HarvardX: PH125.8x | Data Science: Machine Learning

Data Science: Machine Learning

Course Instructor: Rafael Irizarry

Abstract

This is the eighth course in the HarvardX Professional Certificate in Data Science, a series of courses that prepare you to do data analysis in R, from simple computations to machine learning.

The textbook for the Data Science course series is freely available online.

Learning Objectives

The basics of machine learning
How to perform cross-validation to avoid overtraining
Several popular machine learning algorithms
How to build a recommendation system
What regularization is and why it is useful

Course Overview

There are six major sections in this course: introduction to machine learning; machine learning basics; linear regression for prediction, smoothing, and working with matrices; distance, knn, cross validation, and generative models; classification with more than two classes and the caret package; and model fitting and recommendation systems.

Introduction to Machine Learning

In this section, you’ll be introduced to some of the terminology and concepts you’ll need going forward.

Machine Learning Basics

In this section, you’ll learn how to start building a machine learning algorithm using training and test data sets and the importance of conditional probabilities for machine learning.

Linear Regression for Prediction, Smoothing, and Working with Matrices

In this section, you’ll learn why linear regression is a useful baseline approach but is often insufficiently flexible for more complex analyses, how to smooth noisy data, and how to use matrices for machine learning.

Distance, Knn, Cross Validation, and Generative Models

In this section, you’ll learn different types of discriminative and generative approaches for machine learning algorithms.

Classification with More than Two Classes and the Caret Package

In this section, you’ll learn how to overcome the curse of dimensionality using methods that adapt to higher dimensions and how to use the caret package to implement many different machine learning algorithms.

Model Fitting and Recommendation Systems

In this section, you’ll learn how to apply the machine learning algorithms you have learned.

Introduction to Machine Learning Overview

In the Introduction to Machine Learning section, you will be introduced to machine learning.

After completing this section, you will be able to:

Explain the difference between the outcome and the features.
Explain when to use classification and when to use prediction.
Explain the importance of prevalence.
Explain the difference between sensitivity and specificity.

This section has one parts: introduction to machine learning.

The textbook for this section is available here

Assessment 1- Introduction to Machine Learning

“Turbochamp” was one of the first algorithms written by famed British computer scientist, mathematician and crypto-analyst Alan Turing in the late 1940s. The artificial intelligence program was based on programmable rules derived from theory or first principles and could ‘think’ two moves ahead.

True or False: A key feature of machine learning is that the algorithms are built on data.

A. True
B. False

True or False: In machine learning, we build algorithms that take feature values (X) and train a model using known outcomes (Y) that is then used to predict outcomes when presented with features without known outcomes.

A. True
B. False

Machine Learning Basics Overview

In the Machine Learning Basics section, you will learn the basics of machine learning.

After completing this section, you will be able to:

Start to use the caret package.
Construct and interpret a confusion matrix.
Use conditional probabilities in the context of machine learning.

This section has two parts: basics of evaluating machine learning algorithms and conditional probabilities.

The textbook for this section is available here

Assessment 1- Basics of Evaluating Machine Learning Algorithms

For each of the following, indicate whether the outcome is continuous or categorical.

Digit reader: categorical
Movie recommendation ratings: continuous
Spam filter: categorical
Number of hospitalizations: continuous
Siri: categorical

How many features are available to us for prediction in the mnist digits dataset?

library(dslabs)
mnist <- read_mnist()
ncol(mnist$train$images)

## [1] 784

In the digit reader example, the outcomes are stored here: y <- mnist$train$labels.

Do the following operations have a practical meaning?

y[5] + y[6]
y[5] > y[6]

A. Yes, because 9 + 2 = 11 and 9 > 2.
B. No, because y is not a numeric vector.
C. No, because 11 is not one digit, it is two digits.
D. No, because these are labels representing a category, not a number. A 9 represents a type of digit, not the number 9.

Assessment 2- Confusion Matrix

The following questions all ask you to work with the dataset described below.

The reported_heights and heights datasets were collected from three classes taught in the Departments of Computer Science and Biostatistics, as well as remotely through the Extension School. The Biostatistics class was taught in 2016 along with an online version offered by the Extension School. On 2016-01-25 at 8:15 AM, during one of the lectures, the instructors asked student to fill in the sex and height questionnaire that populated the reported_height dataset. The online students filled out the survey during the next few days, after the lecture was posted online. We can use this insight to define a variable which we will call type, to denote the type of student, inclass or online.

The code below sets up the dataset for you to analyze in the following exercises:

library(dslabs)
library(dplyr)
library(lubridate)

data("reported_heights")

dat <- mutate(reported_heights, date_time = ymd_hms(time_stamp)) %>%
  filter(date_time >= make_date(2016, 01, 25) & date_time < make_date(2016, 02, 1)) %>%
  mutate(type = ifelse(day(date_time) == 25 & hour(date_time) == 8 & between(minute(date_time), 15, 30), "inclass","online")) %>%
  select(sex, type)

y <- factor(dat$sex, c("Female", "Male"))
x <- dat$type

Questions:

What is the propotion of females in class and online? (That is, calculate the proportion of the in class students who are female and the proportion of the online students who are female.)

dat %>% group_by(type) %>% summarize(prop_female = mean(sex == "Female"))

If you used the type variable to predict sex, what would the prediction accuracy be?

y_hat <- ifelse(x == "online", "Male", "Female") %>% 
      factor(levels = levels(y))
mean(y_hat==y)

## [1] 0.6333333

Write a line of code using the table function to show the confusion matrix, assuming the prediction is y_hat and the truth is y.

table(y_hat, y)

##         y
## y_hat    Female Male
##   Female     26   13
##   Male       42   69

What is the sensitivity of this prediction?

library(caret)
sensitivity(y_hat, y)

## [1] 0.3823529

What is the specificity of this prediction?

library(caret)
specificity(y_hat, y)

## [1] 0.8414634

What is the prevalence (% of females) in the dat dataset defined above?

mean(y == "Female")

## [1] 0.4533333

Assessment 3- Practice with Machine Learning

We will practice building a machine learning algorithm using a new dataset, iris, that provides multiple predictors for us to use to train. To start, we will remove the setosa species and we will focus on the versicolor and virginica iris species using the following code:

library(caret)
data(iris)
iris <- iris[-which(iris$Species=='setosa'),]
y <- iris$Species

The following questions all involve work with this dataset.

First let us create an even split of the data into train and test partitions using createDataPartition. The code with a missing line is given below:

set.seed(2)
test_index <- createDataPartition(y,times=1,p=0.5,list=FALSE)
test <- iris[test_index,]
train <- iris[-test_index,]

Which code should be used in place of # line of code above?

A. test_index <- createDataPartition(y,times=1,p=0.5)
B. test_index <- sample(2,length(y),replace=FALSE)
C. test_index <- createDataPartition(y,times=1,p=0.5,list=FALSE)
D. test_index <- rep(1,length(y))

Next we will figure out the singular feature in the dataset that yields the greatest overall accuracy. You can use the code from the introduction and from Q1 to start your analysis.

Using only the train iris data set, which of the following is the singular feature for which a smart cutoff (simple search) yields the greatest overall accuracy?

A. Sepal.Length
B. Sepal.Width
C. Petal.Length
D. Petal.Width

Note: This sample code can be used to determine that Petal.Length is the most accurate singular feature if you are using R 3.5.1 OR that Petal.Width is the most accurate singular feature if you are using R 3.6.

foo <- function(x){
    rangedValues <- seq(range(x)[1],range(x)[2],by=0.1)
    sapply(rangedValues,function(i){
        y_hat <- ifelse(x>i,'virginica','versicolor')
        mean(y_hat==train$Species)
    })
}
predictions <- apply(train[,-5],2,foo)
sapply(predictions,max)

## Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
##         0.74         0.64         0.96         0.98

Using the smart cutoff value calculated on the training data, what is the overall accuracy in the test data?

predictions <- foo(train[,3])
rangedValues <- seq(range(train[,3])[1],range(train[,3])[2],by=0.1)
cutoffs <-rangedValues[which(predictions==max(predictions))]

y_hat <- ifelse(test[,3]>cutoffs[1],'virginica','versicolor')
mean(y_hat==test$Species)

## [1] 0.9

Notice that we had an overall accuracy greater than 96% in the training data, but the overall accuracy was lower in the test data. This can happen often if we overtrain. In fact, it could be the case that a single feature is not the best choice. For example, a combination of features might be optimal. Using a single feature and optimizing the cutoff as we did on our training data can lead to overfitting.

Given that we know the test data, we can treat it like we did our training data to see if the same feature with a different cutoff will optimize our predictions.

Which feature best optimizes our overall accuracy?

A. Sepal.Length
B. Sepal.Width
C. Petal.Length
D. Petal.Width

Now we will perform some exploratory data analysis on the data.

Notice that Petal.Length and Petal.Width in combination could potentially be more information than either feature alone.

Optimize the combination of the cutoffs for Petal.Length and Petal.Width in the train data and report the overall accuracy when applied to the test dataset. For simplicity, create a rule that if either the length OR the width is greater than the length cutoff or the width cutoff then virginica or versicolor is called. (Note, the F1 will be similarly high in this example.)

What is the overall accuracy for the test data now?

library(caret)
data(iris)
iris <- iris[-which(iris$Species=='setosa'),]
y <- iris$Species

plot(iris,pch=21,bg=iris$Species)

set.seed(2)
test_index <- createDataPartition(y,times=1,p=0.5,list=FALSE)
test <- iris[test_index,]
train <- iris[-test_index,]
            
petalLengthRange <- seq(range(train$Petal.Length)[1],range(train$Petal.Length)[2],by=0.1)
petalWidthRange <- seq(range(train$Petal.Width)[1],range(train$Petal.Width)[2],by=0.1)

length_predictions <- sapply(petalLengthRange,function(i){
        y_hat <- ifelse(train$Petal.Length>i,'virginica','versicolor')
        mean(y_hat==train$Species)
    })
length_cutoff <- petalLengthRange[which.max(length_predictions)] # 4.7

width_predictions <- sapply(petalWidthRange,function(i){
        y_hat <- ifelse(train$Petal.Width>i,'virginica','versicolor')
        mean(y_hat==train$Species)
    })
width_cutoff <- petalWidthRange[which.max(width_predictions)] # 1.5

y_hat <- ifelse(test$Petal.Length>length_cutoff | test$Petal.Width>width_cutoff,'virginica','versicolor')
mean(y_hat==test$Species)

## [1] 0.9

Assessment 4- Conditional Probabilities Review

Q1. In a previous module, we covered Bayes’ theorem and the Bayesian paradigm. Conditional probabilities are a fundamental part of this previous covered rule.

$\ P(A|B) = P(B|A)\frac{P(A)}{P(B)}$

We first review a simple example to go over conditional probabilities.

Assume a patient comes into the doctor’s office to test whether they have a particular disease.

The test is positive 85% of the time when tested on a patient with the disease (high sensitivity): $\ P(\text{disease}) = 0.02$
The test is negative 90% of the time when tested on a healthy patient (high specificity): $\ P(\text{disease}) = 0.02$
The disease is prevalent in about 2% of the community: $\ P(\text{disease}) = 0.02$

Using Bayes’ theorem, calculate the probability that you have the disease if the test is positive.

$\ P(\text{disease} | \text{test}+) = P(\text{test}+ | \text{disease}) \times \frac{P(\text{disease})}{P(\text{test}+)} = \frac{P(\text{test}+ | \text{disease})P(\text{disease})}{P(\text{test}+ | \text{disease})P(\text{disease})+P(\text{test}+ | \text{healthy})P(\text{healthy})]} = \frac{0.85 \times 0.02}{0.85 \times 0.02 + 0.1 \times 0.98} = 0.1478261$

The following 4 questions (Q2-Q5) all relate to implementing this calculation using R.

We have a hypothetical population of 1 million individuals with the following conditional probabilities as described below:

The test is positive 85% of the time when tested on a patient with the disease (high sensitivity): $\ P(\text{test} + | \text{disease}) = 0.85$
The test is negative 90% of the time when tested on a healthy patient (high specificity): $\ P(\text{test} - | \text{heathy}) = 0.90$ The disease is prevalent in about 2% of the community:
Here is some sample code to get you started: $\ P(\text{disease}) = 0.02$

set.seed(1)
disease <- sample(c(0,1), size=1e6, replace=TRUE, prob=c(0.98,0.02))
test <- rep(NA, 1e6)
test[disease==0] <- sample(c(0,1), size=sum(disease==0), replace=TRUE, prob=c(0.90,0.10))
test[disease==1] <- sample(c(0,1), size=sum(disease==1), replace=TRUE, prob=c(0.15, 0.85))

Q2. What is the probability that a test is positive?

mean(test)

## [1] 0.114509

Q3. What is the probability that an individual has the disease if the test is negative?

mean(disease[test==0])

## [1] 0.003461356

Q4. What is the probability that you have the disease if the test is positive? Remember: calculate the conditional probability the disease is positive assuming a positive test.

mean(disease[test==1]==1)

## [1] 0.1471762

Q5. If the test is positive, what is the relative risk of having the disease? First calculate the probability of having the disease given a positive test, then normalize it against the disease prevalence.

mean(disease[test==1]==1)/mean(disease==1)

## [1] 7.389106

Assessment 5- Conditional Probabilities Practice

We are now going to write code to compute conditional probabilities for being male in the heights dataset. Round the heights to the closest inch. Plot the estimated conditional probability $\ P(x) = \mbox{Pr}(\mbox{Male} | \mbox{height}=x)$ for each $\ x$.

Part of the code is provided here:

library(dslabs)
data("heights")
MISSING CODE
    qplot(height, p, data =.)

Which of the following blocks of code can be used to replace MISSING CODE to make the correct plot?

heights %>% 
    group_by(height) %>%
    summarize(p = mean(sex == "Male")) %>%

heights %>% 
    mutate(height = round(height)) %>%
    group_by(height) %>%
    summarize(p = mean(sex == "Female")) %>%

heights %>% 
    mutate(height = round(height)) %>%
    summarize(p = mean(sex == "Male")) %>%

D.

heights %>% 
    mutate(height = round(height)) %>%
    group_by(height) %>%
    summarize(p = mean(sex == "Male")) %>%

library(dslabs)
data("heights")
# With Missing Code:
heights %>% 
    mutate(height = round(height)) %>%
    group_by(height) %>%
    summarize(p = mean(sex == "Male")) %>%
    qplot(height, p, data =.)

In the plot we just made in Q1 we see high variability for low values of height. This is because we have few data points. This time use the quantile ( 0.1,0.2,,0.9 )and the cut function to assure each group has the same number of points. Note that for any numeric vector x, you can create groups based on quantiles like this: cut(x, quantile(x, seq(0, 1, 0.1)), include.lowest = TRUE).

Part of the code is provided here:

ps <- seq(0, 1, 0.1)
heights %>% 
  MISSING CODE
    group_by(g) %>%
    summarize(p = mean(sex == "Male"), height = mean(height)) %>%
    qplot(height, p, data =.)

Which of the following lines of code can be used to replace MISSING CODE to make the correct plot?

mutate(g = cut(male, quantile(height, ps), include.lowest = TRUE)) %>%

B.

mutate(g = cut(height, quantile(height, ps), include.lowest = TRUE)) %>%

mutate(g = cut(female, quantile(height, ps), include.lowest = TRUE)) %>%

mutate(g = cut(height, quantile(height, ps))) %>%

ps <- seq(0, 1, 0.1)
heights %>% 
  # With missing code:
  mutate(g = cut(height, quantile(height, ps), include.lowest = TRUE)) %>%
    group_by(g) %>%
    summarize(p = mean(sex == "Male"), height = mean(height)) %>%
    qplot(height, p, data =.)

You can generate data from a bivariate normal distrubution using the MASS package using the following code.

Sigma <- 9*matrix(c(1,0.5,0.5,1), 2, 2)
dat <- MASS::mvrnorm(n = 10000, c(69, 69), Sigma) %>%
    data.frame() %>% setNames(c("x", "y"))

And make a quick plot using

plot(dat)

Using an approach similar to that used in the previous exercise, let’s estimate the conditional expectations and make a plot. Part of the code has been provided for you:

ps <- seq(0, 1, 0.1)
dat %>% 
    MISSING CODE
    qplot(x, y, data =.)

Which of the following blocks of code can be used to replace MISSING CODE to make the correct plot?

A.

mutate(g = cut(x, quantile(x, ps), include.lowest = TRUE)) %>%
group_by(g) %>%
summarize(y = mean(y), x = mean(x)) %>%

mutate(g = cut(x, quantile(x, ps))) %>%
group_by(g) %>%
summarize(y = mean(y), x = mean(x)) %>%

mutate(g = cut(x, quantile(x, ps), include.lowest = TRUE)) %>%
summarize(y = mean(y), x = mean(x)) %>%

mutate(g = cut(x, quantile(x, ps), include.lowest = TRUE)) %>%
group_by(g) %>%
summarize(y =(y), x =(x)) %>%

ps <- seq(0, 1, 0.1)
dat %>% 
  # With missing code
    mutate(g = cut(x, quantile(x, ps), include.lowest = TRUE)) %>%
group_by(g) %>%
summarize(y = mean(y), x = mean(x)) %>% 
    qplot(x, y, data =.)

Linear Regression for Prediction, Smoothing, and Working with Matrices Overview

In the Linear Regression for Prediction, Smoothing, and Working with Matrices Overview section, you will learn why linear regression is a useful baseline approach but is often insufficiently flexible for more complex analyses, how to smooth noisy data, and how to use matrices for machine learning.

After completing this section, you will be able to:

Use linear regression for prediction as a baseline approach.
Use logistic regression for categorical data.
Detect trends in noisy data using smoothing (also known as curve fitting or low pass filtering).
Convert predictors to matrices and outcomes to vectors when all predictors are numeric (or can be converted to numerics in a meaningful way).
Perform basic matrix algebra calculations.

This section has three parts: linear regression for prediction, smoothing, and working with matrices.

Assessment 1- Linear Regression

Create a data set using the following code:

set.seed(1)
n <- 100
Sigma <- 9*matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2)
dat <- MASS::mvrnorm(n = 100, c(69, 69), Sigma) %>%
         data.frame() %>% setNames(c("x", "y"))

We will build 100 linear models using the data above and calculate the mean and standard deviation of the combined models. First, set the seed to 1. Within a replicate loop, (1) partition the dataset into test and training sets of equal size using dat$y to generate your indices, (2) train a linear model predicting y from x, (3) generate predictions on the test set, and (4) calculate the RMSE of that model. Then, report the mean and standard deviation of the RMSEs from all 100 models.

set.seed(1)
rmse <- replicate(100, {
    test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
    train_set <- dat %>% slice(-test_index)
    test_set <- dat %>% slice(test_index)
    fit <- lm(y ~ x, data = train_set)
    y_hat <- predict(fit, newdata = test_set)
    sqrt(mean((y_hat-test_set$y)^2))
})

mean(rmse)

## [1] 2.485441

sd(rmse)

## [1] 0.1316324

Mean: 2.488661
SD: 0.1243952

Now we will repeat the exercise above but using larger datasets. Write a function that takes a size n, then (1) builds a dataset using the code provided in Q1 but with n observations instead of 100 and without the set.seed(1), (2) runs the replicate loop that you wrote to answer Q1, which builds 100 linear models and returns a vector of RMSEs, and (3) calculates the mean and standard deviation. Set the seed to 1 and then use sapply or map to apply this function to n <- c(100, 500, 1000, 5000, 10000).

Hint: You only need to set the seed once before running your function; do not set a seed within your function. Also be sure to use sapply or map as you will get different answers running the simulations individually due to setting the seed.

set.seed(1)
n <- c(100, 500, 1000, 5000, 10000)
res <- sapply(n, function(n){
    Sigma <- 9*matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2)
    dat <- MASS::mvrnorm(n, c(69, 69), Sigma) %>%
        data.frame() %>% setNames(c("x", "y"))
    rmse <- replicate(100, {
        test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
        train_set <- dat %>% slice(-test_index)
        test_set <- dat %>% slice(test_index)
        fit <- lm(y ~ x, data = train_set)
        y_hat <- predict(fit, newdata = test_set)
        sqrt(mean((y_hat-test_set$y)^2))
    })
    c(avg = mean(rmse), sd = sd(rmse))
})

res

##          [,1]       [,2]      [,3]       [,4]       [,5]
## avg 2.4812746 2.56921641 2.6114774 2.52740601 2.57768962
## sd  0.1276984 0.08059855 0.0415685 0.02106627 0.01690299

Mean, 100: 2.497754
SD, 100: 0.1180821
Mean, 500: 2.722652
SD, 500: 0.08002108
Mean, 1000: 2.55554451
SD, 1000: 0.04560258
Mean, 5000: 2.62482800
SD, 5000: 0.02355433
Mean, 10000: 2.6184427
SD, 10000: 0.0163413

What happens to the RMSE as the size of the dataset becomes larger?

A. On average, the RMSE does not change much as n gets larger, but the variability of the RMSE decreases.
B. Because of the law of large numbers the RMSE decreases; more data means more precise estimates.
C. n = 10000 is not sufficiently large. To see a decrease in the RMSE we would need to make it larger.
D. The RMSE is not a random variable.

Now repeat the exercise from Q1, this time making the correlation between x and y larger, as in the following code:

set.seed(1)
n <- 100
Sigma <- 9*matrix(c(1.0, 0.95, 0.95, 1.0), 2, 2)
dat <- MASS::mvrnorm(n = 100, c(69, 69), Sigma) %>%
    data.frame() %>% setNames(c("x", "y"))

Note what happens to RMSE - set the seed to 1 as before.

set.seed(1)
rmse <- replicate(100, {
    test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
    train_set <- dat %>% slice(-test_index)
    test_set <- dat %>% slice(test_index)
    fit <- lm(y ~ x, data = train_set)
    y_hat <- predict(fit, newdata = test_set)
    sqrt(mean((y_hat-test_set$y)^2))
})

mean(rmse)

## [1] 0.9078124

sd(rmse)

## [1] 0.05821304

Mean: 0.9167387
SD: 0.06244347

Which of the following best explains why the RMSE in question 4 is so much lower than the RMSE in question 1?

A. It is just luck. If we do it again, it will be larger.
B. The central limit theorem tells us that the RMSE is normal.
C. When we increase the correlation between x and y, x has more predictive power and thus provides a better estimate of y.
D. These are both examples of regression so the RMSE has to be the same.

Create a data set using the following code.

set.seed(1)
Sigma <- matrix(c(1.0, 0.75, 0.75, 0.75, 1.0, 0.25, 0.75, 0.25, 1.0), 3, 3)
dat <- MASS::mvrnorm(n = 100, c(0, 0, 0), Sigma) %>%
    data.frame() %>% setNames(c("y", "x_1", "x_2"))

Note that y is correlated with both x_1 and x_2 but the two predictors are independent of each other, as seen by cor(dat).

Use the caret package to partition into a test and training set of equal size. Compare the RMSE when using just x_1, just x_2 and both x_1 and x_2. Train a linear model for each.

Which of the three models performs the best (has the lowest RMSE)?

A. x_1 B. x_2 C. x_1 and x_2

set.seed(1)
test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
train_set <- dat %>% slice(-test_index)
test_set <- dat %>% slice(test_index)

fit <- lm(y ~ x_1, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))

## [1] 0.6175662

fit <- lm(y ~ x_2, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))

## [1] 0.5881607

fit <- lm(y ~ x_1 + x_2, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))

## [1] 0.3161433

Report the lowest RMSE of the three models tested in Q6.

Lowest: 0.3070962

Repeat the exercise from q6 but now create an example in which x_1 and x_2 are highly correlated.

set.seed(1)
Sigma <- matrix(c(1.0, 0.75, 0.75, 0.75, 1.0, 0.95, 0.75, 0.95, 1.0), 3, 3)
dat <- MASS::mvrnorm(n = 100, c(0, 0, 0), Sigma) %>%
    data.frame() %>% setNames(c("y", "x_1", "x_2"))

Set the seed to 1, then use the caret package to partition into a test and training set of equal size. Compare the RMSE when using just x_1, just x_2, and both x_1 and x_2.

Compare the results from q6 and q8. What can you conclude?

A. Unless we include all predictors we have no predictive power.
B. Adding extra predictors improves RMSE regardless of whether the added predictors are correlated with other predictors or not.
C. Adding extra predictors results in over fitting. D. Adding extra predictors can improve RMSE substantially, but not when the added predictors are highly correlated with other predictors.

set.seed(1)
test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
train_set <- dat %>% slice(-test_index)
test_set <- dat %>% slice(test_index)

fit <- lm(y ~ x_1, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))

## [1] 0.6175662

fit <- lm(y ~ x_2, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))

## [1] 0.5881607

fit <- lm(y ~ x_1 + x_2, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))

## [1] 0.3161433

Assessment 2- Logistic Regression

Define a dataset using the following code:

set.seed(2)
make_data <- function(n = 1000, p = 0.5, 
                mu_0 = 0, mu_1 = 2, 
                sigma_0 = 1,  sigma_1 = 1){

y <- rbinom(n, 1, p)
f_0 <- rnorm(n, mu_0, sigma_0)
f_1 <- rnorm(n, mu_1, sigma_1)
x <- ifelse(y == 1, f_1, f_0)
  
test_index <- createDataPartition(y, times = 1, p = 0.5, list = FALSE)

list(train = data.frame(x = x, y = as.factor(y)) %>% slice(-test_index),
    test = data.frame(x = x, y = as.factor(y)) %>% slice(test_index))
}
dat <- make_data()

Note that we have defined a variable x that is predictive of a binary outcome y: dat$train %>% ggplot(aes(x, color = y)) + geom_density().

Set the seed to 1, then use the make_data function defined above to generate 25 different datasets with mu_1 <- seq(0, 3, len=25). Perform logistic regression on each of the 25 different datasets (predict 1 if p>0.5) and plot accuracy (res in the figures) vs mu_1 (delta in the figures).”

Which is the correct plot?

#The correct one:
set.seed(1)
delta <- seq(0, 3, len = 25)
res <- sapply(delta, function(d){
    dat <- make_data(mu_1 = d)
    fit_glm <- dat$train %>% glm(y ~ x, family = "binomial", data = .)
    y_hat_glm <- ifelse(predict(fit_glm, dat$test) > 0.5, 1, 0) %>% factor(levels = c(0, 1))
    mean(y_hat_glm == dat$test$y)
})
qplot(delta, res)

Assessment 3- Smoothing

In the Wrangling course of this series, PH125.6x, we used the following code to obtain mortality counts for Puerto Rico for 2015-2018:

library(tidyverse)
library(lubridate)
library(purrr)
library(pdftools)
    
fn <- system.file("extdata", "RD-Mortality-Report_2015-18-180531.pdf", package="dslabs")
dat <- map_df(str_split(pdf_text(fn), "\n"), function(s){
    s <- str_trim(s)
    header_index <- str_which(s, "2015")[1]
    tmp <- str_split(s[header_index], "\\s+", simplify = TRUE)
    month <- tmp[1]
    header <- tmp[-1]
    tail_index  <- str_which(s, "Total")
    n <- str_count(s, "\\d+")
    out <- c(1:header_index, which(n==1), which(n>=28), tail_index:length(s))
    s[-out] %>%
        str_remove_all("[^\\d\\s]") %>%
        str_trim() %>%
        str_split_fixed("\\s+", n = 6) %>%
        .[,1:5] %>%
        as_data_frame() %>% 
        setNames(c("day", header)) %>%
        mutate(month = month,
            day = as.numeric(day)) %>%
        gather(year, deaths, -c(day, month)) %>%
        mutate(deaths = as.numeric(deaths))
}) %>%
    mutate(month = recode(month, "JAN" = 1, "FEB" = 2, "MAR" = 3, "APR" = 4, "MAY" = 5, "JUN" = 6, 
                          "JUL" = 7, "AGO" = 8, "SEP" = 9, "OCT" = 10, "NOV" = 11, "DEC" = 12)) %>%
    mutate(date = make_date(year, month, day)) %>%
    filter(date <= "2018-05-01")

Use the loess function to obtain a smooth estimate of the expected number of deaths as a function of date. Plot this resulting smooth function. Make the span about two months long.

Which of the following plots is correct?

span <- 60 / as.numeric(diff(range(dat$date)))
fit <- dat %>% mutate(x = as.numeric(date)) %>% loess(deaths ~ x, data = ., span = span, degree = 1)
dat %>% mutate(smooth = predict(fit, as.numeric(date))) %>%
    ggplot() +
    geom_point(aes(date, deaths)) +
    geom_line(aes(date, smooth), lwd = 2, col = 2)

Work with the same data as in Q1 to plot smooth estimates against day of the year, all on the same plot, but with different colors for each year.

Which code produces the desired plot?

span <- 60 / as.numeric(diff(range(dat$date)))
fit <- dat %>% mutate(x = as.numeric(date)) %>% loess(deaths ~ x, data = ., span = span, degree = 1)
dat %>% 
    mutate(smooth = predict(fit, as.numeric(date)), day = yday(date), year = as.character(year(date))) %>%
    ggplot(aes(day, smooth, col = year)) +
    geom_line(lwd = 2)

dat %>% 
    mutate(smooth = predict(fit), day = yday(date), year = as.character(year(date))) %>%
    ggplot(aes(day, smooth, col = year)) +
    geom_line(lwd = 2)

dat %>% 
    mutate(smooth = predict(fit, as.numeric(date)), day = mday(date), year = as.character(year(date))) %>%
    ggplot(aes(day, smooth, col = year)) +
    geom_line(lwd = 2)

dat %>% 
    mutate(smooth = predict(fit, as.numeric(date)), day = yday(date), year = as.character(year(date))) %>%
    ggplot(aes(day, smooth)) +
    geom_line(lwd = 2)

D.

dat %>% 
    mutate(smooth = predict(fit, as.numeric(date)), day = yday(date), year = as.character(year(date))) %>%
    ggplot(aes(day, smooth, col = year)) +
    geom_line(lwd = 2)

Suppose we want to predict 2s and 7s in the mnist_27 dataset with just the second covariate. Can we do this? On first inspection it appears the data does not have much predictive power.

In fact, if we fit a regular logistic regression the coefficient for x_2 is not significant!

This can be seen using this code:

library(broom)
library(dslabs)
data(mnist_27)
mnist_27$train %>% glm(y ~ x_2, family = "binomial", data = .) %>% tidy()

Plotting a scatterplot here is not useful since y is binary:

qplot(x_2, y, data = mnist_27$train)

Fit a loess line to the data above and plot the results. What do you observe?

A. There is no predictive power and the conditional probability is linear.
B. There is no predictive power and the conditional probability is non-linear.
C. There is predictive power and the conditional probability is linear.
D. There is predictive power and the conditional probability is non-linear.

#Note that there is indeed predictive power, but that the conditional probability is non-linear.
#The loess line can be plotted using the following code:
mnist_27$train %>% 
    mutate(y = ifelse(y=="7", 1, 0)) %>%
    ggplot(aes(x_2, y)) + 
    geom_smooth(method = "loess")

Assessment 4- Working with Matrices

Which line of code correctly creates a 100 by 10 matrix of randomly generated normal numbers and assigns it to x?

A. x <- matrix(rnorm(1000), 100, 100)
B. x <- matrix(rnorm(100*10), 100, 10)
C. x <- matrix(rnorm(100*10), 10, 10)
D. x <- matrix(rnorm(100*10), 10, 100)

Write the line of code that would give you the specified information about the matrix x that you generated in q1. Do not include any spaces in your line of code.

Dimension of x.

dim(x)

Number of rows of x.

nrow(x)

Number of columns of x.

ncol(x)

Which of the following lines of code would add the scalar 1 to row 1, the scalar 2 to row 2, and so on, for the matrix x?

A. x <- x + seq(nrow(x))
B. x <- 1:nrow(x)
C. x <- sweep(x, 2, 1:nrow(x),"+")
D. x <- sweep(x, 1, 1:nrow(x),"+")

Which of the following lines of code would add the scalar 1 to column 1, the scalar 2 to column 2, and so on, for the matrix x?

A. x <- 1:ncol(x)
B. x <- 1:col(x)
C. x <- sweep(x, 2, 1:ncol(x), FUN = "+")
D. x <- -x

Which code correctly computes the average of each row of x?

A. mean(x)
B. rowMedians(x)
C. sapply(x,mean)
D. rowSums(x)
E. rowMeans(x)

Which code correctly computes the average of each column of x?

A. mean(x)
B. sapply(x,mean)
C. colMeans(x)
D. colMedians(x)
E. colSums(x)

For each digit in the mnist training data, compute the proportion of pixels that are in the grey area, defined as values between 50 and 205. (To visualize this, you can make a boxplot by digit class.)

What proportion of pixels are in the grey area overall, defined as values between 50 and 205?
- 0.06183703

#The matrix and plot can be calculated using the following code:
mnist <- read_mnist()
y <- rowMeans(mnist$train$images>50 & mnist$train$images<205)
qplot(as.factor(mnist$train$labels), y, geom = "boxplot")

#The proportion of pixels can be calculated using 
mean(y)

## [1] 0.06183703

Distance, Knn, Cross Validation, and Generative Models

In the Distance, kNN, Cross Validation, and Generative Models section, you will learn about different types of discriminative and generative approaches for machine learning algorithms.

After completing this section, you will be able to:

Use the k-nearest neighbors (kNN) algorithm.
Understand the problems of overtraining and oversmoothing.
Use cross-validation to reduce the true error and the apparent error.
Use generative models such as naive Bayes, quadratic discriminant analysis (qda), and linear discriminant analysis (lda) for machine learning.

This section has three parts: nearest neighbors, cross-validation, and generative models.

Assessment 1- Distance

Load the following dataset:

library(dslabs)
data("tissue_gene_expression")

This dataset includes a matrix x:

dim(tissue_gene_expression$x)

## [1] 189 500

This matrix has the gene expression levels of 500 genes from 189 biological samples representing seven different tissues. The tissue type is stored in y:

table(tissue_gene_expression$y)

## 
##  cerebellum       colon endometrium hippocampus      kidney       liver 
##          38          34          15          31          39          26 
##    placenta 
##           6

Which of the following lines of code computes the Euclidean distance between each observation and stores it in the object d?

d <- dist(tissue_gene_expression$x)

A. d <- dist(tissue_gene_expression$x, distance='maximum')
B. d <- dist(tissue_gene_expression)
C. d <- dist(tissue_gene_expression$x)
D. d <- cor(tissue_gene_expression$x)

Compare the distances between observations 1 and 2 (both cerebellum), observations 39 and 40 (both colon), and observations 73 and 74 (both endometrium).

Distance-wise, are samples from tissues of the same type closer to each other?

A. No, the samples from the same tissue type are not necessarily closer.
B. The two colon samples are closest to each other, but the samples from the other two tissues are not.
C. The two cerebellum samples are closest to each other, but the samples from the other two tissues are not.
D. Yes, the samples from the same tissue type are closest to each other.

ind <- c(1, 2, 39, 40, 73, 74)
as.matrix(d)[ind,ind]

##               cerebellum_1 cerebellum_2   colon_1   colon_2 endometrium_1
## cerebellum_1      0.000000     7.005922 22.694801 22.699755      21.12763
## cerebellum_2      7.005922     0.000000 22.384821 22.069557      20.87910
## colon_1          22.694801    22.384821  0.000000  8.191935      14.99672
## colon_2          22.699755    22.069557  8.191935  0.000000      14.80355
## endometrium_1    21.127629    20.879099 14.996715 14.803545       0.00000
## endometrium_2    21.780792    20.674802 18.089213 17.004456      14.29405
##               endometrium_2
## cerebellum_1       21.78079
## cerebellum_2       20.67480
## colon_1            18.08921
## colon_2            17.00446
## endometrium_1      14.29405
## endometrium_2       0.00000

Make a plot of all the distances using the image function to see if the pattern you observed in Q2 is general.

Which code would correctly make the desired plot?

A. image(d)
B. image(as.matrix(d))
C. d
D. image()

Assessment 2- Nearest Neighbors

Previously, we used logistic regression to predict sex based on height. Now we are going to use knn to do the same. Set the seed to 1, then use the caret package to partition the dslabs “heights” data into a training and test set of equal size. Use the sapply function to perform knn with k values of seq(1, 101, 3) and calculate F_1 scores.

What is the max value of F_1? 0.60194
At what value of k does the max occur? 46

library(dslabs)
library(tidyverse)
library(caret)
data("heights")

set.seed(1)
test_index <- createDataPartition(heights$sex, times = 1, p = 0.5, list = FALSE)
test_set <- heights[test_index, ]
train_set <- heights[-test_index, ]     
                
ks <- seq(1, 101, 3)
F_1 <- sapply(ks, function(k){
    fit <- knn3(sex ~ height, data = train_set, k = k)
    y_hat <- predict(fit, test_set, type = "class") %>% 
        factor(levels = levels(train_set$sex))
    F_meas(data = y_hat, reference = test_set$sex)
})
plot(ks, F_1)

max(F_1)

## [1] 0.619469

ks[which.max(F_1)]

## [1] 40

Next we will use the same gene expression example used in the Comprehension Check: Distance exercises. You can load it like this:

library(dslabs)
data("tissue_gene_expression")

Split the data into training and test sets, and report the accuracy you obtain. Try it for k = 1, 3, 5, 7, 9, 11. Set the seed to 1.

k=1: 0.9784946
k=3: 0.9677419
k=5: 0.9892473
k=7: 0.9677419
k=9: 0.9569892
k=11: 0.9569892

set.seed(1)
library(caret)
y <- tissue_gene_expression$y
x <- tissue_gene_expression$x
train_index <- createDataPartition(y, list = FALSE)
sapply(seq(1, 11, 2), function(k){
    fit <- knn3(x[train_index,], y[train_index], k = k)
    y_hat <- predict(fit, newdata = data.frame(x=x[-train_index,]),
                type = "class")
mean(y_hat == y[-train_index])
})

## [1] 1.0000000 0.9892473 1.0000000 0.9462366 0.9247312 0.9354839

Assessment 3- Cross-validation

Generate a set of random predictors and outcomes using the following code:

library(dbplyr)
library(dslabs)
library(tidyverse)
library(caret)

set.seed(1996)
n <- 1000
p <- 10000
x <- matrix(rnorm(n*p), n, p)
colnames(x) <- paste("x", 1:ncol(x), sep = "_")
y <- rbinom(n, 1, 0.5) %>% factor()

x_subset <- x[ ,sample(p, 100)]

Because x and y are completely independent, you should not be able to predict y using x with accuracy greater than 0.5. Confirm this by running cross-validation using logistic regression to fit the model. Because we have so many predictors, we selected a random sample x_subset. Use the subset when training the model.

Which code correctly performs this cross-validation?

fit <- train(x_subset, y)
fit$results

fit <- train(x_subset, y, method = "glm")
fit$results

fit <- train(x_subset, y, method = "glm")
fit$results [X]

fit <- train(y, x_subset, method = "glm")
fit$results

fit <- test(x_subset, y, method = "glm")
fit$results

Now, instead of using a random selection of predictors, we are going to search for those that are most predictive of the outcome. We can do this by comparing the values for $\ y = 1$ the group to those in the $\ y = 0$ group, for each predictor, using a t-test. You can do perform this step like this:

library(devtools)
#devtools::install_bioc("genefilter")
library(genefilter)
tt <- colttests(x, y)

Which of the following lines of code correctly creates a vector of the p-values called pvals?

A. pvals <- tt$dm
B. pvals <- tt$statistic
C. pvals <- tt
D. pvals <- tt$p.value

pvals <- tt$p.value

Create an index ind with the column numbers of the predictors that were “statistically significantly” associated with y. Use a p-value cutoff of 0.01 to define “statistically significantly.”

How many predictors survive this cutoff? - 108

ind <- which(pvals <= 0.01)
length(ind)

## [1] 108

Now re-run the cross-validation after redefinining x_subset to be the subset of x defined by the columns showing “statistically significant” association with y.

What is the accuracy now?
- 0.754

x_subset <- x[,ind]
fit <- train(x_subset, y, method = "glm")
fit$results

Re-run the cross-validation again, but this time using kNN. Try out the following grid k = seq(101, 301, 25) of tuning parameters. Make a plot of the resulting accuracies.

Which code is correct?

A.

fit <- train(x_subset, y, method = "knn", tuneGrid = data.frame(k = seq(101, 301, 25)))
ggplot(fit)

fit <- train(x_subset, y, method = "knn")
ggplot(fit)

fit <- train(x_subset, y, method = "knn", tuneGrid = data.frame(k = seq(103, 301, 25)))
ggplot(fit)

fit <- train(x_subset, y, method = "knn", tuneGrid = data.frame(k = seq(101, 301, 5)))
ggplot(fit)

In the previous exercises, we see that despite the fact that x and y are completely independent, we were able to predict y with accuracy higher than 70%. We must be doing something wrong then.

What is it?

A. The function train estimates accuracy on the same data it uses to train the algorithm.
B. We are overfitting the model by including 100 predictors.
C. We used the entire dataset to select the columns used in the model.
D. The high accuracy is just due to random variability.

Explanation: Because we used the entire dataset to select the columns in the model, the accuracy is too high. The selection step needs to be included as part of the cross-validation algorithm, and then the cross-validation itself is performed after the column selection step.

As a follow-up exercise, try to re-do the cross-validation, this time including the selection step in the cross-validation algorithm. The accuracy should now be close to 50%.

Use the train function to predict tissue from gene expression in the tissue_gene_expression dataset. Use kNN.

What value of k works best? - 1

data("tissue_gene_expression")
fit <- with(tissue_gene_expression, train(x, y, method = "knn", tuneGrid = data.frame( k = seq(1, 7, 2))))
ggplot(fit)

fit$results

Assessment 4- Bootstrap

The createResample function can be used to create bootstrap samples. For example, we can create 10 bootstrap samples for the mnist_27 dataset like this:

set.seed(1995)
indexes <- createResample(mnist_27$train$y, 10)

How many times do 3, 4, and 7 appear in the first resampled index?

Enter the number of times 3 appears: 1
Enter the number of times 4 appears: 4
Enter the number of times 7 appears: 0

sum(indexes[[1]] == 3)

## [1] 1

sum(indexes[[1]] == 4)

## [1] 2

sum(indexes[[1]] == 7)

## [1] 1

We see that some numbers appear more than once and others appear no times. This has to be this way for each dataset to be independent. Repeat the exercise for all the resampled indexes.

What is the total number of times that 3 appears in all of the resampled indexes? - 11

x=sapply(indexes, function(ind){
    sum(ind == 3)
})
sum(x)

## [1] 12

Generate a random dataset using the following code:

set.seed(1)
y <- rnorm(100, 0, 1)

Estimate the 75th quantile, which we know is qnorm(0.75), with the sample quantile: quantile(y, 0.75).

Run a Monte Carlo simulation with 10,000 repetitions to learn the expected value and standard error of this random variable. Set the seed to 1.

Expected value: 0.6654465
Standard error: 0.1351181

set.seed(1)
B <- 10000
q_75 <- replicate(B, {
    y <- rnorm(100, 0, 1)
    quantile(y, 0.75)
})

mean(q_75)

## [1] 0.6656107

sd(q_75)

## [1] 0.1353809

In practice, we can’t run a Monte Carlo simulation. Use the sample:

set.seed(1)
y <- rnorm(100, 0, 1)

Set the seed to 1 again after generating y and use 10 bootstrap samples to estimate the expected value and standard error of the 75th quantile.

Expected value: 0.731
Standard error: 0.0742

set.seed(1)
indexes <- createResample(y, 10)
q_75_star <- sapply(indexes, function(ind){
    y_star <- y[ind]
    quantile(y_star, 0.75)
})
mean(q_75_star)

## [1] 0.6712146

sd(q_75_star)

## [1] 0.07858009

Repeat the exercise from Q4 but with 10,000 bootstrap samples instead of 10. Set the seed to 1.

Expected value 0.6737512
Standard error 0.0930575

set.seed(1)
indexes <- createResample(y, 10000)
q_75_star <- sapply(indexes, function(ind){
    y_star <- y[ind]
    quantile(y_star, 0.75)
})
mean(q_75_star)

## [1] 0.6739372

sd(q_75_star)

## [1] 0.09259491

Compare the SD values obtained using 10 vs 10,000 bootstrap samples.

What do you observe?

A. The SD is substantially lower with 10,000 bootstrap samples than with 10.
B. The SD is roughly the same in both cases.
C. The SD is substantially higher with 10,000 bootstrap samples than with 10.

Assessment 5- Generative Models

In the following exercises, we are going to apply LDA and QDA to the tissue_gene_expression dataset. We will start with simple examples based on this dataset and then develop a realistic example.

Create a dataset of samples from just cerebellum and hippocampus, two parts of the brain, and a predictor matrix with 10 randomly selected columns using the following code:

library(dslabs)
library(caret)
data("tissue_gene_expression")
      
set.seed(1993, sample.kind = "Rounding") # use this line of code if you are using R 3.6 or later
#set.seed(1993) # use this line of code if you are using R 3.5 or earlier
ind <- which(tissue_gene_expression$y %in% c("cerebellum", "hippocampus"))
y <- droplevels(tissue_gene_expression$y[ind])
x <- tissue_gene_expression$x[ind, ]
x <- x[, sample(ncol(x), 10)]

Use the train function to estimate the accuracy of LDA.

What is the accuracy? - 0.8946508

fit_lda <- train(x, y, method = "lda")
fit_lda$results["Accuracy"]

In this case, LDA fits two 10-dimensional normal distributions. Look at the fitted model by looking at the finalModel component of the result of train. Notice there is a component called means that includes the estimated means of both distributions. Plot the mean vectors against each other and determine which predictors (genes) appear to be driving the algorithm.

Which TWO genes appear to be driving the algorithm?

A. PLCB1
B. RAB1B
C. MSH4
D. OAZ2
E. SPI1
F. SAPCD1
G. HEMK1

t(fit_lda$finalModel$means) %>% data.frame() %>%
    mutate(predictor_name = rownames(.)) %>%
    ggplot(aes(cerebellum, hippocampus, label = predictor_name)) +
    geom_point() +
    geom_text() +
    geom_abline()

Repeat the exercise in Q1 with QDA.

Create a dataset of samples from just cerebellum and hippocampus, two parts of the brain, and a predictor matrix with 10 randomly selected columns using the following code:

library(dslabs)      
library(caret)
data("tissue_gene_expression")
      
set.seed(1993, sample.kind = "Rounding") # use this line of code if you are using R 3.6 or later
#set.seed(1993) # use this line of code if you are using R 3.5 or earlier
ind <- which(tissue_gene_expression$y %in% c("cerebellum", "hippocampus"))
y <- droplevels(tissue_gene_expression$y[ind])
x <- tissue_gene_expression$x[ind, ]
x <- x[, sample(ncol(x), 10)]

Use the train function to estimate the accuracy of QDA. For this question, use the entire tissue_gene_expression dataset: do not split it into training and test sets (understand this can lead to overfitting).

What is the accuracy? - 0.8147954

fit_qda <- train(x, y, method = "qda")
fit_qda$results["Accuracy"]

Which TWO genes drive the algorithm when using QDA instead of LDA?

A. PLCB1
B. RAB1B
C. MSH4
D. OAZ2
E. SPI1
F. SAPCD1
G. HEMK1

t(fit_qda$finalModel$means) %>% data.frame() %>%
    mutate(predictor_name = rownames(.)) %>%
    ggplot(aes(cerebellum, hippocampus, label = predictor_name)) +
    geom_point() +
    geom_text() +
    geom_abline()

One thing we saw in the previous plots is that the values of the predictors correlate in both groups: some predictors are low in both groups and others high in both groups. The mean value of each predictor found in colMeans(x) is not informative or useful for prediction and often for purposes of interpretation, it is useful to center or scale each column. This can be achieved with the preProcessing argument in train. Re-run LDA with preProcessing = "scale". Note that accuracy does not change, but it is now easier to identify the predictors that differ more between groups than based on the plot made in Q2.

Which TWO genes drive the algorithm after performing the scaling?

A. C21orf62
B. PLCB1
C. RAB1B
D. MSH4
E. OAZ2
F. SPI1
G. SAPCD1
H. IL18R1

fit_lda <- train(x, y, method = "lda", preProcess = "center")
fit_lda$results["Accuracy"]

t(fit_lda$finalModel$means) %>% data.frame() %>%
    mutate(predictor_name = rownames(.)) %>%
    ggplot(aes(predictor_name, hippocampus)) +
    geom_point() +
    coord_flip()

d <- apply(fit_lda$finalModel$means, 2, diff)
ind <- order(abs(d), decreasing = TRUE)[1:2]
plot(x[, ind], col = y)

Now we are going to increase the complexity of the challenge slightly: we will consider all the tissue types. Use the following code to create your dataset:

library(dslabs)      
library(caret)
data("tissue_gene_expression")
           
set.seed(1993, sample.kind = "Rounding") # use this line of code if you are using R 3.6 or later
#set.seed(1993) # use this line of code if you are using R 3.5 or earlier
y <- tissue_gene_expression$y
x <- tissue_gene_expression$x
x <- x[, sample(ncol(x), 10)]

What is the accuracy using LDA? - 0.8194837

fit_lda <- train(x, y, method = "lda", preProcess = c("center"))
fit_lda$results["Accuracy"]

Classification with More than Two Classes and the Caret Package

In the Classification with More than Two Classes and the Caret Package section, you will learn how to overcome the curse of dimensionality using methods that adapt to higher dimensions and how to use the caret package to implement many different machine learning algorithms.

After completing this section, you will be able to:

Use classification and regression trees.
Use classification (decision) trees.
Apply random forests to address the shortcomings of decision trees.
Use the caret package to implement a variety of machine learning algorithms.

This section has two parts: classification with more than two classes and caret package.

Assessment 1- Trees and Random Forests

Create a simple dataset where the outcome grows 0.75 units on average for every increase in a predictor, using this code:

library(rpart)
library(dplyr)
library(ggplot2)

n <- 1000
sigma <- 0.25
set.seed(1)
x <- rnorm(n, 0, 1)
y <- 0.75 * x + rnorm(n, 0, sigma)
dat <- data.frame(x = x, y = y)

Which code correctly uses rpart to fit a regression tree and saves the result to fit?

A. fit <- rpart(y ~ .)
B. fit <- rpart(y, ., data = dat)
C. fit <- rpart(x ~ ., data = dat)
D. fit <- rpart(y ~ ., data = dat)

fit <- rpart(y ~ ., data = dat)

Which of the following plots correctly shows the final tree obtained in Q1?

plot(fit)

text(fit)

Below is most of the code to make a scatter plot of y versus x along with the predicted values based on the fit.

dat %>% 
    mutate(y_hat = predict(fit)) %>% 
    ggplot() +
    geom_point(aes(x, y)) +
    
  geom_step(aes(x, y_hat), col=2) #BLANK

Which line of code should be used to replace #BLANK in the code above?

A. geom_step(aes(x, y_hat), col=2)
B. geom_smooth(aes(y_hat, x), col=2)
C. geom_quantile(aes(x, y_hat), col=2)
D. geom_step(aes(y_hat, x), col=2)

Now run Random Forests instead of a regression tree using randomForest from the __randomForest__ package, and remake the scatterplot with the prediction line. Part of the code is provided for you below.

library(randomForest)
#fit <- #BLANK 
fit <- randomForest(y ~ x, data = dat)
dat %>% 
    mutate(y_hat = predict(fit)) %>% 
    ggplot() +
    geom_point(aes(x, y)) +
    geom_step(aes(x, y_hat), col = 2)

What code should replace #BLANK in the provided code?

A. randomForest(y ~ x, data = dat)
B. randomForest(x ~ y, data = dat)
C. randomForest(y ~ x, data = data)
D. randomForest(x ~ y)

Use the plot function to see if the Random Forest from Q4 has converged or if we need more trees.

plot(fit)

It seems that the default values for the Random Forest result in an estimate that is too flexible (unsmooth). Re-run the Random Forest but this time with a node size of 50 and a maximum of 25 nodes. Remake the plot.

Part of the code is provided for you below.

library(randomForest)
#fit <- #BLANK
fit <- randomForest(y ~ x, data = dat, nodesize = 50, maxnodes = 25)

dat %>% 
    mutate(y_hat = predict(fit)) %>% 
    ggplot() +
    geom_point(aes(x, y)) +
    geom_step(aes(x, y_hat), col = 2)

What code should replace #BLANK in the provided code?

A. randomForest(y ~ x, data = dat, nodesize = 25, maxnodes = 25)
B. randomForest(y ~ x, data = dat, nodes = 50, max = 25)
C. randomForest(x ~ y, data = dat, nodes = 50, max = 25)
D. randomForest(y ~ x, data = dat, nodesize = 50, maxnodes = 25)
E. randomForest(x ~ y, data = dat, nodesize = 50, maxnodes = 25)

Assessment 2- Caret Package

The exercises in Q1 and Q2 continue the analysis you began in the last set of assessments.

Q1. In the exercise in Q6 from Comprehension Check: Trees and Random Forests, we saw that changing nodesize to 50 and setting maxnodes to 25 yielded smoother results. Let’s use the train function to help us pick what the values of nodesize and maxnodes should be.

From the caret description of methods, we see that we can’t tune the maxnodes parameter or the nodesize argument with randomForests. So we will use the __Rborist__ package and tune the minNode argument. Use the train function to try values minNode <- seq(25, 100, 25). Set the seed to 1.

Which value minimizes the estimated RMSE? - 50

set.seed(1)
library(caret)
library(Rborist)
fit <- train(y ~ ., method = "Rborist",   
                tuneGrid = data.frame(predFixed = 1, 
                                      minNode = seq(25, 100, 25)),
                data = dat)
ggplot(fit)

Q2. Part of the code to make a scatterplot along with the prediction from the best fitted model is provided below.

library(caret)
dat %>% 
    mutate(y_hat = predict(fit)) %>% 
    ggplot() +
    geom_point(aes(x, y)) +
  #BLANK
    geom_step(aes(x, y_hat), col = 2)

Which code correctly can be used to replace #BLANK in the code above?

A. geom_step(aes(y_hat, x), col = 2)
B. geom_step(aes(x, y_hat), col = 2)
C. geom_step(aes(x, y), col = 2)
D. geom_step(aes(x_hat, y_hat), col = 2)
E. geom_smooth(aes(x, y_hat), col = 2)
F. geom_smooth(aes(y_hat, x), col = 2)

Q3. Use the rpart function to fit a classification tree to the tissue_gene_expression dataset. Use the train function to estimate the accuracy. Try out cp values of seq(0, 0.1, 0.01). Plot the accuracies to report the results of the best model. Set the seed to 1991.

Which value of cp gives the highest accuracy? - 0

library(caret)
library(dslabs)
set.seed(1991)
data("tissue_gene_expression")
    
fit <- with(tissue_gene_expression, 
                train(x, y, method = "rpart",
                      tuneGrid = data.frame(cp = seq(0, 0.1, 0.01))))
    
ggplot(fit)

Q4. Study the confusion matrix for the best fitting classification tree from the exercise in Q3.

What do you observe happening for the placenta samples?

A. Placenta samples are all accurately classified.
B. Placenta samples are being classified as two similar tissues.
C. Placenta samples are being classified somewhat evenly across tissues.
D. Placenta samples not being classified into any of the classes.

Explanation: confusionMatrix(fit) will show the confusion matrix for the classification tree from the tissue gene expression dataset. Looking at the confusion matrix, you can see that placenta is classified somewhat evenly across different tissue types, and in fact, placentas are called endometriums more frequently than they are called placentas.

Q5. Note that there are only 6 placentas in the dataset. By default, rpart requires 20 observations before splitting a node. That means that it is difficult to have a node in which placentas are the majority. Rerun the analysis you did in the exercise in Q3, but this time, allow rpart to split any node by using the argument control = rpart.control(minsplit = 0). Look at the confusion matrix again to determine whether the accuracy increases. Again, set the seed to 1991.

What is the accuracy now? - 0.9141

set.seed(1991)
data("tissue_gene_expression")
    
fit_rpart <- with(tissue_gene_expression, 
                      train(x, y, method = "rpart",
                            tuneGrid = data.frame(cp = seq(0, 0.10, 0.01)),
                            control = rpart.control(minsplit = 0)))
ggplot(fit_rpart)

confusionMatrix(fit_rpart)

## Bootstrapped (25 reps) Confusion Matrix 
## 
## (entries are percentual average cell counts across resamples)
##  
##              Reference
## Prediction    cerebellum colon endometrium hippocampus kidney liver
##   cerebellum        19.5   0.0         0.2         0.9    0.4   0.0
##   colon              0.3  16.5         0.1         0.0    0.1   0.0
##   endometrium        0.1   0.2         6.4         0.1    0.9   0.1
##   hippocampus        0.2   0.0         0.0        15.6    0.1   0.0
##   kidney             0.3   0.3         0.9         0.1   19.1   0.5
##   liver              0.0   0.0         0.3         0.0    0.3  12.6
##   placenta           0.1   0.1         0.5         0.0    0.6   0.1
##              Reference
## Prediction    placenta
##   cerebellum       0.1
##   colon            0.1
##   endometrium      0.5
##   hippocampus      0.0
##   kidney           0.3
##   liver            0.2
##   placenta         1.8
##                             
##  Accuracy (average) : 0.9141

Plot the tree from the best fitting model of the analysis you ran in Q5.

Which gene is at the first split?

A. B3GNT4
B. CAPN3
C. CES2
D. CFHR4
E. CLIP3
F. GPA33
G. HRH1

plot(fit_rpart$finalModel)
text(fit_rpart$finalModel)

We can see that with just seven genes, we are able to predict the tissue type. Now let’s see if we can predict the tissue type with even fewer genes using a Random Forest. Use the train function and the rf method to train a Random Forest. Try out values of mtry ranging from seq(50, 200, 25) (you can also explore other values on your own). What mtry value maximizes accuracy? To permit small nodesize to grow as we did with the classification trees, use the following argument: nodesize = 1.

Note: This exercise will take some time to run. If you want to test out your code first, try using smaller values with ntree. Set the seed to 1991 again.

What value of mtry maximizes accuracy? - 100

set.seed(1991)
library(randomForest)
fit <- with(tissue_gene_expression, 
                train(x, y, method = "rf", 
                      nodesize = 1,
                      tuneGrid = data.frame(mtry = seq(50, 200, 25))))
    
ggplot(fit)

Use the function varImp on the output of train and save it to an object called imp.

#imp <- #BLANK
imp <- varImp(fit)
imp

## rf variable importance
## 
##   only 20 most important variables shown (out of 500)
## 
##          Overall
## GPA33     100.00
## BIN1       64.65
## GPM6B      62.35
## KIF2C      62.15
## CLIP3      52.09
## COLGALT2   46.48
## CFHR4      35.03
## SHANK2     34.90
## TFR2       33.61
## GALNT11    30.70
## CEP55      30.49
## TCN2       27.96
## CAPN3      27.52
## CYP4F11    25.74
## GTF2IRD1   24.89
## KCTD2      24.34
## FCN3       22.68
## SUSD6      22.24
## DOCK4      22.02
## RARRES2    21.53

What should replace #BLANK in the code above?

The rpart model we ran above produced a tree that used just seven predictors. Extracting the predictor names is not straightforward, but can be done. If the output of the call to train was fit_rpart, we can extract the names like this:

tree_terms <- as.character(unique(fit_rpart$finalModel$frame$var[!(fit_rpart$finalModel$frame$var == "<leaf>")]))
tree_terms

## [1] "GPA33"  "CLIP3"  "CAPN3"  "CFHR4"  "CES2"   "HRH1"   "B3GNT4"

Calculate the variable importance in the Random Forest call for these seven predictors and examine where they rank.

What is the importance of the CFHR4 gene in the Random Forest call? - 35

What is the rank of the CFHR4 gene in the Random Forest call? - 7

data_frame(term = rownames(imp$importance), 
            importance = imp$importance$Overall) %>%
    mutate(rank = rank(-importance)) %>% arrange(desc(importance)) %>%
    filter(term %in% tree_terms)

Model Fitting and Recommendation Systems Overview

In the Model Fitting and Recommendation Systems section, you will learn how to apply the machine learning algorithms you have learned.

After completing this section, you will be able to:

Apply the methods we have learned to an example, the MNIST digits.
Build a movie recommendation system using machine learning.
Penalize large estimates that come from small sample sizes using regularization.

This section has three parts: case study: MNIST, recommendation systems, and regularization.

Assessment 1- Ensembles

For these exercises we are going to build several machine learning models for the mnist_27 dataset and then build an ensemble. Each of the exercises in this comprehension check builds on the last.

Use the training set to build a model with several of the models available from the caret package. We will test out all of the following models in this exercise:

models <- c("glm", "lda",  "naive_bayes",  "svmLinear", 
                "gamboost",  "gamLoess", "qda", 
                "knn", "kknn", "loclda", "gam",
                "rf", "ranger",  "wsrf", "Rborist", 
                "avNNet", "mlp", "monmlp",
                "adaboost", "gbm",
                "svmRadial", "svmRadialCost", "svmRadialSigma")

We have not explained many of these, but apply them anyway using train with all the default parameters. You will likely need to install some packages. Keep in mind that you will probably get some warnings. Also, it will probably take a while to train all of the models - be patient!

Run the following code to train the various models:

library(caret)
library(dslabs)
set.seed(1)
data("mnist_27")

fits <- lapply(models, function(model){ 
    print(model)
    train(y ~ ., method = model, data = mnist_27$train)
})

## [1] "glm"
## [1] "lda"
## [1] "naive_bayes"
## [1] "svmLinear"
## [1] "gamboost"
## [1] "gamLoess"
## [1] "qda"
## [1] "knn"
## [1] "kknn"
## [1] "loclda"
## [1] "gam"
## [1] "rf"
## note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
## 
## [1] "ranger"
## note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
## 
## [1] "wsrf"
## note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
## 
## [1] "Rborist"
## note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
## 
## [1] "avNNet"
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 403.533539 
## iter  10 value 218.513879
## iter  20 value 210.777587
## iter  30 value 210.383660
## iter  40 value 210.364859
## iter  50 value 210.360071
## final  value 210.359975 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 399.175450 
## iter  10 value 270.235782
## iter  20 value 210.474135
## iter  30 value 210.414166
## iter  40 value 210.367478
## iter  50 value 210.364399
## iter  60 value 210.361999
## final  value 210.360762 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 400.129360 
## iter  10 value 288.051653
## iter  20 value 275.606213
## iter  30 value 243.152388
## iter  40 value 227.361612
## iter  50 value 211.690758
## iter  60 value 210.609944
## iter  70 value 210.408370
## iter  80 value 210.403145
## final  value 210.403004 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 412.929173 
## iter  10 value 329.943178
## iter  20 value 211.256887
## iter  30 value 210.456666
## iter  40 value 210.373368
## iter  50 value 210.361964
## iter  60 value 210.361105
## iter  70 value 210.359921
## final  value 210.359886 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 429.265327 
## iter  10 value 340.706678
## iter  20 value 214.736322
## iter  30 value 210.422929
## iter  40 value 210.397071
## iter  50 value 210.362408
## iter  50 value 210.362406
## iter  60 value 210.361780
## iter  70 value 210.360275
## final  value 210.360271 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 409.557688 
## iter  10 value 270.620722
## iter  20 value 210.861150
## iter  30 value 210.416305
## iter  40 value 210.410010
## iter  50 value 210.397060
## iter  60 value 210.392276
## iter  70 value 210.379826
## iter  80 value 210.373205
## iter  90 value 210.372118
## iter 100 value 210.368982
## final  value 210.368982 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 418.111453 
## iter  10 value 334.033257
## iter  20 value 201.556361
## iter  30 value 160.430395
## iter  40 value 160.139076
## iter  50 value 159.701634
## iter  60 value 159.535226
## iter  70 value 159.501538
## iter  80 value 159.499649
## iter  90 value 159.441300
## iter 100 value 159.369560
## final  value 159.369560 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 411.998071 
## iter  10 value 220.285446
## iter  20 value 210.722638
## iter  30 value 210.400479
## iter  40 value 210.358731
## iter  50 value 209.410786
## iter  60 value 179.620373
## iter  70 value 168.006438
## iter  80 value 162.562318
## iter  90 value 161.694279
## iter 100 value 161.307734
## final  value 161.307734 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 434.393495 
## iter  10 value 231.547655
## iter  20 value 214.413805
## iter  30 value 209.857730
## iter  40 value 209.621086
## iter  50 value 208.949462
## iter  60 value 208.817087
## iter  70 value 208.287210
## iter  80 value 206.595663
## iter  90 value 205.768454
## iter 100 value 192.461647
## final  value 192.461647 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 449.965148 
## iter  10 value 314.695358
## iter  20 value 225.974089
## iter  30 value 210.879783
## iter  40 value 210.453540
## iter  50 value 210.417538
## iter  60 value 210.402497
## iter  70 value 210.393509
## iter  80 value 210.390960
## iter  90 value 210.385210
## iter 100 value 210.369880
## final  value 210.369880 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 440.337273 
## iter  10 value 296.286539
## iter  20 value 274.131511
## iter  30 value 239.536066
## iter  40 value 232.106630
## iter  50 value 214.596241
## iter  60 value 210.608684
## iter  70 value 210.112858
## iter  80 value 194.943289
## iter  90 value 176.425689
## iter 100 value 161.339530
## final  value 161.339530 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 501.103587 
## iter  10 value 303.351022
## iter  20 value 216.173904
## iter  30 value 209.557432
## iter  40 value 172.596784
## iter  50 value 162.884735
## iter  60 value 161.263587
## iter  70 value 159.893613
## iter  80 value 159.087298
## iter  90 value 158.613992
## iter 100 value 158.073939
## final  value 158.073939 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 397.836520 
## iter  10 value 224.071164
## iter  20 value 208.586101
## iter  30 value 172.620319
## iter  40 value 160.770659
## iter  50 value 158.811165
## iter  60 value 157.781747
## iter  70 value 157.292851
## iter  80 value 157.104609
## iter  90 value 156.910712
## iter 100 value 155.552672
## final  value 155.552672 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 422.145808 
## iter  10 value 264.646487
## iter  20 value 213.159856
## iter  30 value 210.495789
## iter  40 value 210.347105
## iter  50 value 210.331976
## iter  60 value 210.313194
## iter  70 value 210.291446
## iter  80 value 210.215266
## iter  90 value 209.835396
## iter 100 value 207.763011
## final  value 207.763011 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 403.196973 
## iter  10 value 239.197553
## iter  20 value 196.071975
## iter  30 value 174.982666
## iter  40 value 161.239803
## iter  50 value 159.857205
## iter  60 value 159.579776
## iter  70 value 158.945222
## iter  80 value 158.594489
## iter  90 value 158.276733
## iter 100 value 158.070362
## final  value 158.070362 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 431.672959 
## iter  10 value 347.699259
## iter  20 value 244.149146
## final  value 243.821792 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 409.854803 
## iter  10 value 262.690840
## iter  20 value 243.823691
## final  value 243.821792 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 420.676692 
## iter  10 value 358.529025
## iter  20 value 243.892322
## iter  30 value 243.821798
## final  value 243.821792 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 410.475616 
## iter  10 value 356.552748
## iter  20 value 244.515557
## iter  30 value 243.821850
## final  value 243.821792 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 400.822323 
## iter  10 value 305.876119
## iter  20 value 243.873193
## final  value 243.821792 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 413.931065 
## iter  10 value 251.679702
## iter  20 value 240.295026
## iter  30 value 239.845864
## iter  40 value 239.813517
## iter  50 value 239.808781
## iter  60 value 239.800120
## final  value 239.799987 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 421.859293 
## iter  10 value 349.290697
## iter  20 value 249.416970
## iter  30 value 245.499531
## iter  40 value 242.941103
## iter  50 value 240.363629
## iter  60 value 239.950029
## iter  70 value 239.833379
## iter  80 value 239.823204
## iter  90 value 239.801505
## final  value 239.799987 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 446.956227 
## iter  10 value 302.545398
## iter  20 value 252.845455
## iter  30 value 245.809660
## iter  40 value 243.835249
## iter  50 value 241.698733
## iter  60 value 239.999529
## iter  70 value 239.808173
## iter  80 value 239.800593
## iter  90 value 239.800232
## final  value 239.799987 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 412.535246 
## iter  10 value 289.701675
## iter  20 value 243.915665
## iter  30 value 241.075162
## iter  40 value 239.927142
## iter  50 value 239.887889
## iter  60 value 239.858744
## iter  70 value 239.802939
## final  value 239.800048 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 417.492587 
## iter  10 value 279.041680
## iter  20 value 240.112431
## iter  30 value 239.903246
## iter  40 value 239.888282
## iter  50 value 239.887702
## iter  60 value 239.884598
## final  value 239.883704 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 423.724322 
## iter  10 value 259.038313
## iter  20 value 241.810383
## iter  30 value 240.104706
## iter  40 value 239.856810
## iter  50 value 239.802812
## iter  60 value 239.709260
## iter  70 value 239.627780
## iter  80 value 239.571584
## iter  90 value 239.559555
## iter 100 value 239.555032
## final  value 239.555032 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 411.298634 
## iter  10 value 284.482458
## iter  20 value 247.914377
## iter  30 value 241.752683
## iter  40 value 240.563076
## iter  50 value 240.395552
## iter  60 value 239.946610
## iter  70 value 239.619774
## iter  80 value 239.598101
## iter  90 value 239.592728
## iter 100 value 239.589107
## final  value 239.589107 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 415.253543 
## iter  10 value 269.221581
## iter  20 value 241.523168
## iter  30 value 240.234834
## iter  40 value 239.955956
## iter  50 value 239.889454
## iter  60 value 239.813849
## iter  70 value 239.704839
## iter  80 value 239.573675
## iter  90 value 239.563852
## iter 100 value 239.556738
## final  value 239.556738 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 409.758337 
## iter  10 value 265.866977
## iter  20 value 240.995549
## iter  30 value 239.890806
## iter  40 value 239.762134
## iter  50 value 239.732250
## iter  60 value 239.636534
## iter  70 value 239.598717
## iter  80 value 239.570013
## iter  90 value 239.560878
## iter 100 value 239.554263
## final  value 239.554263 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 416.112955 
## iter  10 value 280.928644
## iter  20 value 240.845794
## iter  30 value 240.081083
## iter  40 value 239.795406
## iter  50 value 239.678196
## iter  60 value 239.642767
## iter  70 value 239.604484
## iter  80 value 239.592131
## iter  90 value 239.586621
## iter 100 value 239.583478
## final  value 239.583478 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 415.096263 
## iter  10 value 226.586215
## iter  20 value 210.483167
## iter  30 value 210.437309
## iter  40 value 210.414663
## iter  50 value 210.410646
## final  value 210.409605 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 422.738055 
## iter  10 value 273.183361
## iter  20 value 210.760057
## iter  30 value 210.464956
## iter  40 value 210.415062
## iter  40 value 210.415062
## iter  40 value 210.415062
## final  value 210.415062 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 432.774841 
## iter  10 value 291.337627
## iter  20 value 219.135353
## iter  30 value 211.636359
## iter  40 value 210.414098
## final  value 210.404771 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 417.511126 
## iter  10 value 230.974043
## iter  20 value 210.595361
## iter  30 value 210.466439
## iter  40 value 210.412967
## iter  50 value 210.410387
## final  value 210.409555 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 432.312798 
## iter  10 value 244.721634
## iter  20 value 210.778939
## iter  30 value 210.503394
## iter  40 value 210.424869
## iter  50 value 210.410447
## iter  60 value 210.410162
## final  value 210.409567 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 429.500647 
## iter  10 value 224.157968
## iter  20 value 185.365746
## iter  30 value 163.263843
## iter  40 value 161.141640
## iter  50 value 160.438531
## iter  60 value 160.363082
## iter  70 value 160.132490
## iter  80 value 159.756574
## iter  90 value 159.548297
## iter 100 value 159.445945
## final  value 159.445945 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 402.739117 
## iter  10 value 230.200270
## iter  20 value 211.335020
## iter  30 value 209.947487
## iter  40 value 199.256903
## iter  50 value 177.882563
## iter  60 value 167.583863
## iter  70 value 162.027491
## iter  80 value 160.551665
## iter  90 value 160.356891
## iter 100 value 160.216989
## final  value 160.216989 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 398.800281 
## iter  10 value 216.398483
## iter  20 value 210.646555
## iter  30 value 210.369518
## iter  40 value 209.568394
## iter  50 value 180.698957
## iter  60 value 166.792511
## iter  70 value 162.906658
## iter  80 value 161.033838
## iter  90 value 160.197767
## iter 100 value 159.950741
## final  value 159.950741 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 424.530933 
## iter  10 value 312.508747
## iter  20 value 299.036333
## iter  30 value 288.299078
## iter  40 value 287.674665
## iter  50 value 285.436711
## iter  60 value 279.084316
## iter  70 value 242.066935
## iter  80 value 239.670748
## iter  90 value 215.085851
## iter 100 value 213.009478
## final  value 213.009478 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 399.553826 
## iter  10 value 270.287737
## iter  20 value 210.406266
## iter  30 value 210.078077
## iter  40 value 191.273387
## iter  50 value 166.375161
## iter  60 value 162.751324
## iter  70 value 162.219954
## iter  80 value 161.367078
## iter  90 value 160.294637
## iter 100 value 160.015328
## final  value 160.015328 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 400.114153 
## iter  10 value 216.855040
## iter  20 value 166.544682
## iter  30 value 161.230150
## iter  40 value 160.437724
## iter  50 value 159.942031
## iter  60 value 159.895047
## iter  70 value 159.708500
## iter  80 value 159.472953
## iter  90 value 159.067347
## iter 100 value 158.853348
## final  value 158.853348 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 421.239170 
## iter  10 value 332.355254
## iter  20 value 210.511194
## iter  30 value 200.112854
## iter  40 value 168.901120
## iter  50 value 160.916759
## iter  60 value 159.696564
## iter  70 value 159.411608
## iter  80 value 159.308382
## iter  90 value 159.225282
## iter 100 value 158.876466
## final  value 158.876466 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 411.635238 
## iter  10 value 236.110572
## iter  20 value 218.730687
## iter  30 value 210.944716
## iter  40 value 210.526420
## iter  50 value 210.513131
## iter  60 value 210.435031
## iter  70 value 209.136386
## iter  80 value 173.539487
## iter  90 value 162.550782
## iter 100 value 160.461575
## final  value 160.461575 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 426.841970 
## iter  10 value 248.708968
## iter  20 value 210.726101
## iter  30 value 203.201041
## iter  40 value 161.840599
## iter  50 value 159.930238
## iter  60 value 159.254900
## iter  70 value 158.861835
## iter  80 value 158.621743
## iter  90 value 158.239118
## iter 100 value 157.893154
## final  value 157.893154 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 399.568919 
## iter  10 value 232.297292
## iter  20 value 165.712473
## iter  30 value 161.013634
## iter  40 value 160.385225
## iter  50 value 160.246782
## iter  60 value 160.101526
## iter  70 value 159.919704
## iter  80 value 159.700134
## iter  90 value 159.428660
## iter 100 value 159.010521
## final  value 159.010521 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 413.223734 
## iter  10 value 237.990325
## iter  20 value 226.648614
## iter  30 value 226.369510
## iter  40 value 226.306749
## iter  50 value 226.251310
## iter  60 value 226.246152
## iter  70 value 226.244473
## iter  80 value 226.241684
## iter  90 value 226.239572
## iter 100 value 226.236948
## final  value 226.236948 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 408.205379 
## iter  10 value 253.276789
## iter  20 value 234.941576
## iter  30 value 227.812313
## iter  40 value 226.362074
## iter  50 value 226.230204
## iter  60 value 226.228684
## iter  70 value 226.228196
## final  value 226.226652 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 418.900155 
## iter  10 value 259.683873
## iter  20 value 226.394544
## iter  30 value 226.348401
## iter  40 value 226.272337
## iter  50 value 226.251454
## iter  60 value 226.242253
## iter  70 value 226.239336
## iter  80 value 226.235701
## iter  90 value 226.230246
## iter 100 value 226.228742
## final  value 226.228742 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 451.813439 
## iter  10 value 364.913992
## iter  20 value 229.578191
## iter  30 value 227.405926
## iter  40 value 226.542842
## iter  50 value 226.300238
## iter  60 value 226.281284
## iter  70 value 226.262185
## iter  80 value 226.254430
## iter  90 value 226.242769
## iter 100 value 226.241474
## final  value 226.241474 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 410.080169 
## iter  10 value 274.070538
## iter  20 value 227.123551
## iter  30 value 226.467398
## iter  40 value 226.327769
## iter  50 value 226.272127
## iter  60 value 226.255467
## iter  70 value 226.250947
## iter  80 value 226.246610
## iter  90 value 226.243591
## iter 100 value 226.239191
## final  value 226.239191 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 435.756476 
## iter  10 value 327.865191
## iter  20 value 322.520568
## iter  30 value 321.985247
## iter  40 value 313.969160
## iter  50 value 258.675771
## iter  60 value 257.113424
## iter  70 value 251.810058
## iter  80 value 242.044557
## iter  90 value 229.019874
## iter 100 value 227.568562
## final  value 227.568562 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 418.829043 
## iter  10 value 279.032048
## iter  20 value 230.053867
## iter  30 value 226.321354
## iter  40 value 226.295695
## iter  50 value 226.254976
## iter  60 value 226.248701
## iter  70 value 226.231391
## iter  80 value 226.223705
## iter  90 value 226.216306
## iter 100 value 226.211596
## final  value 226.211596 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 439.724247 
## iter  10 value 255.074469
## iter  20 value 195.959706
## iter  30 value 173.937856
## iter  40 value 168.626493
## iter  50 value 167.945226
## iter  60 value 167.597949
## iter  70 value 167.508123
## iter  80 value 167.493827
## iter  90 value 167.448845
## iter 100 value 167.366428
## final  value 167.366428 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 433.902393 
## iter  10 value 328.218887
## iter  20 value 291.622187
## iter  30 value 273.985079
## iter  40 value 264.497711
## iter  50 value 251.105798
## iter  60 value 234.471400
## iter  70 value 229.136545
## iter  80 value 227.574949
## iter  90 value 227.031169
## iter 100 value 226.744878
## final  value 226.744878 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 421.326756 
## iter  10 value 242.468489
## iter  20 value 227.288105
## iter  30 value 226.480005
## iter  40 value 226.317165
## iter  50 value 226.290451
## iter  60 value 226.238635
## iter  70 value 226.230412
## iter  80 value 226.217840
## iter  90 value 226.217252
## iter 100 value 226.213197
## final  value 226.213197 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 399.844436 
## iter  10 value 229.901875
## iter  20 value 189.662291
## iter  30 value 173.574758
## iter  40 value 169.354920
## iter  50 value 167.998124
## iter  60 value 167.722673
## iter  70 value 167.487691
## iter  80 value 167.358469
## iter  90 value 167.273012
## iter 100 value 166.969095
## final  value 166.969095 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 420.593575 
## iter  10 value 255.190643
## iter  20 value 223.740503
## iter  30 value 177.120700
## iter  40 value 169.016539
## iter  50 value 167.372459
## iter  60 value 166.418523
## iter  70 value 165.502998
## iter  80 value 164.492071
## iter  90 value 163.209778
## iter 100 value 161.455742
## final  value 161.455742 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 421.276926 
## iter  10 value 254.651425
## iter  20 value 169.784319
## iter  30 value 166.785301
## iter  40 value 166.269789
## iter  50 value 165.463276
## iter  60 value 163.433757
## iter  70 value 162.602376
## iter  80 value 162.148198
## iter  90 value 160.984135
## iter 100 value 159.814580
## final  value 159.814580 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 419.560047 
## iter  10 value 280.234546
## iter  20 value 217.523561
## iter  30 value 179.786602
## iter  40 value 167.654361
## iter  50 value 166.961751
## iter  60 value 166.826717
## iter  70 value 166.789952
## iter  80 value 166.520201
## iter  90 value 165.920060
## iter 100 value 161.253841
## final  value 161.253841 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 446.656533 
## iter  10 value 356.947574
## iter  20 value 236.527325
## iter  30 value 226.671248
## iter  40 value 226.375965
## iter  50 value 224.145613
## iter  60 value 193.111459
## iter  70 value 170.075433
## iter  80 value 167.851388
## iter  90 value 167.617527
## iter 100 value 167.597414
## final  value 167.597414 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 482.630148 
## iter  10 value 298.636252
## iter  20 value 257.478019
## final  value 257.068271 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 417.289932 
## iter  10 value 287.952740
## iter  20 value 265.046061
## iter  30 value 256.936101
## final  value 256.876898 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 419.204808 
## iter  10 value 333.375787
## iter  20 value 257.406334
## iter  30 value 256.876896
## iter  30 value 256.876896
## iter  30 value 256.876896
## final  value 256.876896 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 401.880772 
## iter  10 value 272.052721
## iter  20 value 257.797178
## final  value 257.068271 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 414.930420 
## iter  10 value 341.367118
## iter  20 value 257.289580
## final  value 257.068283 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 415.666957 
## iter  10 value 291.017976
## iter  20 value 258.649795
## iter  30 value 256.283344
## iter  40 value 253.969303
## iter  50 value 253.372238
## iter  60 value 253.356456
## iter  70 value 253.352783
## final  value 253.352768 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 397.465209 
## iter  10 value 327.713773
## iter  20 value 256.507011
## iter  30 value 253.699952
## iter  40 value 253.504783
## iter  50 value 253.435678
## iter  60 value 253.414224
## iter  70 value 253.354127
## iter  80 value 253.352778
## final  value 253.352765 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 404.072401 
## iter  10 value 265.156434
## iter  20 value 254.566959
## iter  30 value 250.833990
## iter  40 value 248.013894
## iter  50 value 247.166635
## iter  60 value 247.144966
## final  value 247.144945 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 398.547609 
## iter  10 value 370.362091
## iter  20 value 260.248362
## iter  30 value 256.479093
## iter  40 value 253.564744
## iter  50 value 253.367753
## iter  60 value 253.352857
## final  value 253.352760 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 424.822454 
## iter  10 value 292.094821
## iter  20 value 255.192799
## iter  30 value 249.616961
## iter  40 value 248.151608
## iter  50 value 247.157320
## iter  60 value 247.060420
## iter  70 value 246.862064
## iter  80 value 246.853198
## final  value 246.853152 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 438.851944 
## iter  10 value 294.770415
## iter  20 value 257.401039
## iter  30 value 250.630402
## iter  40 value 249.711852
## iter  50 value 248.304953
## iter  60 value 247.834424
## iter  70 value 246.966777
## iter  80 value 246.775566
## iter  90 value 246.753383
## iter 100 value 246.743681
## final  value 246.743681 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 437.707610 
## iter  10 value 278.031644
## iter  20 value 253.638336
## iter  30 value 253.217152
## iter  40 value 253.136246
## iter  50 value 253.076324
## iter  60 value 253.066036
## iter  70 value 253.061627
## iter  80 value 253.057990
## iter  90 value 253.057382
## final  value 253.057362 
## converged
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 400.335009 
## iter  10 value 303.459720
## iter  20 value 262.737749
## iter  30 value 255.691486
## iter  40 value 253.954553
## iter  50 value 253.685217
## iter  60 value 253.327398
## iter  70 value 253.188353
## iter  80 value 253.100741
## iter  90 value 253.093718
## iter 100 value 253.090088
## final  value 253.090088 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 436.467528 
## iter  10 value 273.134588
## iter  20 value 255.533019
## iter  30 value 254.191422
## iter  40 value 253.366821
## iter  50 value 253.285844
## iter  60 value 253.238757
## iter  70 value 253.144315
## iter  80 value 253.062253
## iter  90 value 253.057894
## iter 100 value 253.057491
## final  value 253.057491 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 481.168829 
## iter  10 value 265.835205
## iter  20 value 253.948991
## iter  30 value 253.620281
## iter  40 value 253.386724
## iter  50 value 253.281379
## iter  60 value 253.231227
## iter  70 value 253.120409
## iter  80 value 253.090854
## iter  90 value 253.080522
## iter 100 value 253.076298
## final  value 253.076298 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 406.114930 
## iter  10 value 271.886241
## iter  20 value 226.525807
## iter  30 value 226.444397
## iter  40 value 226.330361
## iter  50 value 226.319530
## iter  60 value 226.312498
## iter  70 value 226.310626
## iter  80 value 226.309681
## iter  90 value 226.308837
## iter 100 value 226.308233
## final  value 226.308233 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 405.743739 
## iter  10 value 233.114822
## iter  20 value 226.464130
## iter  30 value 226.391951
## iter  40 value 226.338764
## iter  50 value 226.325264
## iter  60 value 226.319274
## iter  70 value 226.315490
## iter  80 value 226.313025
## iter  90 value 226.310924
## iter 100 value 226.309862
## final  value 226.309862 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 410.194374 
## iter  10 value 254.619171
## iter  20 value 226.459955
## iter  30 value 226.401953
## iter  40 value 226.336739
## iter  50 value 226.324574
## iter  60 value 226.319432
## iter  70 value 226.310624
## iter  80 value 226.309253
## final  value 226.309195 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 448.078122 
## iter  10 value 230.197459
## iter  20 value 226.406576
## iter  30 value 226.347830
## iter  40 value 226.315711
## iter  50 value 226.306645
## iter  60 value 226.303193
## iter  70 value 226.295150
## iter  80 value 226.294322
## iter  90 value 226.294227
## final  value 226.293686 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 415.502700 
## iter  10 value 375.956122
## iter  20 value 337.577547
## iter  30 value 227.274729
## iter  40 value 226.385917
## iter  50 value 226.352793
## iter  60 value 226.314711
## final  value 226.307330 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 479.947205 
## iter  10 value 234.902070
## iter  20 value 185.275577
## iter  30 value 171.059454
## iter  40 value 168.756842
## iter  50 value 167.909281
## iter  60 value 167.681280
## iter  70 value 167.563396
## iter  80 value 167.515784
## iter  90 value 167.360537
## iter 100 value 166.602208
## final  value 166.602208 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 459.207787 
## iter  10 value 236.340562
## iter  20 value 227.040440
## iter  30 value 226.411322
## iter  40 value 226.269103
## iter  50 value 224.818398
## iter  60 value 190.897355
## iter  70 value 176.055576
## iter  80 value 171.781153
## iter  90 value 168.935314
## iter 100 value 167.757533
## final  value 167.757533 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 397.212654 
## iter  10 value 233.061387
## iter  20 value 206.207539
## iter  30 value 172.978516
## iter  40 value 168.219645
## iter  50 value 167.756399
## iter  60 value 167.290293
## iter  70 value 167.125412
## iter  80 value 167.092085
## iter  90 value 167.011550
## iter 100 value 166.849776
## final  value 166.849776 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 417.835232 
## iter  10 value 228.038220
## iter  20 value 226.550772
## iter  30 value 226.400608
## iter  40 value 226.386162
## iter  50 value 226.372121
## iter  60 value 226.339899
## iter  70 value 226.319572
## iter  80 value 226.258691
## iter  90 value 223.578926
## iter 100 value 177.285999
## final  value 177.285999 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 400.033646 
## iter  10 value 318.399136
## iter  20 value 299.708032
## iter  30 value 236.299729
## iter  40 value 192.252445
## iter  50 value 181.436227
## iter  60 value 179.159838
## iter  70 value 173.144349
## iter  80 value 169.302685
## iter  90 value 168.710994
## iter 100 value 168.581367
## final  value 168.581367 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 459.697021 
## iter  10 value 274.146951
## iter  20 value 222.636980
## iter  30 value 172.048687
## iter  40 value 167.985213
## iter  50 value 167.525563
## iter  60 value 167.425593
## iter  70 value 167.024759
## iter  80 value 166.933651
## iter  90 value 166.683531
## iter 100 value 166.562879
## final  value 166.562879 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 426.403563 
## iter  10 value 244.845873
## iter  20 value 226.827625
## iter  30 value 226.518035
## iter  40 value 226.361027
## iter  50 value 224.963267
## iter  60 value 203.408609
## iter  70 value 171.868845
## iter  80 value 168.520754
## iter  90 value 167.959585
## iter 100 value 167.885362
## final  value 167.885362 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 442.104770 
## iter  10 value 268.980075
## iter  20 value 225.419307
## iter  30 value 185.922734
## iter  40 value 170.758226
## iter  50 value 168.363220
## iter  60 value 167.234823
## iter  70 value 167.059050
## iter  80 value 166.793918
## iter  90 value 166.366281
## iter 100 value 165.164202
## final  value 165.164202 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 404.711962 
## iter  10 value 242.156719
## iter  20 value 227.071420
## iter  30 value 206.454826
## iter  40 value 177.633575
## iter  50 value 172.597410
## iter  60 value 169.782209
## iter  70 value 168.283037
## iter  80 value 167.096062
## iter  90 value 165.813126
## iter 100 value 164.095945
## final  value 164.095945 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 412.985297 
## iter  10 value 237.020859
## iter  20 value 227.296707
## iter  30 value 225.675643
## iter  40 value 178.334938
## iter  50 value 169.415665
## iter  60 value 168.342493
## iter  70 value 167.839509
## iter  80 value 167.566922
## iter  90 value 167.474565
## iter 100 value 167.120806
## final  value 167.120806 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 415.348371 
## iter  10 value 342.467259
## iter  20 value 262.045414
## iter  30 value 260.575216
## final  value 260.574378 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 433.372223 
## iter  10 value 275.051717
## iter  20 value 260.787731
## iter  30 value 260.613824
## iter  40 value 260.572469
## final  value 260.564476 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 428.544987 
## iter  10 value 262.278565
## iter  20 value 260.882520
## iter  30 value 260.635451
## iter  40 value 260.618022
## iter  50 value 260.580951
## iter  60 value 260.565006
## final  value 260.564479 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 409.219724 
## iter  10 value 276.730635
## iter  20 value 260.618747
## iter  30 value 260.565980
## final  value 260.564477 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 413.954078 
## iter  10 value 319.831919
## iter  20 value 270.096318
## iter  30 value 267.565754
## iter  40 value 264.715743
## iter  50 value 261.150947
## iter  60 value 260.595519
## iter  70 value 260.564708
## final  value 260.564550 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 444.327913 
## iter  10 value 299.765814
## iter  20 value 260.793662
## iter  30 value 233.193680
## iter  40 value 189.467926
## iter  50 value 188.674647
## iter  60 value 188.497886
## iter  70 value 188.463641
## iter  80 value 188.410040
## iter  90 value 188.356884
## iter 100 value 188.230422
## final  value 188.230422 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 411.322562 
## iter  10 value 316.039440
## iter  20 value 260.548680
## iter  30 value 249.668072
## iter  40 value 197.493328
## iter  50 value 190.857302
## iter  60 value 188.337407
## iter  70 value 187.659316
## iter  80 value 187.566896
## iter  90 value 187.434976
## iter 100 value 187.166100
## final  value 187.166100 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 444.553104 
## iter  10 value 307.725598
## iter  20 value 261.291605
## iter  30 value 260.712679
## iter  40 value 260.675284
## iter  50 value 260.596247
## iter  60 value 229.074438
## iter  70 value 188.919252
## iter  80 value 188.789005
## iter  90 value 188.663705
## iter 100 value 188.622345
## final  value 188.622345 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 433.148542 
## iter  10 value 331.605809
## iter  20 value 260.615470
## iter  30 value 249.322859
## iter  40 value 191.215338
## iter  50 value 188.522816
## iter  60 value 188.422226
## iter  70 value 188.314864
## iter  80 value 188.069797
## iter  90 value 187.818522
## iter 100 value 187.676656
## final  value 187.676656 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 406.199272 
## iter  10 value 308.846806
## iter  20 value 260.356401
## iter  30 value 207.459909
## iter  40 value 194.368865
## iter  50 value 190.886502
## iter  60 value 189.156249
## iter  70 value 188.844865
## iter  80 value 188.830156
## iter  90 value 188.776861
## iter 100 value 188.688642
## final  value 188.688642 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 408.577827 
## iter  10 value 268.075459
## iter  20 value 260.657678
## iter  30 value 257.917257
## iter  40 value 196.608854
## iter  50 value 188.904804
## iter  60 value 187.916985
## iter  70 value 187.765131
## iter  80 value 187.555175
## iter  90 value 187.232064
## iter 100 value 187.163648
## final  value 187.163648 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 437.037948 
## iter  10 value 300.254538
## iter  20 value 260.659829
## iter  30 value 260.380193
## iter  40 value 238.287625
## iter  50 value 194.889660
## iter  60 value 190.979197
## iter  70 value 188.852884
## iter  80 value 188.409627
## iter  90 value 188.097304
## iter 100 value 187.445259
## final  value 187.445259 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 439.838705 
## iter  10 value 348.113780
## iter  20 value 261.415648
## iter  30 value 212.414521
## iter  40 value 192.031480
## iter  50 value 188.703766
## iter  60 value 187.926861
## iter  70 value 187.513308
## iter  80 value 187.390943
## iter  90 value 187.174727
## iter 100 value 186.697995
## final  value 186.697995 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 486.471628 
## iter  10 value 279.672170
## iter  20 value 260.893762
## iter  30 value 233.664020
## iter  40 value 194.294475
## iter  50 value 188.827682
## iter  60 value 187.792958
## iter  70 value 186.714347
## iter  80 value 184.487807
## iter  90 value 182.571298
## iter 100 value 181.576387
## final  value 181.576387 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 443.409520 
## iter  10 value 265.359960
## iter  20 value 260.523898
## iter  30 value 233.241468
## iter  40 value 189.130075
## iter  50 value 188.161578
## iter  60 value 187.607511
## iter  70 value 187.314096
## iter  80 value 187.025294
## iter  90 value 186.523054
## iter 100 value 185.939163
## final  value 185.939163 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 425.410079 
## iter  10 value 340.729038
## iter  20 value 286.338520
## final  value 286.257536 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 424.746528 
## iter  10 value 290.914869
## iter  20 value 286.258759
## final  value 286.257536 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 411.144141 
## iter  10 value 369.056583
## iter  20 value 286.267169
## final  value 286.257538 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 436.694733 
## iter  10 value 377.123870
## iter  20 value 286.304366
## final  value 286.257536 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 426.448371 
## iter  10 value 300.902411
## iter  20 value 286.259006
## final  value 286.257603 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 409.027504 
## iter  10 value 303.281635
## iter  20 value 288.538097
## iter  30 value 286.153614
## iter  40 value 284.672602
## iter  50 value 282.124104
## iter  60 value 271.626441
## iter  70 value 270.583220
## iter  80 value 270.529922
## final  value 270.528347 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 410.351530 
## iter  10 value 319.709394
## iter  20 value 284.497344
## iter  30 value 284.054573
## iter  40 value 283.385731
## iter  50 value 283.263581
## iter  60 value 283.262626
## final  value 283.261304 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 465.109832 
## iter  10 value 374.201779
## iter  20 value 285.857395
## iter  30 value 283.466794
## iter  40 value 283.252641
## iter  50 value 283.213597
## iter  60 value 283.208438
## final  value 283.205256 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 426.997214 
## iter  10 value 337.332744
## iter  20 value 287.349004
## iter  30 value 285.306513
## iter  40 value 284.067374
## iter  50 value 283.280457
## iter  60 value 283.269689
## final  value 283.269673 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 414.185445 
## iter  10 value 345.276436
## iter  20 value 287.873622
## iter  30 value 285.147224
## iter  40 value 283.956227
## iter  50 value 283.269375
## iter  60 value 283.258039
## iter  70 value 283.238991
## iter  80 value 283.227961
## iter  90 value 283.210847
## iter 100 value 283.205263
## final  value 283.205263 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 412.067844 
## iter  10 value 308.398566
## iter  20 value 290.599324
## iter  30 value 286.982173
## iter  40 value 284.220122
## iter  50 value 275.459607
## iter  60 value 272.944816
## iter  70 value 270.477292
## iter  80 value 270.234372
## iter  90 value 270.213110
## iter 100 value 270.204441
## final  value 270.204441 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 407.081480 
## iter  10 value 307.215417
## iter  20 value 284.811914
## iter  30 value 283.822436
## iter  40 value 283.374961
## iter  50 value 283.237873
## iter  60 value 283.155375
## iter  70 value 283.068697
## iter  80 value 283.050250
## iter  90 value 283.049824
## final  value 283.049224 
## converged
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 405.088020 
## iter  10 value 303.693784
## iter  20 value 285.937563
## iter  30 value 284.325759
## iter  40 value 283.688008
## iter  50 value 283.399776
## iter  60 value 283.293094
## iter  70 value 283.209380
## iter  80 value 283.094488
## iter  90 value 283.049405
## iter 100 value 283.034776
## final  value 283.034776 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 416.548948 
## iter  10 value 303.201115
## iter  20 value 286.114052
## iter  30 value 284.094495
## iter  40 value 283.815817
## iter  50 value 283.450434
## iter  60 value 283.261921
## iter  70 value 283.180824
## iter  80 value 283.058492
## iter  90 value 283.042063
## iter 100 value 283.033823
## final  value 283.033823 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 498.256370 
## iter  10 value 326.903816
## iter  20 value 288.639610
## iter  30 value 282.430803
## iter  40 value 277.300653
## iter  50 value 276.154777
## iter  60 value 274.401881
## iter  70 value 272.381922
## iter  80 value 270.882529
## iter  90 value 270.296752
## iter 100 value 270.270332
## final  value 270.270332 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 409.618868 
## iter  10 value 279.463086
## iter  20 value 260.731249
## iter  30 value 260.612003
## iter  40 value 260.598230
## final  value 260.598223 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 422.648241 
## iter  10 value 287.573508
## iter  20 value 260.822145
## iter  30 value 260.599143
## iter  40 value 260.596344
## final  value 260.596212 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 410.414360 
## iter  10 value 334.157600
## iter  20 value 261.152921
## iter  30 value 260.605595
## final  value 260.596217 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 414.184356 
## iter  10 value 273.123103
## iter  20 value 261.432220
## iter  30 value 260.601126
## iter  40 value 260.597103
## final  value 260.596212 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 449.105311 
## iter  10 value 296.347948
## iter  20 value 260.941768
## iter  30 value 260.602161
## final  value 260.598158 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 401.935707 
## iter  10 value 278.652898
## iter  20 value 261.187275
## iter  30 value 248.045773
## iter  40 value 215.784653
## iter  50 value 193.141288
## iter  60 value 188.756067
## iter  70 value 188.086117
## iter  80 value 188.017612
## iter  90 value 187.655469
## iter 100 value 187.351811
## final  value 187.351811 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 400.458162 
## iter  10 value 277.786610
## iter  20 value 260.936462
## iter  30 value 260.653452
## iter  40 value 260.618326
## iter  50 value 260.615685
## iter  60 value 260.605834
## iter  70 value 260.597761
## iter  80 value 259.849346
## iter  90 value 221.799872
## iter 100 value 196.108132
## final  value 196.108132 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 457.054676 
## iter  10 value 310.193442
## iter  20 value 264.580050
## iter  30 value 260.809590
## iter  40 value 259.587564
## iter  50 value 224.712891
## iter  60 value 189.121305
## iter  70 value 188.596663
## iter  80 value 187.854074
## iter  90 value 186.866331
## iter 100 value 186.062035
## final  value 186.062035 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 423.584118 
## iter  10 value 358.854394
## iter  20 value 262.159876
## iter  30 value 260.595692
## iter  40 value 260.523835
## iter  50 value 232.310569
## iter  60 value 203.183966
## iter  70 value 192.884533
## iter  80 value 191.429491
## iter  90 value 189.537625
## iter 100 value 189.043380
## final  value 189.043380 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 411.290519 
## iter  10 value 269.703841
## iter  20 value 260.232538
## iter  30 value 240.848256
## iter  40 value 197.045130
## iter  50 value 191.447189
## iter  60 value 189.076140
## iter  70 value 188.413880
## iter  80 value 188.176191
## iter  90 value 188.083533
## iter 100 value 187.960477
## final  value 187.960477 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 421.788960 
## iter  10 value 310.141447
## iter  20 value 258.890352
## iter  30 value 201.832142
## iter  40 value 189.072876
## iter  50 value 188.692479
## iter  60 value 187.917295
## iter  70 value 187.055294
## iter  80 value 186.531284
## iter  90 value 185.657418
## iter 100 value 184.156428
## final  value 184.156428 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 420.109448 
## iter  10 value 276.758543
## iter  20 value 261.135495
## iter  30 value 212.090411
## iter  40 value 190.063601
## iter  50 value 189.102782
## iter  60 value 189.041008
## iter  70 value 188.960333
## iter  80 value 188.853475
## iter  90 value 188.645238
## iter 100 value 188.124035
## final  value 188.124035 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 441.888773 
## iter  10 value 288.195177
## iter  20 value 260.769853
## iter  30 value 260.225420
## iter  40 value 250.159128
## iter  50 value 191.899513
## iter  60 value 189.352947
## iter  70 value 188.853333
## iter  80 value 188.294451
## iter  90 value 187.700432
## iter 100 value 187.224844
## final  value 187.224844 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 404.287963 
## iter  10 value 268.982892
## iter  20 value 195.485838
## iter  30 value 188.804841
## iter  40 value 188.659847
## iter  50 value 188.597510
## iter  60 value 188.579990
## iter  70 value 188.554525
## iter  80 value 188.518989
## iter  90 value 188.395846
## iter 100 value 188.018986
## final  value 188.018986 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 450.973884 
## iter  10 value 292.340006
## iter  20 value 260.249442
## iter  30 value 214.090099
## iter  40 value 188.833047
## iter  50 value 188.152666
## iter  60 value 187.860884
## iter  70 value 187.434455
## iter  80 value 186.781852
## iter  90 value 186.708977
## iter 100 value 186.242042
## final  value 186.242042 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 415.101533 
## iter  10 value 311.199359
## iter  20 value 213.752995
## iter  30 value 210.056720
## iter  40 value 209.629643
## iter  50 value 209.593287
## iter  60 value 209.553453
## iter  70 value 209.523690
## iter  80 value 209.506866
## iter  90 value 209.494377
## iter 100 value 209.491087
## final  value 209.491087 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 442.225272 
## iter  10 value 366.660341
## iter  20 value 210.100735
## iter  30 value 209.688810
## iter  40 value 209.635671
## iter  50 value 209.593747
## iter  60 value 209.554563
## iter  70 value 209.535529
## iter  80 value 209.521432
## iter  90 value 209.510123
## final  value 209.508494 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 407.663765 
## iter  10 value 213.487907
## iter  20 value 209.568417
## iter  30 value 209.512825
## iter  40 value 209.510640
## iter  50 value 209.489099
## iter  60 value 209.482039
## iter  70 value 209.481246
## iter  80 value 209.469123
## iter  90 value 209.466137
## final  value 209.465793 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 411.989183 
## iter  10 value 255.553305
## iter  20 value 223.148234
## iter  30 value 221.746832
## iter  40 value 213.298012
## iter  50 value 211.003759
## iter  60 value 209.929767
## iter  70 value 209.647426
## iter  80 value 209.617290
## iter  90 value 209.545840
## iter 100 value 209.534545
## final  value 209.534545 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 407.781917 
## iter  10 value 211.306604
## iter  20 value 209.892532
## iter  30 value 209.712887
## iter  40 value 209.586332
## iter  50 value 209.570743
## iter  60 value 209.558587
## iter  70 value 209.537139
## iter  80 value 209.516763
## iter  90 value 209.511976
## iter 100 value 209.507535
## final  value 209.507535 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 446.535458 
## iter  10 value 290.577853
## iter  20 value 230.759552
## iter  30 value 223.474164
## iter  40 value 220.384237
## iter  50 value 212.819742
## iter  60 value 211.329850
## iter  70 value 210.804733
## iter  80 value 209.317279
## iter  90 value 208.648461
## iter 100 value 207.285929
## final  value 207.285929 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 403.341393 
## iter  10 value 240.745174
## iter  20 value 196.658688
## iter  30 value 173.559507
## iter  40 value 170.343521
## iter  50 value 169.305697
## iter  60 value 166.370031
## iter  70 value 163.926655
## iter  80 value 163.715944
## iter  90 value 163.364518
## iter 100 value 163.161922
## final  value 163.161922 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 410.178736 
## iter  10 value 212.933555
## iter  20 value 209.483868
## iter  30 value 182.910447
## iter  40 value 175.193125
## iter  50 value 171.156832
## iter  60 value 168.936177
## iter  70 value 167.998121
## iter  80 value 167.776883
## iter  90 value 167.641112
## iter 100 value 167.536844
## final  value 167.536844 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 469.058940 
## iter  10 value 250.169089
## iter  20 value 205.305708
## iter  30 value 176.284649
## iter  40 value 165.068201
## iter  50 value 164.140534
## iter  60 value 163.338384
## iter  70 value 163.072216
## iter  80 value 162.948059
## iter  90 value 162.824514
## iter 100 value 162.673424
## final  value 162.673424 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 418.369672 
## iter  10 value 290.889915
## iter  20 value 274.348883
## iter  30 value 243.160467
## iter  40 value 235.593799
## iter  50 value 216.151072
## iter  60 value 211.502959
## iter  70 value 210.001306
## iter  80 value 209.567845
## final  value 209.538912 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 422.423863 
## iter  10 value 295.622894
## iter  20 value 196.291300
## iter  30 value 172.082787
## iter  40 value 166.248934
## iter  50 value 164.583570
## iter  60 value 163.618109
## iter  70 value 163.289426
## iter  80 value 162.978905
## iter  90 value 162.769919
## iter 100 value 162.617540
## final  value 162.617540 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 427.465352 
## iter  10 value 269.851361
## iter  20 value 213.743889
## iter  30 value 209.956156
## iter  40 value 209.573044
## iter  50 value 208.848487
## iter  60 value 195.784643
## iter  70 value 168.032974
## iter  80 value 165.891991
## iter  90 value 164.610177
## iter 100 value 163.409299
## final  value 163.409299 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 507.581483 
## iter  10 value 311.153087
## iter  20 value 299.009574
## iter  30 value 295.565276
## iter  40 value 273.361010
## iter  50 value 270.346558
## iter  60 value 269.590242
## iter  70 value 262.695652
## iter  80 value 219.298980
## iter  90 value 188.437177
## iter 100 value 180.872650
## final  value 180.872650 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 427.363003 
## iter  10 value 257.490931
## iter  20 value 207.017317
## iter  30 value 173.649237
## iter  40 value 166.830523
## iter  50 value 164.702281
## iter  60 value 163.526068
## iter  70 value 163.306313
## iter  80 value 162.898617
## iter  90 value 162.564071
## iter 100 value 162.278739
## final  value 162.278739 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 447.466870 
## iter  10 value 271.663034
## iter  20 value 209.619743
## iter  30 value 209.465866
## iter  40 value 209.021369
## iter  50 value 199.701843
## iter  60 value 176.343508
## iter  70 value 169.946550
## iter  80 value 168.800587
## iter  90 value 167.501177
## iter 100 value 166.628661
## final  value 166.628661 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 437.065396 
## iter  10 value 337.984971
## iter  20 value 244.637732
## iter  30 value 244.625372
## final  value 244.625358 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 412.760483 
## iter  10 value 250.866354
## iter  20 value 244.290915
## final  value 244.066122 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 413.141334 
## iter  10 value 251.568219
## final  value 244.625400 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 417.600088 
## iter  10 value 245.493993
## final  value 244.066122 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 405.605992 
## iter  10 value 271.481865
## iter  20 value 244.626013
## final  value 244.625356 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 451.109217 
## iter  10 value 264.651686
## iter  20 value 243.733482
## iter  30 value 240.772277
## iter  40 value 240.422700
## iter  50 value 240.383959
## iter  60 value 240.324676
## iter  70 value 240.320012
## final  value 240.320006 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 400.942469 
## iter  10 value 301.685863
## iter  20 value 246.772565
## iter  30 value 243.430121
## iter  40 value 242.177075
## iter  50 value 240.429933
## iter  60 value 240.332942
## iter  70 value 240.322406
## iter  80 value 240.320148
## iter  90 value 240.320004
## final  value 240.319989 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 400.773762 
## iter  10 value 265.839672
## iter  20 value 241.164055
## iter  30 value 240.471201
## iter  40 value 240.438668
## iter  50 value 240.422936
## iter  60 value 240.399107
## iter  70 value 240.322647
## final  value 240.320010 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 436.580706 
## iter  10 value 259.817664
## iter  20 value 241.860153
## iter  30 value 240.674305
## iter  40 value 240.437247
## iter  50 value 240.428647
## iter  60 value 240.414803
## iter  70 value 240.405663
## final  value 240.405632 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 411.430317 
## iter  10 value 265.187743
## iter  20 value 245.493035
## iter  30 value 242.852514
## iter  40 value 241.269590
## iter  50 value 240.448856
## iter  60 value 240.440120
## iter  70 value 240.434585
## iter  80 value 240.410574
## iter  90 value 240.339237
## iter 100 value 240.320211
## final  value 240.320211 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 403.634029 
## iter  10 value 271.357343
## iter  20 value 245.807130
## iter  30 value 241.433799
## iter  40 value 240.586191
## iter  50 value 240.210561
## iter  60 value 240.159720
## iter  70 value 240.069542
## iter  80 value 240.052974
## iter  90 value 240.034081
## iter 100 value 240.015106
## final  value 240.015106 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 419.799301 
## iter  10 value 273.217185
## iter  20 value 247.946049
## iter  30 value 244.786241
## iter  40 value 242.894157
## iter  50 value 241.413956
## iter  60 value 240.368697
## iter  70 value 240.141829
## iter  80 value 240.043127
## iter  90 value 240.026813
## iter 100 value 240.018432
## final  value 240.018432 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 418.533360 
## iter  10 value 272.755000
## iter  20 value 244.116597
## iter  30 value 241.480856
## iter  40 value 240.698672
## iter  50 value 240.434262
## iter  60 value 240.162858
## iter  70 value 240.065652
## iter  80 value 240.045747
## iter  90 value 240.023925
## iter 100 value 240.015347
## final  value 240.015347 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 402.023343 
## iter  10 value 296.735941
## iter  20 value 240.979015
## iter  30 value 240.336619
## iter  40 value 240.171879
## iter  50 value 240.071946
## iter  60 value 240.056822
## iter  70 value 240.051361
## iter  80 value 240.035144
## iter  90 value 240.023180
## iter 100 value 240.015134
## final  value 240.015134 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 422.981019 
## iter  10 value 356.697750
## iter  20 value 253.547630
## iter  30 value 241.981997
## iter  40 value 240.724444
## iter  50 value 240.557811
## iter  60 value 240.453832
## iter  70 value 240.288478
## iter  80 value 240.073429
## iter  90 value 240.059449
## iter 100 value 240.051653
## final  value 240.051653 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 417.717786 
## iter  10 value 320.722835
## iter  20 value 210.987572
## iter  30 value 209.863573
## iter  40 value 209.702206
## iter  50 value 209.642812
## iter  60 value 209.630002
## final  value 209.629969 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 436.689468 
## iter  10 value 379.490849
## iter  20 value 210.252576
## iter  30 value 209.790452
## iter  40 value 209.627918
## iter  50 value 209.606172
## iter  60 value 209.599098
## iter  70 value 209.594604
## iter  80 value 209.592059
## iter  90 value 209.591275
## iter 100 value 209.590202
## final  value 209.590202 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 426.800719 
## iter  10 value 355.691082
## iter  20 value 210.632838
## iter  30 value 210.166014
## iter  40 value 209.744870
## iter  50 value 209.646998
## iter  60 value 209.634973
## iter  70 value 209.632352
## iter  80 value 209.630371
## iter  90 value 209.627848
## iter 100 value 209.625025
## final  value 209.625025 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 406.177517 
## iter  10 value 351.396176
## iter  20 value 322.423652
## iter  30 value 320.616184
## iter  40 value 315.401050
## iter  50 value 295.349013
## iter  60 value 240.791209
## iter  70 value 239.491300
## iter  80 value 232.939736
## iter  90 value 220.971390
## iter 100 value 214.876172
## final  value 214.876172 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 412.281591 
## iter  10 value 214.805836
## iter  20 value 210.026320
## iter  30 value 209.792579
## iter  40 value 209.678337
## iter  50 value 209.651337
## iter  60 value 209.635571
## iter  70 value 209.625024
## iter  80 value 209.624289
## iter  80 value 209.624288
## iter  80 value 209.624288
## final  value 209.624288 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 406.403014 
## iter  10 value 299.029284
## iter  20 value 240.291055
## iter  30 value 217.189457
## iter  40 value 210.194975
## iter  50 value 184.799165
## iter  60 value 173.881650
## iter  70 value 170.642443
## iter  80 value 169.059972
## iter  90 value 168.515844
## iter 100 value 168.464187
## final  value 168.464187 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 416.709347 
## iter  10 value 344.167056
## iter  20 value 209.958834
## iter  30 value 209.702818
## iter  40 value 193.685695
## iter  50 value 173.021670
## iter  60 value 170.262833
## iter  70 value 168.864823
## iter  80 value 168.755279
## iter  90 value 168.531060
## iter 100 value 168.410966
## final  value 168.410966 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 461.073856 
## iter  10 value 291.069916
## iter  20 value 224.077079
## iter  30 value 207.956458
## iter  40 value 205.012811
## iter  50 value 192.526017
## iter  60 value 189.857373
## iter  70 value 184.041666
## iter  80 value 179.934006
## iter  90 value 176.319429
## iter 100 value 170.168120
## final  value 170.168120 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 433.869123 
## iter  10 value 232.250558
## iter  20 value 208.219189
## iter  30 value 198.929601
## iter  40 value 181.094692
## iter  50 value 174.828768
## iter  60 value 168.866543
## iter  70 value 165.609019
## iter  80 value 164.470319
## iter  90 value 163.779160
## iter 100 value 163.457805
## final  value 163.457805 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 428.776965 
## iter  10 value 255.155820
## iter  20 value 209.933994
## iter  30 value 189.407631
## iter  40 value 172.984685
## iter  50 value 169.687909
## iter  60 value 168.867135
## iter  70 value 168.547970
## iter  80 value 168.419127
## iter  90 value 168.290893
## iter 100 value 168.271687
## final  value 168.271687 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 420.143902 
## iter  10 value 223.109012
## iter  20 value 210.554581
## iter  30 value 209.769389
## iter  40 value 209.709793
## iter  50 value 209.524348
## iter  60 value 209.390456
## iter  70 value 207.413041
## iter  80 value 186.147460
## iter  90 value 176.509679
## iter 100 value 172.557553
## final  value 172.557553 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 487.936170 
## iter  10 value 215.075126
## iter  20 value 209.698466
## iter  30 value 191.838294
## iter  40 value 168.107306
## iter  50 value 165.649073
## iter  60 value 163.605694
## iter  70 value 163.499778
## iter  80 value 163.379676
## iter  90 value 163.100870
## iter 100 value 162.849093
## final  value 162.849093 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 426.799715 
## iter  10 value 254.312572
## iter  20 value 209.478101
## iter  30 value 190.655008
## iter  40 value 166.816269
## iter  50 value 164.373433
## iter  60 value 163.845442
## iter  70 value 163.336915
## iter  80 value 163.258049
## iter  90 value 163.106551
## iter 100 value 163.060435
## final  value 163.060435 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 425.777549 
## iter  10 value 230.049145
## iter  20 value 203.921576
## iter  30 value 172.328973
## iter  40 value 164.842064
## iter  50 value 163.555709
## iter  60 value 163.348962
## iter  70 value 163.266113
## iter  80 value 163.186430
## iter  90 value 163.006780
## iter 100 value 162.896155
## final  value 162.896155 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 417.710553 
## iter  10 value 240.644554
## iter  20 value 211.039766
## iter  30 value 181.864000
## iter  40 value 171.519494
## iter  50 value 167.564845
## iter  60 value 164.631116
## iter  70 value 164.030886
## iter  80 value 163.348907
## iter  90 value 163.096554
## iter 100 value 162.935541
## final  value 162.935541 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 408.262650 
## iter  10 value 249.576011
## iter  20 value 224.936001
## iter  30 value 224.068433
## final  value 224.065000 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 438.610223 
## iter  10 value 226.508372
## iter  20 value 224.212468
## iter  30 value 224.076702
## iter  40 value 224.061356
## final  value 224.060112 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 399.085541 
## iter  10 value 305.014226
## iter  20 value 231.781301
## iter  30 value 224.633943
## iter  40 value 224.144491
## iter  50 value 224.061953
## final  value 224.060680 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 440.400290 
## iter  10 value 320.428512
## iter  20 value 311.336558
## iter  30 value 311.048903
## iter  40 value 310.651748
## iter  50 value 310.144666
## iter  60 value 310.131324
## iter  70 value 310.127218
## iter  80 value 310.082964
## iter  90 value 310.078660
## iter 100 value 310.075882
## final  value 310.075882 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 398.337398 
## iter  10 value 290.429717
## iter  20 value 228.776835
## iter  30 value 224.105361
## iter  40 value 224.083379
## iter  50 value 224.060150
## final  value 224.060144 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 415.996609 
## iter  10 value 284.928193
## iter  20 value 224.156536
## iter  30 value 223.357257
## iter  40 value 176.462967
## iter  50 value 162.329352
## iter  60 value 161.837127
## iter  70 value 161.668294
## iter  80 value 161.522176
## iter  90 value 161.409988
## iter 100 value 161.073675
## final  value 161.073675 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 431.504202 
## iter  10 value 282.340062
## iter  20 value 222.369799
## iter  30 value 179.209993
## iter  40 value 164.542287
## iter  50 value 164.052266
## iter  60 value 163.620949
## iter  70 value 163.365173
## iter  80 value 163.117528
## iter  90 value 163.079645
## iter 100 value 163.013131
## final  value 163.013131 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 396.109916 
## iter  10 value 327.107972
## iter  20 value 320.863316
## iter  30 value 312.378446
## iter  40 value 310.790611
## iter  50 value 310.439209
## iter  60 value 310.233652
## iter  70 value 310.133349
## iter  80 value 310.076433
## iter  90 value 310.069108
## iter 100 value 310.011149
## final  value 310.011149 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 422.227193 
## iter  10 value 278.858368
## iter  20 value 235.585801
## iter  30 value 225.138338
## iter  40 value 224.214590
## iter  50 value 224.085742
## iter  60 value 224.084122
## iter  70 value 224.067361
## final  value 224.061072 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 476.445134 
## iter  10 value 277.704166
## iter  20 value 225.614227
## iter  30 value 224.193535
## iter  40 value 224.089293
## iter  50 value 224.077232
## iter  60 value 224.072412
## iter  70 value 224.060971
## iter  80 value 224.060122
## iter  90 value 224.059855
## final  value 224.059598 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 422.312536 
## iter  10 value 254.097159
## iter  20 value 217.420647
## iter  30 value 167.886478
## iter  40 value 161.744956
## iter  50 value 161.500302
## iter  60 value 161.391248
## iter  70 value 161.351363
## iter  80 value 161.145479
## iter  90 value 160.516014
## iter 100 value 159.724756
## final  value 159.724756 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 445.188215 
## iter  10 value 282.031761
## iter  20 value 231.136795
## iter  30 value 223.221761
## iter  40 value 183.430930
## iter  50 value 173.957246
## iter  60 value 170.560951
## iter  70 value 166.705365
## iter  80 value 164.394735
## iter  90 value 163.432460
## iter 100 value 163.112128
## final  value 163.112128 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 445.101939 
## iter  10 value 323.330776
## iter  20 value 225.209757
## iter  30 value 221.685386
## iter  40 value 174.941807
## iter  50 value 163.280187
## iter  60 value 162.069166
## iter  70 value 161.451301
## iter  80 value 161.436892
## iter  90 value 161.422962
## iter 100 value 161.413587
## final  value 161.413587 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 454.462432 
## iter  10 value 361.588905
## iter  20 value 233.218302
## iter  30 value 218.985118
## iter  40 value 164.477129
## iter  50 value 163.843505
## iter  60 value 163.173401
## iter  70 value 162.893151
## iter  80 value 162.767885
## iter  90 value 161.893640
## iter 100 value 161.487670
## final  value 161.487670 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 412.155280 
## iter  10 value 230.558379
## iter  20 value 223.985882
## iter  30 value 223.572382
## iter  40 value 199.337682
## iter  50 value 164.537431
## iter  60 value 162.105956
## iter  70 value 161.556225
## iter  80 value 161.154328
## iter  90 value 159.618654
## iter 100 value 159.230308
## final  value 159.230308 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 400.295844 
## iter  10 value 298.050115
## iter  20 value 255.441672
## final  value 254.987470 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 430.487568 
## iter  10 value 332.473905
## iter  20 value 259.539549
## iter  30 value 254.987472
## iter  30 value 254.987470
## iter  30 value 254.987470
## final  value 254.987470 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 411.899277 
## iter  10 value 256.134235
## iter  20 value 255.028602
## final  value 255.005694 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 408.886854 
## iter  10 value 276.067130
## iter  20 value 255.035766
## final  value 255.005694 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 425.460854 
## iter  10 value 302.738905
## iter  20 value 255.011453
## final  value 255.005695 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 401.683551 
## iter  10 value 292.398966
## iter  20 value 259.746582
## iter  30 value 256.110149
## iter  40 value 254.035007
## iter  50 value 251.987480
## iter  60 value 251.519411
## iter  70 value 251.503017
## iter  80 value 251.498195
## iter  90 value 251.451943
## iter 100 value 251.443372
## final  value 251.443372 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 415.147167 
## iter  10 value 286.447668
## iter  20 value 257.805825
## iter  30 value 256.289474
## iter  40 value 252.001404
## iter  50 value 251.462878
## iter  60 value 251.443291
## iter  70 value 251.443145
## final  value 251.443133 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 416.007378 
## iter  10 value 272.795274
## iter  20 value 257.183278
## iter  30 value 255.647468
## iter  40 value 252.816816
## iter  50 value 251.549257
## iter  60 value 251.509996
## iter  70 value 251.484061
## iter  80 value 251.467657
## iter  90 value 251.452295
## final  value 251.443124 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 422.260135 
## iter  10 value 304.125043
## iter  20 value 251.937584
## iter  30 value 251.720745
## iter  40 value 251.518059
## iter  50 value 251.498611
## iter  60 value 251.472561
## iter  70 value 251.468076
## final  value 251.468066 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 425.129243 
## iter  10 value 344.010174
## iter  20 value 259.382788
## iter  30 value 252.844393
## iter  40 value 251.840186
## iter  50 value 251.446863
## final  value 251.443229 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 494.157581 
## iter  10 value 269.564804
## iter  20 value 255.628700
## iter  30 value 253.278294
## iter  40 value 251.626846
## iter  50 value 251.522321
## iter  60 value 251.490149
## iter  70 value 251.424523
## iter  80 value 251.235408
## iter  90 value 251.181279
## iter 100 value 251.171706
## final  value 251.171706 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 444.171842 
## iter  10 value 275.130250
## iter  20 value 251.917271
## iter  30 value 251.528145
## iter  40 value 251.423787
## iter  50 value 251.301509
## iter  60 value 251.233113
## iter  70 value 251.194282
## iter  80 value 251.172238
## iter  90 value 251.170103
## final  value 251.170031 
## converged
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 421.100596 
## iter  10 value 314.083933
## iter  20 value 254.877631
## iter  30 value 253.339072
## iter  40 value 252.169983
## iter  50 value 251.557974
## iter  60 value 251.387117
## iter  70 value 251.299634
## iter  80 value 251.203477
## iter  90 value 251.181502
## iter 100 value 251.172602
## final  value 251.172602 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 423.472254 
## iter  10 value 326.328717
## iter  20 value 254.375355
## iter  30 value 251.599505
## iter  40 value 251.517535
## iter  50 value 251.509553
## iter  60 value 251.449112
## iter  70 value 251.330411
## iter  80 value 251.226162
## iter  90 value 251.197606
## iter 100 value 251.191591
## final  value 251.191591 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 413.804564 
## iter  10 value 286.513521
## iter  20 value 260.017125
## iter  30 value 255.331129
## iter  40 value 254.027283
## iter  50 value 252.593937
## iter  60 value 251.647810
## iter  70 value 251.242053
## iter  80 value 251.188692
## iter  90 value 251.182065
## iter 100 value 251.171218
## final  value 251.171218 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 400.632364 
## iter  10 value 270.740077
## iter  20 value 246.820847
## iter  30 value 243.033997
## iter  40 value 241.983952
## iter  50 value 240.525160
## iter  60 value 234.718972
## iter  70 value 226.602471
## iter  80 value 224.317915
## iter  90 value 224.152071
## iter 100 value 224.100124
## final  value 224.100124 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 419.008963 
## iter  10 value 355.213091
## iter  20 value 224.247405
## iter  30 value 224.107531
## iter  40 value 224.098895
## final  value 224.098719 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 445.819096 
## iter  10 value 275.250777
## iter  20 value 225.165127
## iter  30 value 224.304369
## iter  40 value 224.118648
## iter  50 value 224.098781
## final  value 224.098717 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 401.410482 
## iter  10 value 299.208690
## iter  20 value 224.469167
## iter  30 value 224.164465
## iter  40 value 224.101373
## iter  50 value 224.098853
## final  value 224.098711 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 443.859589 
## iter  10 value 335.959503
## iter  20 value 224.205075
## iter  30 value 224.106916
## iter  40 value 224.098725
## iter  40 value 224.098725
## final  value 224.098725 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 413.284664 
## iter  10 value 317.888819
## iter  20 value 267.518941
## iter  30 value 256.105345
## iter  40 value 236.990281
## iter  50 value 226.651784
## iter  60 value 225.343551
## iter  70 value 224.822481
## iter  80 value 224.531422
## iter  90 value 224.503633
## iter 100 value 224.459152
## final  value 224.459152 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 416.333216 
## iter  10 value 268.270436
## iter  20 value 224.807606
## iter  30 value 172.405298
## iter  40 value 165.615076
## iter  50 value 164.335129
## iter  60 value 163.730273
## iter  70 value 163.389672
## iter  80 value 163.307401
## iter  90 value 163.227956
## iter 100 value 163.177305
## final  value 163.177305 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 409.997661 
## iter  10 value 262.816763
## iter  20 value 225.144521
## iter  30 value 221.859243
## iter  40 value 180.257522
## iter  50 value 164.401899
## iter  60 value 163.792180
## iter  70 value 163.650056
## iter  80 value 163.379128
## iter  90 value 163.275113
## iter 100 value 163.232880
## final  value 163.232880 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 415.034394 
## iter  10 value 298.728979
## iter  20 value 223.978518
## iter  30 value 220.774401
## iter  40 value 168.273870
## iter  50 value 164.233851
## iter  60 value 162.949848
## iter  70 value 162.621693
## iter  80 value 162.331991
## iter  90 value 162.069440
## iter 100 value 162.004840
## final  value 162.004840 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 426.925462 
## iter  10 value 226.918851
## iter  20 value 224.319630
## iter  30 value 224.194320
## iter  40 value 224.188700
## iter  50 value 224.167962
## iter  60 value 224.142143
## iter  70 value 224.136140
## iter  80 value 224.134530
## iter  90 value 224.118194
## iter 100 value 224.102927
## final  value 224.102927 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 458.162624 
## iter  10 value 281.437113
## iter  20 value 223.839358
## iter  30 value 184.631661
## iter  40 value 164.024638
## iter  50 value 162.678410
## iter  60 value 162.159287
## iter  70 value 162.006966
## iter  80 value 161.960577
## iter  90 value 161.848104
## iter 100 value 161.798008
## final  value 161.798008 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 443.617138 
## iter  10 value 292.587031
## iter  20 value 224.071069
## iter  30 value 219.552175
## iter  40 value 167.531322
## iter  50 value 162.431523
## iter  60 value 162.076948
## iter  70 value 161.864613
## iter  80 value 161.833213
## iter  90 value 161.784801
## iter 100 value 161.707579
## final  value 161.707579 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 401.398100 
## iter  10 value 296.181302
## iter  20 value 224.338603
## iter  30 value 220.716120
## iter  40 value 171.305944
## iter  50 value 164.110718
## iter  60 value 163.613915
## iter  70 value 163.400485
## iter  80 value 163.285517
## iter  90 value 163.251758
## iter 100 value 163.243012
## final  value 163.243012 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 434.141872 
## iter  10 value 239.329600
## iter  20 value 218.097532
## iter  30 value 170.994327
## iter  40 value 164.197081
## iter  50 value 162.583823
## iter  60 value 161.713988
## iter  70 value 161.526084
## iter  80 value 161.018562
## iter  90 value 160.673841
## iter 100 value 160.251505
## final  value 160.251505 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 411.300698 
## iter  10 value 259.398613
## iter  20 value 222.953075
## iter  30 value 164.873749
## iter  40 value 161.762829
## iter  50 value 161.636695
## iter  60 value 161.608764
## iter  70 value 161.594324
## iter  80 value 161.554415
## iter  90 value 161.417845
## iter 100 value 161.087399
## final  value 161.087399 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 406.015185 
## iter  10 value 246.150384
## iter  20 value 238.126980
## iter  30 value 237.549855
## iter  40 value 237.460093
## iter  50 value 237.389969
## iter  60 value 237.382311
## iter  70 value 237.377823
## iter  80 value 237.372786
## iter  90 value 237.369267
## iter 100 value 237.362882
## final  value 237.362882 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 413.522541 
## iter  10 value 331.496141
## iter  20 value 330.808701
## iter  30 value 327.045655
## iter  40 value 326.873982
## iter  50 value 326.009500
## iter  60 value 326.002499
## final  value 326.002446 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 412.593757 
## iter  10 value 372.161992
## iter  20 value 325.989868
## iter  30 value 324.941651
## iter  40 value 324.001745
## final  value 324.000052 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 405.108728 
## iter  10 value 269.638359
## iter  20 value 238.552520
## iter  30 value 237.641012
## iter  40 value 237.547041
## iter  50 value 237.436757
## iter  60 value 237.417568
## iter  70 value 237.408434
## iter  80 value 237.381002
## iter  90 value 237.369042
## iter 100 value 237.367293
## final  value 237.367293 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 425.369203 
## iter  10 value 265.546308
## iter  20 value 239.131729
## iter  30 value 238.010438
## iter  40 value 237.600842
## iter  50 value 237.446787
## iter  60 value 237.422529
## iter  70 value 237.386423
## iter  80 value 237.376721
## iter  90 value 237.374660
## iter 100 value 237.355801
## final  value 237.355801 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 426.906534 
## iter  10 value 331.821103
## iter  20 value 238.913135
## iter  30 value 213.745585
## iter  40 value 198.937000
## iter  50 value 186.202173
## iter  60 value 180.705878
## iter  70 value 177.960796
## iter  80 value 177.377749
## iter  90 value 177.183341
## iter 100 value 177.088166
## final  value 177.088166 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 442.062506 
## iter  10 value 290.532442
## iter  20 value 233.719220
## iter  30 value 189.455884
## iter  40 value 182.334337
## iter  50 value 179.226264
## iter  60 value 177.425483
## iter  70 value 177.256269
## iter  80 value 176.800947
## final  value 176.625634 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 404.660903 
## iter  10 value 317.168870
## iter  20 value 316.568213
## iter  30 value 308.145181
## iter  40 value 307.274809
## iter  50 value 295.254028
## iter  60 value 284.179636
## iter  70 value 270.353089
## iter  80 value 252.205801
## iter  90 value 242.089278
## iter 100 value 240.818402
## final  value 240.818402 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 400.014073 
## iter  10 value 330.215069
## iter  20 value 308.037814
## iter  30 value 300.752011
## iter  40 value 259.533852
## iter  50 value 255.413672
## iter  60 value 255.341773
## iter  70 value 255.145792
## iter  80 value 255.113411
## iter  90 value 254.276485
## iter 100 value 248.348396
## final  value 248.348396 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 413.531443 
## iter  10 value 315.767454
## iter  20 value 238.165417
## iter  30 value 237.439558
## iter  40 value 237.386550
## iter  50 value 237.373858
## iter  60 value 237.362375
## iter  70 value 237.331466
## iter  80 value 237.247790
## iter  90 value 237.120453
## iter 100 value 236.794272
## final  value 236.794272 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 414.022557 
## iter  10 value 245.891622
## iter  20 value 235.085935
## iter  30 value 191.953768
## iter  40 value 173.877200
## iter  50 value 173.466702
## iter  60 value 173.127895
## iter  70 value 172.214603
## iter  80 value 172.123136
## iter  90 value 172.081058
## iter 100 value 172.072439
## final  value 172.072439 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 405.507523 
## iter  10 value 251.249673
## iter  20 value 199.920432
## iter  30 value 183.043679
## iter  40 value 178.952210
## iter  50 value 176.193874
## iter  60 value 174.775778
## iter  70 value 173.850381
## iter  80 value 173.330253
## iter  90 value 172.777368
## iter 100 value 172.392489
## final  value 172.392489 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 484.880802 
## iter  10 value 326.000589
## iter  20 value 307.296675
## iter  30 value 260.005312
## iter  40 value 257.777438
## iter  50 value 244.981442
## iter  60 value 237.930626
## iter  70 value 237.532103
## iter  80 value 237.384756
## iter  90 value 237.329336
## iter 100 value 237.325003
## final  value 237.325003 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 422.708814 
## iter  10 value 244.455257
## iter  20 value 233.811953
## iter  30 value 180.799921
## iter  40 value 174.799833
## iter  50 value 173.631049
## iter  60 value 172.957333
## iter  70 value 172.693140
## iter  80 value 172.470322
## iter  90 value 172.063788
## iter 100 value 171.837725
## final  value 171.837725 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 444.443793 
## iter  10 value 307.979138
## iter  20 value 191.867125
## iter  30 value 176.051601
## iter  40 value 174.366723
## iter  50 value 173.855564
## iter  60 value 173.504678
## iter  70 value 173.036950
## iter  80 value 172.609421
## iter  90 value 172.484266
## iter 100 value 172.347003
## final  value 172.347003 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 419.864003 
## iter  10 value 324.049676
## iter  20 value 267.263409
## final  value 267.257390 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 412.067963 
## iter  10 value 290.187688
## iter  20 value 267.292604
## final  value 267.257390 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 425.358850 
## iter  10 value 373.106803
## iter  20 value 267.863924
## iter  30 value 267.261231
## final  value 267.257390 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 426.211209 
## iter  10 value 291.551723
## iter  20 value 266.883242
## final  value 266.511223 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 416.772361 
## iter  10 value 295.663139
## iter  20 value 266.973477
## final  value 266.511223 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 399.166727 
## iter  10 value 273.480524
## iter  20 value 264.113570
## iter  30 value 263.661334
## iter  40 value 263.656617
## iter  50 value 263.655315
## iter  60 value 263.649215
## iter  70 value 263.646791
## final  value 263.646724 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 414.283137 
## iter  10 value 310.017214
## iter  20 value 271.535812
## iter  30 value 266.359009
## iter  40 value 264.249440
## iter  50 value 263.661070
## iter  60 value 263.644324
## iter  70 value 263.639796
## iter  80 value 263.628394
## iter  90 value 263.565760
## iter 100 value 263.546560
## final  value 263.546560 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 434.618713 
## iter  10 value 337.879340
## iter  20 value 267.037604
## iter  30 value 265.367735
## iter  40 value 257.971137
## iter  50 value 257.206950
## iter  60 value 256.515625
## iter  70 value 256.341326
## final  value 256.340856 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 403.987461 
## iter  10 value 275.447860
## iter  20 value 264.926522
## iter  30 value 263.983253
## iter  40 value 263.681127
## iter  50 value 263.653308
## iter  60 value 263.648838
## iter  70 value 263.646743
## final  value 263.646732 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 442.727067 
## iter  10 value 275.324074
## iter  20 value 265.146577
## iter  30 value 263.888095
## iter  40 value 263.704759
## iter  50 value 263.656748
## iter  60 value 263.655375
## iter  70 value 263.605750
## iter  80 value 263.578218
## iter  90 value 263.552588
## iter 100 value 263.546145
## final  value 263.546145 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 463.534415 
## iter  10 value 278.309959
## iter  20 value 264.387727
## iter  30 value 263.807896
## iter  40 value 263.475942
## iter  50 value 263.436933
## iter  60 value 263.385057
## iter  70 value 263.366459
## iter  80 value 263.358598
## iter  90 value 263.335730
## iter 100 value 263.327895
## final  value 263.327895 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 403.894542 
## iter  10 value 268.520103
## iter  20 value 263.948446
## iter  30 value 263.594959
## iter  40 value 263.480999
## iter  50 value 263.365806
## iter  60 value 263.339557
## iter  70 value 263.332418
## iter  80 value 263.328461
## iter  90 value 263.327718
## final  value 263.327683 
## converged
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 489.005795 
## iter  10 value 271.608697
## iter  20 value 264.641538
## iter  30 value 263.576684
## iter  40 value 263.470382
## iter  50 value 263.375921
## iter  60 value 263.352340
## iter  70 value 263.339895
## iter  80 value 263.329723
## iter  90 value 263.328066
## final  value 263.327741 
## converged
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 421.549411 
## iter  10 value 304.583301
## iter  20 value 272.891204
## iter  30 value 267.962596
## iter  40 value 263.848190
## iter  50 value 259.861165
## iter  60 value 257.779141
## iter  70 value 256.097426
## iter  80 value 256.075617
## iter  90 value 255.959926
## iter 100 value 255.938648
## final  value 255.938648 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 445.329229 
## iter  10 value 301.825675
## iter  20 value 263.659580
## iter  30 value 263.572241
## iter  40 value 263.438404
## iter  50 value 263.377225
## iter  60 value 263.362908
## iter  70 value 263.359533
## iter  80 value 263.343650
## iter  90 value 263.332021
## iter 100 value 263.327948
## final  value 263.327948 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 443.707962 
## iter  10 value 276.767488
## iter  20 value 238.927377
## iter  30 value 237.822795
## iter  40 value 237.645220
## iter  50 value 237.515629
## iter  60 value 237.497860
## iter  70 value 237.483896
## iter  80 value 237.479649
## iter  90 value 237.470018
## iter 100 value 237.466080
## final  value 237.466080 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 405.524807 
## iter  10 value 259.826991
## iter  20 value 238.583378
## iter  30 value 237.566018
## iter  40 value 237.544209
## iter  50 value 237.483805
## iter  60 value 237.472480
## final  value 237.471075 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 431.543490 
## iter  10 value 252.675156
## iter  20 value 237.774886
## iter  30 value 237.582867
## iter  40 value 237.448598
## iter  50 value 237.442943
## iter  60 value 237.437268
## iter  70 value 237.430627
## iter  80 value 237.428237
## final  value 237.427831 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 400.327636 
## iter  10 value 282.523080
## iter  20 value 237.639082
## iter  30 value 237.516802
## iter  40 value 237.454814
## iter  50 value 237.446979
## iter  60 value 237.433150
## iter  70 value 237.430457
## iter  80 value 237.428894
## iter  90 value 237.428343
## iter 100 value 237.426902
## final  value 237.426902 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 427.670978 
## iter  10 value 350.096714
## iter  20 value 239.167693
## iter  30 value 238.001930
## iter  40 value 237.540620
## iter  50 value 237.516496
## iter  60 value 237.473225
## iter  70 value 237.471397
## iter  80 value 237.470261
## iter  90 value 237.467879
## final  value 237.466895 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 413.085323 
## iter  10 value 330.437420
## iter  20 value 324.160067
## iter  30 value 323.176639
## iter  40 value 300.841094
## iter  50 value 286.123298
## iter  60 value 248.183340
## iter  70 value 208.702539
## iter  80 value 204.447581
## iter  90 value 189.499268
## iter 100 value 182.811664
## final  value 182.811664 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 404.793793 
## iter  10 value 348.701424
## iter  20 value 344.165718
## iter  30 value 333.342704
## iter  40 value 330.825884
## iter  50 value 326.939431
## iter  60 value 265.072915
## iter  70 value 239.989659
## iter  80 value 235.598758
## iter  90 value 192.506241
## iter 100 value 178.714184
## final  value 178.714184 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 402.465853 
## iter  10 value 307.236157
## iter  20 value 240.305993
## iter  30 value 191.192985
## iter  40 value 179.832873
## iter  50 value 175.455299
## iter  60 value 174.297473
## iter  70 value 173.629674
## iter  80 value 173.370589
## iter  90 value 173.222521
## iter 100 value 173.036097
## final  value 173.036097 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 418.968933 
## iter  10 value 327.863735
## iter  20 value 302.726920
## iter  30 value 262.657737
## iter  40 value 233.222366
## iter  50 value 219.379927
## iter  60 value 206.296482
## iter  70 value 197.377875
## iter  80 value 188.519423
## iter  90 value 179.286512
## iter 100 value 173.858303
## final  value 173.858303 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 429.139863 
## iter  10 value 241.109930
## iter  20 value 237.404978
## iter  30 value 224.941486
## iter  40 value 185.963557
## iter  50 value 181.264574
## iter  60 value 178.912931
## iter  70 value 178.390754
## iter  80 value 178.148847
## iter  90 value 177.869701
## iter 100 value 177.614483
## final  value 177.614483 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 512.010560 
## iter  10 value 246.754036
## iter  20 value 237.283402
## iter  30 value 222.631540
## iter  40 value 180.665367
## iter  50 value 174.365709
## iter  60 value 173.920996
## iter  70 value 173.494138
## iter  80 value 173.323747
## iter  90 value 173.235485
## iter 100 value 173.122521
## final  value 173.122521 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 420.030394 
## iter  10 value 294.760945
## iter  20 value 220.270702
## iter  30 value 180.987135
## iter  40 value 176.195880
## iter  50 value 173.869043
## iter  60 value 173.045585
## iter  70 value 172.911978
## iter  80 value 172.788190
## iter  90 value 172.688712
## iter 100 value 172.573826
## final  value 172.573826 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 414.103301 
## iter  10 value 241.391548
## iter  20 value 233.598431
## iter  30 value 183.131467
## iter  40 value 176.586016
## iter  50 value 174.524966
## iter  60 value 173.965532
## iter  70 value 173.741722
## iter  80 value 173.639477
## iter  90 value 173.305611
## iter 100 value 173.172142
## final  value 173.172142 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 438.875631 
## iter  10 value 269.444690
## iter  20 value 228.136676
## iter  30 value 181.557913
## iter  40 value 175.678408
## iter  50 value 174.117035
## iter  60 value 173.733212
## iter  70 value 173.526696
## iter  80 value 173.328269
## iter  90 value 173.085084
## iter 100 value 173.001902
## final  value 173.001902 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 446.740542 
## iter  10 value 275.896805
## iter  20 value 230.993474
## iter  30 value 180.969573
## iter  40 value 174.759676
## iter  50 value 173.732012
## iter  60 value 173.407818
## iter  70 value 173.296966
## iter  80 value 173.049304
## iter  90 value 172.839960
## iter 100 value 172.517642
## final  value 172.517642 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 418.402479 
## iter  10 value 263.738929
## iter  20 value 256.001894
## iter  30 value 255.956661
## final  value 255.948806 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 407.225337 
## iter  10 value 263.091041
## iter  20 value 256.097342
## iter  30 value 255.962909
## iter  40 value 255.949914
## final  value 255.948768 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 414.589955 
## iter  10 value 311.103896
## iter  20 value 256.440599
## iter  30 value 255.949251
## final  value 255.948782 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 443.133171 
## iter  10 value 293.252920
## iter  20 value 256.535841
## iter  30 value 256.332940
## iter  40 value 256.067631
## iter  50 value 256.041739
## final  value 256.041168 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 422.342626 
## iter  10 value 290.779432
## iter  20 value 256.641877
## iter  30 value 256.016130
## iter  40 value 256.006403
## iter  50 value 255.999624
## iter  60 value 255.993750
## iter  70 value 255.987731
## iter  80 value 255.968865
## iter  90 value 255.964280
## iter 100 value 255.957686
## final  value 255.957686 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 403.206309 
## iter  10 value 272.828250
## iter  20 value 253.403516
## iter  30 value 214.285680
## iter  40 value 192.333863
## iter  50 value 188.436059
## iter  60 value 187.404489
## iter  70 value 186.817981
## iter  80 value 186.576811
## iter  90 value 186.240044
## iter 100 value 186.095796
## final  value 186.095796 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 436.269846 
## iter  10 value 256.807650
## iter  20 value 220.022623
## iter  30 value 199.175677
## iter  40 value 193.021703
## iter  50 value 191.040641
## iter  60 value 188.865658
## iter  70 value 187.367428
## iter  80 value 187.053477
## iter  90 value 186.769119
## iter 100 value 186.568908
## final  value 186.568908 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 426.901385 
## iter  10 value 334.584227
## iter  20 value 325.958249
## iter  30 value 282.573929
## iter  40 value 264.814444
## iter  50 value 258.129974
## iter  60 value 257.472844
## iter  70 value 254.110098
## iter  80 value 244.250847
## iter  90 value 225.781245
## iter 100 value 218.173058
## final  value 218.173058 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 400.559369 
## iter  10 value 265.809343
## iter  20 value 220.857263
## iter  30 value 198.844732
## iter  40 value 193.415148
## iter  50 value 192.291798
## iter  60 value 191.728996
## iter  70 value 191.167005
## iter  80 value 190.943612
## iter  90 value 190.749349
## iter 100 value 190.742272
## final  value 190.742272 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 407.477335 
## iter  10 value 258.326198
## iter  20 value 255.962452
## iter  30 value 255.588876
## iter  40 value 254.892165
## iter  50 value 232.557384
## iter  60 value 209.262733
## iter  70 value 197.934502
## iter  80 value 196.262237
## iter  90 value 192.690242
## iter 100 value 192.129709
## final  value 192.129709 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 401.827216 
## iter  10 value 309.214317
## iter  20 value 245.577406
## iter  30 value 198.655151
## iter  40 value 190.795064
## iter  50 value 189.796392
## iter  60 value 189.065941
## iter  70 value 188.267954
## iter  80 value 187.319263
## iter  90 value 186.946993
## iter 100 value 186.565493
## final  value 186.565493 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 498.615449 
## iter  10 value 320.558856
## iter  20 value 247.635401
## iter  30 value 197.362100
## iter  40 value 191.579094
## iter  50 value 189.471965
## iter  60 value 188.313400
## iter  70 value 187.713406
## iter  80 value 186.490348
## iter  90 value 186.026947
## iter 100 value 185.523649
## final  value 185.523649 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 424.608632 
## iter  10 value 319.965571
## iter  20 value 252.966219
## iter  30 value 204.724167
## iter  40 value 195.984726
## iter  50 value 193.932652
## iter  60 value 192.240961
## iter  70 value 191.777847
## iter  80 value 191.604337
## iter  90 value 191.364717
## iter 100 value 191.165727
## final  value 191.165727 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 450.780596 
## iter  10 value 288.105376
## iter  20 value 255.800632
## iter  30 value 253.937490
## iter  40 value 226.256265
## iter  50 value 202.292663
## iter  60 value 196.569236
## iter  70 value 193.116734
## iter  80 value 192.172953
## iter  90 value 191.955871
## iter 100 value 191.375410
## final  value 191.375410 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 549.733476 
## iter  10 value 335.242490
## iter  20 value 255.802930
## iter  30 value 236.776689
## iter  40 value 200.968554
## iter  50 value 192.214727
## iter  60 value 190.213830
## iter  70 value 189.815013
## iter  80 value 189.771009
## iter  90 value 189.706571
## iter 100 value 189.499901
## final  value 189.499901 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 402.880302 
## iter  10 value 287.188371
## iter  20 value 283.662659
## iter  20 value 283.662658
## iter  20 value 283.662658
## final  value 283.662658 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 400.421473 
## iter  10 value 292.363424
## iter  20 value 283.163371
## final  value 283.070124 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 426.083250 
## iter  10 value 329.333585
## iter  20 value 283.302007
## final  value 283.070089 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 401.149822 
## iter  10 value 301.394917
## iter  20 value 283.070484
## final  value 283.070089 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 477.217014 
## iter  10 value 373.489744
## iter  20 value 329.630567
## iter  30 value 283.317601
## iter  40 value 283.070171
## final  value 283.070091 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 433.503273 
## iter  10 value 371.447052
## iter  20 value 292.135276
## iter  30 value 281.351778
## iter  40 value 280.567171
## iter  50 value 280.447635
## iter  60 value 280.406888
## iter  70 value 280.391391
## iter  80 value 280.378021
## final  value 280.377946 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 445.079945 
## iter  10 value 289.161525
## iter  20 value 281.257197
## iter  30 value 279.842665
## iter  40 value 273.105470
## iter  50 value 272.732318
## iter  60 value 272.484161
## iter  70 value 272.471135
## final  value 272.470982 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 452.804396 
## iter  10 value 340.806983
## iter  20 value 281.632041
## iter  30 value 280.437226
## iter  40 value 280.417831
## iter  50 value 280.387404
## iter  60 value 280.380733
## iter  70 value 280.378285
## final  value 280.377909 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 400.694090 
## iter  10 value 296.035682
## iter  20 value 281.205288
## iter  30 value 280.504589
## iter  40 value 280.441458
## iter  50 value 280.441142
## iter  60 value 280.440309
## final  value 280.440025 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 446.016742 
## iter  10 value 341.051780
## iter  20 value 281.319209
## iter  30 value 280.580821
## iter  40 value 280.423084
## iter  50 value 280.420241
## iter  60 value 280.390795
## iter  70 value 280.378009
## final  value 280.377911 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 447.456760 
## iter  10 value 332.470927
## iter  20 value 284.930496
## iter  30 value 282.152498
## iter  40 value 280.947726
## iter  50 value 280.712560
## iter  60 value 280.439505
## iter  70 value 280.312074
## iter  80 value 280.235863
## iter  90 value 280.227713
## iter 100 value 280.223541
## final  value 280.223541 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 411.445164 
## iter  10 value 297.855510
## iter  20 value 282.101985
## iter  30 value 280.648999
## iter  40 value 280.470961
## iter  50 value 280.428186
## iter  60 value 280.406144
## iter  70 value 280.358783
## iter  80 value 280.271575
## iter  90 value 280.246977
## iter 100 value 280.240782
## final  value 280.240782 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 455.654140 
## iter  10 value 317.420475
## iter  20 value 283.284312
## iter  30 value 280.841206
## iter  40 value 280.607630
## iter  50 value 280.495270
## iter  60 value 280.444845
## iter  70 value 280.382623
## iter  80 value 280.288935
## iter  90 value 280.256463
## iter 100 value 280.240546
## final  value 280.240546 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 492.633621 
## iter  10 value 368.987724
## iter  20 value 282.314120
## iter  30 value 281.308003
## iter  40 value 280.567199
## iter  50 value 280.401143
## iter  60 value 280.325618
## iter  70 value 280.261092
## iter  80 value 280.248821
## iter  90 value 280.244551
## iter 100 value 280.244101
## final  value 280.244101 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 404.583905 
## iter  10 value 295.349464
## iter  20 value 281.214717
## iter  30 value 280.629151
## iter  40 value 280.422620
## iter  50 value 280.312409
## iter  60 value 280.294932
## iter  70 value 280.261327
## iter  80 value 280.238474
## iter  90 value 280.237844
## final  value 280.237500 
## converged
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 410.503510 
## iter  10 value 294.879393
## iter  20 value 256.514854
## iter  30 value 255.996368
## iter  40 value 255.982529
## final  value 255.981889 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 444.631165 
## iter  10 value 289.567888
## iter  20 value 257.092801
## iter  30 value 255.997345
## iter  40 value 255.981952
## final  value 255.981900 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 411.660916 
## iter  10 value 367.507777
## iter  20 value 256.038986
## iter  30 value 255.991132
## iter  40 value 255.982508
## iter  50 value 255.981895
## final  value 255.981889 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 428.408881 
## iter  10 value 367.839930
## iter  20 value 269.269724
## iter  30 value 256.654346
## iter  40 value 255.998747
## final  value 255.981890 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 460.664773 
## iter  10 value 360.878673
## iter  20 value 260.099327
## iter  30 value 256.109926
## iter  40 value 255.992398
## iter  50 value 255.981893
## final  value 255.981889 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 406.326058 
## iter  10 value 279.899103
## iter  20 value 253.868494
## iter  30 value 212.040180
## iter  40 value 192.038248
## iter  50 value 188.149707
## iter  60 value 187.358399
## iter  70 value 187.122412
## iter  80 value 186.942832
## iter  90 value 186.862063
## iter 100 value 186.832279
## final  value 186.832279 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 420.713111 
## iter  10 value 265.656555
## iter  20 value 255.948177
## iter  30 value 246.471160
## iter  40 value 194.110076
## iter  50 value 190.691359
## iter  60 value 190.109933
## iter  70 value 190.029412
## iter  80 value 189.912997
## iter  90 value 189.564715
## iter 100 value 188.667870
## final  value 188.667870 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 409.778594 
## iter  10 value 259.911833
## iter  20 value 256.025418
## iter  30 value 255.272310
## iter  40 value 248.319324
## iter  50 value 201.662853
## iter  60 value 195.618441
## iter  70 value 193.511113
## iter  80 value 193.103054
## iter  90 value 192.286379
## iter 100 value 191.993404
## final  value 191.993404 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 444.218687 
## iter  10 value 310.055293
## iter  20 value 256.886927
## iter  30 value 255.923110
## iter  40 value 255.546968
## iter  50 value 249.008357
## iter  60 value 204.104800
## iter  70 value 196.163589
## iter  80 value 193.594798
## iter  90 value 192.471208
## iter 100 value 191.848626
## final  value 191.848626 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 427.506184 
## iter  10 value 343.027077
## iter  20 value 256.145934
## iter  30 value 253.434714
## iter  40 value 223.164050
## iter  50 value 198.614521
## iter  60 value 188.995115
## iter  70 value 187.761927
## iter  80 value 187.388274
## iter  90 value 187.045777
## iter 100 value 186.936050
## final  value 186.936050 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 472.790950 
## iter  10 value 258.319856
## iter  20 value 223.938246
## iter  30 value 199.417492
## iter  40 value 190.360537
## iter  50 value 187.938169
## iter  60 value 187.572113
## iter  70 value 187.378998
## iter  80 value 186.376228
## iter  90 value 185.820967
## iter 100 value 185.329423
## final  value 185.329423 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 425.738954 
## iter  10 value 372.058135
## iter  20 value 259.475727
## iter  30 value 251.120097
## iter  40 value 226.750507
## iter  50 value 191.462091
## iter  60 value 188.747049
## iter  70 value 188.075693
## iter  80 value 187.741464
## iter  90 value 187.522949
## iter 100 value 187.064576
## final  value 187.064576 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 421.849525 
## iter  10 value 361.758182
## iter  20 value 265.554309
## iter  30 value 256.153405
## iter  40 value 251.944189
## iter  50 value 200.621223
## iter  60 value 192.570991
## iter  70 value 191.597610
## iter  80 value 190.517079
## iter  90 value 190.213726
## iter 100 value 190.134337
## final  value 190.134337 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 460.588427 
## iter  10 value 390.693240
## iter  20 value 259.958872
## iter  30 value 256.237877
## iter  40 value 254.406261
## iter  50 value 221.487156
## iter  60 value 197.666258
## iter  70 value 190.664739
## iter  80 value 188.576290
## iter  90 value 187.392531
## iter 100 value 187.039935
## final  value 187.039935 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 406.124489 
## iter  10 value 261.450947
## iter  20 value 214.443861
## iter  30 value 192.506493
## iter  40 value 190.105450
## iter  50 value 188.952414
## iter  60 value 187.423045
## iter  70 value 187.220894
## iter  80 value 187.087545
## iter  90 value 186.935694
## iter 100 value 186.826916
## final  value 186.826916 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 454.714744 
## iter  10 value 374.468281
## iter  20 value 245.860025
## iter  30 value 243.371432
## iter  40 value 242.676161
## iter  50 value 242.518910
## iter  60 value 242.486335
## iter  70 value 242.482577
## iter  80 value 242.477719
## iter  90 value 242.476489
## final  value 242.476320 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 414.414714 
## iter  10 value 365.263325
## iter  20 value 357.524838
## iter  30 value 357.314823
## iter  40 value 356.810778
## iter  50 value 356.708619
## iter  60 value 356.181947
## iter  70 value 356.159947
## iter  80 value 356.100796
## final  value 356.099395 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 424.508133 
## iter  10 value 376.508342
## iter  20 value 243.445066
## iter  30 value 243.018272
## iter  40 value 242.550768
## iter  50 value 242.492875
## iter  60 value 242.479881
## iter  70 value 242.478272
## iter  80 value 242.477143
## iter  90 value 242.475955
## iter 100 value 242.475085
## final  value 242.475085 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 402.942344 
## iter  10 value 313.424034
## iter  20 value 243.060430
## iter  30 value 242.725944
## iter  40 value 242.500218
## iter  50 value 242.489680
## iter  60 value 242.478500
## iter  70 value 242.475733
## iter  80 value 242.474877
## iter  90 value 242.473712
## final  value 242.473659 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 416.675433 
## iter  10 value 248.641918
## iter  20 value 242.866726
## iter  30 value 242.531540
## iter  40 value 242.491661
## iter  50 value 242.481807
## iter  60 value 242.479107
## iter  70 value 242.477001
## iter  80 value 242.476052
## iter  90 value 242.474344
## final  value 242.473436 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 396.746778 
## iter  10 value 254.048209
## iter  20 value 202.529506
## iter  30 value 176.301523
## iter  40 value 174.528661
## iter  50 value 172.155461
## iter  60 value 171.263952
## iter  70 value 171.051813
## iter  80 value 170.881336
## iter  90 value 170.514491
## iter 100 value 169.653536
## final  value 169.653536 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 417.286237 
## iter  10 value 362.433174
## iter  20 value 220.259688
## iter  30 value 175.496349
## iter  40 value 173.592387
## iter  50 value 171.196451
## iter  60 value 171.123753
## iter  70 value 171.075458
## iter  80 value 171.014272
## iter  90 value 170.939326
## iter 100 value 170.766139
## final  value 170.766139 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 406.985277 
## iter  10 value 248.339321
## iter  20 value 234.999716
## iter  30 value 177.562643
## iter  40 value 176.367000
## iter  50 value 175.290587
## iter  60 value 175.025410
## iter  70 value 174.837674
## iter  80 value 174.163057
## iter  90 value 173.062481
## iter 100 value 172.124646
## final  value 172.124646 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 478.584043 
## iter  10 value 262.418952
## iter  20 value 239.233234
## iter  30 value 185.777821
## iter  40 value 180.189014
## iter  50 value 173.287622
## iter  60 value 171.498380
## iter  70 value 171.310331
## iter  80 value 171.284745
## iter  90 value 171.194938
## iter 100 value 171.133947
## final  value 171.133947 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 461.414369 
## iter  10 value 287.954612
## iter  20 value 245.703708
## iter  30 value 236.644523
## iter  40 value 196.093601
## iter  50 value 181.583741
## iter  60 value 175.509206
## iter  70 value 173.402710
## iter  80 value 172.852544
## iter  90 value 172.307097
## iter 100 value 171.501538
## final  value 171.501538 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 439.302174 
## iter  10 value 291.197781
## iter  20 value 242.450747
## iter  30 value 193.982306
## iter  40 value 179.001002
## iter  50 value 174.039924
## iter  60 value 171.841480
## iter  70 value 171.350105
## iter  80 value 171.165668
## iter  90 value 171.107343
## iter 100 value 171.104524
## final  value 171.104524 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 412.316713 
## iter  10 value 348.344867
## iter  20 value 242.868150
## iter  30 value 235.664825
## iter  40 value 183.274725
## iter  50 value 174.411807
## iter  60 value 171.326791
## iter  70 value 171.100201
## iter  80 value 170.644244
## iter  90 value 170.518955
## iter 100 value 170.299509
## final  value 170.299509 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 448.555675 
## iter  10 value 287.073815
## iter  20 value 238.049166
## iter  30 value 187.456400
## iter  40 value 174.388273
## iter  50 value 171.553423
## iter  60 value 171.190842
## iter  70 value 171.151171
## iter  80 value 171.067445
## iter  90 value 170.810293
## iter 100 value 169.959481
## final  value 169.959481 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 506.952392 
## iter  10 value 254.025119
## iter  20 value 197.291834
## iter  30 value 177.100687
## iter  40 value 174.003381
## iter  50 value 170.938669
## iter  60 value 170.370495
## iter  70 value 169.513659
## iter  80 value 168.827452
## iter  90 value 168.585903
## iter 100 value 168.510479
## final  value 168.510479 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 433.570637 
## iter  10 value 286.452229
## iter  20 value 243.202314
## iter  30 value 242.444754
## iter  40 value 235.394060
## iter  50 value 183.758720
## iter  60 value 176.174601
## iter  70 value 172.688854
## iter  80 value 171.465812
## iter  90 value 171.208076
## iter 100 value 171.143382
## final  value 171.143382 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 453.644373 
## iter  10 value 378.828185
## iter  20 value 284.681207
## iter  30 value 270.639523
## final  value 270.523925 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 432.079182 
## iter  10 value 360.604367
## iter  20 value 272.197677
## iter  30 value 270.206575
## iter  30 value 270.206574
## iter  30 value 270.206574
## final  value 270.206574 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 490.343934 
## iter  10 value 394.783148
## iter  20 value 271.399499
## iter  30 value 270.525610
## final  value 270.523690 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 401.220820 
## iter  10 value 278.622195
## iter  20 value 270.206653
## final  value 270.206574 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 424.674124 
## iter  10 value 288.243186
## iter  20 value 270.339662
## final  value 270.206574 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 423.607540 
## iter  10 value 350.358173
## iter  20 value 274.093176
## iter  30 value 268.831747
## iter  40 value 267.312653
## iter  50 value 267.125332
## iter  60 value 267.118580
## iter  70 value 267.117706
## iter  70 value 267.117704
## iter  70 value 267.117704
## final  value 267.117704 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 399.325580 
## iter  10 value 299.780267
## iter  20 value 269.202226
## iter  30 value 262.775855
## iter  40 value 259.737786
## iter  50 value 255.878358
## iter  60 value 255.042705
## iter  70 value 254.363515
## iter  80 value 253.905949
## iter  90 value 253.895104
## iter  90 value 253.895102
## iter  90 value 253.895102
## final  value 253.895102 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 400.656950 
## iter  10 value 273.415026
## iter  20 value 268.037614
## iter  30 value 267.289734
## iter  40 value 267.200335
## iter  50 value 267.163553
## iter  60 value 267.146462
## iter  70 value 267.101330
## final  value 267.097836 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 409.560167 
## iter  10 value 364.039067
## iter  20 value 279.387624
## iter  30 value 272.231410
## iter  40 value 271.021147
## iter  50 value 259.039947
## iter  60 value 254.319343
## iter  70 value 253.801858
## iter  80 value 253.797911
## final  value 253.797645 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 430.557775 
## iter  10 value 290.133839
## iter  20 value 269.652165
## iter  30 value 268.484097
## iter  40 value 267.256382
## iter  50 value 267.141796
## iter  60 value 267.130094
## iter  70 value 267.117735
## final  value 267.117704 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 429.154700 
## iter  10 value 306.028350
## iter  20 value 268.269703
## iter  30 value 267.249780
## iter  40 value 266.977328
## iter  50 value 266.913160
## iter  60 value 266.877322
## iter  70 value 266.864918
## iter  80 value 266.856682
## iter  90 value 266.855428
## final  value 266.854910 
## converged
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 396.932931 
## iter  10 value 317.951814
## iter  20 value 273.876470
## iter  30 value 268.236640
## iter  40 value 267.345788
## iter  50 value 267.027538
## iter  60 value 266.913081
## iter  70 value 266.873362
## iter  80 value 266.861226
## iter  90 value 266.858028
## iter 100 value 266.857509
## final  value 266.857509 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 410.831484 
## iter  10 value 292.368714
## iter  20 value 267.953280
## iter  30 value 267.046744
## iter  40 value 266.911242
## iter  50 value 266.888253
## iter  60 value 266.881569
## iter  70 value 266.881211
## iter  80 value 266.868359
## iter  90 value 266.856096
## iter 100 value 266.855049
## final  value 266.855049 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 447.197609 
## iter  10 value 350.805984
## iter  20 value 268.011242
## iter  30 value 266.991865
## iter  40 value 266.890119
## iter  50 value 266.883057
## iter  60 value 266.882480
## final  value 266.882324 
## converged
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 398.315619 
## iter  10 value 292.812574
## iter  20 value 271.261871
## iter  30 value 268.689151
## iter  40 value 267.344314
## iter  50 value 267.078032
## iter  60 value 266.954634
## iter  70 value 266.898515
## iter  80 value 266.864179
## iter  90 value 266.860367
## iter 100 value 266.857996
## final  value 266.857996 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 420.893434 
## iter  10 value 281.626769
## iter  20 value 243.123209
## iter  30 value 242.531241
## final  value 242.529210 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 403.812827 
## iter  10 value 341.645429
## iter  20 value 242.585247
## iter  30 value 242.537093
## iter  40 value 242.517743
## final  value 242.517001 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 428.405144 
## iter  10 value 363.335256
## iter  20 value 242.855604
## iter  30 value 242.579253
## iter  40 value 242.535875
## iter  50 value 242.521807
## iter  60 value 242.517674
## iter  70 value 242.516825
## final  value 242.516767 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 424.881814 
## iter  10 value 272.684529
## iter  20 value 242.603276
## iter  30 value 242.522661
## iter  40 value 242.519218
## iter  50 value 242.517591
## iter  60 value 242.517039
## iter  60 value 242.517038
## iter  60 value 242.517038
## final  value 242.517038 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 415.544795 
## iter  10 value 345.239150
## iter  20 value 242.744296
## iter  30 value 242.568311
## iter  40 value 242.521742
## final  value 242.518246 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 406.832717 
## iter  10 value 310.456814
## iter  20 value 239.652386
## iter  30 value 204.004059
## iter  40 value 176.801458
## iter  50 value 172.848908
## iter  60 value 171.599955
## iter  70 value 171.439516
## iter  80 value 171.400629
## iter  90 value 171.386021
## iter 100 value 171.370612
## final  value 171.370612 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 480.937482 
## iter  10 value 305.554538
## iter  20 value 239.746580
## iter  30 value 182.084904
## iter  40 value 175.500713
## iter  50 value 172.320068
## iter  60 value 171.758016
## iter  70 value 171.556807
## iter  80 value 171.531293
## iter  90 value 171.503768
## iter 100 value 171.479198
## final  value 171.479198 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 424.295342 
## iter  10 value 287.580164
## iter  20 value 238.142065
## iter  30 value 185.974399
## iter  40 value 176.942058
## iter  50 value 174.275779
## iter  60 value 171.884451
## iter  70 value 171.426459
## iter  80 value 171.392558
## iter  90 value 171.355951
## iter 100 value 171.339174
## final  value 171.339174 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 425.964289 
## iter  10 value 288.389327
## iter  20 value 242.629628
## iter  30 value 241.864029
## iter  40 value 226.987080
## iter  50 value 217.585134
## iter  60 value 208.330632
## iter  70 value 176.082849
## iter  80 value 172.201099
## iter  90 value 171.522047
## iter 100 value 171.461129
## final  value 171.461129 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 463.892972 
## iter  10 value 270.323173
## iter  20 value 243.485502
## iter  30 value 242.625351
## iter  40 value 241.195500
## iter  50 value 172.809901
## iter  60 value 172.159491
## iter  70 value 171.705959
## iter  80 value 171.472038
## iter  90 value 171.436184
## iter 100 value 171.377892
## final  value 171.377892 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 426.211455 
## iter  10 value 249.907890
## iter  20 value 202.899126
## iter  30 value 171.675594
## iter  40 value 171.473199
## iter  50 value 171.422007
## iter  60 value 171.416541
## iter  70 value 171.380341
## iter  80 value 171.325900
## iter  90 value 171.171272
## iter 100 value 170.998135
## final  value 170.998135 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 420.494834 
## iter  10 value 301.849751
## iter  20 value 242.820893
## iter  30 value 242.402096
## iter  40 value 195.743046
## iter  50 value 175.477301
## iter  60 value 171.983252
## iter  70 value 171.617144
## iter  80 value 171.456998
## iter  90 value 171.400184
## iter 100 value 171.392957
## final  value 171.392957 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 442.380780 
## iter  10 value 347.146533
## iter  20 value 263.206073
## iter  30 value 210.467765
## iter  40 value 187.711318
## iter  50 value 180.444177
## iter  60 value 174.937687
## iter  70 value 172.036343
## iter  80 value 171.828933
## iter  90 value 171.785919
## iter 100 value 171.709036
## final  value 171.709036 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 473.105518 
## iter  10 value 259.061575
## iter  20 value 211.884281
## iter  30 value 179.362009
## iter  40 value 172.106449
## iter  50 value 171.700683
## iter  60 value 171.541351
## iter  70 value 171.454754
## iter  80 value 171.423315
## iter  90 value 171.410221
## iter 100 value 171.396792
## final  value 171.396792 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 399.280726 
## iter  10 value 242.859826
## iter  20 value 232.256200
## iter  30 value 179.348544
## iter  40 value 172.763170
## iter  50 value 170.534669
## iter  60 value 168.438032
## iter  70 value 168.019172
## iter  80 value 167.344907
## iter  90 value 166.833779
## iter 100 value 166.457345
## final  value 166.457345 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 421.253263 
## iter  10 value 250.162552
## iter  20 value 231.438494
## iter  30 value 231.244816
## iter  40 value 231.043623
## iter  50 value 231.015445
## iter  60 value 231.006688
## iter  70 value 230.973336
## iter  80 value 230.966915
## iter  90 value 230.961204
## iter 100 value 230.945986
## final  value 230.945986 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 414.019652 
## iter  10 value 278.824772
## iter  20 value 232.330719
## iter  30 value 231.480525
## iter  40 value 231.224174
## iter  50 value 231.139526
## iter  60 value 231.074414
## iter  70 value 231.044736
## iter  80 value 231.021422
## iter  90 value 231.000365
## iter 100 value 230.989157
## final  value 230.989157 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 455.562976 
## iter  10 value 346.326400
## iter  20 value 231.497585
## iter  30 value 231.333617
## iter  40 value 231.087953
## iter  50 value 231.055271
## iter  60 value 231.038182
## iter  70 value 231.025304
## iter  80 value 231.017031
## iter  90 value 231.004894
## iter 100 value 230.975558
## final  value 230.975558 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 431.626265 
## iter  10 value 288.435241
## iter  20 value 232.188436
## iter  30 value 231.519235
## iter  40 value 231.032105
## iter  50 value 231.020736
## iter  60 value 230.974750
## iter  70 value 230.949992
## iter  80 value 230.948857
## iter  90 value 230.945723
## iter 100 value 230.944330
## final  value 230.944330 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 411.639442 
## iter  10 value 347.353333
## iter  20 value 233.109222
## iter  30 value 231.523160
## iter  40 value 231.149609
## iter  50 value 231.074814
## iter  60 value 231.010008
## iter  70 value 230.999170
## iter  80 value 230.989687
## iter  90 value 230.971528
## iter 100 value 230.967754
## final  value 230.967754 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 403.833414 
## iter  10 value 326.406015
## iter  20 value 323.615184
## iter  30 value 323.222530
## iter  40 value 323.027814
## iter  50 value 322.982958
## iter  60 value 322.768343
## iter  70 value 322.657816
## iter  80 value 322.504385
## iter  90 value 322.498528
## iter 100 value 322.486654
## final  value 322.486654 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 413.370925 
## iter  10 value 242.151151
## iter  20 value 224.003181
## iter  30 value 221.742503
## iter  40 value 220.350230
## iter  50 value 212.455812
## iter  60 value 204.127270
## iter  70 value 187.795588
## iter  80 value 181.492330
## iter  90 value 173.147367
## iter 100 value 168.728364
## final  value 168.728364 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 464.275617 
## iter  10 value 244.345004
## iter  20 value 219.380410
## iter  30 value 187.400951
## iter  40 value 173.591957
## iter  50 value 167.646066
## iter  60 value 165.801873
## iter  70 value 165.333998
## iter  80 value 165.227249
## iter  90 value 165.050230
## iter 100 value 164.834050
## final  value 164.834050 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 431.189022 
## iter  10 value 232.528268
## iter  20 value 230.693499
## iter  30 value 227.666105
## iter  40 value 214.376267
## iter  50 value 181.456864
## iter  60 value 172.588436
## iter  70 value 170.063427
## iter  80 value 168.580716
## iter  90 value 167.252455
## iter 100 value 166.763284
## final  value 166.763284 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 430.281317 
## iter  10 value 335.122827
## iter  20 value 311.429494
## iter  30 value 301.461952
## iter  40 value 278.119244
## iter  50 value 256.035647
## iter  60 value 255.727191
## iter  70 value 255.354269
## iter  80 value 252.808209
## iter  90 value 249.736890
## iter 100 value 248.377128
## final  value 248.377128 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 458.942968 
## iter  10 value 270.524839
## iter  20 value 202.715954
## iter  30 value 172.117193
## iter  40 value 166.608851
## iter  50 value 166.089985
## iter  60 value 165.326733
## iter  70 value 164.465770
## iter  80 value 164.061377
## iter  90 value 161.815198
## iter 100 value 160.156614
## final  value 160.156614 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 515.700202 
## iter  10 value 261.464463
## iter  20 value 226.634414
## iter  30 value 186.380396
## iter  40 value 170.889143
## iter  50 value 166.641403
## iter  60 value 165.042169
## iter  70 value 164.546763
## iter  80 value 163.738664
## iter  90 value 163.294676
## iter 100 value 163.053677
## final  value 163.053677 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 410.765409 
## iter  10 value 245.382330
## iter  20 value 197.488439
## iter  30 value 176.811321
## iter  40 value 170.095657
## iter  50 value 168.091763
## iter  60 value 167.369298
## iter  70 value 167.164412
## iter  80 value 166.843636
## iter  90 value 166.664040
## iter 100 value 166.216445
## final  value 166.216445 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 409.071927 
## iter  10 value 330.996660
## iter  20 value 328.008202
## iter  30 value 328.001559
## final  value 328.000781 
## converged
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 466.628181 
## iter  10 value 285.717433
## iter  20 value 227.541723
## iter  30 value 194.883217
## iter  40 value 173.618287
## iter  50 value 167.409537
## iter  60 value 166.666758
## iter  70 value 165.827932
## iter  80 value 165.635347
## iter  90 value 165.514694
## iter 100 value 165.237801
## final  value 165.237801 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 476.370191 
## iter  10 value 318.885601
## iter  20 value 261.856977
## iter  30 value 261.572551
## iter  30 value 261.572551
## iter  30 value 261.572551
## final  value 261.572551 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 437.848475 
## iter  10 value 346.031740
## iter  20 value 263.536595
## iter  30 value 262.492780
## iter  30 value 262.492779
## iter  30 value 262.492779
## final  value 262.492779 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 405.482673 
## iter  10 value 322.789445
## iter  20 value 263.536862
## iter  30 value 261.572555
## final  value 261.572551 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 402.984343 
## iter  10 value 293.622877
## iter  20 value 262.492923
## final  value 262.492801 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 429.536453 
## iter  10 value 372.901269
## iter  20 value 263.885802
## iter  30 value 262.492804
## final  value 262.492779 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 400.913079 
## iter  10 value 284.458542
## iter  20 value 260.667664
## iter  30 value 258.914457
## iter  40 value 258.535825
## iter  50 value 258.480637
## iter  60 value 258.474556
## final  value 258.474400 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 457.004840 
## iter  10 value 390.958293
## iter  20 value 263.473059
## iter  30 value 258.681898
## iter  40 value 255.441265
## iter  50 value 254.324951
## iter  60 value 252.760447
## iter  70 value 252.531853
## iter  80 value 252.527061
## final  value 252.527057 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 422.107583 
## iter  10 value 306.936034
## iter  20 value 261.537213
## iter  30 value 259.825555
## iter  40 value 258.655386
## iter  50 value 258.522340
## iter  60 value 258.480351
## iter  70 value 258.474406
## iter  70 value 258.474404
## iter  70 value 258.474404
## final  value 258.474404 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 412.603271 
## iter  10 value 297.320749
## iter  20 value 265.527644
## iter  30 value 263.319703
## iter  40 value 260.244471
## iter  50 value 257.727589
## iter  60 value 253.419901
## iter  70 value 252.797715
## iter  80 value 252.771022
## iter  90 value 252.269384
## iter 100 value 252.246007
## final  value 252.246007 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 460.455772 
## iter  10 value 322.297183
## iter  20 value 259.101733
## iter  30 value 258.606288
## iter  40 value 258.597240
## iter  50 value 258.590295
## iter  60 value 258.501613
## iter  70 value 258.475040
## iter  80 value 258.474480
## final  value 258.474439 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 430.786531 
## iter  10 value 294.262012
## iter  20 value 261.575831
## iter  30 value 259.331684
## iter  40 value 258.562860
## iter  50 value 258.318863
## iter  60 value 258.273895
## iter  70 value 258.241154
## iter  80 value 258.230926
## iter  90 value 258.230438
## final  value 258.230423 
## converged
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 420.505623 
## iter  10 value 278.580173
## iter  20 value 258.795770
## iter  30 value 258.608806
## iter  40 value 258.568116
## iter  50 value 258.381960
## iter  60 value 258.301694
## iter  70 value 258.241829
## iter  80 value 258.232018
## iter  90 value 258.230706
## final  value 258.230426 
## converged
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 411.063672 
## iter  10 value 295.191388
## iter  20 value 263.066267
## iter  30 value 257.698317
## iter  40 value 254.755980
## iter  50 value 254.159329
## iter  60 value 253.290015
## iter  70 value 252.457194
## iter  80 value 252.366666
## iter  90 value 252.189570
## iter 100 value 252.087824
## final  value 252.087824 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 453.971923 
## iter  10 value 275.025065
## iter  20 value 260.529697
## iter  30 value 259.343834
## iter  40 value 258.588110
## iter  50 value 258.399710
## iter  60 value 258.322351
## iter  70 value 258.295559
## iter  80 value 258.278162
## iter  90 value 258.273182
## iter 100 value 258.272573
## final  value 258.272573 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 416.002197 
## iter  10 value 290.812179
## iter  20 value 259.306154
## iter  30 value 258.589972
## iter  40 value 258.546956
## iter  50 value 258.465594
## iter  60 value 258.432731
## iter  70 value 258.374627
## iter  80 value 258.277037
## iter  90 value 258.239906
## iter 100 value 258.231539
## final  value 258.231539 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 445.231915 
## iter  10 value 258.472177
## iter  20 value 233.201384
## iter  30 value 231.952401
## iter  40 value 231.328784
## iter  50 value 231.120302
## iter  60 value 231.106395
## iter  70 value 231.089323
## iter  80 value 231.083993
## final  value 231.083804 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 399.583621 
## iter  10 value 234.234455
## iter  20 value 232.344420
## iter  30 value 231.270767
## iter  40 value 231.215963
## iter  50 value 231.143567
## iter  60 value 231.117929
## iter  70 value 231.110306
## iter  80 value 231.083748
## iter  90 value 231.082662
## final  value 231.082244 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 431.225321 
## iter  10 value 234.091599
## iter  20 value 231.972485
## iter  30 value 231.303898
## iter  40 value 231.207524
## iter  50 value 231.171831
## iter  60 value 231.156634
## iter  70 value 231.135675
## iter  80 value 231.134344
## final  value 231.133859 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 455.178160 
## iter  10 value 267.591536
## iter  20 value 233.137352
## iter  30 value 231.758663
## iter  40 value 231.345145
## iter  50 value 231.202996
## iter  60 value 231.138251
## iter  70 value 231.104503
## final  value 231.097283 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 401.925411 
## iter  10 value 248.921058
## iter  20 value 233.241993
## iter  30 value 231.354860
## iter  40 value 231.281296
## iter  50 value 231.218277
## iter  60 value 231.174643
## iter  70 value 231.144328
## iter  80 value 231.136560
## iter  90 value 231.134318
## final  value 231.134171 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 411.826805 
## iter  10 value 282.043429
## iter  20 value 231.745626
## iter  30 value 231.246495
## iter  40 value 231.153725
## iter  50 value 231.001349
## iter  60 value 228.056359
## iter  70 value 186.382137
## iter  80 value 173.590309
## iter  90 value 170.089281
## iter 100 value 168.728908
## final  value 168.728908 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 436.655481 
## iter  10 value 261.205451
## iter  20 value 230.806420
## iter  30 value 209.257931
## iter  40 value 179.096386
## iter  50 value 175.036605
## iter  60 value 169.073682
## iter  70 value 166.328269
## iter  80 value 165.901068
## iter  90 value 165.846432
## iter 100 value 165.718879
## final  value 165.718879 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 420.941712 
## iter  10 value 305.494706
## iter  20 value 227.086150
## iter  30 value 196.462383
## iter  40 value 177.117331
## iter  50 value 171.949391
## iter  60 value 169.663118
## iter  70 value 167.959946
## iter  80 value 167.185921
## iter  90 value 167.039500
## iter 100 value 166.919104
## final  value 166.919104 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 471.384265 
## iter  10 value 353.115676
## iter  20 value 330.604279
## iter  30 value 319.969612
## iter  40 value 319.550789
## iter  50 value 319.080053
## iter  60 value 318.878538
## iter  70 value 318.754979
## iter  80 value 318.690103
## iter  90 value 318.652599
## iter 100 value 318.607836
## final  value 318.607836 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 446.350318 
## iter  10 value 307.523498
## iter  20 value 226.963516
## iter  30 value 187.612550
## iter  40 value 172.549635
## iter  50 value 169.958275
## iter  60 value 166.532619
## iter  70 value 166.119359
## iter  80 value 165.981325
## iter  90 value 165.753852
## iter 100 value 165.592020
## final  value 165.592020 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 411.620331 
## iter  10 value 240.558139
## iter  20 value 229.910330
## iter  30 value 183.368711
## iter  40 value 169.153444
## iter  50 value 166.717644
## iter  60 value 166.185639
## iter  70 value 166.085958
## iter  80 value 165.665575
## iter  90 value 165.208456
## iter 100 value 164.728220
## final  value 164.728220 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 407.817840 
## iter  10 value 255.803964
## iter  20 value 229.085875
## iter  30 value 220.195012
## iter  40 value 202.887412
## iter  50 value 182.757450
## iter  60 value 170.954487
## iter  70 value 168.655290
## iter  80 value 167.967587
## iter  90 value 167.559736
## iter 100 value 167.389301
## final  value 167.389301 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 401.739193 
## iter  10 value 257.026324
## iter  20 value 193.379846
## iter  30 value 174.029362
## iter  40 value 166.804468
## iter  50 value 163.209338
## iter  60 value 162.409801
## iter  70 value 161.962920
## iter  80 value 161.651785
## iter  90 value 161.433252
## iter 100 value 161.305886
## final  value 161.305886 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 428.412369 
## iter  10 value 232.490813
## iter  20 value 231.436856
## iter  30 value 231.364058
## iter  40 value 231.297851
## iter  50 value 231.170476
## iter  60 value 231.071513
## iter  70 value 228.142550
## iter  80 value 185.747206
## iter  90 value 171.668769
## iter 100 value 168.327113
## final  value 168.327113 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 410.236804 
## iter  10 value 241.232054
## iter  20 value 182.764713
## iter  30 value 169.856330
## iter  40 value 166.767075
## iter  50 value 166.622893
## iter  60 value 166.364026
## iter  70 value 166.087953
## iter  80 value 165.749852
## iter  90 value 165.643677
## iter 100 value 165.554972
## final  value 165.554972 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 455.126738 
## iter  10 value 368.115241
## iter  20 value 200.007476
## iter  30 value 199.930032
## final  value 199.929849 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 405.618328 
## iter  10 value 233.139469
## iter  20 value 200.092899
## iter  30 value 199.930287
## iter  40 value 199.929894
## final  value 199.929871 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 404.828627 
## iter  10 value 275.876574
## iter  20 value 201.461647
## iter  30 value 200.575540
## iter  40 value 199.937180
## iter  50 value 199.930565
## final  value 199.929856 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 404.382524 
## iter  10 value 216.751399
## iter  20 value 199.976199
## iter  30 value 199.942778
## iter  40 value 199.929848
## iter  40 value 199.929846
## iter  40 value 199.929846
## final  value 199.929846 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 431.720512 
## iter  10 value 319.285975
## iter  20 value 217.678964
## iter  30 value 204.208206
## iter  40 value 199.932597
## final  value 199.929987 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 397.434729 
## iter  10 value 250.412766
## iter  20 value 199.943217
## iter  30 value 175.588857
## iter  40 value 169.858248
## iter  50 value 169.318291
## iter  60 value 169.026570
## iter  70 value 168.820873
## iter  80 value 168.801511
## iter  90 value 168.784526
## iter 100 value 168.766553
## final  value 168.766553 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 427.643718 
## iter  10 value 262.023038
## iter  20 value 233.444588
## iter  30 value 227.950847
## iter  40 value 224.260804
## iter  50 value 209.026993
## iter  60 value 203.359715
## iter  70 value 200.598555
## iter  80 value 200.530217
## iter  90 value 200.335480
## iter 100 value 200.301585
## final  value 200.301585 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 455.446114 
## iter  10 value 251.711683
## iter  20 value 220.516160
## iter  30 value 201.091649
## iter  40 value 200.384035
## iter  50 value 197.515950
## iter  60 value 174.315316
## iter  70 value 172.969861
## iter  80 value 172.255307
## iter  90 value 170.986104
## iter 100 value 170.772776
## final  value 170.772776 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 469.518756 
## iter  10 value 226.623506
## iter  20 value 205.706306
## iter  30 value 178.074072
## iter  40 value 172.901133
## iter  50 value 170.787135
## iter  60 value 170.682796
## iter  70 value 170.621850
## iter  80 value 170.594777
## iter  90 value 170.568577
## iter 100 value 170.532521
## final  value 170.532521 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 418.518951 
## iter  10 value 225.580596
## iter  20 value 200.418088
## iter  30 value 200.090370
## iter  40 value 199.840858
## iter  50 value 198.518590
## iter  60 value 184.449542
## iter  70 value 171.999265
## iter  80 value 169.536158
## iter  90 value 169.168624
## iter 100 value 169.048354
## final  value 169.048354 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 426.637030 
## iter  10 value 281.642256
## iter  20 value 243.863537
## iter  30 value 225.253425
## iter  40 value 214.066975
## iter  50 value 202.470783
## iter  60 value 196.731311
## iter  70 value 193.444431
## iter  80 value 181.479839
## iter  90 value 175.504953
## iter 100 value 169.694715
## final  value 169.694715 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 443.299905 
## iter  10 value 247.486094
## iter  20 value 201.658576
## iter  30 value 200.164302
## iter  40 value 199.943748
## iter  50 value 199.746973
## iter  60 value 185.750278
## iter  70 value 173.152331
## iter  80 value 170.335886
## iter  90 value 169.645003
## iter 100 value 169.103582
## final  value 169.103582 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 395.262206 
## iter  10 value 265.295703
## iter  20 value 190.670710
## iter  30 value 171.143811
## iter  40 value 169.563301
## iter  50 value 169.192738
## iter  60 value 169.142677
## iter  70 value 169.090768
## iter  80 value 168.981573
## iter  90 value 168.803411
## iter 100 value 168.503863
## final  value 168.503863 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 440.349630 
## iter  10 value 351.582616
## iter  20 value 200.433950
## iter  30 value 186.352977
## iter  40 value 171.731608
## iter  50 value 169.409501
## iter  60 value 169.109376
## iter  70 value 169.041323
## iter  80 value 168.945375
## iter  90 value 168.867371
## iter 100 value 168.812273
## final  value 168.812273 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 426.047679 
## iter  10 value 219.407070
## iter  20 value 199.946499
## iter  30 value 199.848346
## iter  40 value 199.762947
## iter  50 value 199.123597
## iter  60 value 198.925918
## iter  70 value 198.817556
## iter  80 value 198.703068
## iter  90 value 197.785424
## iter 100 value 191.388409
## final  value 191.388409 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 426.208125 
## iter  10 value 277.623076
## iter  20 value 235.898553
## iter  30 value 235.672680
## iter  30 value 235.672679
## iter  30 value 235.672679
## final  value 235.672679 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 463.682627 
## iter  10 value 239.286209
## iter  20 value 235.672892
## final  value 235.672679 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 483.692779 
## iter  10 value 286.531203
## iter  20 value 235.791896
## iter  30 value 235.428822
## iter  30 value 235.428822
## iter  30 value 235.428822
## final  value 235.428822 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 405.542636 
## iter  10 value 391.245271
## iter  20 value 235.636909
## final  value 235.428844 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 399.986738 
## iter  10 value 291.098257
## iter  20 value 235.532794
## final  value 235.428822 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 393.820820 
## iter  10 value 298.351028
## iter  20 value 236.326795
## iter  30 value 232.534384
## iter  40 value 232.032153
## iter  50 value 231.894741
## final  value 231.893388 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 437.478150 
## iter  10 value 262.506178
## iter  20 value 239.585477
## iter  30 value 237.347342
## iter  40 value 233.040670
## iter  50 value 232.010656
## iter  60 value 231.891696
## iter  70 value 231.891241
## iter  80 value 231.881673
## iter  90 value 231.872357
## iter 100 value 231.841795
## final  value 231.841795 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 407.476323 
## iter  10 value 278.870712
## iter  20 value 234.302939
## iter  30 value 232.065351
## iter  40 value 231.868494
## iter  50 value 231.851609
## iter  60 value 231.844539
## iter  70 value 231.833181
## final  value 231.833163 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 411.173765 
## iter  10 value 281.658374
## iter  20 value 233.676021
## iter  30 value 232.423544
## iter  40 value 232.068769
## iter  50 value 231.859504
## iter  60 value 231.844744
## iter  70 value 231.833195
## final  value 231.833163 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 441.602984 
## iter  10 value 313.335557
## iter  20 value 236.944809
## iter  30 value 233.965594
## iter  40 value 233.010868
## iter  50 value 231.956137
## iter  60 value 231.833469
## iter  70 value 231.833320
## final  value 231.833295 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 404.008756 
## iter  10 value 252.390294
## iter  20 value 236.796270
## iter  30 value 233.108071
## iter  40 value 231.907102
## iter  50 value 231.834914
## iter  60 value 231.706541
## iter  70 value 231.662505
## iter  80 value 231.619497
## iter  90 value 231.616800
## iter 100 value 231.614610
## final  value 231.614610 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 478.145067 
## iter  10 value 294.299766
## iter  20 value 232.319032
## iter  30 value 231.954637
## iter  40 value 231.796537
## iter  50 value 231.703094
## iter  60 value 231.648257
## iter  70 value 231.642956
## iter  80 value 231.640351
## iter  90 value 231.636355
## iter 100 value 231.618212
## final  value 231.618212 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 442.701737 
## iter  10 value 305.835205
## iter  20 value 236.367394
## iter  30 value 234.011053
## iter  40 value 232.433348
## iter  50 value 231.784905
## iter  60 value 231.726248
## iter  70 value 231.679172
## iter  80 value 231.632484
## iter  90 value 231.629485
## iter 100 value 231.628503
## final  value 231.628503 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 395.276018 
## iter  10 value 246.471980
## iter  20 value 232.860594
## iter  30 value 231.988823
## iter  40 value 231.838711
## iter  50 value 231.796261
## iter  60 value 231.743931
## iter  70 value 231.708842
## iter  80 value 231.623835
## iter  90 value 231.615150
## iter 100 value 231.613870
## final  value 231.613870 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 430.920654 
## iter  10 value 307.316147
## iter  20 value 234.457321
## iter  30 value 232.353807
## iter  40 value 231.791130
## iter  50 value 231.719212
## iter  60 value 231.654742
## iter  70 value 231.642679
## iter  80 value 231.617224
## iter  90 value 231.614821
## iter 100 value 231.613773
## final  value 231.613773 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 425.713886 
## iter  10 value 256.929469
## iter  20 value 228.542432
## iter  30 value 221.866553
## iter  40 value 206.711653
## iter  50 value 205.475663
## iter  60 value 202.699857
## iter  70 value 200.283736
## iter  80 value 199.989153
## iter  90 value 199.986431
## final  value 199.986420 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 415.248578 
## iter  10 value 239.303063
## iter  20 value 205.970254
## iter  30 value 203.591431
## iter  40 value 201.231411
## iter  50 value 200.045406
## iter  60 value 199.984105
## final  value 199.984102 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 398.347271 
## iter  10 value 236.830209
## iter  20 value 200.603727
## iter  30 value 200.031404
## iter  40 value 200.022522
## iter  50 value 199.984555
## final  value 199.984097 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 413.777270 
## iter  10 value 256.591264
## iter  20 value 202.356103
## iter  30 value 200.173984
## iter  40 value 199.991513
## iter  50 value 199.986423
## iter  50 value 199.986423
## final  value 199.986423 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 476.435732 
## iter  10 value 235.285097
## iter  20 value 200.339732
## iter  30 value 199.994856
## final  value 199.986479 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 464.158274 
## iter  10 value 265.685508
## iter  20 value 206.484008
## iter  30 value 200.099750
## iter  40 value 200.026089
## iter  50 value 200.005395
## iter  60 value 199.983786
## iter  70 value 199.786635
## iter  80 value 177.301242
## iter  90 value 171.786678
## iter 100 value 169.921735
## final  value 169.921735 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 426.840184 
## iter  10 value 212.671642
## iter  20 value 196.460157
## iter  30 value 177.437019
## iter  40 value 174.354767
## iter  50 value 173.468650
## iter  60 value 172.664243
## iter  70 value 171.598614
## iter  80 value 171.169335
## iter  90 value 171.032226
## iter 100 value 170.876239
## final  value 170.876239 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 406.443706 
## iter  10 value 213.728408
## iter  20 value 194.134716
## iter  30 value 173.825103
## iter  40 value 171.183415
## iter  50 value 169.757033
## iter  60 value 169.404868
## iter  70 value 169.244435
## iter  80 value 169.185634
## iter  90 value 169.154822
## iter 100 value 169.121024
## final  value 169.121024 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 441.271096 
## iter  10 value 234.434786
## iter  20 value 200.216696
## iter  30 value 200.087319
## iter  40 value 200.037374
## iter  50 value 199.941786
## iter  60 value 194.322046
## iter  70 value 179.979109
## iter  80 value 173.795427
## iter  90 value 170.793937
## iter 100 value 170.073551
## final  value 170.073551 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 402.306555 
## iter  10 value 215.187248
## iter  20 value 200.218838
## iter  30 value 188.253085
## iter  40 value 172.220520
## iter  50 value 170.518436
## iter  60 value 169.663638
## iter  70 value 169.294993
## iter  80 value 169.151867
## iter  90 value 169.022306
## iter 100 value 168.641844
## final  value 168.641844 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 417.786971 
## iter  10 value 254.301783
## iter  20 value 193.907657
## iter  30 value 172.392158
## iter  40 value 170.277478
## iter  50 value 169.549554
## iter  60 value 169.444788
## iter  70 value 169.299401
## iter  80 value 169.160241
## iter  90 value 169.077461
## iter 100 value 169.028606
## final  value 169.028606 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 439.335943 
## iter  10 value 261.592511
## iter  20 value 203.678061
## iter  30 value 200.312127
## iter  40 value 200.114676
## iter  50 value 199.947418
## iter  60 value 199.838735
## iter  70 value 198.095267
## iter  80 value 184.130033
## iter  90 value 176.319007
## iter 100 value 175.325606
## final  value 175.325606 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 427.533228 
## iter  10 value 301.850023
## iter  20 value 267.632061
## iter  30 value 264.860701
## iter  40 value 262.066653
## iter  50 value 255.692215
## iter  60 value 240.092828
## iter  70 value 224.813097
## iter  80 value 209.204270
## iter  90 value 202.487097
## iter 100 value 201.171457
## final  value 201.171457 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 488.663138 
## iter  10 value 249.391993
## iter  20 value 200.529247
## iter  30 value 199.910525
## iter  40 value 198.895050
## iter  50 value 180.012367
## iter  60 value 171.152268
## iter  70 value 169.723538
## iter  80 value 169.202885
## iter  90 value 169.144366
## iter 100 value 169.124372
## final  value 169.124372 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 446.385890 
## iter  10 value 388.349524
## iter  20 value 234.362055
## iter  30 value 200.032645
## iter  40 value 199.961442
## iter  50 value 199.126092
## iter  60 value 182.500162
## iter  70 value 172.441917
## iter  80 value 170.558394
## iter  90 value 170.089197
## iter 100 value 169.713372
## final  value 169.713372 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 446.059228 
## iter  10 value 249.228935
## iter  20 value 242.069102
## iter  30 value 242.013710
## iter  40 value 241.969875
## iter  50 value 241.959187
## iter  60 value 241.952774
## iter  70 value 241.942088
## iter  80 value 241.941113
## iter  90 value 241.939809
## iter 100 value 241.939461
## final  value 241.939461 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 409.928851 
## iter  10 value 354.492511
## iter  20 value 242.433732
## iter  30 value 242.091800
## iter  40 value 242.002972
## iter  50 value 241.978555
## iter  60 value 241.961641
## iter  70 value 241.944227
## iter  80 value 241.943197
## iter  90 value 241.942318
## iter 100 value 241.941632
## final  value 241.941632 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 412.297594 
## iter  10 value 358.214553
## iter  20 value 249.735009
## iter  30 value 242.789386
## iter  40 value 242.126032
## iter  50 value 242.004442
## iter  60 value 241.955918
## iter  70 value 241.953079
## iter  80 value 241.946082
## final  value 241.944780 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 404.044657 
## iter  10 value 347.298371
## iter  20 value 346.210231
## iter  30 value 346.181075
## iter  40 value 346.178375
## iter  50 value 346.044168
## iter  60 value 345.976241
## iter  70 value 345.971004
## final  value 345.969862 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 423.507211 
## iter  10 value 261.007751
## iter  20 value 242.094749
## iter  30 value 242.021665
## iter  40 value 241.955612
## iter  50 value 241.952078
## iter  60 value 241.947339
## iter  70 value 241.945250
## iter  80 value 241.943634
## iter  90 value 241.942639
## iter 100 value 241.935418
## final  value 241.935418 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 429.358452 
## iter  10 value 339.639996
## iter  20 value 242.671789
## iter  30 value 241.555853
## iter  40 value 216.015284
## iter  50 value 189.609379
## iter  60 value 186.953238
## iter  70 value 186.317554
## iter  80 value 185.456285
## iter  90 value 184.817860
## iter 100 value 184.213080
## final  value 184.213080 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 420.888835 
## iter  10 value 268.452482
## iter  20 value 242.033868
## iter  30 value 230.615233
## iter  40 value 208.613402
## iter  50 value 194.494202
## iter  60 value 189.556360
## iter  70 value 187.991104
## iter  80 value 187.357962
## iter  90 value 186.547433
## iter 100 value 186.045459
## final  value 186.045459 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 414.985390 
## iter  10 value 256.069429
## iter  20 value 238.825333
## iter  30 value 197.034330
## iter  40 value 186.184073
## iter  50 value 185.835753
## iter  60 value 185.621682
## iter  70 value 185.187617
## iter  80 value 185.105783
## iter  90 value 185.045922
## iter 100 value 185.033647
## final  value 185.033647 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 406.862212 
## iter  10 value 257.622509
## iter  20 value 234.403570
## iter  30 value 195.222111
## iter  40 value 190.483518
## iter  50 value 187.034906
## iter  60 value 186.657486
## iter  70 value 186.186691
## iter  80 value 186.095825
## iter  90 value 185.937982
## iter 100 value 185.387456
## final  value 185.387456 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 421.556615 
## iter  10 value 308.889689
## iter  20 value 240.148512
## iter  30 value 227.592236
## iter  40 value 189.203551
## iter  50 value 185.808728
## iter  60 value 185.107485
## iter  70 value 185.058020
## iter  80 value 185.031816
## iter  90 value 184.953755
## iter 100 value 184.913115
## final  value 184.913115 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 439.643035 
## iter  10 value 303.251120
## iter  20 value 246.879170
## iter  30 value 242.598782
## iter  40 value 238.117961
## iter  50 value 206.048711
## iter  60 value 187.880484
## iter  70 value 186.997580
## iter  80 value 186.409537
## iter  90 value 186.375449
## iter 100 value 186.335884
## final  value 186.335884 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 431.110687 
## iter  10 value 268.849345
## iter  20 value 220.711226
## iter  30 value 190.490846
## iter  40 value 186.587499
## iter  50 value 186.112122
## iter  60 value 185.007772
## iter  70 value 184.348800
## iter  80 value 183.751014
## iter  90 value 183.379562
## iter 100 value 182.892512
## final  value 182.892512 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 411.413427 
## iter  10 value 278.277245
## iter  20 value 220.526414
## iter  30 value 189.229941
## iter  40 value 185.340731
## iter  50 value 184.354325
## iter  60 value 184.177908
## iter  70 value 183.796016
## iter  80 value 182.156781
## iter  90 value 180.826836
## iter 100 value 180.589635
## final  value 180.589635 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 494.130835 
## iter  10 value 279.985280
## iter  20 value 241.315801
## iter  30 value 216.775150
## iter  40 value 190.732495
## iter  50 value 187.042568
## iter  60 value 186.389721
## iter  70 value 186.249119
## iter  80 value 185.825042
## iter  90 value 185.362783
## iter 100 value 185.279276
## final  value 185.279276 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 404.585404 
## iter  10 value 248.234745
## iter  20 value 192.081062
## iter  30 value 186.819497
## iter  40 value 185.700016
## iter  50 value 185.449347
## iter  60 value 185.191484
## iter  70 value 184.899239
## iter  80 value 184.505481
## iter  90 value 184.028870
## iter 100 value 183.435161
## final  value 183.435161 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 400.837238 
## iter  10 value 352.722717
## iter  20 value 270.643890
## iter  30 value 270.567051
## iter  30 value 270.567049
## iter  30 value 270.567049
## final  value 270.567049 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 408.499996 
## iter  10 value 335.430446
## iter  20 value 270.474685
## iter  30 value 270.404757
## iter  30 value 270.404756
## iter  30 value 270.404756
## final  value 270.404756 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 425.274432 
## iter  10 value 395.409830
## iter  20 value 286.160362
## iter  30 value 270.582423
## final  value 270.567049 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 427.743874 
## iter  10 value 370.861141
## iter  20 value 276.288388
## iter  30 value 270.567052
## iter  30 value 270.567050
## iter  30 value 270.567050
## final  value 270.567050 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 443.274113 
## iter  10 value 298.159171
## iter  20 value 270.550116
## final  value 270.404756 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 477.341420 
## iter  10 value 296.881029
## iter  20 value 268.187908
## iter  30 value 267.570666
## iter  40 value 267.241624
## iter  50 value 267.198806
## iter  60 value 267.182981
## iter  70 value 267.170689
## iter  70 value 267.170687
## iter  70 value 267.170687
## final  value 267.170687 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 417.593412 
## iter  10 value 311.475506
## iter  20 value 270.657469
## iter  30 value 268.015738
## iter  40 value 267.309221
## iter  50 value 267.232103
## iter  60 value 267.214116
## iter  70 value 267.171817
## final  value 267.170689 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 432.365095 
## iter  10 value 320.307529
## iter  20 value 271.927302
## iter  30 value 270.630290
## iter  40 value 268.577538
## iter  50 value 267.281236
## iter  60 value 267.235506
## iter  70 value 267.234042
## iter  80 value 267.230677
## iter  90 value 267.181190
## iter 100 value 267.171006
## final  value 267.171006 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 465.790640 
## iter  10 value 319.930108
## iter  20 value 269.835000
## iter  30 value 268.169563
## iter  40 value 267.387491
## iter  50 value 267.182729
## iter  60 value 267.170895
## final  value 267.170685 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 435.155224 
## iter  10 value 341.243554
## iter  20 value 270.373508
## iter  30 value 267.863199
## iter  40 value 267.312478
## iter  50 value 267.235162
## iter  60 value 267.223641
## iter  70 value 267.203152
## final  value 267.203084 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 429.182693 
## iter  10 value 298.906086
## iter  20 value 270.200387
## iter  30 value 268.186715
## iter  40 value 267.593745
## iter  50 value 267.237872
## iter  60 value 267.112700
## iter  70 value 267.012273
## iter  80 value 266.943519
## iter  90 value 266.939142
## iter 100 value 266.926178
## final  value 266.926178 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 458.088910 
## iter  10 value 297.986773
## iter  20 value 271.447866
## iter  30 value 268.355528
## iter  40 value 267.351264
## iter  50 value 267.059470
## iter  60 value 266.969344
## iter  70 value 266.958807
## iter  80 value 266.949962
## final  value 266.949894 
## converged
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 400.862171 
## iter  10 value 300.630876
## iter  20 value 269.467627
## iter  30 value 267.433558
## iter  40 value 267.217057
## iter  50 value 267.102709
## iter  60 value 267.070838
## iter  70 value 266.982614
## iter  80 value 266.939980
## iter  90 value 266.934622
## iter 100 value 266.923394
## final  value 266.923394 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 450.172791 
## iter  10 value 327.262580
## iter  20 value 274.226983
## iter  30 value 269.111601
## iter  40 value 267.393455
## iter  50 value 267.058999
## iter  60 value 266.971647
## iter  70 value 266.954178
## iter  80 value 266.948232
## iter  90 value 266.943952
## iter 100 value 266.933737
## final  value 266.933737 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 478.536863 
## iter  10 value 302.632110
## iter  20 value 267.577230
## iter  30 value 267.263163
## iter  40 value 267.207263
## iter  50 value 267.072185
## iter  60 value 267.009854
## iter  70 value 266.983777
## iter  80 value 266.950860
## iter  90 value 266.949880
## iter 100 value 266.949710
## final  value 266.949710 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 419.673075 
## iter  10 value 299.136003
## iter  20 value 243.244040
## iter  30 value 242.113425
## iter  40 value 242.045166
## iter  50 value 242.014548
## iter  60 value 242.007089
## iter  70 value 242.002047
## iter  80 value 242.001196
## iter  80 value 242.001196
## final  value 242.001196 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 432.992213 
## iter  10 value 289.775041
## iter  20 value 244.723726
## iter  30 value 242.080038
## iter  40 value 242.012826
## iter  50 value 242.006033
## iter  60 value 242.004095
## iter  70 value 242.003219
## iter  80 value 242.000866
## final  value 242.000563 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 456.280916 
## iter  10 value 319.925677
## iter  20 value 242.979161
## iter  30 value 242.118959
## iter  40 value 242.017829
## iter  50 value 242.001492
## iter  60 value 241.997049
## iter  70 value 241.995617
## iter  80 value 241.994935
## iter  90 value 241.994336
## iter 100 value 241.993893
## final  value 241.993893 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 417.445527 
## iter  10 value 279.384059
## iter  20 value 242.954736
## iter  30 value 242.185760
## iter  40 value 242.047180
## iter  50 value 242.011923
## iter  60 value 242.007765
## iter  70 value 242.003166
## iter  80 value 242.002169
## iter  90 value 242.001731
## iter 100 value 242.001113
## final  value 242.001113 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 400.457912 
## iter  10 value 337.981076
## iter  20 value 292.443932
## iter  30 value 262.050159
## iter  40 value 248.253515
## iter  50 value 243.593352
## iter  60 value 242.205244
## iter  70 value 242.007348
## iter  80 value 242.003802
## iter  90 value 242.003489
## iter 100 value 242.002738
## final  value 242.002738 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 444.079218 
## iter  10 value 241.231277
## iter  20 value 230.931175
## iter  30 value 194.965509
## iter  40 value 188.730333
## iter  50 value 187.340153
## iter  60 value 186.565311
## iter  70 value 186.127969
## iter  80 value 186.039697
## iter  90 value 185.536125
## iter 100 value 185.140154
## final  value 185.140154 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 440.700958 
## iter  10 value 256.725579
## iter  20 value 244.213642
## iter  30 value 242.782037
## iter  40 value 242.588977
## iter  50 value 197.157632
## iter  60 value 188.735200
## iter  70 value 187.516901
## iter  80 value 187.455817
## iter  90 value 187.197768
## iter 100 value 186.770515
## final  value 186.770515 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 406.081157 
## iter  10 value 291.360836
## iter  20 value 242.021581
## iter  30 value 241.074617
## iter  40 value 201.726479
## iter  50 value 189.340137
## iter  60 value 187.462247
## iter  70 value 187.251445
## iter  80 value 186.837983
## iter  90 value 186.568615
## iter 100 value 186.306853
## final  value 186.306853 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 433.759356 
## iter  10 value 253.693130
## iter  20 value 239.770256
## iter  30 value 194.425846
## iter  40 value 190.113349
## iter  50 value 187.850782
## iter  60 value 187.194295
## iter  70 value 186.821793
## iter  80 value 186.726204
## iter  90 value 186.567739
## iter 100 value 186.353543
## final  value 186.353543 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 420.313415 
## iter  10 value 312.978648
## iter  20 value 243.986805
## iter  30 value 242.039756
## iter  40 value 225.406729
## iter  50 value 193.509795
## iter  60 value 188.499988
## iter  70 value 187.874018
## iter  80 value 187.399483
## iter  90 value 186.967283
## iter 100 value 186.264559
## final  value 186.264559 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 415.728059 
## iter  10 value 248.478726
## iter  20 value 227.552359
## iter  30 value 189.334599
## iter  40 value 186.145117
## iter  50 value 186.021330
## iter  60 value 186.018249
## iter  70 value 185.935276
## iter  80 value 185.623688
## iter  90 value 185.351291
## iter 100 value 185.229971
## final  value 185.229971 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 417.123676 
## iter  10 value 327.708286
## iter  20 value 239.923568
## iter  30 value 194.924516
## iter  40 value 188.497033
## iter  50 value 186.398964
## iter  60 value 185.584380
## iter  70 value 185.450079
## iter  80 value 185.359228
## iter  90 value 185.299081
## iter 100 value 185.259751
## final  value 185.259751 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 421.263916 
## iter  10 value 277.304525
## iter  20 value 215.615999
## iter  30 value 187.777686
## iter  40 value 186.039242
## iter  50 value 185.473546
## iter  60 value 185.398241
## iter  70 value 185.324202
## iter  80 value 185.266523
## iter  90 value 185.203631
## iter 100 value 184.762445
## final  value 184.762445 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 406.647558 
## iter  10 value 261.520395
## iter  20 value 232.403148
## iter  30 value 194.166301
## iter  40 value 187.871643
## iter  50 value 186.915030
## iter  60 value 186.824629
## iter  70 value 186.771709
## iter  80 value 186.520429
## iter  90 value 186.426307
## iter 100 value 186.359169
## final  value 186.359169 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 437.249547 
## iter  10 value 263.403237
## iter  20 value 238.135826
## iter  30 value 192.571473
## iter  40 value 188.045314
## iter  50 value 187.202406
## iter  60 value 186.879149
## iter  70 value 186.784823
## iter  80 value 186.552546
## iter  90 value 186.187895
## iter 100 value 185.660301
## final  value 185.660301 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 406.596997 
## iter  10 value 223.741347
## iter  20 value 217.424692
## final  value 217.424637 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 415.265515 
## iter  10 value 304.950869
## iter  20 value 217.671723
## iter  30 value 217.445909
## iter  40 value 217.429517
## iter  50 value 217.425118
## final  value 217.424621 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 421.417407 
## iter  10 value 337.867710
## iter  20 value 217.684216
## iter  30 value 217.446179
## iter  40 value 217.424964
## final  value 217.424693 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 436.703239 
## iter  10 value 249.529686
## iter  20 value 238.861215
## iter  30 value 236.780825
## iter  40 value 227.378539
## iter  50 value 224.038036
## iter  60 value 223.304474
## iter  70 value 220.503750
## iter  80 value 217.916915
## iter  90 value 217.464029
## iter 100 value 217.424785
## final  value 217.424785 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 411.105781 
## iter  10 value 233.268404
## iter  20 value 217.885022
## iter  30 value 217.557632
## iter  40 value 217.553792
## iter  50 value 217.526954
## iter  60 value 217.506595
## iter  70 value 217.490763
## iter  80 value 217.445235
## iter  90 value 217.430281
## iter 100 value 217.424771
## final  value 217.424771 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 420.472726 
## iter  10 value 231.387106
## iter  20 value 199.745335
## iter  30 value 191.118490
## iter  40 value 187.842904
## iter  50 value 184.637260
## iter  60 value 183.900077
## iter  70 value 183.259654
## iter  80 value 182.779116
## iter  90 value 182.495736
## iter 100 value 182.382118
## final  value 182.382118 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 395.982651 
## iter  10 value 238.126590
## iter  20 value 205.843977
## iter  30 value 192.748406
## iter  40 value 190.048043
## iter  50 value 184.394153
## iter  60 value 182.330755
## iter  70 value 181.058248
## iter  80 value 180.652218
## iter  90 value 179.920850
## iter 100 value 179.640311
## final  value 179.640311 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 406.249333 
## final  value 328.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 442.531622 
## iter  10 value 273.554055
## iter  20 value 217.574804
## iter  30 value 208.201030
## iter  40 value 186.872567
## iter  50 value 182.564070
## iter  60 value 181.759236
## iter  70 value 180.633464
## iter  80 value 179.851352
## iter  90 value 179.423025
## iter 100 value 179.316785
## final  value 179.316785 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 433.732294 
## iter  10 value 230.590882
## iter  20 value 208.172707
## iter  30 value 184.072821
## iter  40 value 182.790113
## iter  50 value 182.446786
## iter  60 value 182.372964
## iter  70 value 182.351102
## iter  80 value 182.339156
## iter  90 value 182.331911
## iter 100 value 182.328832
## final  value 182.328832 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 423.236948 
## iter  10 value 326.179383
## iter  20 value 216.736709
## iter  30 value 205.978156
## iter  40 value 190.096285
## iter  50 value 183.493381
## iter  60 value 182.087868
## iter  70 value 181.678611
## iter  80 value 181.038852
## iter  90 value 180.498651
## iter 100 value 180.095523
## final  value 180.095523 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 440.381304 
## iter  10 value 304.928293
## iter  20 value 279.821634
## iter  30 value 278.347208
## iter  40 value 272.436908
## iter  50 value 231.060060
## iter  60 value 222.283421
## iter  70 value 218.925943
## iter  80 value 218.729710
## iter  90 value 218.613013
## iter 100 value 218.554715
## final  value 218.554715 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 444.765950 
## iter  10 value 268.703400
## iter  20 value 216.122373
## iter  30 value 198.941487
## iter  40 value 189.633396
## iter  50 value 184.206686
## iter  60 value 181.964414
## iter  70 value 181.615686
## iter  80 value 181.302515
## iter  90 value 180.250231
## iter 100 value 179.264620
## final  value 179.264620 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 424.695565 
## iter  10 value 278.466848
## iter  20 value 241.720490
## iter  30 value 226.545675
## iter  40 value 219.859448
## iter  50 value 219.443247
## iter  60 value 218.127814
## iter  70 value 217.580038
## iter  80 value 217.075073
## iter  90 value 215.633626
## iter 100 value 194.039594
## final  value 194.039594 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 416.399882 
## iter  10 value 302.601040
## iter  20 value 289.711449
## iter  30 value 289.386123
## iter  40 value 277.262351
## iter  50 value 254.361335
## iter  60 value 241.693176
## iter  70 value 240.996790
## iter  80 value 239.187703
## iter  90 value 236.630375
## iter 100 value 231.820659
## final  value 231.820659 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 423.050996 
## iter  10 value 263.173387
## iter  20 value 250.862903
## final  value 250.503115 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 463.537905 
## iter  10 value 318.019808
## iter  20 value 250.703348
## final  value 250.503115 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 447.244219 
## iter  10 value 382.775140
## iter  20 value 251.102852
## iter  30 value 250.503617
## final  value 250.503115 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 403.265376 
## iter  10 value 296.130888
## iter  20 value 253.356932
## iter  30 value 250.503124
## final  value 250.503115 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 403.310727 
## iter  10 value 297.180217
## iter  20 value 251.368405
## final  value 250.503115 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 432.048254 
## iter  10 value 282.058478
## iter  20 value 249.585319
## iter  30 value 247.912213
## iter  40 value 247.128945
## iter  50 value 246.845599
## iter  60 value 246.844183
## iter  70 value 246.835482
## iter  80 value 246.826062
## iter  90 value 246.817027
## iter 100 value 246.806822
## final  value 246.806822 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 426.049882 
## iter  10 value 271.243393
## iter  20 value 252.098002
## iter  30 value 250.482312
## iter  40 value 247.856989
## iter  50 value 246.832778
## iter  60 value 246.811696
## iter  70 value 246.807053
## final  value 246.806895 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 414.024663 
## iter  10 value 338.421398
## iter  20 value 249.032142
## iter  30 value 247.120828
## iter  40 value 246.839254
## iter  50 value 246.826899
## iter  60 value 246.813187
## iter  70 value 246.806812
## final  value 246.806804 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 421.806984 
## iter  10 value 307.211189
## iter  20 value 247.868315
## iter  30 value 246.872195
## iter  40 value 246.840678
## iter  50 value 246.832039
## iter  60 value 246.798637
## iter  70 value 246.776215
## final  value 246.775669 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 399.039997 
## iter  10 value 311.964491
## iter  20 value 250.607917
## iter  30 value 247.276507
## iter  40 value 247.021689
## iter  50 value 246.830867
## iter  60 value 246.812016
## iter  70 value 246.779644
## iter  80 value 246.775680
## final  value 246.775659 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 401.309185 
## iter  10 value 277.235103
## iter  20 value 250.197264
## iter  30 value 247.474333
## iter  40 value 246.866933
## iter  50 value 246.796171
## iter  60 value 246.725842
## iter  70 value 246.633435
## iter  80 value 246.550360
## iter  90 value 246.541521
## iter 100 value 246.538323
## final  value 246.538323 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 413.769337 
## iter  10 value 320.908434
## iter  20 value 252.842666
## iter  30 value 248.386887
## iter  40 value 247.350667
## iter  50 value 247.104295
## iter  60 value 246.912019
## iter  70 value 246.843297
## iter  80 value 246.706082
## iter  90 value 246.571166
## iter 100 value 246.542418
## final  value 246.542418 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 404.197021 
## iter  10 value 283.483030
## iter  20 value 254.445464
## iter  30 value 249.172837
## iter  40 value 247.744796
## iter  50 value 247.288650
## iter  60 value 246.904893
## iter  70 value 246.650107
## iter  80 value 246.564330
## iter  90 value 246.546765
## iter 100 value 246.546089
## final  value 246.546089 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 432.836764 
## iter  10 value 312.353391
## iter  20 value 247.814739
## iter  30 value 246.965956
## iter  40 value 246.777269
## iter  50 value 246.720601
## iter  60 value 246.668140
## iter  70 value 246.598925
## iter  80 value 246.517633
## iter  90 value 246.515007
## iter 100 value 246.514134
## final  value 246.514134 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 407.177415 
## iter  10 value 273.268619
## iter  20 value 249.491874
## iter  30 value 246.913364
## iter  40 value 246.788003
## iter  50 value 246.736971
## iter  60 value 246.629636
## iter  70 value 246.580323
## iter  80 value 246.534341
## iter  90 value 246.530507
## iter 100 value 246.528002
## final  value 246.528002 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 403.131835 
## iter  10 value 364.831301
## iter  20 value 218.003536
## iter  30 value 217.534113
## iter  40 value 217.501617
## iter  50 value 217.487252
## iter  60 value 217.472478
## final  value 217.471765 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 414.637895 
## iter  10 value 222.815666
## iter  20 value 217.498020
## iter  30 value 217.474209
## final  value 217.469889 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 408.859295 
## iter  10 value 254.055579
## iter  20 value 217.481399
## iter  30 value 217.475448
## final  value 217.471772 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 404.540880 
## iter  10 value 217.700631
## iter  20 value 217.501350
## iter  30 value 217.474981
## final  value 217.471768 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 420.128954 
## iter  10 value 241.086850
## iter  20 value 217.613292
## iter  30 value 217.484395
## iter  40 value 217.472006
## iter  50 value 217.471765
## iter  50 value 217.471764
## iter  50 value 217.471764
## final  value 217.471764 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 412.452432 
## iter  10 value 279.420022
## iter  20 value 235.892046
## iter  30 value 219.673637
## iter  40 value 217.414209
## iter  50 value 211.223849
## iter  60 value 191.293275
## iter  70 value 186.254530
## iter  80 value 183.751210
## iter  90 value 182.918702
## iter 100 value 181.845422
## final  value 181.845422 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 453.797112 
## iter  10 value 239.713630
## iter  20 value 218.056299
## iter  30 value 217.490969
## iter  40 value 217.375276
## iter  50 value 216.812823
## iter  60 value 211.272396
## iter  70 value 194.303393
## iter  80 value 190.095507
## iter  90 value 185.957373
## iter 100 value 182.948252
## final  value 182.948252 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 402.363721 
## iter  10 value 288.152504
## iter  20 value 218.441142
## iter  30 value 217.787396
## iter  40 value 213.382162
## iter  50 value 193.036370
## iter  60 value 186.363596
## iter  70 value 182.254385
## iter  80 value 178.037913
## iter  90 value 174.279019
## iter 100 value 173.359635
## final  value 173.359635 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 405.401607 
## iter  10 value 228.864076
## iter  20 value 216.973352
## iter  30 value 216.435372
## iter  40 value 215.705076
## iter  50 value 208.357412
## iter  60 value 203.461380
## iter  70 value 196.148851
## iter  80 value 185.135500
## iter  90 value 181.785023
## iter 100 value 181.033259
## final  value 181.033259 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 436.055664 
## iter  10 value 278.025783
## iter  20 value 245.968879
## iter  30 value 228.852392
## iter  40 value 223.806135
## iter  50 value 223.090513
## iter  60 value 222.504485
## iter  70 value 222.188909
## iter  80 value 219.141979
## iter  90 value 219.000203
## iter 100 value 218.687862
## final  value 218.687862 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 404.148251 
## iter  10 value 306.466046
## iter  20 value 286.710251
## iter  30 value 286.232019
## iter  40 value 282.392737
## iter  50 value 253.894326
## iter  60 value 219.735038
## iter  70 value 208.812185
## iter  80 value 203.129736
## iter  90 value 198.205943
## iter 100 value 189.086981
## final  value 189.086981 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 402.958820 
## iter  10 value 231.475542
## iter  20 value 203.256589
## iter  30 value 189.344904
## iter  40 value 185.725257
## iter  50 value 183.853242
## iter  60 value 183.228065
## iter  70 value 182.869314
## iter  80 value 181.847601
## iter  90 value 180.810001
## iter 100 value 178.667382
## final  value 178.667382 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 421.052707 
## iter  10 value 266.617687
## iter  20 value 218.928163
## iter  30 value 216.341714
## iter  40 value 206.817382
## iter  50 value 185.112464
## iter  60 value 182.942909
## iter  70 value 181.556024
## iter  80 value 180.437858
## iter  90 value 180.034601
## iter 100 value 179.820962
## final  value 179.820962 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 431.311635 
## iter  10 value 285.960856
## iter  20 value 215.833861
## iter  30 value 194.836887
## iter  40 value 188.092008
## iter  50 value 185.871751
## iter  60 value 184.003308
## iter  70 value 182.308141
## iter  80 value 181.836030
## iter  90 value 181.613515
## iter 100 value 181.011898
## final  value 181.011898 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 401.327719 
## iter  10 value 257.324387
## iter  20 value 210.149816
## iter  30 value 186.768372
## iter  40 value 184.934949
## iter  50 value 183.328331
## iter  60 value 182.711868
## iter  70 value 182.062131
## iter  80 value 181.803536
## iter  90 value 181.544965
## iter 100 value 180.871099
## final  value 180.871099 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 406.360835 
## iter  10 value 239.537307
## iter  20 value 229.967796
## iter  30 value 229.660659
## iter  40 value 229.500475
## iter  50 value 229.466237
## iter  60 value 229.442773
## iter  70 value 229.426529
## iter  80 value 229.413618
## iter  90 value 229.401933
## final  value 229.401418 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 430.128997 
## iter  10 value 314.644941
## iter  20 value 230.499294
## iter  30 value 229.869967
## iter  40 value 229.542877
## iter  50 value 229.487678
## iter  60 value 229.442307
## iter  70 value 229.428062
## iter  80 value 229.420257
## iter  90 value 229.415816
## iter 100 value 229.399786
## final  value 229.399786 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 399.943002 
## iter  10 value 372.247022
## iter  20 value 229.878259
## iter  30 value 229.675627
## iter  40 value 229.513073
## iter  50 value 229.449865
## iter  60 value 229.444282
## iter  70 value 229.404250
## iter  80 value 229.400793
## iter  90 value 229.394248
## iter 100 value 229.392069
## final  value 229.392069 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 442.010600 
## iter  10 value 265.596588
## iter  20 value 229.613277
## iter  30 value 229.550979
## iter  40 value 229.464171
## iter  50 value 229.450963
## iter  60 value 229.429272
## iter  70 value 229.420132
## iter  80 value 229.415434
## iter  90 value 229.407112
## iter 100 value 229.403936
## final  value 229.403936 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 432.166367 
## iter  10 value 244.910153
## iter  20 value 230.164301
## iter  30 value 229.638257
## iter  40 value 229.524232
## iter  50 value 229.440875
## iter  60 value 229.413166
## iter  70 value 229.405160
## iter  80 value 229.402120
## iter  90 value 229.391229
## iter 100 value 229.384564
## final  value 229.384564 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 422.035624 
## iter  10 value 380.458771
## iter  20 value 227.070940
## iter  30 value 183.826235
## iter  40 value 177.623060
## iter  50 value 176.211267
## iter  60 value 175.558976
## iter  70 value 174.863247
## iter  80 value 174.386126
## iter  90 value 174.242729
## iter 100 value 174.146708
## final  value 174.146708 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 438.791789 
## iter  10 value 334.051091
## iter  20 value 236.464007
## iter  30 value 222.802696
## iter  40 value 210.838921
## iter  50 value 182.817260
## iter  60 value 174.555486
## iter  70 value 172.121084
## iter  80 value 168.959385
## iter  90 value 167.530876
## iter 100 value 167.221872
## final  value 167.221872 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 435.177182 
## iter  10 value 351.100655
## iter  20 value 223.200467
## iter  30 value 186.443382
## iter  40 value 172.097814
## iter  50 value 170.078120
## iter  60 value 169.138715
## iter  70 value 168.566265
## iter  80 value 168.404052
## iter  90 value 167.896218
## iter 100 value 167.838692
## final  value 167.838692 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 402.581600 
## iter  10 value 244.806463
## iter  20 value 231.674459
## iter  30 value 229.613872
## iter  40 value 229.557375
## iter  50 value 229.435572
## iter  60 value 229.405385
## iter  70 value 229.376891
## iter  80 value 229.371573
## iter  90 value 229.363419
## iter 100 value 229.349512
## final  value 229.349512 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 402.423982 
## iter  10 value 268.579985
## iter  20 value 221.578925
## iter  30 value 173.879166
## iter  40 value 169.683954
## iter  50 value 168.843060
## iter  60 value 168.614969
## iter  70 value 168.244001
## iter  80 value 167.987675
## iter  90 value 167.854276
## iter 100 value 167.675452
## final  value 167.675452 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 431.693024 
## iter  10 value 258.744463
## iter  20 value 227.678660
## iter  30 value 211.434995
## iter  40 value 169.632565
## iter  50 value 167.062715
## iter  60 value 166.536323
## iter  70 value 166.295916
## iter  80 value 165.986276
## iter  90 value 165.911284
## iter 100 value 165.894959
## final  value 165.894959 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 406.554823 
## iter  10 value 236.377490
## iter  20 value 172.639259
## iter  30 value 169.370269
## iter  40 value 168.885027
## iter  50 value 168.417341
## iter  60 value 168.233896
## iter  70 value 168.164876
## iter  80 value 168.035835
## iter  90 value 166.465940
## iter 100 value 165.948284
## final  value 165.948284 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 400.135491 
## iter  10 value 247.397817
## iter  20 value 229.461308
## iter  30 value 226.472515
## iter  40 value 186.283354
## iter  50 value 168.431155
## iter  60 value 166.668859
## iter  70 value 166.468677
## iter  80 value 166.229062
## iter  90 value 166.043540
## iter 100 value 165.967490
## final  value 165.967490 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 487.998836 
## iter  10 value 240.904466
## iter  20 value 224.029029
## iter  30 value 185.587969
## iter  40 value 178.014348
## iter  50 value 173.992286
## iter  60 value 171.255183
## iter  70 value 167.265677
## iter  80 value 166.745840
## iter  90 value 166.654468
## iter 100 value 166.538855
## final  value 166.538855 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 405.620677 
## iter  10 value 351.010923
## iter  20 value 349.978308
## iter  30 value 349.014122
## iter  40 value 349.002241
## iter  40 value 349.002239
## iter  40 value 349.002238
## final  value 349.002238 
## converged
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 444.311398 
## iter  10 value 365.219591
## iter  20 value 261.950883
## iter  30 value 260.893867
## final  value 260.893754 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 429.385655 
## iter  10 value 342.904755
## iter  20 value 261.595775
## final  value 260.666618 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 436.793078 
## iter  10 value 354.517647
## iter  20 value 263.599944
## final  value 260.893755 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 438.328485 
## iter  10 value 283.594962
## iter  20 value 260.897020
## final  value 260.893754 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 403.796500 
## iter  10 value 349.421799
## iter  20 value 261.997455
## iter  30 value 260.666833
## final  value 260.666618 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 406.097956 
## iter  10 value 273.399626
## iter  20 value 258.479312
## iter  30 value 257.313697
## iter  40 value 257.232694
## iter  50 value 257.222583
## iter  60 value 257.209292
## final  value 257.208962 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 434.377831 
## iter  10 value 281.358157
## iter  20 value 263.339134
## iter  30 value 260.100274
## iter  40 value 257.913832
## iter  50 value 257.221291
## iter  60 value 257.209001
## final  value 257.208992 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 452.043615 
## iter  10 value 302.459570
## iter  20 value 261.720651
## iter  30 value 259.959149
## iter  40 value 257.888003
## iter  50 value 257.237819
## iter  60 value 257.226587
## iter  70 value 257.178339
## iter  80 value 257.165593
## iter  90 value 257.164683
## final  value 257.164311 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 413.901755 
## iter  10 value 321.435908
## iter  20 value 260.664044
## iter  30 value 257.643268
## iter  40 value 257.292661
## iter  50 value 257.218802
## iter  60 value 257.210124
## final  value 257.208962 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 435.544112 
## iter  10 value 309.910818
## iter  20 value 258.248995
## iter  30 value 257.491593
## iter  40 value 257.277333
## iter  50 value 257.218180
## iter  60 value 257.185397
## iter  70 value 257.164614
## final  value 257.164341 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 441.819615 
## iter  10 value 339.088560
## iter  20 value 258.450089
## iter  30 value 257.682850
## iter  40 value 257.142819
## iter  50 value 257.011694
## iter  60 value 256.908842
## iter  70 value 256.895028
## iter  80 value 256.888285
## iter  90 value 256.885749
## final  value 256.885508 
## converged
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 411.976716 
## iter  10 value 322.870565
## iter  20 value 260.111771
## iter  30 value 258.295719
## iter  40 value 257.430285
## iter  50 value 257.123296
## iter  60 value 256.968113
## iter  70 value 256.924582
## iter  80 value 256.921959
## iter  90 value 256.920656
## iter 100 value 256.901394
## final  value 256.901394 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 418.267433 
## iter  10 value 314.329902
## iter  20 value 262.161898
## iter  30 value 258.820221
## iter  40 value 257.431434
## iter  50 value 257.329953
## iter  60 value 257.197191
## iter  70 value 257.064705
## iter  80 value 256.923055
## iter  90 value 256.917090
## iter 100 value 256.908334
## final  value 256.908334 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 403.004010 
## iter  10 value 323.388175
## iter  20 value 265.797874
## iter  30 value 259.999256
## iter  40 value 259.010671
## iter  50 value 257.882038
## iter  60 value 257.308792
## iter  70 value 256.959604
## iter  80 value 256.926481
## iter  90 value 256.923515
## iter 100 value 256.923236
## final  value 256.923236 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 474.604534 
## iter  10 value 323.596807
## iter  20 value 263.804998
## iter  30 value 259.551382
## iter  40 value 258.182824
## iter  50 value 257.534253
## iter  60 value 257.298696
## iter  70 value 257.223364
## iter  80 value 257.053804
## iter  90 value 256.936999
## iter 100 value 256.931035
## final  value 256.931035 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 400.655311 
## iter  10 value 242.662453
## iter  20 value 229.709509
## iter  30 value 229.622162
## iter  40 value 229.542625
## iter  50 value 229.512159
## iter  60 value 229.504809
## iter  70 value 229.494781
## iter  80 value 229.492305
## iter  90 value 229.491470
## final  value 229.490239 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 420.969443 
## iter  10 value 327.141189
## iter  20 value 231.020041
## iter  30 value 230.133884
## iter  40 value 229.576320
## iter  50 value 229.521921
## iter  60 value 229.475999
## iter  70 value 229.471394
## iter  80 value 229.469885
## iter  90 value 229.466442
## iter 100 value 229.465161
## final  value 229.465161 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 400.542604 
## iter  10 value 334.652167
## iter  20 value 246.714067
## iter  30 value 232.329088
## iter  40 value 229.781816
## iter  50 value 229.475378
## iter  60 value 229.468306
## final  value 229.468287 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 442.763826 
## iter  10 value 354.701204
## iter  20 value 299.539138
## iter  30 value 236.745697
## iter  40 value 230.367285
## iter  50 value 229.539600
## iter  60 value 229.492427
## iter  70 value 229.487285
## iter  80 value 229.467498
## iter  90 value 229.464778
## iter 100 value 229.463196
## final  value 229.463196 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 407.862205 
## iter  10 value 356.561362
## iter  20 value 258.040936
## iter  30 value 239.679889
## iter  40 value 231.019121
## iter  50 value 229.611819
## iter  60 value 229.530650
## iter  70 value 229.484124
## iter  80 value 229.472540
## iter  90 value 229.467042
## iter 100 value 229.465951
## final  value 229.465951 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 400.863640 
## iter  10 value 320.416885
## iter  20 value 300.979592
## iter  30 value 269.453421
## iter  40 value 213.074393
## iter  50 value 203.011954
## iter  60 value 201.847009
## iter  70 value 201.677626
## iter  80 value 200.422798
## iter  90 value 197.447366
## iter 100 value 182.420493
## final  value 182.420493 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 399.346315 
## iter  10 value 218.404179
## iter  20 value 202.020702
## iter  30 value 190.701183
## iter  40 value 178.887080
## iter  50 value 169.706664
## iter  60 value 168.908650
## iter  70 value 168.107076
## iter  80 value 167.408710
## iter  90 value 164.740848
## iter 100 value 163.967427
## final  value 163.967427 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 410.324382 
## iter  10 value 251.183939
## iter  20 value 212.702173
## iter  30 value 173.966476
## iter  40 value 169.834263
## iter  50 value 169.243398
## iter  60 value 168.324443
## iter  70 value 167.934565
## iter  80 value 167.868000
## iter  90 value 167.689994
## iter 100 value 167.460827
## final  value 167.460827 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 443.555648 
## iter  10 value 345.312034
## iter  20 value 315.049436
## iter  30 value 270.957078
## iter  40 value 250.021107
## iter  50 value 240.596458
## iter  60 value 233.563104
## iter  70 value 230.826425
## iter  80 value 230.306312
## iter  90 value 230.238575
## iter 100 value 230.189142
## final  value 230.189142 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 399.371680 
## iter  10 value 238.523695
## iter  20 value 226.458840
## iter  30 value 185.827199
## iter  40 value 172.374666
## iter  50 value 169.637851
## iter  60 value 168.866104
## iter  70 value 168.617372
## iter  80 value 168.563543
## iter  90 value 168.495545
## iter 100 value 168.476809
## final  value 168.476809 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 443.760132 
## iter  10 value 287.971067
## iter  20 value 204.879209
## iter  30 value 179.844938
## iter  40 value 171.709906
## iter  50 value 169.912943
## iter  60 value 167.801770
## iter  70 value 166.842286
## iter  80 value 166.658761
## iter  90 value 166.424084
## iter 100 value 166.339621
## final  value 166.339621 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 446.943453 
## iter  10 value 309.812259
## iter  20 value 229.288275
## iter  30 value 202.619637
## iter  40 value 171.129650
## iter  50 value 167.037162
## iter  60 value 166.915692
## iter  70 value 166.865975
## iter  80 value 166.629082
## iter  90 value 166.384447
## iter 100 value 166.251367
## final  value 166.251367 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 405.488912 
## iter  10 value 320.932151
## iter  20 value 274.008884
## iter  30 value 254.295213
## iter  40 value 234.531300
## iter  50 value 231.691075
## iter  60 value 230.819358
## iter  70 value 230.219355
## iter  80 value 230.117124
## iter  90 value 230.056276
## iter 100 value 229.919151
## final  value 229.919151 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 454.125540 
## iter  10 value 314.401556
## iter  20 value 224.092834
## iter  30 value 187.545584
## iter  40 value 171.321702
## iter  50 value 167.821028
## iter  60 value 167.186970
## iter  70 value 166.713925
## iter  80 value 166.515282
## iter  90 value 166.214314
## iter 100 value 166.139121
## final  value 166.139121 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 417.640856 
## iter  10 value 252.017345
## iter  20 value 224.961378
## iter  30 value 179.408521
## iter  40 value 170.212492
## iter  50 value 167.924367
## iter  60 value 167.443626
## iter  70 value 166.862786
## iter  80 value 166.533473
## iter  90 value 166.248999
## iter 100 value 166.237214
## final  value 166.237214 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 401.076257 
## iter  10 value 301.081112
## iter  20 value 237.541370
## iter  30 value 237.203051
## iter  40 value 237.162609
## iter  50 value 237.160714
## final  value 237.160532 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 400.871000 
## iter  10 value 342.371122
## iter  20 value 238.255143
## iter  30 value 237.573554
## iter  40 value 237.204824
## iter  50 value 237.164385
## iter  60 value 237.161478
## final  value 237.160553 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 429.807058 
## iter  10 value 300.167662
## iter  20 value 237.208022
## iter  30 value 237.181341
## iter  40 value 237.160840
## final  value 237.160794 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 439.188025 
## iter  10 value 339.710023
## iter  20 value 285.797617
## iter  30 value 240.236948
## iter  40 value 237.534438
## iter  50 value 237.516413
## iter  60 value 237.479754
## iter  70 value 237.437754
## iter  80 value 237.407844
## iter  90 value 237.375972
## iter 100 value 237.340204
## final  value 237.340204 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 401.070012 
## iter  10 value 259.079589
## iter  20 value 242.747166
## iter  30 value 237.192505
## iter  40 value 237.165967
## iter  50 value 237.160819
## iter  60 value 237.160620
## final  value 237.160552 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 454.632558 
## iter  10 value 274.263521
## iter  20 value 236.255550
## iter  30 value 189.621352
## iter  40 value 176.522278
## iter  50 value 174.139973
## iter  60 value 173.028017
## iter  70 value 172.054421
## iter  80 value 171.730939
## iter  90 value 171.654653
## iter 100 value 171.623228
## final  value 171.623228 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 420.015471 
## iter  10 value 273.551657
## iter  20 value 237.619291
## iter  30 value 237.262898
## iter  40 value 237.163068
## iter  50 value 237.161111
## final  value 237.160740 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 457.299962 
## iter  10 value 321.798352
## iter  20 value 252.760013
## iter  30 value 240.826674
## iter  40 value 201.535877
## iter  50 value 176.356972
## iter  60 value 173.888003
## iter  70 value 173.163397
## iter  80 value 172.055093
## iter  90 value 171.671524
## iter 100 value 171.622683
## final  value 171.622683 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 406.855714 
## iter  10 value 278.857680
## iter  20 value 236.206268
## iter  30 value 213.098506
## iter  40 value 188.584934
## iter  50 value 177.790445
## iter  60 value 173.076557
## iter  70 value 170.831390
## iter  80 value 170.477161
## iter  90 value 169.725313
## iter 100 value 168.798650
## final  value 168.798650 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 427.885232 
## iter  10 value 287.178960
## iter  20 value 245.247475
## iter  30 value 190.334571
## iter  40 value 179.851786
## iter  50 value 173.432618
## iter  60 value 170.835976
## iter  70 value 170.447697
## iter  80 value 170.219262
## iter  90 value 170.093635
## iter 100 value 169.834932
## final  value 169.834932 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 426.103068 
## iter  10 value 242.364581
## iter  20 value 237.301780
## iter  30 value 228.371151
## iter  40 value 179.450143
## iter  50 value 171.861786
## iter  60 value 171.297071
## iter  70 value 170.744238
## iter  80 value 170.304373
## iter  90 value 170.171132
## iter 100 value 169.916854
## final  value 169.916854 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 414.008619 
## iter  10 value 278.131745
## iter  20 value 237.578613
## iter  30 value 236.795680
## iter  40 value 212.351439
## iter  50 value 176.106732
## iter  60 value 173.195345
## iter  70 value 171.821327
## iter  80 value 171.637654
## iter  90 value 170.735633
## iter 100 value 170.560009
## final  value 170.560009 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 429.771191 
## iter  10 value 319.129962
## iter  20 value 218.486894
## iter  30 value 171.424710
## iter  40 value 170.789589
## iter  50 value 169.448030
## iter  60 value 168.409630
## iter  70 value 167.902287
## iter  80 value 167.783667
## iter  90 value 166.815736
## iter 100 value 165.819948
## final  value 165.819948 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 439.589143 
## iter  10 value 250.435087
## iter  20 value 235.416161
## iter  30 value 179.139228
## iter  40 value 176.023804
## iter  50 value 175.062771
## iter  60 value 174.026905
## iter  70 value 172.627437
## iter  80 value 172.098200
## iter  90 value 172.025419
## iter 100 value 171.796585
## final  value 171.796585 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 419.737393 
## iter  10 value 285.082620
## iter  20 value 237.674205
## iter  30 value 236.222354
## iter  40 value 199.610233
## iter  50 value 175.075141
## iter  60 value 171.328542
## iter  70 value 170.625037
## iter  80 value 170.316817
## iter  90 value 168.736405
## iter 100 value 167.705525
## final  value 167.705525 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 414.923439 
## iter  10 value 306.644951
## iter  20 value 266.981978
## final  value 266.645265 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 426.085206 
## iter  10 value 317.478520
## iter  20 value 266.664090
## final  value 266.645267 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 436.970941 
## iter  10 value 379.402324
## iter  20 value 268.330303
## iter  30 value 266.645569
## final  value 266.645266 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 400.426917 
## iter  10 value 380.940037
## iter  20 value 266.461675
## iter  30 value 266.383712
## final  value 266.383685 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 426.032992 
## iter  10 value 276.929965
## iter  20 value 266.647836
## final  value 266.645265 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 408.709005 
## iter  10 value 304.692133
## iter  20 value 270.512939
## iter  30 value 265.860368
## iter  40 value 264.148950
## iter  50 value 263.269919
## iter  60 value 263.256752
## iter  70 value 263.256591
## iter  80 value 263.256297
## iter  90 value 263.228308
## iter 100 value 263.185590
## final  value 263.185590 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 418.574057 
## iter  10 value 267.734061
## iter  20 value 263.801681
## iter  30 value 263.246782
## iter  40 value 263.233145
## iter  50 value 263.232641
## iter  60 value 263.231623
## final  value 263.231559 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 401.179508 
## iter  10 value 302.692253
## iter  20 value 264.246155
## iter  30 value 260.512662
## iter  40 value 259.660356
## iter  50 value 258.512805
## iter  60 value 257.254540
## iter  70 value 256.506617
## iter  80 value 256.045580
## iter  90 value 255.985811
## final  value 255.985785 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 432.147426 
## iter  10 value 344.237802
## iter  20 value 263.837062
## iter  30 value 263.374158
## iter  40 value 263.189328
## iter  50 value 263.183629
## iter  60 value 263.183270
## final  value 263.183101 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 408.353689 
## iter  10 value 289.473117
## iter  20 value 267.612041
## iter  30 value 266.873226
## iter  40 value 265.026253
## iter  50 value 263.433344
## iter  60 value 263.241912
## iter  70 value 263.236890
## iter  80 value 263.231719
## final  value 263.231588 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 468.141114 
## iter  10 value 317.561225
## iter  20 value 267.875724
## iter  30 value 266.353716
## iter  40 value 264.705851
## iter  50 value 264.029796
## iter  60 value 263.469735
## iter  70 value 263.201377
## iter  80 value 263.044138
## iter  90 value 262.978791
## iter 100 value 262.972985
## final  value 262.972985 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 460.583692 
## iter  10 value 274.877049
## iter  20 value 263.497424
## iter  30 value 263.194453
## iter  40 value 263.169689
## iter  50 value 263.140991
## iter  60 value 263.069182
## iter  70 value 262.987412
## iter  80 value 262.957845
## iter  90 value 262.952318
## iter 100 value 262.949341
## final  value 262.949341 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 421.613392 
## iter  10 value 297.183071
## iter  20 value 267.645124
## iter  30 value 264.563452
## iter  40 value 263.493973
## iter  50 value 263.186610
## iter  60 value 263.134498
## iter  70 value 263.027454
## iter  80 value 262.983428
## iter  90 value 262.983097
## iter 100 value 262.982749
## final  value 262.982749 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 405.103110 
## iter  10 value 327.241089
## iter  20 value 268.154662
## iter  30 value 265.641974
## iter  40 value 264.283747
## iter  50 value 263.600860
## iter  60 value 263.256448
## iter  70 value 263.114543
## iter  80 value 262.990136
## iter  90 value 262.958366
## iter 100 value 262.953285
## final  value 262.953285 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 456.971547 
## iter  10 value 337.022253
## iter  20 value 268.369407
## iter  30 value 264.299952
## iter  40 value 263.617019
## iter  50 value 263.161991
## iter  60 value 263.012758
## iter  70 value 262.978619
## iter  80 value 262.975157
## iter  90 value 262.968996
## final  value 262.968606 
## converged
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 406.005250 
## iter  10 value 240.025137
## iter  20 value 237.205564
## iter  30 value 237.201897
## iter  40 value 237.199930
## final  value 237.199851 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 443.124121 
## iter  10 value 397.050621
## iter  20 value 237.374769
## iter  30 value 237.226134
## iter  40 value 237.203552
## iter  50 value 237.199759
## iter  50 value 237.199758
## iter  50 value 237.199758
## final  value 237.199758 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 418.069732 
## iter  10 value 356.440560
## iter  20 value 238.621861
## iter  30 value 237.622726
## iter  40 value 237.222341
## iter  50 value 237.195981
## final  value 237.195843 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 401.731686 
## iter  10 value 269.833243
## iter  20 value 237.470223
## iter  30 value 237.247856
## iter  40 value 237.200852
## iter  50 value 237.195871
## final  value 237.195867 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 401.864009 
## iter  10 value 342.641907
## iter  20 value 279.525023
## iter  30 value 259.836472
## iter  40 value 257.329826
## iter  50 value 245.300935
## iter  60 value 238.404061
## iter  70 value 237.207383
## final  value 237.195973 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 411.700952 
## iter  10 value 267.811859
## iter  20 value 179.570097
## iter  30 value 175.649207
## iter  40 value 175.406207
## iter  50 value 174.896515
## iter  60 value 174.680730
## iter  70 value 174.669786
## iter  80 value 174.464916
## iter  90 value 173.250221
## iter 100 value 172.529130
## final  value 172.529130 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 399.959269 
## iter  10 value 273.726556
## iter  20 value 238.933980
## iter  30 value 237.656787
## iter  40 value 237.537152
## iter  50 value 237.419685
## iter  60 value 237.402792
## iter  70 value 237.003157
## iter  80 value 225.507172
## iter  90 value 180.731466
## iter 100 value 176.249982
## final  value 176.249982 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 427.864369 
## iter  10 value 245.214704
## iter  20 value 237.343121
## iter  30 value 237.280703
## iter  40 value 237.259996
## iter  50 value 237.254561
## iter  60 value 237.240953
## iter  70 value 237.232382
## final  value 237.232261 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 441.840515 
## iter  10 value 243.424179
## iter  20 value 237.563243
## iter  30 value 237.275198
## iter  40 value 237.264850
## iter  50 value 237.244744
## iter  60 value 237.156680
## iter  70 value 224.550389
## iter  80 value 181.829773
## iter  90 value 174.972310
## iter 100 value 174.009323
## final  value 174.009323 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 404.809556 
## iter  10 value 240.690572
## iter  20 value 236.363719
## iter  30 value 181.540259
## iter  40 value 170.976522
## iter  50 value 170.657852
## iter  60 value 170.568718
## iter  70 value 170.478276
## iter  80 value 170.278024
## iter  90 value 169.618825
## iter 100 value 169.428277
## final  value 169.428277 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 436.376105 
## iter  10 value 342.517336
## iter  20 value 238.693608
## iter  30 value 230.664260
## iter  40 value 197.398276
## iter  50 value 181.329725
## iter  60 value 177.522509
## iter  70 value 176.629602
## iter  80 value 175.321968
## iter  90 value 175.078925
## iter 100 value 174.875200
## final  value 174.875200 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 421.279843 
## iter  10 value 289.247261
## iter  20 value 234.618671
## iter  30 value 176.904893
## iter  40 value 175.653717
## iter  50 value 174.594788
## iter  60 value 173.322967
## iter  70 value 171.458946
## iter  80 value 171.254348
## iter  90 value 171.009842
## iter 100 value 170.185558
## final  value 170.185558 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 483.505235 
## iter  10 value 261.423535
## iter  20 value 236.588366
## iter  30 value 181.089520
## iter  40 value 176.454315
## iter  50 value 175.426533
## iter  60 value 175.080014
## iter  70 value 174.546087
## iter  80 value 174.361785
## iter  90 value 174.245535
## iter 100 value 173.909041
## final  value 173.909041 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 464.178337 
## iter  10 value 365.025256
## iter  20 value 238.206861
## iter  30 value 237.224662
## iter  40 value 227.470087
## iter  50 value 179.539019
## iter  60 value 172.757378
## iter  70 value 171.527030
## iter  80 value 171.072988
## iter  90 value 170.783909
## iter 100 value 170.714595
## final  value 170.714595 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 407.294525 
## iter  10 value 260.896538
## iter  20 value 189.852044
## iter  30 value 172.884261
## iter  40 value 171.560676
## iter  50 value 171.178854
## iter  60 value 170.945607
## iter  70 value 170.815361
## iter  80 value 170.501104
## iter  90 value 170.009558
## iter 100 value 169.624451
## final  value 169.624451 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 458.115405 
## iter  10 value 346.001465
## iter  20 value 346.000011
## final  value 346.000002 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 409.446833 
## iter  10 value 246.805750
## iter  20 value 230.264376
## iter  30 value 229.148521
## iter  40 value 229.107584
## final  value 229.104188 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 423.095862 
## iter  10 value 332.658227
## iter  20 value 234.203125
## iter  30 value 229.386267
## iter  40 value 229.133323
## final  value 229.104188 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 404.260868 
## iter  10 value 315.777571
## iter  20 value 229.211015
## iter  30 value 229.106430
## final  value 229.104188 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 412.398826 
## iter  10 value 269.316957
## iter  20 value 233.895989
## iter  30 value 229.452011
## iter  40 value 229.185960
## iter  50 value 229.104231
## iter  50 value 229.104228
## iter  50 value 229.104228
## final  value 229.104228 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 417.066311 
## iter  10 value 289.955014
## iter  20 value 232.291016
## iter  30 value 213.347648
## iter  40 value 190.922714
## iter  50 value 185.880113
## iter  60 value 182.177150
## iter  70 value 180.838873
## iter  80 value 179.932363
## iter  90 value 178.749976
## iter 100 value 178.158477
## final  value 178.158477 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 419.587810 
## iter  10 value 248.015110
## iter  20 value 231.086735
## iter  30 value 229.679219
## iter  40 value 229.575476
## iter  50 value 229.491505
## iter  60 value 229.471513
## iter  70 value 229.444576
## iter  80 value 229.435064
## iter  90 value 229.404632
## iter 100 value 229.231762
## final  value 229.231762 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 510.544598 
## iter  10 value 232.826216
## iter  20 value 229.360464
## iter  30 value 229.212228
## iter  40 value 222.470050
## iter  50 value 186.801555
## iter  60 value 183.268947
## iter  70 value 181.291356
## iter  80 value 180.925191
## iter  90 value 180.639120
## iter 100 value 180.085087
## final  value 180.085087 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 419.230111 
## iter  10 value 255.104721
## iter  20 value 222.869264
## iter  30 value 186.908944
## iter  40 value 185.530653
## iter  50 value 182.812006
## iter  60 value 180.968229
## iter  70 value 180.020233
## iter  80 value 179.879149
## iter  90 value 179.815140
## iter 100 value 179.684408
## final  value 179.684408 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 404.588067 
## iter  10 value 324.169571
## iter  20 value 228.901868
## iter  30 value 183.743111
## iter  40 value 180.161925
## iter  50 value 178.681773
## iter  60 value 178.099482
## iter  70 value 177.832049
## iter  80 value 177.671355
## iter  90 value 177.189839
## iter 100 value 177.061727
## final  value 177.061727 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 435.462044 
## iter  10 value 373.885026
## iter  20 value 230.225306
## iter  30 value 193.656379
## iter  40 value 181.575282
## iter  50 value 179.172689
## iter  60 value 178.098611
## iter  70 value 177.594875
## iter  80 value 177.192872
## iter  90 value 176.819924
## iter 100 value 176.617469
## final  value 176.617469 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 424.921016 
## iter  10 value 270.287581
## iter  20 value 228.938389
## iter  30 value 195.021298
## iter  40 value 182.418152
## iter  50 value 178.590489
## iter  60 value 177.213850
## iter  70 value 177.054505
## iter  80 value 176.869959
## iter  90 value 175.929634
## iter 100 value 175.261760
## final  value 175.261760 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 420.969019 
## iter  10 value 360.525737
## iter  20 value 229.226758
## iter  30 value 212.651257
## iter  40 value 188.318623
## iter  50 value 184.251343
## iter  60 value 181.968342
## iter  70 value 180.848950
## iter  80 value 179.435811
## iter  90 value 178.964868
## iter 100 value 178.319053
## final  value 178.319053 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 406.692626 
## iter  10 value 240.139325
## iter  20 value 230.624785
## iter  30 value 228.533008
## iter  40 value 186.914200
## iter  50 value 179.825874
## iter  60 value 178.739004
## iter  70 value 178.455489
## iter  80 value 178.359020
## iter  90 value 178.249527
## iter 100 value 178.213865
## final  value 178.213865 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 420.974485 
## iter  10 value 248.073105
## iter  20 value 225.620756
## iter  30 value 189.426234
## iter  40 value 180.501608
## iter  50 value 178.755457
## iter  60 value 178.403073
## iter  70 value 177.850001
## iter  80 value 177.618054
## iter  90 value 177.301940
## iter 100 value 177.200791
## final  value 177.200791 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 402.822589 
## iter  10 value 263.124132
## final  value 260.589353 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 422.761746 
## iter  10 value 325.143202
## iter  20 value 260.962863
## iter  30 value 260.847754
## final  value 260.847751 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 415.834088 
## iter  10 value 279.453504
## iter  20 value 261.067755
## final  value 260.847751 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 442.233710 
## iter  10 value 301.606597
## iter  20 value 261.047923
## final  value 260.589349 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 430.546654 
## iter  10 value 371.943024
## iter  20 value 275.080741
## iter  30 value 260.610960
## final  value 260.589350 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 400.468980 
## iter  10 value 260.746942
## iter  20 value 257.425874
## iter  30 value 257.389577
## iter  40 value 257.384790
## iter  50 value 257.382550
## iter  60 value 257.378288
## final  value 257.378251 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 414.520008 
## iter  10 value 308.393810
## iter  20 value 265.601533
## iter  30 value 261.983071
## iter  40 value 259.174292
## iter  50 value 257.432416
## iter  60 value 257.393727
## iter  70 value 257.384959
## iter  80 value 257.375244
## iter  90 value 257.363154
## iter 100 value 257.343916
## final  value 257.343916 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 427.975157 
## iter  10 value 349.086254
## iter  20 value 269.266639
## iter  30 value 262.603956
## iter  40 value 261.679269
## iter  50 value 260.127247
## iter  60 value 258.190047
## iter  70 value 257.471966
## iter  80 value 257.359265
## iter  90 value 257.352261
## iter 100 value 257.343359
## final  value 257.343359 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 468.389125 
## iter  10 value 306.722718
## iter  20 value 257.576139
## iter  30 value 257.415455
## iter  40 value 257.381087
## final  value 257.381064 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 450.352966 
## iter  10 value 294.270630
## iter  20 value 260.796845
## iter  30 value 259.733787
## iter  40 value 258.503679
## iter  50 value 257.479500
## iter  60 value 257.388448
## iter  70 value 257.387191
## iter  80 value 257.383627
## iter  90 value 257.372319
## iter 100 value 257.347988
## final  value 257.347988 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 408.074480 
## iter  10 value 283.171996
## iter  20 value 260.840896
## iter  30 value 257.930180
## iter  40 value 257.470477
## iter  50 value 257.395624
## iter  60 value 257.375213
## iter  70 value 257.337005
## iter  80 value 257.178748
## iter  90 value 257.161252
## iter 100 value 257.155166
## final  value 257.155166 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 445.877592 
## iter  10 value 377.595940
## iter  20 value 262.681837
## iter  30 value 258.400251
## iter  40 value 257.382028
## iter  50 value 257.253959
## iter  60 value 257.230123
## iter  70 value 257.219981
## iter  80 value 257.173011
## iter  90 value 257.162308
## iter 100 value 257.159361
## final  value 257.159361 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 413.782252 
## iter  10 value 287.655151
## iter  20 value 260.221197
## iter  30 value 259.100135
## iter  40 value 258.012510
## iter  50 value 257.486058
## iter  60 value 257.374184
## iter  70 value 257.296868
## iter  80 value 257.170050
## iter  90 value 257.162433
## iter 100 value 257.156744
## final  value 257.156744 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 450.775043 
## iter  10 value 343.000194
## iter  20 value 258.238766
## iter  30 value 257.372118
## iter  40 value 257.211561
## iter  50 value 257.189203
## iter  60 value 257.175808
## iter  70 value 257.168221
## iter  80 value 257.160416
## iter  90 value 257.157035
## iter 100 value 257.144811
## final  value 257.144811 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 474.803326 
## iter  10 value 338.793595
## iter  20 value 257.830913
## iter  30 value 257.407892
## iter  40 value 257.376037
## iter  50 value 257.319794
## iter  60 value 257.236862
## iter  70 value 257.187092
## iter  80 value 257.156694
## iter  90 value 257.145956
## iter 100 value 257.142778
## final  value 257.142778 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 409.542911 
## iter  10 value 279.737026
## iter  20 value 229.795173
## iter  30 value 229.213360
## final  value 229.149785 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 400.119670 
## iter  10 value 236.106815
## iter  20 value 229.380195
## iter  30 value 229.284446
## iter  40 value 229.198139
## final  value 229.149744 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 442.400311 
## iter  10 value 363.546329
## iter  20 value 340.241701
## iter  30 value 328.165130
## iter  40 value 299.653963
## iter  50 value 297.409961
## iter  60 value 295.890855
## iter  70 value 295.076874
## iter  80 value 269.661733
## iter  90 value 267.568672
## iter 100 value 266.283501
## final  value 266.283501 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 437.263856 
## iter  10 value 230.769941
## iter  20 value 229.178592
## iter  30 value 229.149886
## final  value 229.149723 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 426.353091 
## iter  10 value 311.494508
## iter  20 value 250.218533
## iter  30 value 232.013568
## iter  40 value 229.413757
## iter  50 value 229.222699
## iter  60 value 229.181898
## iter  70 value 229.150905
## final  value 229.150738 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 407.500854 
## iter  10 value 260.860532
## iter  20 value 229.335818
## iter  30 value 229.205634
## iter  40 value 229.199821
## iter  50 value 229.085386
## iter  60 value 215.667263
## iter  70 value 190.771733
## iter  80 value 184.847559
## iter  90 value 183.375530
## iter 100 value 181.894136
## final  value 181.894136 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 404.973933 
## iter  10 value 322.798821
## iter  20 value 318.482117
## iter  30 value 299.301835
## iter  40 value 248.788657
## iter  50 value 238.282489
## iter  60 value 237.005385
## iter  70 value 232.620132
## iter  80 value 231.974200
## iter  90 value 231.839604
## iter 100 value 231.193376
## final  value 231.193376 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 413.367829 
## iter  10 value 234.745069
## iter  20 value 229.176277
## iter  30 value 228.375255
## iter  40 value 207.772143
## iter  50 value 187.103159
## iter  60 value 182.951640
## iter  70 value 181.829889
## iter  80 value 180.079790
## iter  90 value 178.869621
## iter 100 value 178.693773
## final  value 178.693773 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 432.345279 
## iter  10 value 254.863230
## iter  20 value 227.809935
## iter  30 value 193.803076
## iter  40 value 187.103091
## iter  50 value 182.216721
## iter  60 value 179.339156
## iter  70 value 177.989808
## iter  80 value 177.811128
## iter  90 value 177.531767
## iter 100 value 177.454010
## final  value 177.454010 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 468.574647 
## iter  10 value 271.097561
## iter  20 value 229.382618
## iter  30 value 228.855624
## iter  40 value 203.868490
## iter  50 value 187.859940
## iter  60 value 180.364660
## iter  70 value 178.888488
## iter  80 value 178.112484
## iter  90 value 177.729735
## iter 100 value 177.624785
## final  value 177.624785 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 476.370965 
## iter  10 value 236.303430
## iter  20 value 203.175457
## iter  30 value 184.155059
## iter  40 value 180.524555
## iter  50 value 178.513124
## iter  60 value 178.227055
## iter  70 value 177.955439
## iter  80 value 177.779661
## iter  90 value 177.607735
## iter 100 value 177.538685
## final  value 177.538685 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 410.698005 
## iter  10 value 237.862348
## iter  20 value 229.471407
## iter  30 value 225.608627
## iter  40 value 183.836587
## iter  50 value 180.255277
## iter  60 value 179.409761
## iter  70 value 178.191493
## iter  80 value 177.879604
## iter  90 value 177.716390
## iter 100 value 177.572703
## final  value 177.572703 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 455.696565 
## iter  10 value 239.617249
## iter  20 value 229.369949
## iter  30 value 222.565126
## iter  40 value 183.051906
## iter  50 value 179.434906
## iter  60 value 178.529317
## iter  70 value 178.430508
## iter  80 value 178.352641
## iter  90 value 178.307138
## iter 100 value 178.279372
## final  value 178.279372 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 467.439797 
## iter  10 value 244.657873
## iter  20 value 227.134959
## iter  30 value 195.561861
## iter  40 value 184.928376
## iter  50 value 182.335606
## iter  60 value 181.570172
## iter  70 value 180.881173
## iter  80 value 180.768213
## iter  90 value 180.582685
## iter 100 value 179.691512
## final  value 179.691512 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 412.161143 
## iter  10 value 366.485837
## iter  20 value 236.966264
## iter  30 value 227.024114
## iter  40 value 185.757528
## iter  50 value 180.019407
## iter  60 value 178.460957
## iter  70 value 178.227709
## iter  80 value 177.614116
## iter  90 value 177.514825
## iter 100 value 177.363042
## final  value 177.363042 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 440.856695 
## iter  10 value 240.982504
## iter  20 value 225.887937
## iter  30 value 224.953418
## iter  40 value 224.802752
## iter  50 value 224.681623
## iter  60 value 224.670628
## iter  70 value 224.662970
## iter  80 value 224.645481
## iter  90 value 224.638926
## final  value 224.638740 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 413.741660 
## iter  10 value 281.329949
## iter  20 value 227.734857
## iter  30 value 225.431045
## iter  40 value 224.915622
## iter  50 value 224.696846
## iter  60 value 224.675593
## iter  70 value 224.652011
## iter  80 value 224.647735
## iter  90 value 224.645195
## iter 100 value 224.641913
## final  value 224.641913 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 411.134894 
## iter  10 value 270.468338
## iter  20 value 226.738806
## iter  30 value 225.164692
## iter  40 value 224.814531
## iter  50 value 224.712726
## iter  60 value 224.685021
## iter  70 value 224.670582
## iter  80 value 224.654886
## iter  90 value 224.646566
## iter 100 value 224.642039
## final  value 224.642039 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 396.043789 
## iter  10 value 228.858949
## iter  20 value 224.951677
## iter  30 value 224.774219
## iter  40 value 224.738081
## iter  50 value 224.669113
## iter  60 value 224.659651
## iter  70 value 224.655222
## iter  80 value 224.631543
## iter  90 value 224.628636
## final  value 224.628585 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 408.483995 
## iter  10 value 270.229710
## iter  20 value 226.106955
## iter  30 value 224.750876
## iter  40 value 224.730979
## iter  50 value 224.676584
## iter  60 value 224.651109
## final  value 224.647429 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 403.095062 
## iter  10 value 322.849987
## iter  20 value 244.236628
## iter  30 value 221.309542
## iter  40 value 215.361939
## iter  50 value 183.087432
## iter  60 value 174.198677
## iter  70 value 170.576904
## iter  80 value 167.972821
## iter  90 value 167.411121
## iter 100 value 166.954480
## final  value 166.954480 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 447.878930 
## iter  10 value 243.955889
## iter  20 value 211.133038
## iter  30 value 176.282803
## iter  40 value 173.623198
## iter  50 value 171.377855
## iter  60 value 169.258644
## iter  70 value 167.368973
## iter  80 value 167.186786
## iter  90 value 166.983926
## iter 100 value 166.904366
## final  value 166.904366 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 417.397872 
## iter  10 value 236.888357
## iter  20 value 225.210618
## iter  30 value 224.959755
## iter  40 value 224.521047
## iter  50 value 216.460084
## iter  60 value 167.389034
## iter  70 value 164.475014
## iter  80 value 164.133330
## iter  90 value 164.018139
## iter 100 value 163.885017
## final  value 163.885017 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 431.264155 
## iter  10 value 239.958510
## iter  20 value 224.456161
## iter  30 value 223.890565
## iter  40 value 212.318476
## iter  50 value 184.463036
## iter  60 value 168.469999
## iter  70 value 166.821753
## iter  80 value 166.620133
## iter  90 value 166.614231
## iter 100 value 166.602798
## final  value 166.602798 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 417.835977 
## iter  10 value 272.491685
## iter  20 value 224.508253
## iter  30 value 184.380303
## iter  40 value 169.258659
## iter  50 value 166.412526
## iter  60 value 165.513523
## iter  70 value 165.467063
## iter  80 value 165.415656
## iter  90 value 165.364688
## iter 100 value 165.345654
## final  value 165.345654 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 409.569621 
## iter  10 value 230.692504
## iter  20 value 215.913753
## iter  30 value 168.519594
## iter  40 value 167.209995
## iter  50 value 166.973519
## iter  60 value 166.938960
## iter  70 value 166.910868
## iter  80 value 166.896330
## iter  90 value 166.817271
## iter 100 value 166.643367
## final  value 166.643367 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 397.962179 
## iter  10 value 235.484575
## iter  20 value 195.452903
## iter  30 value 170.505910
## iter  40 value 167.344888
## iter  50 value 165.631545
## iter  60 value 164.436567
## iter  70 value 164.087398
## iter  80 value 163.931099
## iter  90 value 163.605366
## iter 100 value 163.208049
## final  value 163.208049 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 402.037642 
## iter  10 value 232.049743
## iter  20 value 217.533190
## iter  30 value 176.115993
## iter  40 value 167.985491
## iter  50 value 164.086489
## iter  60 value 163.679902
## iter  70 value 163.479750
## iter  80 value 163.253551
## iter  90 value 162.605321
## iter 100 value 161.993250
## final  value 161.993250 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 486.828289 
## iter  10 value 277.525138
## iter  20 value 222.296647
## iter  30 value 221.662352
## iter  40 value 219.667044
## iter  50 value 191.646911
## iter  60 value 182.801544
## iter  70 value 173.865938
## iter  80 value 167.326563
## iter  90 value 165.478847
## iter 100 value 164.945703
## final  value 164.945703 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 396.288017 
## iter  10 value 262.353470
## iter  20 value 224.811133
## iter  30 value 190.723251
## iter  40 value 167.601276
## iter  50 value 164.529539
## iter  60 value 163.404273
## iter  70 value 163.129452
## iter  80 value 162.368986
## iter  90 value 161.544904
## iter 100 value 160.217033
## final  value 160.217033 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 413.613669 
## iter  10 value 338.544857
## iter  20 value 255.351481
## final  value 255.317771 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 454.001174 
## iter  10 value 332.064455
## iter  20 value 255.399235
## final  value 255.317771 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 396.030217 
## iter  10 value 298.312419
## iter  20 value 255.693228
## final  value 255.530880 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 400.353387 
## iter  10 value 298.570224
## iter  20 value 255.340210
## final  value 255.317772 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 462.108616 
## iter  10 value 272.894235
## iter  20 value 255.320358
## final  value 255.317771 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 451.014768 
## iter  10 value 283.588468
## iter  20 value 253.937421
## iter  30 value 252.279963
## iter  40 value 251.908266
## iter  50 value 251.832907
## iter  60 value 251.799153
## iter  70 value 251.771757
## final  value 251.770516 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 460.610553 
## iter  10 value 343.666659
## iter  20 value 264.627749
## iter  30 value 257.081762
## iter  40 value 252.598436
## iter  50 value 251.843732
## iter  60 value 251.811804
## iter  70 value 251.770100
## iter  80 value 251.766538
## iter  90 value 251.765231
## final  value 251.765050 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 430.194309 
## iter  10 value 277.636950
## iter  20 value 257.308660
## iter  30 value 252.969550
## iter  40 value 252.229223
## iter  50 value 251.825195
## iter  60 value 251.802417
## iter  70 value 251.768118
## iter  80 value 251.765115
## iter  80 value 251.765113
## iter  80 value 251.765113
## final  value 251.765113 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 426.486516 
## iter  10 value 271.229603
## iter  20 value 254.283669
## iter  30 value 252.244665
## iter  40 value 251.922882
## iter  50 value 251.845366
## iter  60 value 251.843785
## iter  70 value 251.804412
## iter  80 value 251.775702
## iter  90 value 251.771431
## final  value 251.770511 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 439.588860 
## iter  10 value 349.612988
## iter  20 value 258.405805
## iter  30 value 256.082805
## iter  40 value 254.506320
## iter  50 value 252.279477
## iter  60 value 251.841518
## iter  70 value 251.808473
## iter  80 value 251.796443
## iter  90 value 251.765279
## final  value 251.765051 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 471.110012 
## iter  10 value 313.083653
## iter  20 value 258.488605
## iter  30 value 253.986509
## iter  40 value 252.857959
## iter  50 value 252.346907
## iter  60 value 251.871255
## iter  70 value 251.619155
## iter  80 value 251.488442
## iter  90 value 251.482444
## iter 100 value 251.482329
## final  value 251.482329 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 397.724628 
## iter  10 value 273.514174
## iter  20 value 254.327364
## iter  30 value 253.014707
## iter  40 value 252.209206
## iter  50 value 251.945145
## iter  60 value 251.855332
## iter  70 value 251.801223
## iter  80 value 251.554289
## iter  90 value 251.482059
## iter 100 value 251.478117
## final  value 251.478117 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 478.216718 
## iter  10 value 368.806850
## iter  20 value 261.691835
## iter  30 value 253.330865
## iter  40 value 251.917258
## iter  50 value 251.648433
## iter  60 value 251.531396
## iter  70 value 251.476230
## iter  80 value 251.458383
## iter  90 value 251.452852
## iter 100 value 251.451252
## final  value 251.451252 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 433.819667 
## iter  10 value 333.055717
## iter  20 value 258.897807
## iter  30 value 254.417522
## iter  40 value 253.221543
## iter  50 value 252.190690
## iter  60 value 251.968437
## iter  70 value 251.765454
## iter  80 value 251.554641
## iter  90 value 251.484212
## iter 100 value 251.482780
## final  value 251.482780 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 523.553596 
## iter  10 value 255.700658
## iter  20 value 251.630344
## iter  30 value 251.517589
## iter  40 value 251.488297
## iter  50 value 251.480518
## iter  60 value 251.465462
## iter  70 value 251.456923
## iter  80 value 251.450425
## final  value 251.448260 
## converged
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 399.162201 
## iter  10 value 277.241819
## iter  20 value 226.939255
## iter  30 value 225.506759
## iter  40 value 224.897095
## iter  50 value 224.727932
## iter  60 value 224.724123
## iter  70 value 224.714739
## final  value 224.712075 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 430.508616 
## iter  10 value 249.904896
## iter  20 value 225.480448
## iter  30 value 224.785565
## iter  40 value 224.772400
## iter  50 value 224.720918
## iter  60 value 224.715559
## iter  70 value 224.714672
## iter  80 value 224.707200
## iter  80 value 224.707199
## final  value 224.707199 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 395.250722 
## iter  10 value 239.806637
## iter  20 value 226.272163
## iter  30 value 224.985633
## iter  40 value 224.843854
## iter  50 value 224.731374
## iter  60 value 224.724441
## iter  70 value 224.720531
## iter  80 value 224.715393
## iter  90 value 224.710418
## iter 100 value 224.708961
## final  value 224.708961 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 398.658739 
## iter  10 value 239.311355
## iter  20 value 225.266254
## iter  30 value 224.905025
## iter  40 value 224.752523
## iter  50 value 224.737559
## iter  60 value 224.726778
## iter  70 value 224.722643
## iter  80 value 224.719684
## iter  90 value 224.718247
## iter 100 value 224.715145
## final  value 224.715145 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 442.122839 
## iter  10 value 352.195911
## iter  20 value 265.583740
## iter  30 value 229.553418
## iter  40 value 224.982483
## iter  50 value 224.766965
## iter  60 value 224.717821
## iter  70 value 224.708919
## final  value 224.708364 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 441.077738 
## iter  10 value 264.009605
## iter  20 value 227.022636
## iter  30 value 197.895950
## iter  40 value 175.817971
## iter  50 value 171.497385
## iter  60 value 170.233164
## iter  70 value 169.521077
## iter  80 value 169.187394
## iter  90 value 168.995823
## iter 100 value 168.658872
## final  value 168.658872 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 496.456543 
## iter  10 value 273.780692
## iter  20 value 215.084057
## iter  30 value 167.968852
## iter  40 value 164.710212
## iter  50 value 164.363605
## iter  60 value 164.077614
## iter  70 value 163.918087
## iter  80 value 163.869152
## iter  90 value 163.715786
## iter 100 value 163.526300
## final  value 163.526300 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 399.820125 
## iter  10 value 304.225244
## iter  20 value 288.197596
## iter  30 value 237.985678
## iter  40 value 227.745735
## iter  50 value 225.992641
## iter  60 value 225.653032
## iter  70 value 225.463544
## iter  80 value 225.392825
## iter  90 value 225.143428
## iter 100 value 225.121514
## final  value 225.121514 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 422.703817 
## iter  10 value 255.920296
## iter  20 value 225.579118
## iter  30 value 225.067797
## iter  40 value 224.880791
## iter  50 value 224.812691
## iter  60 value 224.754771
## iter  70 value 224.749554
## final  value 224.749144 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 469.765549 
## iter  10 value 263.064453
## iter  20 value 229.801187
## iter  30 value 225.541783
## iter  40 value 224.978087
## iter  50 value 224.841967
## iter  60 value 224.796477
## iter  70 value 224.792148
## iter  80 value 224.776915
## iter  90 value 224.648353
## iter 100 value 203.724363
## final  value 203.724363 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 528.653847 
## iter  10 value 309.103703
## iter  20 value 223.104838
## iter  30 value 187.877245
## iter  40 value 170.014358
## iter  50 value 165.565002
## iter  60 value 164.601746
## iter  70 value 163.943256
## iter  80 value 163.529105
## iter  90 value 163.130707
## iter 100 value 162.723547
## final  value 162.723547 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 393.212231 
## iter  10 value 311.897091
## iter  20 value 208.200602
## iter  30 value 187.666069
## iter  40 value 175.100814
## iter  50 value 169.507470
## iter  60 value 166.153225
## iter  70 value 164.183908
## iter  80 value 163.602383
## iter  90 value 163.022286
## iter 100 value 162.659229
## final  value 162.659229 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 395.532035 
## iter  10 value 244.204271
## iter  20 value 182.111618
## iter  30 value 170.092794
## iter  40 value 165.835554
## iter  50 value 165.165333
## iter  60 value 165.040008
## iter  70 value 164.738507
## iter  80 value 164.400849
## iter  90 value 164.008893
## iter 100 value 163.734367
## final  value 163.734367 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 484.773391 
## iter  10 value 305.659569
## iter  20 value 253.581316
## iter  30 value 226.565753
## iter  40 value 195.425762
## iter  50 value 172.549629
## iter  60 value 169.308440
## iter  70 value 168.706295
## iter  80 value 167.684161
## iter  90 value 167.073439
## iter 100 value 166.900708
## final  value 166.900708 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 414.325063 
## iter  10 value 245.668138
## iter  20 value 223.428503
## iter  30 value 178.962304
## iter  40 value 169.160822
## iter  50 value 166.808143
## iter  60 value 165.215900
## iter  70 value 164.432739
## iter  80 value 164.116421
## iter  90 value 164.025271
## iter 100 value 163.726645
## final  value 163.726645 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 403.926699 
## iter  10 value 257.858581
## iter  20 value 206.446318
## iter  30 value 205.825820
## iter  40 value 205.824267
## iter  50 value 205.794994
## iter  60 value 205.790094
## final  value 205.781556 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 427.277341 
## iter  10 value 275.509165
## iter  20 value 205.988342
## iter  30 value 205.784631
## final  value 205.781461 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 415.571224 
## iter  10 value 257.775129
## iter  20 value 216.995955
## iter  30 value 207.432201
## iter  40 value 205.791241
## iter  50 value 205.781579
## final  value 205.781452 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 445.356905 
## iter  10 value 219.734662
## iter  20 value 205.926136
## iter  30 value 205.823636
## iter  40 value 205.818768
## iter  50 value 205.783528
## iter  60 value 205.781547
## final  value 205.781458 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 432.607921 
## iter  10 value 287.935770
## iter  20 value 205.808975
## iter  30 value 205.787494
## final  value 205.781453 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 402.008658 
## iter  10 value 228.147569
## iter  20 value 207.499988
## iter  30 value 179.270374
## iter  40 value 147.031320
## iter  50 value 143.870542
## iter  60 value 142.810349
## iter  70 value 142.314066
## iter  80 value 142.159484
## iter  90 value 142.107745
## iter 100 value 142.026087
## final  value 142.026087 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 452.968188 
## iter  10 value 253.658055
## iter  20 value 218.959317
## iter  30 value 207.356357
## iter  40 value 206.007314
## iter  50 value 205.836202
## iter  60 value 205.807763
## final  value 205.799496 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 435.501978 
## iter  10 value 244.767648
## iter  20 value 214.199156
## iter  30 value 206.097996
## iter  40 value 205.792298
## iter  50 value 205.782825
## iter  60 value 205.781883
## final  value 205.781536 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 404.365455 
## iter  10 value 215.826335
## iter  20 value 205.977618
## iter  30 value 205.750871
## iter  40 value 205.724701
## iter  50 value 205.698223
## iter  60 value 205.569257
## iter  70 value 205.452001
## iter  80 value 205.391495
## iter  90 value 205.381416
## iter 100 value 205.327129
## final  value 205.327129 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 433.002258 
## iter  10 value 282.187836
## iter  20 value 205.843260
## iter  30 value 202.978873
## iter  40 value 146.291181
## iter  50 value 144.968172
## iter  60 value 144.723956
## iter  70 value 144.315739
## iter  80 value 143.360623
## iter  90 value 142.691000
## iter 100 value 142.289089
## final  value 142.289089 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 402.701070 
## iter  10 value 235.208796
## iter  20 value 168.692026
## iter  30 value 145.824481
## iter  40 value 143.833030
## iter  50 value 142.664312
## iter  60 value 142.310794
## iter  70 value 142.127439
## iter  80 value 142.053145
## iter  90 value 142.027164
## iter 100 value 141.997146
## final  value 141.997146 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 441.220987 
## iter  10 value 291.274197
## iter  20 value 206.628529
## iter  30 value 202.920819
## iter  40 value 147.455010
## iter  50 value 144.926048
## iter  60 value 144.080713
## iter  70 value 143.253108
## iter  80 value 142.923891
## iter  90 value 142.813068
## iter 100 value 142.646178
## final  value 142.646178 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 426.391165 
## iter  10 value 349.167052
## iter  20 value 208.747549
## iter  30 value 177.777473
## iter  40 value 147.424641
## iter  50 value 145.322727
## iter  60 value 144.963900
## iter  70 value 144.434916
## iter  80 value 143.874315
## iter  90 value 143.740749
## iter 100 value 143.691786
## final  value 143.691786 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 442.952193 
## iter  10 value 281.140816
## iter  20 value 268.698378
## iter  30 value 268.263641
## iter  40 value 265.407282
## iter  50 value 231.221859
## iter  60 value 222.977659
## iter  70 value 215.151005
## iter  80 value 207.769789
## iter  90 value 206.297344
## iter 100 value 206.018104
## final  value 206.018104 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 446.620672 
## iter  10 value 240.701805
## iter  20 value 176.246835
## iter  30 value 146.388886
## iter  40 value 143.821753
## iter  50 value 143.587338
## iter  60 value 143.360309
## iter  70 value 142.919593
## iter  80 value 142.433718
## iter  90 value 142.059860
## iter 100 value 141.936716
## final  value 141.936716 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 456.364413 
## iter  10 value 262.146035
## iter  20 value 239.306279
## final  value 239.303021 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 420.839875 
## iter  10 value 260.866757
## iter  20 value 239.524150
## final  value 239.303021 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 424.464752 
## iter  10 value 394.808778
## iter  20 value 239.754311
## iter  30 value 239.305467
## final  value 239.303021 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 410.290563 
## iter  10 value 321.982315
## iter  20 value 239.520150
## final  value 239.303021 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 421.912108 
## iter  10 value 376.361530
## iter  20 value 246.245625
## iter  30 value 239.295733
## final  value 239.259115 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 402.332147 
## iter  10 value 261.548591
## iter  20 value 236.050923
## iter  30 value 235.463184
## iter  40 value 235.446332
## iter  50 value 235.445252
## iter  60 value 235.443655
## final  value 235.443584 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 406.436631 
## iter  10 value 255.828508
## iter  20 value 235.746421
## iter  30 value 235.525241
## iter  40 value 235.509343
## iter  50 value 235.503851
## iter  60 value 235.466393
## iter  70 value 235.444066
## final  value 235.443629 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 414.335436 
## iter  10 value 266.444351
## iter  20 value 239.374428
## iter  30 value 238.324406
## iter  40 value 236.261260
## iter  50 value 235.526303
## iter  60 value 235.514183
## iter  70 value 235.512914
## iter  80 value 235.509941
## iter  90 value 235.472979
## iter 100 value 235.457927
## final  value 235.457927 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 461.544271 
## iter  10 value 240.300632
## iter  20 value 235.810064
## iter  30 value 235.539021
## iter  40 value 235.516790
## iter  50 value 235.515909
## iter  60 value 235.474074
## final  value 235.457876 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 483.014537 
## iter  10 value 289.263792
## iter  20 value 236.444626
## iter  30 value 235.614735
## iter  40 value 235.500789
## iter  50 value 235.465138
## iter  60 value 235.448734
## iter  70 value 235.443585
## iter  70 value 235.443584
## iter  70 value 235.443584
## final  value 235.443584 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 410.614867 
## iter  10 value 257.472500
## iter  20 value 237.631580
## iter  30 value 236.040897
## iter  40 value 235.546470
## iter  50 value 235.489680
## iter  60 value 235.411726
## iter  70 value 235.279961
## iter  80 value 235.150874
## iter  90 value 235.140797
## iter 100 value 235.137170
## final  value 235.137170 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 423.106826 
## iter  10 value 325.591946
## iter  20 value 246.394706
## iter  30 value 238.778713
## iter  40 value 235.895770
## iter  50 value 235.488036
## iter  60 value 235.321853
## iter  70 value 235.215918
## iter  80 value 235.168722
## iter  90 value 235.166216
## iter 100 value 235.152795
## final  value 235.152795 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 469.097405 
## iter  10 value 358.669277
## iter  20 value 241.359831
## iter  30 value 236.175749
## iter  40 value 235.218397
## iter  50 value 235.159698
## iter  60 value 235.142265
## iter  70 value 235.138663
## iter  80 value 235.136028
## iter  90 value 235.135780
## final  value 235.135738 
## converged
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 483.588159 
## iter  10 value 265.536529
## iter  20 value 236.059241
## iter  30 value 235.508362
## iter  40 value 235.361042
## iter  50 value 235.258360
## iter  60 value 235.185327
## iter  70 value 235.168824
## iter  80 value 235.151266
## iter  90 value 235.140729
## iter 100 value 235.135858
## final  value 235.135858 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 428.440764 
## iter  10 value 265.872459
## iter  20 value 240.704833
## iter  30 value 238.710388
## iter  40 value 236.802534
## iter  50 value 236.018668
## iter  60 value 235.675536
## iter  70 value 235.489111
## iter  80 value 235.303859
## iter  90 value 235.144444
## iter 100 value 235.139063
## final  value 235.139063 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 466.399880 
## iter  10 value 299.336525
## iter  20 value 206.070075
## iter  30 value 205.949711
## iter  40 value 205.824529
## iter  50 value 205.824231
## final  value 205.824074 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 430.825438 
## iter  10 value 296.320849
## iter  20 value 206.373299
## iter  30 value 205.906977
## iter  40 value 205.822525
## iter  50 value 205.822302
## final  value 205.822295 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 448.787958 
## iter  10 value 235.081457
## iter  20 value 205.967575
## iter  30 value 205.825387
## iter  40 value 205.822518
## final  value 205.822295 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 414.659479 
## iter  10 value 286.493065
## iter  20 value 277.503911
## iter  30 value 256.332351
## iter  40 value 241.282832
## iter  50 value 236.442120
## iter  60 value 224.754880
## iter  70 value 211.498506
## iter  80 value 208.156337
## iter  90 value 207.171177
## iter 100 value 206.744497
## final  value 206.744497 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 437.646355 
## iter  10 value 289.212652
## iter  20 value 205.848153
## final  value 205.825354 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 428.954985 
## iter  10 value 245.780466
## iter  20 value 198.206078
## iter  30 value 150.130513
## iter  40 value 148.038092
## iter  50 value 147.112826
## iter  60 value 146.617016
## iter  70 value 146.066096
## iter  80 value 145.998974
## iter  90 value 145.974049
## iter 100 value 145.946981
## final  value 145.946981 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 428.172411 
## iter  10 value 311.570388
## iter  20 value 208.381693
## iter  30 value 203.349744
## iter  40 value 168.712645
## iter  50 value 153.447874
## iter  60 value 147.657009
## iter  70 value 145.978685
## iter  80 value 145.768652
## iter  90 value 145.649591
## iter 100 value 145.574128
## final  value 145.574128 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 404.972319 
## iter  10 value 223.308883
## iter  20 value 206.175877
## iter  30 value 206.124737
## iter  40 value 206.030940
## iter  50 value 205.983489
## iter  60 value 205.928943
## iter  70 value 203.838949
## iter  80 value 168.757531
## iter  90 value 150.600370
## iter 100 value 146.742007
## final  value 146.742007 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 436.199636 
## iter  10 value 270.013293
## iter  20 value 206.048670
## iter  30 value 200.552876
## iter  40 value 162.623153
## iter  50 value 150.412958
## iter  60 value 148.623707
## iter  70 value 147.929473
## iter  80 value 145.711401
## iter  90 value 145.222461
## iter 100 value 145.188516
## final  value 145.188516 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 418.457389 
## iter  10 value 284.350353
## iter  20 value 205.501125
## iter  30 value 160.371761
## iter  40 value 150.454374
## iter  50 value 145.321246
## iter  60 value 144.465383
## iter  70 value 143.581793
## iter  80 value 143.236934
## iter  90 value 142.958080
## iter 100 value 142.846182
## final  value 142.846182 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 418.395621 
## iter  10 value 262.889901
## iter  20 value 206.396257
## iter  30 value 194.775198
## iter  40 value 150.168864
## iter  50 value 146.416208
## iter  60 value 145.644922
## iter  70 value 145.245352
## iter  80 value 145.115936
## iter  90 value 145.005081
## iter 100 value 144.936198
## final  value 144.936198 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 417.394709 
## iter  10 value 297.568249
## iter  20 value 248.563724
## iter  30 value 222.349852
## iter  40 value 218.893521
## iter  50 value 218.169926
## iter  60 value 211.366726
## iter  70 value 209.141324
## iter  80 value 207.962582
## iter  90 value 207.618936
## iter 100 value 207.232168
## final  value 207.232168 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 467.889745 
## iter  10 value 228.424783
## iter  20 value 150.152806
## iter  30 value 143.804643
## iter  40 value 143.221819
## iter  50 value 142.970277
## iter  60 value 142.919628
## iter  70 value 142.889402
## iter  80 value 142.865782
## iter  90 value 142.804427
## iter 100 value 142.780601
## final  value 142.780601 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 446.122731 
## iter  10 value 243.245266
## iter  20 value 206.903332
## iter  30 value 194.096898
## iter  40 value 161.638165
## iter  50 value 150.183853
## iter  60 value 147.096739
## iter  70 value 144.682717
## iter  80 value 144.078365
## iter  90 value 143.617318
## iter 100 value 143.112012
## final  value 143.112012 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 417.761008 
## iter  10 value 248.929330
## iter  20 value 207.478271
## iter  30 value 206.100888
## iter  40 value 178.089517
## iter  50 value 146.122025
## iter  60 value 145.211624
## iter  70 value 144.999221
## iter  80 value 144.946317
## iter  90 value 144.910843
## iter 100 value 144.870236
## final  value 144.870236 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 412.598608 
## iter  10 value 242.076998
## iter  20 value 229.363795
## iter  30 value 224.792733
## iter  40 value 223.699231
## iter  50 value 223.361006
## iter  60 value 223.287599
## iter  70 value 223.248251
## iter  80 value 223.233244
## iter  90 value 223.211203
## iter 100 value 223.197499
## final  value 223.197499 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 425.689185 
## iter  10 value 335.753123
## iter  20 value 223.751784
## iter  30 value 223.458497
## iter  40 value 223.255976
## iter  50 value 223.237963
## iter  60 value 223.227526
## iter  70 value 223.214977
## iter  80 value 223.202794
## iter  90 value 223.190713
## iter 100 value 223.172143
## final  value 223.172143 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 407.695723 
## iter  10 value 228.718291
## iter  20 value 223.422372
## iter  30 value 223.228568
## iter  40 value 223.224101
## iter  50 value 223.188491
## iter  60 value 223.181012
## final  value 223.177569 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 426.282544 
## iter  10 value 276.350126
## iter  20 value 225.157372
## iter  30 value 223.251843
## iter  40 value 223.232346
## iter  50 value 223.220729
## iter  60 value 223.204739
## final  value 223.192349 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 424.200999 
## iter  10 value 335.610428
## iter  20 value 272.402224
## iter  30 value 257.930851
## iter  40 value 254.964387
## iter  50 value 248.968601
## iter  60 value 239.038679
## iter  70 value 225.858573
## iter  80 value 223.715349
## iter  90 value 223.357918
## iter 100 value 223.191208
## final  value 223.191208 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 416.497363 
## iter  10 value 362.090670
## iter  20 value 278.586800
## iter  30 value 245.159030
## iter  40 value 227.465161
## iter  50 value 223.816162
## iter  60 value 223.415271
## iter  70 value 223.191556
## iter  80 value 223.173363
## iter  90 value 223.152484
## iter 100 value 223.141285
## final  value 223.141285 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 431.839172 
## iter  10 value 290.667523
## iter  20 value 213.544697
## iter  30 value 178.745469
## iter  40 value 175.528980
## iter  50 value 171.975593
## iter  60 value 169.386672
## iter  70 value 168.881472
## iter  80 value 168.851105
## iter  90 value 168.819589
## iter 100 value 168.815813
## final  value 168.815813 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 423.499345 
## iter  10 value 260.851407
## iter  20 value 249.804258
## iter  30 value 222.152643
## iter  40 value 193.728747
## iter  50 value 184.099000
## iter  60 value 179.965790
## iter  70 value 177.238566
## iter  80 value 172.745655
## iter  90 value 169.301480
## iter 100 value 168.939917
## final  value 168.939917 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 425.154395 
## iter  10 value 343.458665
## iter  20 value 333.015238
## iter  30 value 332.408571
## iter  40 value 330.514259
## iter  50 value 310.604405
## iter  60 value 305.718051
## iter  70 value 259.356285
## iter  80 value 242.582103
## iter  90 value 226.673812
## iter 100 value 224.432520
## final  value 224.432520 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 448.875468 
## iter  10 value 251.426033
## iter  20 value 224.191686
## iter  30 value 223.631750
## iter  40 value 222.957325
## iter  50 value 220.135202
## iter  60 value 188.753135
## iter  70 value 177.022551
## iter  80 value 173.842098
## iter  90 value 169.926477
## iter 100 value 168.955863
## final  value 168.955863 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 405.163539 
## iter  10 value 295.479873
## iter  20 value 225.053861
## iter  30 value 205.468510
## iter  40 value 185.720411
## iter  50 value 175.390847
## iter  60 value 171.610299
## iter  70 value 170.417904
## iter  80 value 169.709461
## iter  90 value 169.383587
## iter 100 value 169.210947
## final  value 169.210947 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 444.304901 
## iter  10 value 374.624307
## iter  20 value 221.829976
## iter  30 value 178.112795
## iter  40 value 169.383060
## iter  50 value 168.388809
## iter  60 value 168.322881
## iter  70 value 168.317989
## iter  80 value 168.279797
## iter  90 value 168.238247
## iter 100 value 168.207970
## final  value 168.207970 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 433.324592 
## iter  10 value 249.303880
## iter  20 value 221.242085
## iter  30 value 196.245136
## iter  40 value 174.038507
## iter  50 value 169.354666
## iter  60 value 168.125458
## iter  70 value 167.130649
## iter  80 value 166.080698
## iter  90 value 166.000770
## iter 100 value 165.747074
## final  value 165.747074 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 446.736812 
## iter  10 value 324.127879
## iter  20 value 229.078853
## iter  30 value 220.065361
## iter  40 value 202.824798
## iter  50 value 181.019385
## iter  60 value 173.410845
## iter  70 value 171.150595
## iter  80 value 169.925643
## iter  90 value 169.517488
## iter 100 value 169.117720
## final  value 169.117720 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 413.323241 
## iter  10 value 226.493430
## iter  20 value 218.244629
## iter  30 value 176.401250
## iter  40 value 171.143283
## iter  50 value 168.935890
## iter  60 value 168.382023
## iter  70 value 168.138165
## iter  80 value 167.853135
## iter  90 value 166.816446
## iter 100 value 165.629741
## final  value 165.629741 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 415.124776 
## iter  10 value 330.596574
## iter  20 value 253.516883
## final  value 253.510585 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 414.981943 
## iter  10 value 320.693066
## iter  20 value 253.834296
## final  value 253.333209 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 399.702596 
## iter  10 value 259.937067
## iter  20 value 253.672498
## final  value 253.510585 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 405.297423 
## iter  10 value 254.938952
## iter  20 value 253.408025
## final  value 253.333209 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 407.441624 
## iter  10 value 299.967360
## iter  20 value 253.809327
## final  value 253.510585 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 428.190075 
## iter  10 value 323.220093
## iter  20 value 253.284242
## iter  30 value 252.157240
## iter  40 value 251.095283
## iter  50 value 249.801568
## iter  60 value 249.774370
## iter  70 value 249.772589
## final  value 249.772540 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 464.296755 
## iter  10 value 268.760151
## iter  20 value 250.962582
## iter  30 value 249.961245
## iter  40 value 249.866363
## iter  50 value 249.763625
## iter  60 value 249.754170
## iter  70 value 249.685220
## iter  80 value 249.665297
## iter  90 value 249.665032
## final  value 249.664909 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 486.854815 
## iter  10 value 256.043067
## iter  20 value 249.869214
## iter  30 value 249.761117
## iter  40 value 249.752434
## iter  50 value 249.737282
## iter  60 value 249.671192
## iter  70 value 249.665047
## final  value 249.664915 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 444.743146 
## iter  10 value 277.702556
## iter  20 value 251.681514
## iter  30 value 250.813071
## iter  40 value 250.058063
## iter  50 value 249.779623
## iter  60 value 249.766293
## iter  70 value 249.730207
## iter  80 value 249.703666
## iter  90 value 249.701089
## iter 100 value 249.699530
## final  value 249.699530 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 410.048041 
## iter  10 value 258.935685
## iter  20 value 253.874727
## iter  30 value 252.399472
## iter  40 value 249.976164
## iter  50 value 249.776378
## iter  60 value 249.769214
## iter  70 value 249.709343
## iter  80 value 249.700049
## final  value 249.699667 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 406.411385 
## iter  10 value 288.255458
## iter  20 value 250.506242
## iter  30 value 249.900122
## iter  40 value 249.712147
## iter  50 value 249.635990
## iter  60 value 249.499057
## iter  70 value 249.388762
## iter  80 value 249.357513
## iter  90 value 249.348612
## iter 100 value 249.336837
## final  value 249.336837 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 420.363839 
## iter  10 value 286.878344
## iter  20 value 254.676143
## iter  30 value 249.614786
## iter  40 value 247.829412
## iter  50 value 246.509143
## iter  60 value 246.370758
## iter  70 value 246.115985
## iter  80 value 245.970814
## iter  90 value 245.804673
## iter 100 value 245.794959
## final  value 245.794959 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 430.485966 
## iter  10 value 326.549974
## iter  20 value 265.445581
## iter  30 value 254.298342
## iter  40 value 251.224425
## iter  50 value 249.147740
## iter  60 value 247.814814
## iter  70 value 246.219637
## iter  80 value 245.910967
## iter  90 value 245.868224
## iter 100 value 245.858119
## final  value 245.858119 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 406.273265 
## iter  10 value 262.385339
## iter  20 value 251.382889
## iter  30 value 250.305432
## iter  40 value 249.703465
## iter  50 value 249.547329
## iter  60 value 249.397836
## iter  70 value 249.332221
## iter  80 value 249.320524
## iter  90 value 249.318809
## final  value 249.318415 
## converged
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 441.242565 
## iter  10 value 315.213089
## iter  20 value 255.728450
## iter  30 value 252.480943
## iter  40 value 251.254985
## iter  50 value 250.687620
## iter  60 value 250.038423
## iter  70 value 249.703822
## iter  80 value 249.534788
## iter  90 value 249.371954
## iter 100 value 249.365465
## final  value 249.365465 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 414.391460 
## iter  10 value 274.255713
## iter  20 value 236.605700
## iter  30 value 233.056887
## iter  40 value 226.030015
## iter  50 value 224.007366
## iter  60 value 223.414449
## iter  70 value 223.326972
## iter  80 value 223.282919
## iter  90 value 223.277836
## iter 100 value 223.273325
## final  value 223.273325 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 427.311350 
## iter  10 value 280.455568
## iter  20 value 240.322653
## iter  30 value 235.406576
## iter  40 value 228.294470
## iter  50 value 224.220052
## iter  60 value 223.510237
## iter  70 value 223.351592
## iter  80 value 223.288333
## final  value 223.280457 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 426.292134 
## iter  10 value 397.341125
## iter  20 value 233.112451
## iter  30 value 224.321656
## iter  40 value 223.453300
## iter  50 value 223.365206
## iter  60 value 223.278702
## iter  70 value 223.272563
## final  value 223.272283 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 428.417975 
## iter  10 value 329.233088
## iter  20 value 308.091144
## iter  30 value 303.333135
## iter  40 value 278.153330
## iter  50 value 266.585903
## iter  60 value 262.246370
## iter  70 value 259.016773
## iter  80 value 252.374212
## iter  90 value 247.171917
## iter 100 value 245.283403
## final  value 245.283403 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 406.171783 
## iter  10 value 371.832698
## iter  20 value 223.958454
## iter  30 value 223.575665
## iter  40 value 223.292600
## iter  50 value 223.283505
## iter  60 value 223.277861
## iter  70 value 223.268552
## iter  80 value 223.263400
## final  value 223.262717 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 401.348430 
## iter  10 value 251.034434
## iter  20 value 228.687205
## iter  30 value 224.294719
## iter  40 value 224.024521
## iter  50 value 223.675299
## iter  60 value 223.635613
## iter  70 value 223.472075
## iter  80 value 223.316455
## iter  90 value 223.244080
## iter 100 value 223.139072
## final  value 223.139072 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 421.680949 
## iter  10 value 270.967100
## iter  20 value 230.407220
## iter  30 value 224.081514
## iter  40 value 223.426307
## iter  50 value 223.369540
## iter  60 value 223.307459
## iter  70 value 223.299623
## iter  80 value 223.297714
## iter  90 value 223.268787
## iter 100 value 222.509239
## final  value 222.509239 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 441.987012 
## iter  10 value 285.874365
## iter  20 value 233.300022
## iter  30 value 221.988796
## iter  40 value 208.801302
## iter  50 value 189.841939
## iter  60 value 171.470766
## iter  70 value 170.047754
## iter  80 value 169.044449
## iter  90 value 168.726048
## iter 100 value 168.643107
## final  value 168.643107 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 409.903637 
## iter  10 value 333.982668
## iter  20 value 245.548878
## iter  30 value 227.494602
## iter  40 value 223.765081
## iter  50 value 223.414557
## iter  60 value 223.042932
## iter  70 value 222.524725
## iter  80 value 213.921814
## iter  90 value 178.257495
## iter 100 value 171.634484
## final  value 171.634484 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 428.180492 
## iter  10 value 294.389772
## iter  20 value 220.888084
## iter  30 value 183.463266
## iter  40 value 174.349338
## iter  50 value 171.301752
## iter  60 value 169.441700
## iter  70 value 169.141896
## iter  80 value 169.134124
## iter  90 value 169.094553
## iter 100 value 169.070893
## final  value 169.070893 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 407.732989 
## iter  10 value 234.801143
## iter  20 value 200.709512
## iter  30 value 178.292105
## iter  40 value 173.005691
## iter  50 value 169.393428
## iter  60 value 168.830785
## iter  70 value 168.494968
## iter  80 value 168.388928
## iter  90 value 168.240543
## iter 100 value 167.902889
## final  value 167.902889 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 428.684450 
## iter  10 value 311.583960
## iter  20 value 220.786845
## iter  30 value 191.796310
## iter  40 value 174.244849
## iter  50 value 169.141763
## iter  60 value 168.793239
## iter  70 value 168.778767
## iter  80 value 168.742918
## iter  90 value 168.697641
## iter 100 value 168.605657
## final  value 168.605657 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 421.153706 
## iter  10 value 234.019048
## iter  20 value 187.848026
## iter  30 value 169.251014
## iter  40 value 168.766656
## iter  50 value 168.547356
## iter  60 value 168.480099
## iter  70 value 168.334158
## iter  80 value 168.179246
## iter  90 value 168.111389
## iter 100 value 168.075185
## final  value 168.075185 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 409.816466 
## iter  10 value 312.143217
## iter  20 value 269.301190
## iter  30 value 218.569708
## iter  40 value 216.603919
## iter  50 value 201.449528
## iter  60 value 187.857935
## iter  70 value 178.035766
## iter  80 value 174.276538
## iter  90 value 170.408776
## iter 100 value 170.045344
## final  value 170.045344 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 459.720566 
## iter  10 value 373.883177
## iter  20 value 227.111765
## iter  30 value 223.651591
## iter  40 value 206.226382
## iter  50 value 174.251616
## iter  60 value 170.935150
## iter  70 value 169.027534
## iter  80 value 168.743533
## iter  90 value 168.646526
## iter 100 value 168.557634
## final  value 168.557634 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 448.162974 
## iter  10 value 295.198334
## iter  20 value 268.398942
## iter  30 value 266.128893
## iter  40 value 257.844459
## iter  50 value 254.923578
## iter  60 value 252.163057
## iter  70 value 251.759628
## iter  80 value 246.090797
## iter  90 value 242.203530
## iter 100 value 235.144072
## final  value 235.144072 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 419.174904 
## iter  10 value 314.891841
## iter  20 value 245.456863
## iter  30 value 234.046842
## iter  40 value 230.738205
## iter  50 value 230.154267
## iter  60 value 229.848752
## iter  70 value 229.812364
## iter  80 value 229.762058
## iter  90 value 229.748997
## iter 100 value 229.732458
## final  value 229.732458 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 402.720407 
## iter  10 value 230.149939
## iter  20 value 229.695658
## iter  30 value 229.672058
## iter  40 value 229.662746
## iter  50 value 229.653187
## iter  60 value 229.647309
## iter  70 value 229.644605
## iter  80 value 229.643497
## iter  90 value 229.643178
## iter 100 value 229.642738
## final  value 229.642738 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 440.721698 
## iter  10 value 393.285940
## iter  20 value 230.750274
## iter  30 value 230.005828
## iter  40 value 229.857851
## iter  50 value 229.820912
## iter  60 value 229.760166
## iter  70 value 229.740063
## final  value 229.738938 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 423.569361 
## iter  10 value 250.304866
## iter  20 value 230.321073
## iter  30 value 229.978133
## iter  40 value 229.797940
## iter  50 value 229.722447
## iter  60 value 229.718329
## final  value 229.712283 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 396.286521 
## iter  10 value 233.961391
## iter  20 value 229.644528
## iter  30 value 216.624579
## iter  40 value 186.541624
## iter  50 value 171.568650
## iter  60 value 164.967469
## iter  70 value 163.619669
## iter  80 value 163.365675
## iter  90 value 163.244980
## iter 100 value 163.164801
## final  value 163.164801 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 407.379354 
## iter  10 value 233.716296
## iter  20 value 165.388099
## iter  30 value 163.727221
## iter  40 value 163.651862
## iter  50 value 163.586984
## iter  60 value 163.572835
## iter  70 value 163.526738
## iter  80 value 163.522288
## iter  90 value 163.517468
## iter 100 value 163.506981
## final  value 163.506981 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 399.502060 
## iter  10 value 259.486618
## iter  20 value 221.167176
## iter  30 value 176.678370
## iter  40 value 168.675504
## iter  50 value 165.103285
## iter  60 value 163.719475
## iter  70 value 163.542218
## iter  80 value 163.519640
## iter  90 value 163.311281
## iter 100 value 163.046254
## final  value 163.046254 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 416.666500 
## iter  10 value 354.690709
## iter  20 value 228.042452
## iter  30 value 204.834611
## iter  40 value 181.314969
## iter  50 value 172.740640
## iter  60 value 168.052692
## iter  70 value 166.254032
## iter  80 value 165.562118
## iter  90 value 164.582065
## iter 100 value 164.100345
## final  value 164.100345 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 422.277095 
## iter  10 value 257.349682
## iter  20 value 230.563909
## iter  30 value 229.893712
## iter  40 value 229.745505
## iter  50 value 229.687408
## iter  60 value 229.679072
## iter  70 value 229.656885
## iter  80 value 229.383306
## iter  90 value 225.976887
## iter 100 value 164.738893
## final  value 164.738893 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 419.566766 
## iter  10 value 256.192392
## iter  20 value 226.332252
## iter  30 value 172.715735
## iter  40 value 164.780332
## iter  50 value 163.866890
## iter  60 value 163.621639
## iter  70 value 163.551525
## iter  80 value 163.446839
## iter  90 value 163.274834
## iter 100 value 163.106489
## final  value 163.106489 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 431.751736 
## iter  10 value 261.193863
## iter  20 value 229.564096
## iter  30 value 176.429737
## iter  40 value 164.630754
## iter  50 value 163.791720
## iter  60 value 163.638667
## iter  70 value 163.560272
## iter  80 value 163.483069
## iter  90 value 163.337915
## iter 100 value 163.150197
## final  value 163.150197 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 446.615041 
## iter  10 value 364.982785
## iter  20 value 231.760632
## iter  30 value 230.089230
## iter  40 value 226.281471
## iter  50 value 172.842447
## iter  60 value 165.324957
## iter  70 value 163.846594
## iter  80 value 163.309172
## iter  90 value 163.039889
## iter 100 value 162.939051
## final  value 162.939051 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 437.402750 
## iter  10 value 278.363113
## iter  20 value 228.684795
## iter  30 value 170.128943
## iter  40 value 165.174464
## iter  50 value 164.625457
## iter  60 value 164.266988
## iter  70 value 163.699181
## iter  80 value 163.638969
## iter  90 value 163.587680
## iter 100 value 163.541746
## final  value 163.541746 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 401.719816 
## iter  10 value 287.169484
## iter  20 value 206.281900
## iter  30 value 164.586190
## iter  40 value 163.784378
## iter  50 value 163.624618
## iter  60 value 163.576641
## iter  70 value 163.543463
## iter  80 value 163.462220
## iter  90 value 163.357819
## iter 100 value 163.232869
## final  value 163.232869 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 421.285417 
## iter  10 value 276.531999
## iter  20 value 258.417433
## final  value 258.416450 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 408.213474 
## iter  10 value 324.839331
## iter  20 value 258.449072
## iter  30 value 258.416452
## iter  30 value 258.416450
## iter  30 value 258.416450
## final  value 258.416450 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 431.546671 
## iter  10 value 337.488253
## iter  20 value 258.985528
## final  value 258.897700 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 427.522488 
## iter  10 value 278.915732
## iter  20 value 258.543602
## final  value 258.416449 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 410.428628 
## iter  10 value 336.358281
## iter  20 value 259.253541
## iter  30 value 258.897701
## iter  30 value 258.897699
## iter  30 value 258.897699
## final  value 258.897699 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 409.578582 
## iter  10 value 270.241016
## iter  20 value 256.974505
## iter  30 value 255.100764
## iter  40 value 255.005062
## iter  50 value 254.992793
## final  value 254.989973 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 455.164216 
## iter  10 value 325.322614
## iter  20 value 259.543753
## iter  30 value 256.867555
## iter  40 value 255.451239
## iter  50 value 255.103400
## iter  60 value 255.093534
## iter  70 value 255.025037
## iter  80 value 254.991983
## iter  90 value 254.990240
## final  value 254.989967 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 452.613306 
## iter  10 value 375.460954
## iter  20 value 263.235170
## iter  30 value 258.828897
## iter  40 value 256.653085
## iter  50 value 255.169811
## iter  60 value 255.066737
## iter  70 value 255.008992
## iter  80 value 254.993301
## iter  90 value 254.990300
## final  value 254.989968 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 399.985110 
## iter  10 value 280.641502
## iter  20 value 258.017863
## iter  30 value 256.781490
## iter  40 value 255.578480
## iter  50 value 255.041558
## iter  60 value 254.990194
## final  value 254.989968 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 460.060184 
## iter  10 value 263.852633
## iter  20 value 258.565079
## iter  30 value 255.900017
## iter  40 value 255.125166
## iter  50 value 255.068550
## iter  60 value 255.010231
## iter  70 value 254.989972
## iter  70 value 254.989971
## iter  70 value 254.989970
## final  value 254.989970 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 448.667361 
## iter  10 value 319.520493
## iter  20 value 256.559157
## iter  30 value 255.404141
## iter  40 value 255.136998
## iter  50 value 255.036359
## iter  60 value 254.953404
## iter  70 value 254.836199
## iter  80 value 254.712746
## iter  90 value 254.680601
## iter 100 value 254.660857
## final  value 254.660857 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 413.784702 
## iter  10 value 294.564287
## iter  20 value 263.569954
## iter  30 value 256.842448
## iter  40 value 255.449181
## iter  50 value 255.075054
## iter  60 value 254.844978
## iter  70 value 254.717843
## iter  80 value 254.663698
## iter  90 value 254.655356
## iter 100 value 254.651664
## final  value 254.651664 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 402.938634 
## iter  10 value 279.407911
## iter  20 value 262.476505
## iter  30 value 258.988485
## iter  40 value 256.166907
## iter  50 value 255.498668
## iter  60 value 255.139833
## iter  70 value 254.894941
## iter  80 value 254.711167
## iter  90 value 254.681546
## iter 100 value 254.671905
## final  value 254.671905 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 425.189715 
## iter  10 value 291.897486
## iter  20 value 257.348007
## iter  30 value 256.159686
## iter  40 value 254.974727
## iter  50 value 254.768518
## iter  60 value 254.688034
## iter  70 value 254.672040
## iter  80 value 254.657928
## iter  90 value 254.651622
## iter 100 value 254.651334
## final  value 254.651334 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 439.604063 
## iter  10 value 312.579728
## iter  20 value 256.288833
## iter  30 value 254.705708
## iter  40 value 254.688555
## iter  50 value 254.682757
## iter  60 value 254.670082
## iter  70 value 254.664900
## iter  80 value 254.653661
## iter  90 value 254.651469
## iter 100 value 254.651292
## final  value 254.651292 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 408.549085 
## iter  10 value 242.749410
## iter  20 value 230.339320
## iter  30 value 229.950375
## iter  40 value 229.830982
## iter  50 value 229.810699
## iter  60 value 229.799756
## iter  70 value 229.798699
## iter  80 value 229.788092
## iter  90 value 229.786454
## final  value 229.786240 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 403.939774 
## iter  10 value 346.728344
## iter  20 value 231.456362
## iter  30 value 230.499176
## iter  40 value 230.027050
## iter  50 value 229.860867
## iter  60 value 229.811765
## final  value 229.806390 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 410.071621 
## iter  10 value 380.750658
## iter  20 value 234.438127
## iter  30 value 230.582958
## iter  40 value 229.938730
## iter  50 value 229.889039
## iter  60 value 229.866778
## iter  70 value 229.839136
## iter  80 value 229.821462
## iter  90 value 229.819657
## iter 100 value 229.809718
## final  value 229.809718 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 403.843495 
## iter  10 value 256.591390
## iter  20 value 230.659434
## iter  30 value 230.268357
## iter  40 value 229.955874
## iter  50 value 229.867068
## iter  60 value 229.814208
## iter  70 value 229.810685
## iter  80 value 229.808236
## final  value 229.806644 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 429.992886 
## iter  10 value 251.034870
## iter  20 value 241.186582
## iter  30 value 233.803632
## iter  40 value 231.012776
## iter  50 value 230.095020
## iter  60 value 229.847132
## iter  70 value 229.815010
## iter  80 value 229.803193
## iter  90 value 229.795912
## iter 100 value 229.795127
## final  value 229.795127 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 512.222302 
## iter  10 value 271.531021
## iter  20 value 230.440248
## iter  30 value 227.754692
## iter  40 value 182.970235
## iter  50 value 167.309317
## iter  60 value 164.334732
## iter  70 value 164.032467
## iter  80 value 163.897818
## iter  90 value 163.878046
## iter 100 value 163.833228
## final  value 163.833228 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 447.104085 
## iter  10 value 251.058212
## iter  20 value 227.460250
## iter  30 value 164.967038
## iter  40 value 164.038940
## iter  50 value 163.810187
## iter  60 value 163.796800
## iter  70 value 163.751501
## iter  80 value 163.740907
## iter  90 value 163.739170
## iter 100 value 163.737503
## final  value 163.737503 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 417.238278 
## iter  10 value 271.050104
## iter  20 value 227.272932
## iter  30 value 225.426204
## iter  40 value 220.129530
## iter  50 value 181.056639
## iter  60 value 178.466220
## iter  70 value 176.557026
## iter  80 value 175.231142
## iter  90 value 174.559119
## iter 100 value 170.231812
## final  value 170.231812 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 400.641872 
## iter  10 value 274.059610
## iter  20 value 233.700062
## iter  30 value 230.149172
## iter  40 value 229.953375
## iter  50 value 167.823897
## iter  60 value 164.357364
## iter  70 value 164.015219
## iter  80 value 163.973068
## iter  90 value 163.804200
## iter 100 value 163.755330
## final  value 163.755330 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 443.634742 
## iter  10 value 256.429881
## iter  20 value 230.837271
## iter  30 value 222.568139
## iter  40 value 174.278127
## iter  50 value 164.380784
## iter  60 value 163.531370
## iter  70 value 163.284091
## iter  80 value 163.068485
## iter  90 value 162.938091
## iter 100 value 162.849512
## final  value 162.849512 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 458.570157 
## iter  10 value 323.852225
## iter  20 value 232.123647
## iter  30 value 229.165514
## iter  40 value 221.372911
## iter  50 value 171.562443
## iter  60 value 164.913712
## iter  70 value 163.669558
## iter  80 value 163.370557
## iter  90 value 163.177736
## iter 100 value 162.993613
## final  value 162.993613 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 476.281472 
## iter  10 value 244.172234
## iter  20 value 229.704834
## iter  30 value 210.509472
## iter  40 value 167.086515
## iter  50 value 164.310023
## iter  60 value 163.805162
## iter  70 value 163.780362
## iter  80 value 163.758492
## iter  90 value 163.724657
## iter 100 value 163.602141
## final  value 163.602141 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 418.558028 
## iter  10 value 306.013023
## iter  20 value 218.488835
## iter  30 value 172.672397
## iter  40 value 164.528765
## iter  50 value 163.758227
## iter  60 value 163.377342
## iter  70 value 163.211203
## iter  80 value 163.090982
## iter  90 value 162.947812
## iter 100 value 162.885928
## final  value 162.885928 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 450.666875 
## iter  10 value 325.600676
## iter  20 value 247.825513
## iter  30 value 228.198833
## iter  40 value 187.277120
## iter  50 value 169.808415
## iter  60 value 166.369977
## iter  70 value 164.243286
## iter  80 value 164.011476
## iter  90 value 163.844466
## iter 100 value 163.828926
## final  value 163.828926 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 448.926080 
## iter  10 value 243.441685
## iter  20 value 229.895897
## iter  30 value 228.982405
## iter  40 value 185.327798
## iter  50 value 168.741968
## iter  60 value 166.158022
## iter  70 value 164.354500
## iter  80 value 163.722723
## iter  90 value 163.617347
## iter 100 value 163.512003
## final  value 163.512003 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 463.961061 
## iter  10 value 375.158869
## iter  20 value 227.654393
## iter  30 value 227.422370
## iter  40 value 227.417960
## final  value 227.417945 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 406.209941 
## iter  10 value 327.531831
## iter  20 value 227.442147
## iter  30 value 227.418174
## final  value 227.417945 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 418.591727 
## iter  10 value 268.216731
## iter  20 value 227.449834
## iter  30 value 227.430318
## iter  40 value 227.417964
## final  value 227.417949 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 403.840609 
## iter  10 value 237.635192
## iter  20 value 227.673307
## iter  30 value 227.533442
## iter  40 value 227.507674
## iter  50 value 227.451466
## iter  60 value 227.429472
## final  value 227.417945 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 410.815867 
## iter  10 value 298.302192
## iter  20 value 234.956699
## iter  30 value 231.071799
## iter  40 value 229.124803
## iter  50 value 227.477867
## iter  60 value 227.418065
## final  value 227.417974 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 436.942631 
## iter  10 value 241.956298
## iter  20 value 221.233094
## iter  30 value 193.759879
## iter  40 value 181.622452
## iter  50 value 171.177836
## iter  60 value 166.734792
## iter  70 value 164.330694
## iter  80 value 163.340429
## iter  90 value 162.910715
## iter 100 value 161.662252
## final  value 161.662252 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 446.544037 
## iter  10 value 238.428562
## iter  20 value 222.179074
## iter  30 value 195.900795
## iter  40 value 177.718921
## iter  50 value 171.558897
## iter  60 value 168.457159
## iter  70 value 167.093460
## iter  80 value 166.465001
## iter  90 value 166.196903
## iter 100 value 165.967581
## final  value 165.967581 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 495.999462 
## iter  10 value 260.448778
## iter  20 value 228.287109
## iter  30 value 227.471322
## iter  40 value 227.391323
## iter  50 value 225.392968
## iter  60 value 191.638788
## iter  70 value 180.047952
## iter  80 value 174.841550
## iter  90 value 171.994756
## iter 100 value 169.780803
## final  value 169.780803 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 489.266973 
## iter  10 value 325.217699
## iter  20 value 243.385191
## iter  30 value 225.936727
## iter  40 value 195.367753
## iter  50 value 178.175351
## iter  60 value 170.075168
## iter  70 value 166.744551
## iter  80 value 165.602332
## iter  90 value 164.906624
## iter 100 value 164.104689
## final  value 164.104689 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 406.450150 
## iter  10 value 290.342755
## iter  20 value 227.549986
## iter  30 value 227.080523
## iter  40 value 201.637376
## iter  50 value 178.248815
## iter  60 value 172.567747
## iter  70 value 170.933637
## iter  80 value 166.655119
## iter  90 value 164.238218
## iter 100 value 162.602274
## final  value 162.602274 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 405.554291 
## iter  10 value 240.068492
## iter  20 value 208.197617
## iter  30 value 176.853693
## iter  40 value 169.004723
## iter  50 value 167.759577
## iter  60 value 167.022131
## iter  70 value 166.349768
## iter  80 value 165.087514
## iter  90 value 163.189086
## iter 100 value 162.530465
## final  value 162.530465 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 405.337177 
## iter  10 value 323.151014
## iter  20 value 231.355394
## iter  30 value 222.996905
## iter  40 value 186.708311
## iter  50 value 175.573553
## iter  60 value 172.914871
## iter  70 value 171.037091
## iter  80 value 169.913479
## iter  90 value 169.787984
## iter 100 value 169.712961
## final  value 169.712961 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 458.491370 
## iter  10 value 267.987353
## iter  20 value 228.000103
## iter  30 value 207.989797
## iter  40 value 187.055646
## iter  50 value 171.872933
## iter  60 value 168.079631
## iter  70 value 166.753440
## iter  80 value 166.013910
## iter  90 value 165.036375
## iter 100 value 163.388946
## final  value 163.388946 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 415.837627 
## iter  10 value 235.081702
## iter  20 value 222.632715
## iter  30 value 194.342487
## iter  40 value 174.259717
## iter  50 value 167.617333
## iter  60 value 165.341054
## iter  70 value 162.901319
## iter  80 value 161.694956
## iter  90 value 160.121906
## iter 100 value 159.360220
## final  value 159.360220 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 439.470012 
## iter  10 value 250.654755
## iter  20 value 226.447579
## iter  30 value 189.735602
## iter  40 value 174.198801
## iter  50 value 168.741835
## iter  60 value 165.667979
## iter  70 value 164.019044
## iter  80 value 162.671050
## iter  90 value 162.166286
## iter 100 value 161.332714
## final  value 161.332714 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 435.626610 
## iter  10 value 356.090234
## iter  20 value 259.138609
## iter  30 value 257.804987
## iter  30 value 257.804985
## iter  30 value 257.804985
## final  value 257.804985 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 405.595964 
## iter  10 value 335.680256
## iter  20 value 257.818026
## final  value 257.804986 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 403.488136 
## iter  10 value 296.349945
## iter  20 value 257.087848
## final  value 257.086166 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 404.438962 
## iter  10 value 275.421710
## iter  20 value 257.134598
## final  value 257.086166 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 419.212187 
## iter  10 value 284.799869
## iter  20 value 257.173032
## final  value 257.086166 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 420.189001 
## iter  10 value 284.738327
## iter  20 value 257.727675
## iter  30 value 254.941610
## iter  40 value 254.328764
## iter  50 value 254.147090
## iter  60 value 254.137623
## final  value 254.135919 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 412.484623 
## iter  10 value 286.680916
## iter  20 value 254.563996
## iter  30 value 254.298751
## iter  40 value 254.237953
## iter  50 value 254.224416
## iter  60 value 254.223827
## iter  70 value 254.219991
## final  value 254.219610 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 412.950702 
## iter  10 value 314.870675
## iter  20 value 259.081576
## iter  30 value 256.520587
## iter  40 value 254.965951
## iter  50 value 254.230229
## iter  60 value 254.220850
## iter  70 value 254.199852
## iter  80 value 254.185476
## iter  90 value 254.151478
## iter 100 value 254.136023
## final  value 254.136023 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 459.910968 
## iter  10 value 270.700246
## iter  20 value 255.772451
## iter  30 value 254.601658
## iter  40 value 254.239085
## iter  50 value 254.224555
## iter  60 value 254.224006
## iter  70 value 254.220392
## iter  80 value 254.219639
## final  value 254.219628 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 426.518738 
## iter  10 value 363.889789
## iter  20 value 264.047797
## iter  30 value 258.541057
## iter  40 value 257.314239
## iter  50 value 254.830492
## iter  60 value 254.216856
## iter  70 value 254.143493
## iter  80 value 254.140523
## iter  90 value 254.135968
## final  value 254.135919 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 434.597263 
## iter  10 value 346.989664
## iter  20 value 257.052424
## iter  30 value 255.089677
## iter  40 value 254.390369
## iter  50 value 254.216143
## iter  60 value 254.115405
## iter  70 value 254.024452
## iter  80 value 253.984082
## iter  90 value 253.978163
## final  value 253.977004 
## converged
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 407.588401 
## iter  10 value 286.700395
## iter  20 value 254.848078
## iter  30 value 254.162578
## iter  40 value 254.071690
## iter  50 value 254.041275
## iter  60 value 253.999972
## iter  70 value 253.990231
## iter  80 value 253.982115
## iter  90 value 253.980184
## iter 100 value 253.979076
## final  value 253.979076 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 438.118483 
## iter  10 value 324.712513
## iter  20 value 255.775259
## iter  30 value 254.393972
## iter  40 value 254.202686
## iter  50 value 254.189794
## iter  60 value 254.168400
## iter  70 value 254.118454
## iter  80 value 254.006249
## iter  90 value 253.986494
## iter 100 value 253.981310
## final  value 253.981310 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 419.747596 
## iter  10 value 272.257501
## iter  20 value 255.231341
## iter  30 value 254.450235
## iter  40 value 254.179734
## iter  50 value 254.091340
## iter  60 value 254.046488
## iter  70 value 254.001216
## iter  80 value 253.972598
## iter  90 value 253.962923
## iter 100 value 253.951524
## final  value 253.951524 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 427.544984 
## iter  10 value 278.937703
## iter  20 value 256.458190
## iter  30 value 254.814453
## iter  40 value 254.361714
## iter  50 value 254.292629
## iter  60 value 254.098704
## iter  70 value 254.033920
## iter  80 value 253.981406
## iter  90 value 253.977100
## iter 100 value 253.960771
## final  value 253.960771 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 436.868463 
## iter  10 value 369.706220
## iter  20 value 228.293450
## iter  30 value 227.470993
## iter  40 value 227.462059
## iter  50 value 227.459857
## final  value 227.459844 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 419.303794 
## iter  10 value 275.692678
## iter  20 value 240.099185
## iter  30 value 235.200196
## iter  40 value 232.640156
## iter  50 value 228.388109
## iter  60 value 227.496452
## iter  70 value 227.460991
## final  value 227.459845 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 448.340575 
## iter  10 value 319.171720
## iter  20 value 228.854795
## iter  30 value 228.158712
## iter  40 value 227.482616
## iter  50 value 227.460007
## final  value 227.459868 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 443.397677 
## iter  10 value 231.243037
## iter  20 value 227.722415
## iter  30 value 227.489292
## iter  40 value 227.457402
## final  value 227.457060 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 409.606469 
## iter  10 value 300.918557
## iter  20 value 228.685631
## iter  30 value 227.511787
## iter  40 value 227.478128
## iter  50 value 227.460584
## final  value 227.459843 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 408.046542 
## iter  10 value 289.588754
## iter  20 value 227.690146
## iter  30 value 196.307965
## iter  40 value 170.216112
## iter  50 value 168.764690
## iter  60 value 166.771671
## iter  70 value 165.157011
## iter  80 value 164.644221
## iter  90 value 164.047836
## iter 100 value 163.838005
## final  value 163.838005 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 409.890877 
## iter  10 value 247.032447
## iter  20 value 196.236545
## iter  30 value 176.283139
## iter  40 value 173.840216
## iter  50 value 169.814415
## iter  60 value 168.601404
## iter  70 value 168.260615
## iter  80 value 168.180780
## iter  90 value 167.971149
## iter 100 value 167.835158
## final  value 167.835158 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 492.751206 
## iter  10 value 259.450514
## iter  20 value 227.483653
## iter  30 value 217.662979
## iter  40 value 177.571134
## iter  50 value 173.533055
## iter  60 value 171.715815
## iter  70 value 171.583084
## iter  80 value 171.552540
## iter  90 value 171.524118
## iter 100 value 171.506278
## final  value 171.506278 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 450.031366 
## iter  10 value 303.253211
## iter  20 value 213.578658
## iter  30 value 179.394715
## iter  40 value 176.819425
## iter  50 value 174.239944
## iter  60 value 172.021207
## iter  70 value 171.648129
## iter  80 value 171.565864
## iter  90 value 171.510191
## iter 100 value 171.506026
## final  value 171.506026 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 407.422077 
## iter  10 value 260.239547
## iter  20 value 243.336380
## iter  30 value 230.015605
## iter  40 value 227.984548
## iter  50 value 227.848891
## iter  60 value 227.797556
## iter  70 value 227.667510
## iter  80 value 227.605187
## iter  90 value 227.587299
## iter 100 value 227.549965
## final  value 227.549965 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 430.308674 
## iter  10 value 287.597798
## iter  20 value 223.171827
## iter  30 value 174.294003
## iter  40 value 168.309437
## iter  50 value 166.075771
## iter  60 value 165.537132
## iter  70 value 165.313354
## iter  80 value 165.045398
## iter  90 value 164.934477
## iter 100 value 164.856773
## final  value 164.856773 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 491.107909 
## iter  10 value 258.177200
## iter  20 value 227.844155
## iter  30 value 227.577087
## iter  40 value 192.303663
## iter  50 value 172.163908
## iter  60 value 169.751694
## iter  70 value 167.467277
## iter  80 value 166.399950
## iter  90 value 165.387962
## iter 100 value 165.013104
## final  value 165.013104 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 446.768419 
## iter  10 value 266.496737
## iter  20 value 230.476438
## iter  30 value 226.294977
## iter  40 value 202.717980
## iter  50 value 181.298756
## iter  60 value 169.922961
## iter  70 value 167.653338
## iter  80 value 165.747569
## iter  90 value 165.588120
## iter 100 value 165.428843
## final  value 165.428843 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 479.098104 
## iter  10 value 238.392621
## iter  20 value 207.448816
## iter  30 value 181.684688
## iter  40 value 171.070653
## iter  50 value 168.544330
## iter  60 value 166.993707
## iter  70 value 165.326030
## iter  80 value 164.982765
## iter  90 value 164.663103
## iter 100 value 164.081818
## final  value 164.081818 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 408.657782 
## iter  10 value 243.323513
## iter  20 value 228.795261
## iter  30 value 227.641909
## iter  40 value 227.611137
## iter  50 value 227.562035
## iter  60 value 227.544177
## iter  70 value 227.522603
## iter  80 value 227.496327
## iter  90 value 226.691212
## iter 100 value 176.797031
## final  value 176.797031 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 412.484205 
## iter  10 value 307.774862
## iter  20 value 242.139822
## iter  30 value 224.447195
## iter  40 value 219.448601
## iter  50 value 218.268404
## iter  60 value 218.187635
## iter  70 value 218.130708
## iter  80 value 218.075726
## final  value 218.070594 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 399.236768 
## iter  10 value 290.736375
## iter  20 value 221.932452
## iter  30 value 218.920865
## iter  40 value 218.305493
## iter  50 value 218.187453
## iter  60 value 218.144495
## iter  70 value 218.128719
## iter  80 value 218.071852
## iter  90 value 218.068276
## iter 100 value 218.059503
## final  value 218.059503 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 425.876288 
## iter  10 value 279.763940
## iter  20 value 219.388569
## iter  30 value 218.431811
## iter  40 value 218.267233
## iter  50 value 218.168061
## iter  60 value 218.128480
## iter  70 value 218.103832
## iter  80 value 218.060211
## iter  90 value 218.050347
## iter 100 value 218.046855
## final  value 218.046855 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 413.853565 
## iter  10 value 313.811853
## iter  20 value 218.848330
## iter  30 value 218.344119
## iter  40 value 218.097027
## iter  50 value 218.042986
## final  value 218.042783 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 414.345528 
## iter  10 value 231.679813
## iter  20 value 218.286845
## iter  30 value 218.164953
## iter  40 value 218.088210
## iter  50 value 218.073607
## iter  60 value 218.064567
## iter  70 value 218.048482
## iter  80 value 218.041693
## iter  90 value 218.037900
## iter 100 value 218.013545
## final  value 218.013545 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 421.391002 
## iter  10 value 277.515095
## iter  20 value 217.864812
## iter  30 value 195.905312
## iter  40 value 174.455860
## iter  50 value 165.144845
## iter  60 value 162.780900
## iter  70 value 162.661271
## iter  80 value 162.625833
## iter  90 value 162.574896
## iter 100 value 162.518408
## final  value 162.518408 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 412.913626 
## iter  10 value 290.037799
## iter  20 value 217.996767
## iter  30 value 182.152451
## iter  40 value 173.151782
## iter  50 value 167.251492
## iter  60 value 163.373724
## iter  70 value 162.751230
## iter  80 value 162.620499
## iter  90 value 162.513755
## iter 100 value 162.476363
## final  value 162.476363 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 424.924158 
## iter  10 value 237.004157
## iter  20 value 219.153394
## iter  30 value 199.006140
## iter  40 value 171.299123
## iter  50 value 163.783536
## iter  60 value 162.485047
## iter  70 value 162.316946
## iter  80 value 162.193154
## iter  90 value 162.116341
## iter 100 value 161.869248
## final  value 161.869248 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 412.114834 
## iter  10 value 225.259989
## iter  20 value 218.189585
## iter  30 value 218.069887
## iter  40 value 218.066630
## iter  50 value 218.056900
## iter  60 value 218.012134
## iter  70 value 217.990786
## iter  80 value 217.987139
## iter  90 value 217.979243
## iter 100 value 217.966369
## final  value 217.966369 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 475.394870 
## iter  10 value 297.716053
## iter  20 value 248.207614
## iter  30 value 231.729188
## iter  40 value 219.031905
## iter  50 value 217.992318
## iter  60 value 217.970304
## iter  70 value 217.969445
## iter  80 value 217.963068
## iter  90 value 217.958799
## final  value 217.957662 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 404.547112 
## iter  10 value 263.007054
## iter  20 value 218.521869
## iter  30 value 217.610576
## iter  40 value 192.934340
## iter  50 value 173.766999
## iter  60 value 166.965286
## iter  70 value 163.597610
## iter  80 value 162.747529
## iter  90 value 162.655736
## iter 100 value 162.557610
## final  value 162.557610 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 524.225886 
## iter  10 value 285.212966
## iter  20 value 220.185619
## iter  30 value 218.051674
## iter  40 value 217.573462
## iter  50 value 214.685266
## iter  60 value 191.962750
## iter  70 value 169.399659
## iter  80 value 164.711690
## iter  90 value 162.681729
## iter 100 value 162.520614
## final  value 162.520614 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 432.638692 
## iter  10 value 302.947231
## iter  20 value 219.772329
## iter  30 value 214.616310
## iter  40 value 171.241384
## iter  50 value 163.658040
## iter  60 value 162.373106
## iter  70 value 161.720194
## iter  80 value 161.012010
## iter  90 value 160.211378
## iter 100 value 158.618328
## final  value 158.618328 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 416.140029 
## iter  10 value 233.299804
## iter  20 value 219.078361
## iter  30 value 196.436350
## iter  40 value 163.533287
## iter  50 value 162.891362
## iter  60 value 162.816600
## iter  70 value 162.635590
## iter  80 value 162.581526
## iter  90 value 162.497075
## iter 100 value 162.455490
## final  value 162.455490 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 442.404871 
## iter  10 value 243.177238
## iter  20 value 217.701923
## iter  30 value 194.336851
## iter  40 value 164.503737
## iter  50 value 163.177943
## iter  60 value 162.871353
## iter  70 value 162.696507
## iter  80 value 162.608980
## iter  90 value 162.561727
## iter 100 value 162.505434
## final  value 162.505434 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 408.250410 
## iter  10 value 344.401705
## iter  20 value 250.166614
## iter  30 value 250.030141
## iter  30 value 250.030141
## iter  30 value 250.030141
## final  value 250.030141 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 402.881315 
## iter  10 value 368.967208
## iter  20 value 253.016091
## iter  30 value 250.030646
## final  value 250.030141 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 419.795761 
## iter  10 value 368.703966
## iter  20 value 250.876957
## iter  30 value 250.030151
## final  value 250.030141 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 413.226318 
## iter  10 value 317.445303
## iter  20 value 250.645088
## final  value 250.549994 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 405.807461 
## iter  10 value 266.977199
## iter  20 value 250.550771
## final  value 250.549998 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 401.785500 
## iter  10 value 319.537348
## iter  20 value 252.687713
## iter  30 value 249.291263
## iter  40 value 247.590321
## iter  50 value 246.623511
## iter  60 value 246.585013
## iter  70 value 246.584121
## iter  80 value 246.580742
## iter  90 value 246.516868
## iter 100 value 246.478048
## final  value 246.478048 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 413.488540 
## iter  10 value 313.653986
## iter  20 value 249.801434
## iter  30 value 247.157394
## iter  40 value 246.687385
## iter  50 value 246.505768
## iter  60 value 246.481168
## iter  70 value 246.476154
## iter  70 value 246.476153
## iter  70 value 246.476153
## final  value 246.476153 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 407.455466 
## iter  10 value 281.887743
## iter  20 value 249.257304
## iter  30 value 246.837936
## iter  40 value 246.649788
## iter  50 value 246.586945
## iter  60 value 246.585280
## iter  70 value 246.510573
## iter  80 value 246.495696
## iter  90 value 246.477548
## final  value 246.476153 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 443.086461 
## iter  10 value 311.481486
## iter  20 value 251.376596
## iter  30 value 249.753525
## iter  40 value 248.070051
## iter  50 value 246.738976
## iter  60 value 246.587525
## iter  70 value 246.586692
## final  value 246.586650 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 415.759709 
## iter  10 value 286.900665
## iter  20 value 252.083146
## iter  30 value 250.166340
## iter  40 value 247.254914
## iter  50 value 246.599689
## iter  60 value 246.567559
## iter  70 value 246.527932
## iter  80 value 246.525039
## iter  90 value 246.524506
## final  value 246.524398 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 413.608108 
## iter  10 value 278.077580
## iter  20 value 249.877514
## iter  30 value 247.803423
## iter  40 value 246.650688
## iter  50 value 246.372021
## iter  60 value 246.259254
## iter  70 value 246.210383
## iter  80 value 246.200905
## iter  90 value 246.187286
## iter 100 value 246.178521
## final  value 246.178521 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 477.789539 
## iter  10 value 254.013434
## iter  20 value 246.577671
## iter  30 value 246.291468
## iter  40 value 246.233420
## iter  50 value 246.220342
## iter  60 value 246.206964
## iter  70 value 246.201695
## iter  80 value 246.194310
## iter  90 value 246.169155
## iter 100 value 246.165464
## final  value 246.165464 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 406.692141 
## iter  10 value 306.952992
## iter  20 value 249.090735
## iter  30 value 246.878189
## iter  40 value 246.521440
## iter  50 value 246.404617
## iter  60 value 246.300493
## iter  70 value 246.200920
## iter  80 value 246.180005
## iter  90 value 246.175993
## iter 100 value 246.174999
## final  value 246.174999 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 403.998455 
## iter  10 value 285.663707
## iter  20 value 255.672842
## iter  30 value 251.805298
## iter  40 value 250.572705
## iter  50 value 248.394121
## iter  60 value 247.059474
## iter  70 value 246.567939
## iter  80 value 246.465326
## iter  90 value 246.210720
## iter 100 value 246.182617
## final  value 246.182617 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 410.247607 
## iter  10 value 290.556761
## iter  20 value 251.787044
## iter  30 value 247.629050
## iter  40 value 246.680837
## iter  50 value 246.476253
## iter  60 value 246.374311
## iter  70 value 246.227964
## iter  80 value 246.184391
## iter  90 value 246.176666
## iter 100 value 246.175018
## final  value 246.175018 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 422.345206 
## iter  10 value 314.992439
## iter  20 value 218.838182
## iter  30 value 218.355768
## iter  40 value 218.229477
## iter  50 value 218.196222
## iter  60 value 218.172568
## iter  70 value 218.162207
## iter  80 value 218.149896
## iter  90 value 218.141997
## iter 100 value 218.139034
## final  value 218.139034 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 433.365533 
## iter  10 value 332.493412
## iter  20 value 225.095177
## iter  30 value 219.887277
## iter  40 value 218.461051
## iter  50 value 218.206231
## iter  60 value 218.191716
## iter  70 value 218.185606
## iter  80 value 218.184494
## iter  90 value 218.181823
## iter 100 value 218.179876
## final  value 218.179876 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 422.690163 
## iter  10 value 222.350141
## iter  20 value 218.263804
## iter  30 value 218.183422
## iter  40 value 218.157390
## iter  50 value 218.138993
## iter  60 value 218.137513
## final  value 218.137473 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 476.618240 
## iter  10 value 388.940443
## iter  20 value 231.085210
## iter  30 value 220.358945
## iter  40 value 218.469118
## iter  50 value 218.358725
## iter  60 value 218.227612
## iter  70 value 218.203118
## iter  80 value 218.189344
## iter  90 value 218.187181
## iter 100 value 218.184015
## final  value 218.184015 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 433.499196 
## iter  10 value 262.247365
## iter  20 value 219.279838
## iter  30 value 218.769850
## iter  40 value 218.658477
## iter  50 value 218.486160
## iter  60 value 218.338875
## iter  70 value 218.242609
## iter  80 value 218.216886
## iter  90 value 218.207750
## iter 100 value 218.195909
## final  value 218.195909 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 408.097730 
## iter  10 value 252.836023
## iter  20 value 200.000378
## iter  30 value 180.902607
## iter  40 value 168.956301
## iter  50 value 166.116028
## iter  60 value 164.569033
## iter  70 value 164.152942
## iter  80 value 163.430157
## iter  90 value 163.292929
## iter 100 value 162.969117
## final  value 162.969117 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 414.233112 
## iter  10 value 248.554267
## iter  20 value 219.328957
## iter  30 value 201.243848
## iter  40 value 166.693524
## iter  50 value 163.388003
## iter  60 value 162.922236
## iter  70 value 162.804711
## iter  80 value 162.754666
## iter  90 value 162.745555
## iter 100 value 162.738765
## final  value 162.738765 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 400.972662 
## iter  10 value 222.711486
## iter  20 value 217.739452
## iter  30 value 174.219179
## iter  40 value 168.111089
## iter  50 value 163.597946
## iter  60 value 163.065118
## iter  70 value 162.934275
## iter  80 value 162.901387
## iter  90 value 162.862585
## iter 100 value 162.824765
## final  value 162.824765 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 510.577484 
## iter  10 value 249.350565
## iter  20 value 219.576797
## iter  30 value 218.282661
## iter  40 value 197.474975
## iter  50 value 167.906970
## iter  60 value 163.005118
## iter  70 value 162.823988
## iter  80 value 162.768406
## iter  90 value 162.749059
## iter 100 value 162.734372
## final  value 162.734372 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 412.880713 
## iter  10 value 221.038043
## iter  20 value 218.301927
## iter  30 value 217.790429
## iter  40 value 177.359529
## iter  50 value 168.881658
## iter  60 value 164.499801
## iter  70 value 163.083725
## iter  80 value 162.807065
## iter  90 value 162.747745
## iter 100 value 162.742126
## final  value 162.742126 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 448.950999 
## iter  10 value 351.680602
## iter  20 value 220.593827
## iter  30 value 203.529966
## iter  40 value 168.208850
## iter  50 value 163.912993
## iter  60 value 162.796233
## iter  70 value 162.658789
## iter  80 value 161.982166
## iter  90 value 159.949898
## iter 100 value 158.936772
## final  value 158.936772 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 416.360683 
## iter  10 value 228.449178
## iter  20 value 217.860587
## iter  30 value 211.011488
## iter  40 value 164.477825
## iter  50 value 163.600541
## iter  60 value 162.764102
## iter  70 value 162.378779
## iter  80 value 162.049115
## iter  90 value 161.993803
## iter 100 value 161.869320
## final  value 161.869320 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 428.198773 
## iter  10 value 228.231737
## iter  20 value 218.670975
## iter  30 value 218.378889
## iter  40 value 218.256149
## iter  50 value 218.221945
## iter  60 value 218.205834
## iter  70 value 218.200402
## iter  80 value 218.157077
## iter  90 value 218.056559
## iter 100 value 208.567333
## final  value 208.567333 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 502.112816 
## iter  10 value 256.683172
## iter  20 value 218.754020
## iter  30 value 218.369006
## iter  40 value 218.107451
## iter  50 value 217.352483
## iter  60 value 207.963010
## iter  70 value 169.889254
## iter  80 value 164.076946
## iter  90 value 162.889635
## iter 100 value 162.743218
## final  value 162.743218 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 421.281825 
## iter  10 value 308.539893
## iter  20 value 217.597110
## iter  30 value 183.934581
## iter  40 value 164.609194
## iter  50 value 162.901091
## iter  60 value 162.778875
## iter  70 value 162.743471
## iter  80 value 162.704442
## iter  90 value 162.663088
## iter 100 value 162.526210
## final  value 162.526210 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 420.180114 
## iter  10 value 359.393241
## iter  20 value 239.148686
## iter  30 value 232.393328
## iter  40 value 231.399846
## iter  50 value 230.559711
## iter  60 value 230.446861
## iter  70 value 230.416995
## iter  80 value 230.368493
## iter  90 value 230.362121
## iter 100 value 230.351160
## final  value 230.351160 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 421.632868 
## iter  10 value 259.809194
## iter  20 value 233.692470
## iter  30 value 230.891432
## iter  40 value 230.445070
## iter  50 value 230.419969
## iter  60 value 230.372665
## iter  70 value 230.349883
## iter  80 value 230.345564
## iter  90 value 230.338335
## final  value 230.336987 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 413.401551 
## iter  10 value 285.856381
## iter  20 value 242.465670
## iter  30 value 230.364409
## iter  40 value 230.361559
## iter  50 value 230.355530
## iter  60 value 230.352289
## iter  70 value 230.351050
## iter  80 value 230.341342
## iter  90 value 230.338540
## final  value 230.338174 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 428.183728 
## iter  10 value 260.710212
## iter  20 value 231.134299
## iter  30 value 230.499683
## iter  40 value 230.420041
## iter  50 value 230.363878
## iter  60 value 230.356451
## iter  70 value 230.354432
## final  value 230.341073 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 406.250910 
## iter  10 value 248.905352
## iter  20 value 230.646643
## iter  30 value 230.495209
## iter  40 value 230.421143
## iter  50 value 230.375471
## iter  60 value 230.362968
## final  value 230.361709 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 472.834019 
## iter  10 value 271.079448
## iter  20 value 226.349761
## iter  30 value 186.039453
## iter  40 value 173.270786
## iter  50 value 172.339259
## iter  60 value 171.639324
## iter  70 value 171.212299
## iter  80 value 171.145299
## iter  90 value 171.018143
## iter 100 value 170.960209
## final  value 170.960209 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 401.513800 
## iter  10 value 328.279186
## iter  20 value 256.489731
## iter  30 value 239.538238
## iter  40 value 231.314645
## iter  50 value 230.733577
## iter  60 value 230.509365
## iter  70 value 230.438891
## iter  80 value 230.368701
## iter  90 value 230.290189
## iter 100 value 230.227722
## final  value 230.227722 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 411.000495 
## iter  10 value 340.974728
## iter  20 value 229.438027
## iter  30 value 186.707263
## iter  40 value 175.194802
## iter  50 value 173.321615
## iter  60 value 172.141936
## iter  70 value 171.482847
## iter  80 value 171.314609
## iter  90 value 171.097674
## iter 100 value 171.021979
## final  value 171.021979 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 402.806949 
## iter  10 value 234.645405
## iter  20 value 230.417178
## iter  30 value 228.690021
## iter  40 value 191.677653
## iter  50 value 174.871710
## iter  60 value 171.746662
## iter  70 value 171.189991
## iter  80 value 170.866422
## iter  90 value 170.761403
## iter 100 value 170.638191
## final  value 170.638191 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 412.004622 
## iter  10 value 291.521413
## iter  20 value 230.304533
## iter  30 value 226.866074
## iter  40 value 188.393373
## iter  50 value 181.630658
## iter  60 value 174.492349
## iter  70 value 173.035769
## iter  80 value 171.502038
## iter  90 value 170.812061
## iter 100 value 170.740116
## final  value 170.740116 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 405.322984 
## iter  10 value 275.067264
## iter  20 value 234.685384
## iter  30 value 225.207186
## iter  40 value 196.216781
## iter  50 value 179.868290
## iter  60 value 176.037381
## iter  70 value 173.225679
## iter  80 value 172.153116
## iter  90 value 170.564223
## iter 100 value 168.811423
## final  value 168.811423 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 426.916605 
## iter  10 value 247.873746
## iter  20 value 231.455630
## iter  30 value 228.053948
## iter  40 value 183.027717
## iter  50 value 174.085441
## iter  60 value 173.558106
## iter  70 value 172.415529
## iter  80 value 171.498276
## iter  90 value 170.816074
## iter 100 value 170.173388
## final  value 170.173388 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 435.817117 
## iter  10 value 292.884936
## iter  20 value 228.855352
## iter  30 value 183.485037
## iter  40 value 173.022507
## iter  50 value 171.820481
## iter  60 value 170.923722
## iter  70 value 170.772097
## iter  80 value 170.704908
## iter  90 value 170.635626
## iter 100 value 170.613716
## final  value 170.613716 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 432.754231 
## iter  10 value 256.005191
## iter  20 value 228.691784
## iter  30 value 177.947206
## iter  40 value 173.269384
## iter  50 value 171.095746
## iter  60 value 170.661198
## iter  70 value 170.190940
## iter  80 value 169.762792
## iter  90 value 169.087878
## iter 100 value 167.176251
## final  value 167.176251 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 449.003752 
## iter  10 value 362.638386
## iter  20 value 229.873569
## iter  30 value 199.117419
## iter  40 value 178.132681
## iter  50 value 172.354999
## iter  60 value 171.085943
## iter  70 value 171.010279
## iter  80 value 170.991952
## iter  90 value 170.944816
## iter 100 value 170.876622
## final  value 170.876622 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 406.545457 
## iter  10 value 373.097668
## iter  20 value 261.755776
## iter  30 value 261.202984
## final  value 261.201651 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 405.830204 
## iter  10 value 298.164340
## iter  20 value 261.937809
## final  value 261.638363 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 411.726142 
## iter  10 value 372.929684
## iter  20 value 263.542674
## iter  30 value 261.201680
## final  value 261.201651 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 427.200580 
## iter  10 value 291.899776
## iter  20 value 261.677711
## final  value 261.638363 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 410.650210 
## iter  10 value 360.910720
## iter  20 value 261.312872
## iter  30 value 261.201667
## final  value 261.201651 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 421.943760 
## iter  10 value 292.803503
## iter  20 value 258.433607
## iter  30 value 258.021580
## iter  40 value 257.935008
## iter  50 value 257.919552
## final  value 257.919381 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 402.240182 
## iter  10 value 299.678812
## iter  20 value 258.372070
## iter  30 value 257.912103
## iter  40 value 257.895669
## final  value 257.895401 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 403.686873 
## iter  10 value 294.639938
## iter  20 value 261.062550
## iter  30 value 258.775726
## iter  40 value 258.067484
## iter  50 value 257.919770
## iter  60 value 257.918738
## iter  70 value 257.909181
## iter  80 value 257.899938
## iter  90 value 257.888037
## final  value 257.887352 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 532.129608 
## iter  10 value 292.960988
## iter  20 value 258.201543
## iter  30 value 257.921769
## iter  40 value 257.891288
## iter  50 value 257.890246
## iter  60 value 257.887547
## final  value 257.887351 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 406.769592 
## iter  10 value 324.480220
## iter  20 value 262.693541
## iter  30 value 259.940695
## iter  40 value 258.646071
## iter  50 value 257.975359
## iter  60 value 257.919791
## final  value 257.919392 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 424.145351 
## iter  10 value 293.561255
## iter  20 value 259.324720
## iter  30 value 258.016838
## iter  40 value 257.870534
## iter  50 value 257.815894
## iter  60 value 257.732782
## iter  70 value 257.638616
## iter  80 value 257.570729
## iter  90 value 257.558422
## iter 100 value 257.544173
## final  value 257.544173 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 428.771473 
## iter  10 value 311.952713
## iter  20 value 258.574124
## iter  30 value 257.614355
## iter  40 value 257.570971
## iter  50 value 257.556807
## iter  60 value 257.550721
## iter  70 value 257.546257
## iter  80 value 257.542636
## final  value 257.542437 
## converged
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 482.536936 
## iter  10 value 316.451865
## iter  20 value 259.373207
## iter  30 value 258.795747
## iter  40 value 258.318945
## iter  50 value 258.136440
## iter  60 value 257.984631
## iter  70 value 257.914377
## iter  80 value 257.683686
## iter  90 value 257.551532
## iter 100 value 257.545129
## final  value 257.545129 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 444.210531 
## iter  10 value 292.177774
## iter  20 value 258.675198
## iter  30 value 258.042555
## iter  40 value 257.893895
## iter  50 value 257.802342
## iter  60 value 257.604594
## iter  70 value 257.585197
## iter  80 value 257.570577
## iter  90 value 257.555026
## iter 100 value 257.543965
## final  value 257.543965 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 401.876212 
## iter  10 value 307.084250
## iter  20 value 257.743202
## iter  30 value 254.337069
## iter  40 value 253.862901
## iter  50 value 253.724967
## iter  60 value 253.636362
## iter  70 value 253.509007
## iter  80 value 253.487374
## iter  90 value 253.483455
## iter 100 value 253.482801
## final  value 253.482801 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 410.452312 
## iter  10 value 325.141615
## iter  20 value 298.907012
## iter  30 value 261.309571
## iter  40 value 254.667335
## iter  50 value 240.600727
## iter  60 value 231.828671
## iter  70 value 230.824091
## iter  80 value 230.545598
## iter  90 value 230.534995
## iter 100 value 230.520443
## final  value 230.520443 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 404.431264 
## iter  10 value 392.689792
## iter  20 value 230.726648
## iter  30 value 230.550564
## iter  40 value 230.439871
## iter  50 value 230.433933
## iter  60 value 230.423237
## iter  70 value 230.421697
## iter  80 value 230.420143
## iter  90 value 230.418343
## iter 100 value 230.417464
## final  value 230.417464 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 409.910007 
## iter  10 value 323.228899
## iter  20 value 304.824969
## iter  30 value 277.841620
## iter  40 value 231.438778
## iter  50 value 231.315299
## iter  60 value 231.219869
## iter  70 value 231.164936
## iter  80 value 231.088300
## iter  90 value 231.058308
## iter 100 value 230.972847
## final  value 230.972847 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 422.066456 
## iter  10 value 237.509781
## iter  20 value 230.716553
## iter  30 value 230.476681
## iter  40 value 230.471153
## iter  50 value 230.443077
## iter  60 value 230.441422
## final  value 230.441209 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 440.678990 
## iter  10 value 246.712096
## iter  20 value 231.241743
## iter  30 value 230.664428
## iter  40 value 230.526151
## iter  50 value 230.469814
## iter  60 value 230.457878
## iter  70 value 230.447511
## iter  80 value 230.442551
## iter  90 value 230.440976
## iter 100 value 230.440776
## final  value 230.440776 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 432.323328 
## iter  10 value 256.976269
## iter  20 value 227.771355
## iter  30 value 194.496938
## iter  40 value 175.237345
## iter  50 value 172.230975
## iter  60 value 171.722503
## iter  70 value 171.544301
## iter  80 value 171.387248
## iter  90 value 171.376414
## iter 100 value 171.374704
## final  value 171.374704 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 406.038476 
## iter  10 value 268.648911
## iter  20 value 228.378590
## iter  30 value 198.243922
## iter  40 value 176.581352
## iter  50 value 172.701932
## iter  60 value 171.951959
## iter  70 value 171.500205
## iter  80 value 171.466609
## iter  90 value 171.437176
## iter 100 value 171.417759
## final  value 171.417759 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 405.182796 
## iter  10 value 245.687687
## iter  20 value 207.324129
## iter  30 value 174.643286
## iter  40 value 171.150799
## iter  50 value 170.923261
## iter  60 value 170.843929
## iter  70 value 170.769683
## iter  80 value 170.630822
## iter  90 value 170.530223
## iter 100 value 170.271101
## final  value 170.271101 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 402.153025 
## iter  10 value 249.725974
## iter  20 value 225.622406
## iter  30 value 184.892590
## iter  40 value 171.158496
## iter  50 value 170.800309
## iter  60 value 170.760430
## iter  70 value 170.674491
## iter  80 value 170.577679
## iter  90 value 170.557830
## iter 100 value 170.556920
## final  value 170.556920 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 431.591695 
## iter  10 value 327.629186
## iter  20 value 224.094585
## iter  30 value 188.304004
## iter  40 value 176.699530
## iter  50 value 173.352557
## iter  60 value 171.816795
## iter  70 value 171.108881
## iter  80 value 171.038580
## iter  90 value 170.957889
## iter 100 value 170.883534
## final  value 170.883534 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 398.905794 
## iter  10 value 236.569454
## iter  20 value 222.818428
## iter  30 value 178.571362
## iter  40 value 171.010298
## iter  50 value 170.788004
## iter  60 value 170.685627
## iter  70 value 170.608684
## iter  80 value 170.510575
## iter  90 value 170.416971
## iter 100 value 170.299800
## final  value 170.299800 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 419.305846 
## iter  10 value 291.788205
## iter  20 value 230.298371
## iter  30 value 192.698490
## iter  40 value 174.191716
## iter  50 value 171.925163
## iter  60 value 171.184316
## iter  70 value 170.973808
## iter  80 value 170.709893
## iter  90 value 170.554977
## iter 100 value 169.982642
## final  value 169.982642 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 414.144843 
## iter  10 value 329.598663
## iter  20 value 234.486237
## iter  30 value 209.678456
## iter  40 value 173.486577
## iter  50 value 171.402361
## iter  60 value 170.929641
## iter  70 value 170.725372
## iter  80 value 170.580964
## iter  90 value 170.452367
## iter 100 value 170.135106
## final  value 170.135106 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 424.657683 
## iter  10 value 315.979438
## iter  20 value 223.550808
## iter  30 value 182.100234
## iter  40 value 171.870681
## iter  50 value 171.492374
## iter  60 value 170.933426
## iter  70 value 170.756266
## iter  80 value 170.477806
## iter  90 value 169.890245
## iter 100 value 169.364024
## final  value 169.364024 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 428.455181 
## iter  10 value 325.608773
## iter  20 value 230.665574
## iter  30 value 230.404828
## iter  40 value 230.137131
## iter  50 value 229.086127
## iter  60 value 216.239997
## iter  70 value 177.359122
## iter  80 value 172.138458
## iter  90 value 171.494557
## iter 100 value 171.207110
## final  value 171.207110 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 408.435526 
## iter  10 value 195.474888
## iter  20 value 192.331715
## iter  30 value 192.234088
## iter  40 value 192.173346
## iter  50 value 192.152997
## iter  60 value 192.145831
## iter  70 value 192.134979
## final  value 192.134147 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 419.392489 
## iter  10 value 246.742863
## iter  20 value 212.760115
## iter  30 value 205.700490
## iter  40 value 197.260554
## iter  50 value 195.212161
## iter  60 value 192.550147
## iter  70 value 192.214204
## iter  80 value 192.194569
## iter  90 value 192.155908
## iter 100 value 192.145997
## final  value 192.145997 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 442.268479 
## iter  10 value 328.726442
## iter  20 value 193.384798
## iter  30 value 192.421784
## iter  40 value 192.220742
## iter  50 value 192.165545
## iter  60 value 192.152474
## iter  70 value 192.128348
## final  value 192.126857 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 467.146596 
## iter  10 value 268.661673
## iter  20 value 192.425746
## iter  30 value 192.298967
## iter  40 value 192.172909
## iter  50 value 192.165939
## iter  60 value 192.147759
## iter  70 value 192.139766
## iter  80 value 192.136268
## iter  90 value 192.130441
## iter 100 value 192.129691
## final  value 192.129691 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 397.060089 
## iter  10 value 351.483343
## iter  20 value 194.869390
## iter  30 value 193.045341
## iter  40 value 192.299710
## iter  50 value 192.165114
## iter  60 value 192.158394
## iter  70 value 192.142690
## iter  80 value 192.139295
## iter  90 value 192.138254
## iter 100 value 192.132528
## final  value 192.132528 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 424.127981 
## iter  10 value 213.037325
## iter  20 value 192.455277
## iter  30 value 192.246082
## iter  40 value 192.153442
## iter  50 value 192.148486
## iter  60 value 192.125603
## iter  70 value 192.110377
## final  value 192.107014 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 419.088657 
## iter  10 value 211.234284
## iter  20 value 193.381519
## iter  30 value 192.158430
## iter  40 value 191.932256
## iter  50 value 191.832722
## iter  60 value 190.614836
## iter  70 value 175.536804
## iter  80 value 171.116863
## iter  90 value 166.129938
## iter 100 value 164.777496
## final  value 164.777496 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 401.200261 
## iter  10 value 213.693853
## iter  20 value 169.263716
## iter  30 value 148.306135
## iter  40 value 147.494800
## iter  50 value 146.296346
## iter  60 value 145.409341
## iter  70 value 145.124091
## iter  80 value 144.921279
## iter  90 value 144.549492
## iter 100 value 144.460860
## final  value 144.460860 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 475.867173 
## iter  10 value 305.270855
## iter  20 value 252.047810
## iter  30 value 218.698760
## iter  40 value 199.066714
## iter  50 value 194.066724
## iter  60 value 193.294433
## iter  70 value 192.819644
## iter  80 value 192.489101
## iter  90 value 192.221521
## iter 100 value 192.152214
## final  value 192.152214 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 431.439237 
## iter  10 value 202.560930
## iter  20 value 192.028741
## iter  30 value 172.298707
## iter  40 value 150.111486
## iter  50 value 147.701749
## iter  60 value 145.950876
## iter  70 value 144.430245
## iter  80 value 144.092640
## iter  90 value 144.042149
## iter 100 value 144.025898
## final  value 144.025898 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 428.575404 
## iter  10 value 306.558424
## iter  20 value 203.193112
## iter  30 value 192.187583
## iter  40 value 149.254817
## iter  50 value 145.255706
## iter  60 value 144.118454
## iter  70 value 143.812058
## iter  80 value 143.332626
## iter  90 value 142.668423
## iter 100 value 141.396580
## final  value 141.396580 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 436.076995 
## iter  10 value 278.848799
## iter  20 value 192.581688
## iter  30 value 177.175009
## iter  40 value 150.503146
## iter  50 value 145.847840
## iter  60 value 143.556619
## iter  70 value 143.081257
## iter  80 value 142.328458
## iter  90 value 140.390735
## iter 100 value 139.991956
## final  value 139.991956 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 413.638366 
## iter  10 value 267.804299
## iter  20 value 198.746035
## iter  30 value 192.088640
## iter  40 value 192.043923
## iter  50 value 191.638487
## iter  60 value 162.280963
## iter  70 value 145.783115
## iter  80 value 143.500627
## iter  90 value 143.087408
## iter 100 value 142.780360
## final  value 142.780360 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 401.566836 
## iter  10 value 242.939328
## iter  20 value 192.479226
## iter  30 value 192.074953
## iter  40 value 192.041953
## iter  50 value 191.641237
## iter  60 value 188.478187
## iter  70 value 155.880519
## iter  80 value 153.087390
## iter  90 value 148.881737
## iter 100 value 146.097421
## final  value 146.097421 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 395.796258 
## iter  10 value 201.552697
## iter  20 value 192.318379
## iter  30 value 167.558077
## iter  40 value 147.351357
## iter  50 value 145.321717
## iter  60 value 143.507807
## iter  70 value 142.909921
## iter  80 value 141.566321
## iter  90 value 140.705828
## iter 100 value 140.020969
## final  value 140.020969 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 425.762533 
## iter  10 value 368.929453
## iter  20 value 228.692288
## iter  30 value 227.244577
## final  value 227.244224 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 452.669834 
## iter  10 value 394.207084
## iter  20 value 228.312154
## iter  30 value 227.470506
## final  value 227.466766 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 425.872424 
## iter  10 value 256.150298
## iter  20 value 227.498774
## final  value 227.466766 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 422.930471 
## iter  10 value 289.808722
## iter  20 value 227.654636
## iter  30 value 227.466780
## final  value 227.466767 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 415.087032 
## iter  10 value 230.865867
## iter  20 value 227.466787
## final  value 227.466769 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 402.783491 
## iter  10 value 245.484287
## iter  20 value 227.404042
## iter  30 value 224.675079
## iter  40 value 223.827885
## iter  50 value 223.364646
## iter  60 value 223.339771
## iter  70 value 223.307565
## final  value 223.307220 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 449.883710 
## iter  10 value 341.511738
## iter  20 value 238.834500
## iter  30 value 226.071872
## iter  40 value 224.546820
## iter  50 value 223.447082
## iter  60 value 223.401519
## iter  70 value 223.399484
## iter  80 value 223.369474
## iter  90 value 223.346620
## iter 100 value 223.308109
## final  value 223.308109 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 415.361844 
## iter  10 value 248.052269
## iter  20 value 223.451260
## iter  30 value 223.393405
## iter  40 value 223.369191
## iter  50 value 223.355697
## iter  60 value 223.316121
## final  value 223.307124 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 443.343028 
## iter  10 value 353.066282
## iter  20 value 228.284599
## iter  30 value 226.645140
## iter  40 value 224.576660
## iter  50 value 223.652624
## iter  60 value 223.400063
## iter  70 value 223.392051
## iter  80 value 223.334735
## iter  90 value 223.318885
## iter 100 value 223.307721
## final  value 223.307721 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 426.509518 
## iter  10 value 255.066463
## iter  20 value 227.264587
## iter  30 value 224.675340
## iter  40 value 223.678452
## iter  50 value 223.407409
## iter  60 value 223.404374
## iter  70 value 223.387505
## iter  80 value 223.358500
## iter  90 value 223.314688
## iter 100 value 223.307149
## final  value 223.307149 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 402.483032 
## iter  10 value 278.590890
## iter  20 value 225.246535
## iter  30 value 223.710277
## iter  40 value 223.314640
## iter  50 value 223.234056
## iter  60 value 223.142335
## iter  70 value 223.068890
## iter  80 value 223.029752
## iter  90 value 223.021813
## iter 100 value 223.010096
## final  value 223.010096 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 431.450110 
## iter  10 value 292.093594
## iter  20 value 225.549753
## iter  30 value 223.402512
## iter  40 value 223.238640
## iter  50 value 223.202360
## iter  60 value 223.163729
## iter  70 value 223.110256
## iter  80 value 223.032715
## iter  90 value 223.027890
## iter 100 value 223.001436
## final  value 223.001436 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 470.850502 
## iter  10 value 251.200835
## iter  20 value 224.961928
## iter  30 value 223.800817
## iter  40 value 223.204485
## iter  50 value 223.094528
## iter  60 value 223.034176
## iter  70 value 223.014591
## iter  80 value 223.010829
## final  value 223.009744 
## converged
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 424.464332 
## iter  10 value 294.710692
## iter  20 value 224.592796
## iter  30 value 223.523493
## iter  40 value 223.146589
## iter  50 value 223.084849
## iter  60 value 223.055887
## iter  70 value 223.014539
## iter  80 value 223.000787
## iter  90 value 222.990946
## iter 100 value 222.989319
## final  value 222.989319 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 416.440755 
## iter  10 value 258.976143
## iter  20 value 227.259545
## iter  30 value 224.857335
## iter  40 value 224.091945
## iter  50 value 223.487993
## iter  60 value 223.238040
## iter  70 value 223.107394
## iter  80 value 223.022062
## iter  90 value 223.016298
## iter 100 value 223.000751
## final  value 223.000751 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 408.536810 
## iter  10 value 232.172280
## iter  20 value 192.829066
## iter  30 value 192.356170
## iter  40 value 192.236123
## iter  50 value 192.204121
## iter  60 value 192.202113
## iter  70 value 192.201144
## iter  80 value 192.200252
## iter  80 value 192.200250
## final  value 192.200250 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 408.667648 
## iter  10 value 265.536140
## iter  20 value 194.840478
## iter  30 value 192.206644
## iter  40 value 192.200928
## iter  50 value 192.197802
## iter  60 value 192.197664
## final  value 192.197479 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 410.863496 
## iter  10 value 320.884885
## iter  20 value 192.470612
## iter  30 value 192.327996
## iter  40 value 192.228203
## iter  50 value 192.221708
## iter  60 value 192.214536
## iter  70 value 192.214250
## iter  80 value 192.213744
## final  value 192.213506 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 437.809787 
## iter  10 value 311.822625
## iter  20 value 210.263353
## iter  30 value 193.904692
## iter  40 value 192.712856
## iter  50 value 192.305111
## iter  60 value 192.246488
## iter  70 value 192.224774
## iter  80 value 192.222556
## iter  90 value 192.220155
## iter 100 value 192.217399
## final  value 192.217399 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 404.769754 
## iter  10 value 235.605388
## iter  20 value 192.540740
## iter  30 value 192.213574
## final  value 192.213349 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 461.574265 
## iter  10 value 314.744733
## iter  20 value 194.428847
## iter  30 value 192.656021
## iter  40 value 191.952514
## iter  50 value 163.362381
## iter  60 value 149.860299
## iter  70 value 148.156339
## iter  80 value 146.892096
## iter  90 value 144.906304
## iter 100 value 144.647214
## final  value 144.647214 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 405.902652 
## iter  10 value 296.612514
## iter  20 value 281.816265
## iter  30 value 278.411800
## iter  40 value 268.737043
## iter  50 value 267.239483
## iter  60 value 255.924672
## iter  70 value 222.223033
## iter  80 value 218.521254
## iter  90 value 217.814989
## iter 100 value 194.049227
## final  value 194.049227 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 406.239825 
## iter  10 value 196.156833
## iter  20 value 147.161162
## iter  30 value 144.971767
## iter  40 value 144.583069
## iter  50 value 143.759132
## iter  60 value 143.373591
## iter  70 value 142.461367
## iter  80 value 141.800898
## iter  90 value 141.580262
## iter 100 value 141.241929
## final  value 141.241929 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 427.866637 
## iter  10 value 205.284244
## iter  20 value 192.507435
## iter  30 value 186.407393
## iter  40 value 152.773136
## iter  50 value 148.650038
## iter  60 value 146.169710
## iter  70 value 145.018839
## iter  80 value 144.632757
## iter  90 value 144.478770
## iter 100 value 144.432292
## final  value 144.432292 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 401.028812 
## iter  10 value 232.497791
## iter  20 value 192.580849
## iter  30 value 192.263231
## iter  40 value 169.122506
## iter  50 value 152.865123
## iter  60 value 147.641815
## iter  70 value 146.282434
## iter  80 value 144.630082
## iter  90 value 143.970987
## iter 100 value 143.672930
## final  value 143.672930 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 499.958985 
## iter  10 value 285.065063
## iter  20 value 193.237532
## iter  30 value 164.955326
## iter  40 value 148.450474
## iter  50 value 144.802543
## iter  60 value 143.525183
## iter  70 value 143.094013
## iter  80 value 142.228980
## iter  90 value 141.322412
## iter 100 value 140.391572
## final  value 140.391572 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 399.447170 
## iter  10 value 203.888064
## iter  20 value 189.886902
## iter  30 value 155.335597
## iter  40 value 146.181349
## iter  50 value 144.278252
## iter  60 value 142.841248
## iter  70 value 142.247556
## iter  80 value 141.583173
## iter  90 value 141.403875
## iter 100 value 141.020386
## final  value 141.020386 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 434.223635 
## iter  10 value 198.432357
## iter  20 value 183.220497
## iter  30 value 146.594088
## iter  40 value 145.678442
## iter  50 value 144.983827
## iter  60 value 144.802989
## iter  70 value 144.705326
## iter  80 value 144.625356
## iter  90 value 144.596131
## iter 100 value 144.542493
## final  value 144.542493 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 438.669986 
## iter  10 value 303.713342
## iter  20 value 214.506753
## iter  30 value 191.378168
## iter  40 value 163.743272
## iter  50 value 154.944490
## iter  60 value 149.332460
## iter  70 value 145.712978
## iter  80 value 144.637370
## iter  90 value 144.216782
## iter 100 value 143.800980
## final  value 143.800980 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 401.056737 
## iter  10 value 196.083579
## iter  20 value 186.702803
## iter  30 value 155.215890
## iter  40 value 143.947512
## iter  50 value 142.777329
## iter  60 value 142.434055
## iter  70 value 142.039047
## iter  80 value 141.107358
## iter  90 value 140.783950
## iter 100 value 140.639245
## final  value 140.639245 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 407.171736 
## iter  10 value 285.497339
## iter  20 value 209.906564
## iter  30 value 209.331591
## iter  40 value 209.127063
## iter  50 value 209.085932
## iter  60 value 209.046768
## iter  70 value 209.040871
## iter  80 value 209.017392
## iter  90 value 209.010913
## final  value 209.007902 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 430.059913 
## iter  10 value 233.340942
## iter  20 value 209.638215
## iter  30 value 209.281822
## iter  40 value 209.096393
## iter  50 value 209.075479
## iter  60 value 209.054015
## iter  70 value 209.047470
## iter  80 value 209.037205
## iter  90 value 209.028066
## final  value 209.026000 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 429.215016 
## iter  10 value 354.035993
## iter  20 value 210.552346
## iter  30 value 209.394621
## iter  40 value 209.202060
## iter  50 value 209.132868
## iter  60 value 209.081796
## iter  70 value 209.067526
## iter  80 value 209.054830
## iter  90 value 209.041523
## iter 100 value 209.034101
## final  value 209.034101 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 425.792469 
## iter  10 value 326.379234
## iter  20 value 210.275832
## iter  30 value 209.411402
## iter  40 value 209.133203
## iter  50 value 209.068832
## iter  60 value 209.044852
## iter  70 value 209.040303
## iter  80 value 209.032594
## iter  90 value 209.024456
## iter 100 value 209.019725
## final  value 209.019725 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 425.091119 
## iter  10 value 344.020964
## iter  20 value 295.211164
## iter  30 value 291.151203
## iter  40 value 287.363579
## iter  50 value 251.743366
## iter  60 value 235.364397
## iter  70 value 220.341725
## iter  80 value 211.914869
## iter  90 value 210.118788
## iter 100 value 209.185340
## final  value 209.185340 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 417.217936 
## iter  10 value 302.363960
## iter  20 value 292.479452
## iter  30 value 292.474743
## iter  40 value 292.416908
## iter  50 value 292.050932
## iter  60 value 292.000126
## final  value 292.000008 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 489.818412 
## iter  10 value 329.332459
## iter  20 value 217.558947
## iter  30 value 208.870615
## iter  40 value 208.406405
## iter  50 value 190.696089
## iter  60 value 171.950485
## iter  70 value 165.978408
## iter  80 value 165.520040
## iter  90 value 163.609320
## iter 100 value 162.562573
## final  value 162.562573 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 408.552175 
## iter  10 value 269.036007
## iter  20 value 202.642403
## iter  30 value 166.472896
## iter  40 value 160.455485
## iter  50 value 159.642022
## iter  60 value 159.239683
## iter  70 value 159.082200
## iter  80 value 159.010777
## iter  90 value 158.956124
## iter 100 value 158.938544
## final  value 158.938544 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 448.957903 
## iter  10 value 238.941101
## iter  20 value 210.843244
## iter  30 value 209.062776
## iter  40 value 208.146389
## iter  50 value 206.435353
## iter  60 value 204.120694
## iter  70 value 201.740647
## iter  80 value 194.806208
## iter  90 value 174.986536
## iter 100 value 167.562151
## final  value 167.562151 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 420.151191 
## iter  10 value 257.955335
## iter  20 value 216.202099
## iter  30 value 192.247969
## iter  40 value 165.424393
## iter  50 value 162.142682
## iter  60 value 160.545119
## iter  70 value 159.603328
## iter  80 value 159.379569
## iter  90 value 159.348269
## iter 100 value 159.228269
## final  value 159.228269 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 450.766845 
## iter  10 value 254.218572
## iter  20 value 208.698758
## iter  30 value 173.390740
## iter  40 value 164.875331
## iter  50 value 161.360823
## iter  60 value 159.040277
## iter  70 value 158.832164
## iter  80 value 158.791316
## iter  90 value 158.763343
## iter 100 value 158.705294
## final  value 158.705294 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 446.141552 
## iter  10 value 275.075162
## iter  20 value 208.953959
## iter  30 value 178.676260
## iter  40 value 165.565937
## iter  50 value 161.746682
## iter  60 value 160.886935
## iter  70 value 160.145122
## iter  80 value 159.480553
## iter  90 value 159.351367
## iter 100 value 159.188988
## final  value 159.188988 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 438.857473 
## iter  10 value 386.232378
## iter  20 value 214.908418
## iter  30 value 207.656313
## iter  40 value 207.528855
## iter  50 value 207.396572
## iter  60 value 206.971658
## iter  70 value 205.026972
## iter  80 value 186.061921
## iter  90 value 169.228175
## iter 100 value 167.455212
## final  value 167.455212 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 409.824049 
## iter  10 value 229.468720
## iter  20 value 211.373084
## iter  30 value 209.220295
## iter  40 value 209.154198
## iter  50 value 208.580468
## iter  60 value 203.678971
## iter  70 value 180.368486
## iter  80 value 166.524402
## iter  90 value 164.029774
## iter 100 value 162.069512
## final  value 162.069512 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 407.543267 
## iter  10 value 323.743833
## iter  20 value 310.965391
## iter  30 value 224.905916
## iter  40 value 210.692709
## iter  50 value 209.665830
## iter  60 value 208.888732
## iter  70 value 208.015512
## iter  80 value 207.908840
## iter  90 value 207.755043
## iter 100 value 207.685677
## final  value 207.685677 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 410.914592 
## iter  10 value 283.088575
## iter  20 value 241.413177
## final  value 241.327217 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 456.784331 
## iter  10 value 349.744574
## iter  20 value 242.457501
## iter  30 value 241.270629
## iter  30 value 241.270628
## iter  30 value 241.270628
## final  value 241.270628 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 414.927340 
## iter  10 value 257.850633
## iter  20 value 241.330610
## final  value 241.327217 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 442.701797 
## iter  10 value 382.107372
## iter  20 value 243.275404
## iter  30 value 241.278697
## final  value 241.270628 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 414.097844 
## iter  10 value 337.279300
## iter  20 value 241.442506
## iter  30 value 241.327217
## iter  30 value 241.327216
## iter  30 value 241.327216
## final  value 241.327216 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 423.052219 
## iter  10 value 305.592707
## iter  20 value 237.720436
## iter  30 value 237.449312
## iter  40 value 237.424523
## iter  50 value 237.407857
## iter  60 value 237.393054
## final  value 237.392994 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 436.704795 
## iter  10 value 354.539763
## iter  20 value 240.034959
## iter  30 value 238.039740
## iter  40 value 237.603161
## iter  50 value 237.407960
## iter  60 value 237.366751
## final  value 237.365984 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 404.940768 
## iter  10 value 309.591189
## iter  20 value 244.667458
## iter  30 value 241.720273
## iter  40 value 239.209955
## iter  50 value 237.530657
## iter  60 value 237.465873
## iter  70 value 237.464608
## iter  80 value 237.461707
## iter  90 value 237.392315
## iter 100 value 237.368221
## final  value 237.368221 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 404.546112 
## iter  10 value 263.983681
## iter  20 value 243.241113
## iter  30 value 238.914936
## iter  40 value 237.812994
## iter  50 value 237.398683
## iter  60 value 237.394475
## final  value 237.392993 
## converged
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 450.712976 
## iter  10 value 327.148446
## iter  20 value 245.484801
## iter  30 value 237.929435
## iter  40 value 237.508980
## iter  50 value 237.420559
## iter  60 value 237.399135
## iter  70 value 237.392995
## iter  70 value 237.392993
## iter  70 value 237.392993
## final  value 237.392993 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 473.494674 
## iter  10 value 284.977343
## iter  20 value 244.670792
## iter  30 value 239.342435
## iter  40 value 237.736672
## iter  50 value 237.172323
## iter  60 value 237.075708
## iter  70 value 237.048073
## iter  80 value 237.044429
## iter  90 value 237.038445
## iter 100 value 237.027554
## final  value 237.027554 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 447.596968 
## iter  10 value 305.189694
## iter  20 value 245.751219
## iter  30 value 241.585433
## iter  40 value 238.136096
## iter  50 value 237.611199
## iter  60 value 237.292985
## iter  70 value 237.067546
## iter  80 value 237.025955
## iter  90 value 237.024146
## final  value 237.023369 
## converged
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 441.582810 
## iter  10 value 256.416045
## iter  20 value 238.264967
## iter  30 value 237.691001
## iter  40 value 237.279299
## iter  50 value 237.114315
## iter  60 value 237.089635
## iter  70 value 237.042372
## iter  80 value 237.013399
## iter  90 value 237.010353
## iter 100 value 237.009599
## final  value 237.009599 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 462.419217 
## iter  10 value 333.247899
## iter  20 value 238.377447
## iter  30 value 237.536336
## iter  40 value 237.464893
## iter  50 value 237.380754
## iter  60 value 237.101309
## iter  70 value 237.041914
## iter  80 value 237.033494
## iter  90 value 237.026020
## final  value 237.023385 
## converged
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 444.960684 
## iter  10 value 343.271832
## iter  20 value 250.667083
## iter  30 value 241.016130
## iter  40 value 239.140373
## iter  50 value 237.866291
## iter  60 value 237.502371
## iter  70 value 237.315544
## iter  80 value 237.107906
## iter  90 value 237.051055
## iter 100 value 237.033899
## final  value 237.033899 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 425.033702 
## iter  10 value 246.168425
## iter  20 value 209.473476
## iter  30 value 209.231664
## iter  40 value 209.128383
## iter  50 value 209.121108
## iter  60 value 209.118147
## final  value 209.115292 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 437.024893 
## iter  10 value 334.299165
## iter  20 value 209.572986
## iter  30 value 209.174054
## iter  40 value 209.158795
## iter  50 value 209.136348
## iter  60 value 209.118555
## iter  70 value 209.114305
## iter  80 value 209.110196
## final  value 209.109626 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 432.102736 
## iter  10 value 377.048161
## iter  20 value 209.403256
## iter  30 value 209.214370
## iter  40 value 209.162761
## iter  50 value 209.126858
## iter  60 value 209.120389
## iter  70 value 209.113325
## iter  80 value 209.107910
## iter  90 value 209.107460
## iter 100 value 209.106055
## final  value 209.106055 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 424.559995 
## iter  10 value 312.759212
## iter  20 value 219.677218
## iter  30 value 213.422865
## iter  40 value 210.419342
## iter  50 value 209.275055
## iter  60 value 209.175850
## iter  70 value 209.118513
## iter  80 value 209.114066
## iter  80 value 209.114066
## final  value 209.114066 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 422.146433 
## iter  10 value 211.908365
## iter  20 value 209.652218
## iter  30 value 209.293170
## iter  40 value 209.197672
## iter  50 value 209.113987
## iter  60 value 209.105544
## final  value 209.104718 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 411.270585 
## iter  10 value 218.750997
## iter  20 value 207.037282
## iter  30 value 163.731949
## iter  40 value 162.497308
## iter  50 value 162.295852
## iter  60 value 162.200914
## iter  70 value 162.170291
## iter  80 value 162.135396
## iter  90 value 162.078056
## iter 100 value 161.538538
## final  value 161.538538 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 438.557336 
## iter  10 value 248.008143
## iter  20 value 193.728089
## iter  30 value 163.612324
## iter  40 value 160.421029
## iter  50 value 160.076131
## iter  60 value 159.619977
## iter  70 value 159.472295
## iter  80 value 159.439099
## iter  90 value 159.416694
## iter 100 value 159.399814
## final  value 159.399814 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 418.578657 
## iter  10 value 244.299447
## iter  20 value 216.575540
## iter  30 value 211.146179
## iter  40 value 209.372150
## iter  50 value 209.191788
## iter  60 value 209.167264
## iter  70 value 209.128280
## iter  80 value 209.124900
## iter  80 value 209.124899
## final  value 209.124899 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 433.191274 
## iter  10 value 261.928816
## iter  20 value 209.033762
## iter  30 value 182.749993
## iter  40 value 166.917345
## iter  50 value 164.558465
## iter  60 value 162.648795
## iter  70 value 161.974117
## iter  80 value 161.610489
## iter  90 value 161.470977
## iter 100 value 161.309075
## final  value 161.309075 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 416.369359 
## iter  10 value 282.785916
## iter  20 value 223.228655
## iter  30 value 211.872037
## iter  40 value 210.112443
## iter  50 value 209.783048
## iter  60 value 209.603871
## iter  70 value 209.472356
## iter  80 value 209.442917
## iter  90 value 209.376646
## iter 100 value 209.355126
## final  value 209.355126 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 434.573155 
## iter  10 value 218.420734
## iter  20 value 209.450086
## iter  30 value 182.826295
## iter  40 value 164.178318
## iter  50 value 162.592652
## iter  60 value 162.003132
## iter  70 value 161.910023
## iter  80 value 161.799923
## iter  90 value 161.777880
## iter 100 value 161.737379
## final  value 161.737379 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 406.124623 
## iter  10 value 242.053037
## iter  20 value 201.300979
## iter  30 value 175.823158
## iter  40 value 164.875533
## iter  50 value 161.470883
## iter  60 value 160.964176
## iter  70 value 160.540123
## iter  80 value 160.240567
## iter  90 value 160.013149
## iter 100 value 159.883165
## final  value 159.883165 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 416.256350 
## iter  10 value 212.436167
## iter  20 value 167.206665
## iter  30 value 162.078238
## iter  40 value 160.646904
## iter  50 value 159.680658
## iter  60 value 159.550243
## iter  70 value 159.441998
## iter  80 value 159.398199
## iter  90 value 159.322440
## iter 100 value 159.067146
## final  value 159.067146 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 421.645592 
## iter  10 value 231.635674
## iter  20 value 206.470222
## iter  30 value 172.303860
## iter  40 value 161.713625
## iter  50 value 160.057289
## iter  60 value 159.729724
## iter  70 value 159.596666
## iter  80 value 159.454952
## iter  90 value 159.427329
## iter 100 value 159.297528
## final  value 159.297528 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 422.549800 
## iter  10 value 256.044329
## iter  20 value 209.509886
## iter  30 value 200.801135
## iter  40 value 167.072625
## iter  50 value 163.480196
## iter  60 value 161.950731
## iter  70 value 161.891238
## iter  80 value 161.833743
## iter  90 value 161.807559
## iter 100 value 161.729408
## final  value 161.729408 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 410.092538 
## iter  10 value 258.174647
## iter  20 value 233.525672
## iter  30 value 233.237824
## iter  40 value 233.234530
## iter  50 value 233.208613
## iter  60 value 233.205419
## iter  70 value 233.202792
## iter  80 value 233.201342
## iter  90 value 233.200523
## iter 100 value 233.195839
## final  value 233.195839 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 406.104862 
## iter  10 value 316.204571
## iter  20 value 234.152620
## iter  30 value 233.444896
## iter  40 value 233.298231
## iter  50 value 233.218275
## iter  60 value 233.210804
## iter  70 value 233.205325
## iter  80 value 233.203168
## iter  90 value 233.202226
## final  value 233.201366 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 407.307414 
## iter  10 value 355.308506
## iter  20 value 242.908206
## iter  30 value 234.001697
## iter  40 value 233.285784
## iter  50 value 233.236762
## iter  60 value 233.214047
## iter  70 value 233.208391
## iter  80 value 233.204994
## iter  90 value 233.200296
## iter 100 value 233.199626
## final  value 233.199626 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 427.735294 
## iter  10 value 268.873793
## iter  20 value 234.018828
## iter  30 value 233.274947
## iter  40 value 233.258357
## iter  50 value 233.202919
## iter  60 value 233.201352
## iter  70 value 233.200711
## iter  80 value 233.199506
## iter  80 value 233.199506
## final  value 233.199506 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 436.315555 
## iter  10 value 365.340147
## iter  20 value 286.486963
## iter  30 value 251.046937
## iter  40 value 235.598320
## iter  50 value 233.333111
## iter  60 value 233.310882
## iter  70 value 233.225068
## iter  80 value 233.218759
## iter  90 value 233.203257
## iter 100 value 233.198115
## final  value 233.198115 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 442.570109 
## iter  10 value 245.828919
## iter  20 value 232.699318
## iter  30 value 223.000141
## iter  40 value 187.425188
## iter  50 value 186.588703
## iter  60 value 186.201186
## iter  70 value 185.980750
## iter  80 value 185.822604
## iter  90 value 185.190458
## iter 100 value 184.820481
## final  value 184.820481 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 418.608616 
## iter  10 value 306.757073
## iter  20 value 237.775251
## iter  30 value 233.441511
## iter  40 value 233.251486
## iter  50 value 233.227511
## iter  60 value 233.206390
## iter  70 value 233.194512
## iter  80 value 233.190652
## iter  90 value 233.186941
## final  value 233.186862 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 410.267481 
## iter  10 value 236.672868
## iter  20 value 232.660358
## iter  30 value 199.400110
## iter  40 value 191.761539
## iter  50 value 189.223926
## iter  60 value 188.517107
## iter  70 value 188.367304
## iter  80 value 188.327533
## iter  90 value 188.216762
## iter 100 value 188.100259
## final  value 188.100259 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 417.564370 
## iter  10 value 336.705308
## iter  20 value 227.775901
## iter  30 value 190.496850
## iter  40 value 187.479152
## iter  50 value 186.573019
## iter  60 value 186.131282
## iter  70 value 185.935878
## iter  80 value 185.885627
## iter  90 value 185.506332
## iter 100 value 184.679929
## final  value 184.679929 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 400.249202 
## iter  10 value 234.427870
## iter  20 value 233.188228
## iter  30 value 232.604288
## iter  40 value 217.612435
## iter  50 value 187.686093
## iter  60 value 186.685553
## iter  70 value 186.506418
## iter  80 value 186.364806
## iter  90 value 186.293870
## iter 100 value 186.218726
## final  value 186.218726 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 480.772326 
## iter  10 value 301.616093
## iter  20 value 232.322267
## iter  30 value 202.697776
## iter  40 value 187.332173
## iter  50 value 187.081543
## iter  60 value 186.894784
## iter  70 value 186.654439
## iter  80 value 186.254153
## iter  90 value 185.743389
## iter 100 value 185.519137
## final  value 185.519137 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 416.306126 
## iter  10 value 236.202730
## iter  20 value 227.586960
## iter  30 value 192.434923
## iter  40 value 187.684320
## iter  50 value 186.793860
## iter  60 value 186.507897
## iter  70 value 186.369876
## iter  80 value 186.309303
## iter  90 value 186.248003
## iter 100 value 186.196178
## final  value 186.196178 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 503.210684 
## iter  10 value 237.824040
## iter  20 value 222.977897
## iter  30 value 190.995952
## iter  40 value 187.010765
## iter  50 value 186.541545
## iter  60 value 186.007802
## iter  70 value 185.737703
## iter  80 value 185.554107
## iter  90 value 185.069186
## iter 100 value 184.522989
## final  value 184.522989 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 427.749325 
## iter  10 value 318.230096
## iter  20 value 267.857363
## iter  30 value 255.200969
## iter  40 value 239.208796
## iter  50 value 234.326732
## iter  60 value 233.562452
## iter  70 value 233.242640
## iter  80 value 233.187944
## iter  90 value 233.184271
## final  value 233.184247 
## converged
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 465.485479 
## iter  10 value 243.430528
## iter  20 value 231.388724
## iter  30 value 192.015243
## iter  40 value 187.530819
## iter  50 value 186.782466
## iter  60 value 186.553194
## iter  70 value 186.356815
## iter  80 value 186.303739
## iter  90 value 186.209753
## iter 100 value 186.143775
## final  value 186.143775 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 414.489572 
## iter  10 value 370.747350
## iter  20 value 265.325230
## iter  30 value 263.689423
## final  value 263.687099 
## converged
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 402.699946 
## iter  10 value 265.981271
## iter  20 value 263.698169
## final  value 263.687099 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 409.331497 
## iter  10 value 282.071583
## iter  20 value 263.771672
## final  value 263.687099 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 425.855776 
## iter  10 value 343.107854
## iter  20 value 263.957994
## iter  30 value 263.687099
## iter  30 value 263.687099
## iter  30 value 263.687099
## final  value 263.687099 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 415.233957 
## iter  10 value 315.692226
## iter  20 value 264.844335
## final  value 263.687099 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 424.949190 
## iter  10 value 369.150820
## iter  20 value 274.142727
## iter  30 value 263.272826
## iter  40 value 260.275896
## iter  50 value 260.187235
## iter  60 value 260.159429
## iter  70 value 260.136250
## final  value 260.135788 
## converged
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 434.552727 
## iter  10 value 291.486098
## iter  20 value 262.072881
## iter  30 value 260.875666
## iter  40 value 260.280284
## iter  50 value 260.165999
## iter  60 value 260.155382
## iter  70 value 260.148151
## final  value 260.148147 
## converged
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 424.142444 
## iter  10 value 327.129091
## iter  20 value 264.625929
## iter  30 value 261.477632
## iter  40 value 260.242004
## iter  50 value 260.148540
## iter  60 value 260.137875
## final  value 260.135784 
## converged
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 413.326959 
## iter  10 value 300.621665
## iter  20 value 262.049765
## iter  30 value 260.818719
## iter  40 value 260.416490
## iter  50 value 260.205761
## iter  60 value 260.202076
## iter  70 value 260.176561
## iter  80 value 260.144758
## iter  90 value 260.139982
## iter 100 value 260.135786
## final  value 260.135786 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 432.440677 
## iter  10 value 349.574636
## iter  20 value 265.695922
## iter  30 value 260.850567
## iter  40 value 260.311225
## iter  50 value 260.205345
## iter  60 value 260.197444
## iter  70 value 260.179422
## iter  80 value 260.152335
## iter  90 value 260.150264
## final  value 260.148149 
## converged
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 466.465815 
## iter  10 value 310.852001
## iter  20 value 263.746885
## iter  30 value 261.375758
## iter  40 value 260.439439
## iter  50 value 260.090827
## iter  60 value 259.981672
## iter  70 value 259.910562
## iter  80 value 259.875809
## iter  90 value 259.874388
## iter 100 value 259.863121
## final  value 259.863121 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 411.922841 
## iter  10 value 297.472771
## iter  20 value 261.601525
## iter  30 value 260.518740
## iter  40 value 260.213432
## iter  50 value 260.182396
## iter  60 value 260.122913
## iter  70 value 259.987731
## iter  80 value 259.871052
## iter  90 value 259.862705
## iter 100 value 259.854877
## final  value 259.854877 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 410.771297 
## iter  10 value 314.156251
## iter  20 value 263.613222
## iter  30 value 261.333865
## iter  40 value 260.943534
## iter  50 value 260.640276
## iter  60 value 260.333019
## iter  70 value 260.196186
## iter  80 value 260.008217
## iter  90 value 259.873940
## iter 100 value 259.859065
## final  value 259.859065 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 449.357410 
## iter  10 value 356.946261
## iter  20 value 264.200285
## iter  30 value 260.417287
## iter  40 value 260.125229
## iter  50 value 260.059684
## iter  60 value 260.019858
## iter  70 value 259.989978
## iter  80 value 259.874340
## iter  90 value 259.863886
## iter 100 value 259.854750
## final  value 259.854750 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 405.461136 
## iter  10 value 284.947317
## iter  20 value 262.465564
## iter  30 value 260.307762
## iter  40 value 259.406885
## iter  50 value 258.840926
## iter  60 value 258.105178
## iter  70 value 257.349293
## iter  80 value 256.990344
## iter  90 value 256.930793
## iter 100 value 256.919141
## final  value 256.919141 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  7
## initial  value 417.870241 
## iter  10 value 270.395089
## iter  20 value 235.981095
## iter  30 value 234.319059
## iter  40 value 233.353659
## iter  50 value 233.299571
## iter  60 value 233.261994
## iter  70 value 233.260132
## iter  80 value 233.258741
## iter  90 value 233.256364
## iter 100 value 233.256064
## final  value 233.256064 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  7
## initial  value 426.160018 
## iter  10 value 369.942150
## iter  20 value 236.210679
## iter  30 value 233.314593
## iter  40 value 233.265224
## iter  50 value 233.263094
## iter  60 value 233.257114
## final  value 233.255893 
## converged
## Fitting Repeat 3 
## 
## # weights:  7
## initial  value 408.163344 
## iter  10 value 318.259509
## iter  20 value 266.394454
## iter  30 value 262.055520
## iter  40 value 244.147626
## iter  50 value 239.698038
## iter  60 value 234.446983
## iter  70 value 233.505542
## iter  80 value 233.295946
## iter  90 value 233.273333
## final  value 233.260717 
## converged
## Fitting Repeat 4 
## 
## # weights:  7
## initial  value 422.009501 
## iter  10 value 367.775739
## iter  20 value 233.371260
## iter  30 value 233.303207
## iter  40 value 233.269266
## iter  50 value 233.264427
## iter  60 value 233.260419
## final  value 233.259916 
## converged
## Fitting Repeat 5 
## 
## # weights:  7
## initial  value 419.018015 
## iter  10 value 287.971415
## iter  20 value 233.323787
## iter  30 value 233.303673
## iter  40 value 233.269878
## iter  50 value 233.262276
## iter  60 value 233.259188
## final  value 233.259110 
## converged
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 413.499239 
## iter  10 value 308.548835
## iter  20 value 243.305129
## iter  30 value 235.745658
## iter  40 value 233.684937
## iter  50 value 233.486015
## iter  60 value 233.451827
## iter  70 value 233.372977
## iter  80 value 233.346052
## iter  90 value 233.330367
## iter 100 value 233.324375
## final  value 233.324375 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 405.270608 
## iter  10 value 238.195474
## iter  20 value 224.460065
## iter  30 value 192.850005
## iter  40 value 187.462916
## iter  50 value 187.005767
## iter  60 value 186.762081
## iter  70 value 186.514087
## iter  80 value 186.464910
## iter  90 value 186.384483
## iter 100 value 186.281227
## final  value 186.281227 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 428.565864 
## iter  10 value 273.083512
## iter  20 value 233.012820
## iter  30 value 214.312828
## iter  40 value 191.069359
## iter  50 value 187.983954
## iter  60 value 187.264318
## iter  70 value 186.903647
## iter  80 value 186.767377
## iter  90 value 186.641245
## iter 100 value 186.560642
## final  value 186.560642 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 406.607909 
## iter  10 value 267.024964
## iter  20 value 201.481734
## iter  30 value 190.747942
## iter  40 value 187.173173
## iter  50 value 186.515279
## iter  60 value 186.078924
## iter  70 value 185.883894
## iter  80 value 185.738853
## iter  90 value 185.148842
## iter 100 value 184.373401
## final  value 184.373401 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 429.079595 
## iter  10 value 308.004488
## iter  20 value 250.714872
## iter  30 value 237.624490
## iter  40 value 234.139290
## iter  50 value 233.594592
## iter  60 value 233.473841
## iter  70 value 233.456811
## iter  80 value 233.365424
## iter  90 value 233.309035
## iter 100 value 233.299328
## final  value 233.299328 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  27
## initial  value 425.266130 
## iter  10 value 358.447501
## iter  20 value 252.468357
## iter  30 value 235.203030
## iter  40 value 219.651337
## iter  50 value 189.074574
## iter  60 value 187.054087
## iter  70 value 186.546093
## iter  80 value 186.359612
## iter  90 value 186.279958
## iter 100 value 186.191956
## final  value 186.191956 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  27
## initial  value 426.099951 
## iter  10 value 320.876972
## iter  20 value 254.736670
## iter  30 value 238.723620
## iter  40 value 236.891629
## iter  50 value 234.958417
## iter  60 value 218.724546
## iter  70 value 198.080597
## iter  80 value 195.628785
## iter  90 value 193.178262
## iter 100 value 188.474337
## final  value 188.474337 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  27
## initial  value 401.157321 
## iter  10 value 315.548039
## iter  20 value 256.058790
## iter  30 value 246.789245
## iter  40 value 237.273243
## iter  50 value 236.133814
## iter  60 value 232.491788
## iter  70 value 213.175564
## iter  80 value 207.180022
## iter  90 value 197.393711
## iter 100 value 192.247025
## final  value 192.247025 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  27
## initial  value 405.757512 
## iter  10 value 239.081138
## iter  20 value 233.074184
## iter  30 value 231.220317
## iter  40 value 188.603920
## iter  50 value 187.066728
## iter  60 value 186.923096
## iter  70 value 186.669276
## iter  80 value 186.588595
## iter  90 value 186.510604
## iter 100 value 186.288404
## final  value 186.288404 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  27
## initial  value 440.875866 
## iter  10 value 268.171168
## iter  20 value 223.377547
## iter  30 value 191.968079
## iter  40 value 187.993665
## iter  50 value 187.077650
## iter  60 value 186.928218
## iter  70 value 186.854166
## iter  80 value 186.800261
## iter  90 value 186.708325
## iter 100 value 186.664242
## final  value 186.664242 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  17
## initial  value 402.650170 
## iter  10 value 239.910604
## iter  20 value 228.989319
## iter  30 value 210.617404
## iter  40 value 179.720363
## iter  50 value 176.034241
## iter  60 value 174.424415
## iter  70 value 174.242180
## iter  80 value 174.178573
## iter  90 value 174.067669
## iter 100 value 174.026314
## final  value 174.026314 
## stopped after 100 iterations
## Fitting Repeat 2 
## 
## # weights:  17
## initial  value 407.742897 
## iter  10 value 243.851131
## iter  20 value 224.526433
## iter  30 value 179.469913
## iter  40 value 177.522099
## iter  50 value 175.068513
## iter  60 value 173.579291
## iter  70 value 173.513703
## iter  80 value 173.481438
## iter  90 value 173.403329
## iter 100 value 173.335369
## final  value 173.335369 
## stopped after 100 iterations
## Fitting Repeat 3 
## 
## # weights:  17
## initial  value 404.498653 
## iter  10 value 254.381118
## iter  20 value 232.512427
## iter  30 value 230.266467
## iter  40 value 229.937687
## iter  50 value 229.522337
## iter  60 value 226.575924
## iter  70 value 187.454497
## iter  80 value 181.066632
## iter  90 value 177.038129
## iter 100 value 175.619475
## final  value 175.619475 
## stopped after 100 iterations
## Fitting Repeat 4 
## 
## # weights:  17
## initial  value 452.921369 
## iter  10 value 251.238083
## iter  20 value 229.852411
## iter  30 value 227.981443
## iter  40 value 209.042607
## iter  50 value 185.378249
## iter  60 value 175.371700
## iter  70 value 174.947409
## iter  80 value 174.694446
## iter  90 value 174.459875
## iter 100 value 174.324010
## final  value 174.324010 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  17
## initial  value 412.538952 
## iter  10 value 246.581111
## iter  20 value 228.645911
## iter  30 value 187.701330
## iter  40 value 178.617167
## iter  50 value 175.098679
## iter  60 value 173.257648
## iter  70 value 173.102642
## iter  80 value 173.097158
## iter  90 value 173.067381
## iter 100 value 173.022245
## final  value 173.022245 
## stopped after 100 iterations
## [1] "mlp"
## [1] "monmlp"
## ** Ensemble 1 
## 0.5650904 
## ** 0.5650904 
## 
## ** Ensemble 1 
## 0.4468529 
## ** 0.4468529 
## 
## ** Ensemble 1 
## 0.4234825 
## ** 0.4234825 
## 
## ** Ensemble 1 
## 0.5671791 
## ** 0.5671791 
## 
## ** Ensemble 1 
## 0.4666353 
## ** 0.4666353 
## 
## ** Ensemble 1 
## 0.4413243 
## ** 0.4413243 
## 
## ** Ensemble 1 
## 0.5966755 
## ** 0.5966755 
## 
## ** Ensemble 1 
## 0.4681114 
## ** 0.4681114 
## 
## ** Ensemble 1 
## 0.4426144 
## ** 0.4426144 
## 
## ** Ensemble 1 
## 0.5099163 
## ** 0.5099163 
## 
## ** Ensemble 1 
## 0.3813041 
## ** 0.3813041 
## 
## ** Ensemble 1 
## 0.3536631 
## ** 0.3536631 
## 
## ** Ensemble 1 
## 0.5870448 
## ** 0.5870448 
## 
## ** Ensemble 1 
## 0.4426924 
## ** 0.4426924 
## 
## ** Ensemble 1 
## 0.4273253 
## ** 0.4273253 
## 
## ** Ensemble 1 
## 0.5590184 
## ** 0.5590184 
## 
## ** Ensemble 1 
## 0.4482791 
## ** 0.4482791 
## 
## ** Ensemble 1 
## 0.4345956 
## ** 0.4345956 
## 
## ** Ensemble 1 
## 0.5741628 
## ** 0.5741628 
## 
## ** Ensemble 1 
## 0.4163254 
## ** 0.4163254 
## 
## ** Ensemble 1 
## 0.3914547 
## ** 0.3914547 
## 
## ** Ensemble 1 
## 0.5962694 
## ** 0.5962694 
## 
## ** Ensemble 1 
## 0.4437906 
## ** 0.4437906 
## 
## ** Ensemble 1 
## 0.4342396 
## ** 0.4342396 
## 
## ** Ensemble 1 
## 0.5534097 
## ** 0.5534097 
## 
## ** Ensemble 1 
## 0.4075735 
## ** 0.4075735 
## 
## ** Ensemble 1 
## 0.3983453 
## ** 0.3983453 
## 
## ** Ensemble 1 
## 0.5702727 
## ** 0.5702727 
## 
## ** Ensemble 1 
## 0.4143229 
## ** 0.4143229 
## 
## ** Ensemble 1 
## 0.3924902 
## ** 0.3924902 
## 
## ** Ensemble 1 
## 0.5932968 
## ** 0.5932968 
## 
## ** Ensemble 1 
## 0.4689001 
## ** 0.4689001 
## 
## ** Ensemble 1 
## 0.4449659 
## ** 0.4449659 
## 
## ** Ensemble 1 
## 0.5854919 
## ** 0.5854919 
## 
## ** Ensemble 1 
## 0.4362667 
## ** 0.4362667 
## 
## ** Ensemble 1 
## 0.4085503 
## ** 0.4085503 
## 
## ** Ensemble 1 
## 0.5986158 
## ** 0.5986158 
## 
## ** Ensemble 1 
## 0.4529955 
## ** 0.4529955 
## 
## ** Ensemble 1 
## 0.4083867 
## ** 0.4083867 
## 
## ** Ensemble 1 
## 0.6122039 
## ** 0.6122039 
## 
## ** Ensemble 1 
## 0.5957444 
## ** 0.5957444 
## 
## ** Ensemble 1 
## 0.4186496 
## ** 0.4186496 
## 
## ** Ensemble 1 
## 0.56004 
## ** 0.56004 
## 
## ** Ensemble 1 
## 0.398661 
## ** 0.398661 
## 
## ** Ensemble 1 
## 0.3926989 
## ** 0.3926989 
## 
## ** Ensemble 1 
## 0.5423852 
## ** 0.5423852 
## 
## ** Ensemble 1 
## 0.4291737 
## ** 0.4291737 
## 
## ** Ensemble 1 
## 0.4138618 
## ** 0.4138618 
## 
## ** Ensemble 1 
## 0.6006389 
## ** 0.6006389 
## 
## ** Ensemble 1 
## 0.4699572 
## ** 0.4699572 
## 
## ** Ensemble 1 
## 0.4489593 
## ** 0.4489593 
## 
## ** Ensemble 1 
## 0.6081373 
## ** 0.6081373 
## 
## ** Ensemble 1 
## 0.4548977 
## ** 0.4548977 
## 
## ** Ensemble 1 
## 0.4311419 
## ** 0.4311419 
## 
## ** Ensemble 1 
## 0.5492261 
## ** 0.5492261 
## 
## ** Ensemble 1 
## 0.3770256 
## ** 0.3770256 
## 
## ** Ensemble 1 
## 0.3674743 
## ** 0.3674743 
## 
## ** Ensemble 1 
## 0.5606842 
## ** 0.5606842 
## 
## ** Ensemble 1 
## 0.4284221 
## ** 0.4284221 
## 
## ** Ensemble 1 
## 0.4063609 
## ** 0.4063609 
## 
## ** Ensemble 1 
## 0.5974212 
## ** 0.5974212 
## 
## ** Ensemble 1 
## 0.425973 
## ** 0.425973 
## 
## ** Ensemble 1 
## 0.4088576 
## ** 0.4088576 
## 
## ** Ensemble 1 
## 0.5984095 
## ** 0.5984095 
## 
## ** Ensemble 1 
## 0.4491176 
## ** 0.4491176 
## 
## ** Ensemble 1 
## 0.4242122 
## ** 0.4242122 
## 
## ** Ensemble 1 
## 0.5783237 
## ** 0.5783237 
## 
## ** Ensemble 1 
## 0.4214252 
## ** 0.4214252 
## 
## ** Ensemble 1 
## 0.4034361 
## ** 0.4034361 
## 
## ** Ensemble 1 
## 0.6154374 
## ** 0.6154374 
## 
## ** Ensemble 1 
## 0.4765455 
## ** 0.4765455 
## 
## ** Ensemble 1 
## 0.445054 
## ** 0.445054 
## 
## ** Ensemble 1 
## 0.6035911 
## ** 0.6035911 
## 
## ** Ensemble 1 
## 0.4467982 
## ** 0.4467982 
## 
## ** Ensemble 1 
## 0.4270636 
## ** 0.4270636 
## 
## ** Ensemble 1 
## 0.4246003 
## ** 0.4246003 
## 
## [1] "adaboost"
## [1] "gbm"
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3203             nan     0.1000    0.0321
##      2        1.2639             nan     0.1000    0.0255
##      3        1.2223             nan     0.1000    0.0195
##      4        1.1835             nan     0.1000    0.0194
##      5        1.1466             nan     0.1000    0.0152
##      6        1.1171             nan     0.1000    0.0129
##      7        1.0906             nan     0.1000    0.0112
##      8        1.0689             nan     0.1000    0.0095
##      9        1.0495             nan     0.1000    0.0077
##     10        1.0310             nan     0.1000    0.0069
##     20        0.8905             nan     0.1000    0.0043
##     40        0.7510             nan     0.1000    0.0015
##     60        0.6741             nan     0.1000    0.0005
##     80        0.6328             nan     0.1000   -0.0001
##    100        0.6025             nan     0.1000   -0.0003
##    120        0.5786             nan     0.1000    0.0006
##    140        0.5642             nan     0.1000   -0.0004
##    150        0.5581             nan     0.1000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2949             nan     0.1000    0.0437
##      2        1.2208             nan     0.1000    0.0353
##      3        1.1642             nan     0.1000    0.0268
##      4        1.1153             nan     0.1000    0.0250
##      5        1.0688             nan     0.1000    0.0218
##      6        1.0300             nan     0.1000    0.0175
##      7        0.9972             nan     0.1000    0.0162
##      8        0.9705             nan     0.1000    0.0127
##      9        0.9416             nan     0.1000    0.0132
##     10        0.9152             nan     0.1000    0.0117
##     20        0.7561             nan     0.1000    0.0037
##     40        0.6167             nan     0.1000    0.0012
##     60        0.5524             nan     0.1000   -0.0001
##     80        0.5158             nan     0.1000   -0.0003
##    100        0.4918             nan     0.1000   -0.0001
##    120        0.4702             nan     0.1000   -0.0013
##    140        0.4583             nan     0.1000   -0.0010
##    150        0.4483             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2917             nan     0.1000    0.0480
##      2        1.2147             nan     0.1000    0.0351
##      3        1.1496             nan     0.1000    0.0317
##      4        1.0909             nan     0.1000    0.0251
##      5        1.0453             nan     0.1000    0.0222
##      6        1.0021             nan     0.1000    0.0175
##      7        0.9561             nan     0.1000    0.0214
##      8        0.9264             nan     0.1000    0.0131
##      9        0.8999             nan     0.1000    0.0115
##     10        0.8716             nan     0.1000    0.0141
##     20        0.6730             nan     0.1000    0.0027
##     40        0.5493             nan     0.1000    0.0004
##     60        0.5009             nan     0.1000   -0.0009
##     80        0.4665             nan     0.1000   -0.0016
##    100        0.4364             nan     0.1000   -0.0004
##    120        0.4138             nan     0.1000   -0.0009
##    140        0.3911             nan     0.1000   -0.0002
##    150        0.3835             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3161             nan     0.1000    0.0318
##      2        1.2664             nan     0.1000    0.0234
##      3        1.2176             nan     0.1000    0.0223
##      4        1.1824             nan     0.1000    0.0144
##      5        1.1500             nan     0.1000    0.0134
##      6        1.1211             nan     0.1000    0.0144
##      7        1.0989             nan     0.1000    0.0119
##      8        1.0773             nan     0.1000    0.0101
##      9        1.0579             nan     0.1000    0.0093
##     10        1.0429             nan     0.1000    0.0074
##     20        0.9140             nan     0.1000    0.0032
##     40        0.7801             nan     0.1000    0.0008
##     60        0.7166             nan     0.1000    0.0002
##     80        0.6807             nan     0.1000    0.0002
##    100        0.6542             nan     0.1000   -0.0005
##    120        0.6388             nan     0.1000    0.0001
##    140        0.6262             nan     0.1000   -0.0009
##    150        0.6199             nan     0.1000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3094             nan     0.1000    0.0405
##      2        1.2369             nan     0.1000    0.0320
##      3        1.1849             nan     0.1000    0.0256
##      4        1.1349             nan     0.1000    0.0204
##      5        1.0932             nan     0.1000    0.0186
##      6        1.0527             nan     0.1000    0.0164
##      7        1.0169             nan     0.1000    0.0153
##      8        0.9875             nan     0.1000    0.0134
##      9        0.9616             nan     0.1000    0.0108
##     10        0.9349             nan     0.1000    0.0117
##     20        0.7912             nan     0.1000    0.0043
##     40        0.6765             nan     0.1000   -0.0014
##     60        0.6269             nan     0.1000   -0.0000
##     80        0.5961             nan     0.1000    0.0003
##    100        0.5722             nan     0.1000   -0.0001
##    120        0.5495             nan     0.1000   -0.0015
##    140        0.5306             nan     0.1000   -0.0005
##    150        0.5228             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2958             nan     0.1000    0.0409
##      2        1.2227             nan     0.1000    0.0316
##      3        1.1614             nan     0.1000    0.0276
##      4        1.1114             nan     0.1000    0.0220
##      5        1.0567             nan     0.1000    0.0255
##      6        1.0142             nan     0.1000    0.0197
##      7        0.9821             nan     0.1000    0.0137
##      8        0.9473             nan     0.1000    0.0171
##      9        0.9236             nan     0.1000    0.0101
##     10        0.9007             nan     0.1000    0.0084
##     20        0.7339             nan     0.1000    0.0041
##     40        0.6221             nan     0.1000   -0.0002
##     60        0.5740             nan     0.1000   -0.0007
##     80        0.5369             nan     0.1000   -0.0006
##    100        0.5004             nan     0.1000   -0.0008
##    120        0.4785             nan     0.1000   -0.0008
##    140        0.4532             nan     0.1000   -0.0009
##    150        0.4460             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3185             nan     0.1000    0.0367
##      2        1.2568             nan     0.1000    0.0303
##      3        1.2046             nan     0.1000    0.0239
##      4        1.1665             nan     0.1000    0.0208
##      5        1.1283             nan     0.1000    0.0168
##      6        1.0986             nan     0.1000    0.0134
##      7        1.0720             nan     0.1000    0.0109
##      8        1.0486             nan     0.1000    0.0106
##      9        1.0300             nan     0.1000    0.0074
##     10        1.0131             nan     0.1000    0.0072
##     20        0.8936             nan     0.1000    0.0035
##     40        0.7716             nan     0.1000    0.0008
##     60        0.7081             nan     0.1000    0.0000
##     80        0.6666             nan     0.1000   -0.0002
##    100        0.6415             nan     0.1000   -0.0010
##    120        0.6228             nan     0.1000   -0.0004
##    140        0.6106             nan     0.1000    0.0005
##    150        0.6047             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2978             nan     0.1000    0.0427
##      2        1.2283             nan     0.1000    0.0351
##      3        1.1699             nan     0.1000    0.0290
##      4        1.1204             nan     0.1000    0.0234
##      5        1.0736             nan     0.1000    0.0199
##      6        1.0394             nan     0.1000    0.0169
##      7        1.0087             nan     0.1000    0.0156
##      8        0.9813             nan     0.1000    0.0128
##      9        0.9545             nan     0.1000    0.0128
##     10        0.9330             nan     0.1000    0.0093
##     20        0.7844             nan     0.1000    0.0024
##     40        0.6528             nan     0.1000    0.0006
##     60        0.5985             nan     0.1000   -0.0002
##     80        0.5704             nan     0.1000   -0.0005
##    100        0.5515             nan     0.1000   -0.0011
##    120        0.5280             nan     0.1000   -0.0009
##    140        0.5093             nan     0.1000    0.0003
##    150        0.4989             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3001             nan     0.1000    0.0413
##      2        1.2242             nan     0.1000    0.0395
##      3        1.1582             nan     0.1000    0.0286
##      4        1.1003             nan     0.1000    0.0285
##      5        1.0489             nan     0.1000    0.0227
##      6        1.0078             nan     0.1000    0.0178
##      7        0.9666             nan     0.1000    0.0185
##      8        0.9342             nan     0.1000    0.0148
##      9        0.9072             nan     0.1000    0.0127
##     10        0.8800             nan     0.1000    0.0118
##     20        0.7271             nan     0.1000    0.0045
##     40        0.6090             nan     0.1000    0.0006
##     60        0.5530             nan     0.1000   -0.0002
##     80        0.5155             nan     0.1000   -0.0002
##    100        0.4882             nan     0.1000   -0.0003
##    120        0.4618             nan     0.1000   -0.0011
##    140        0.4389             nan     0.1000   -0.0011
##    150        0.4293             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3191             nan     0.1000    0.0312
##      2        1.2600             nan     0.1000    0.0286
##      3        1.2157             nan     0.1000    0.0209
##      4        1.1747             nan     0.1000    0.0196
##      5        1.1427             nan     0.1000    0.0160
##      6        1.1136             nan     0.1000    0.0131
##      7        1.0915             nan     0.1000    0.0100
##      8        1.0691             nan     0.1000    0.0068
##      9        1.0487             nan     0.1000    0.0100
##     10        1.0306             nan     0.1000    0.0085
##     20        0.8935             nan     0.1000    0.0038
##     40        0.7560             nan     0.1000    0.0020
##     60        0.6812             nan     0.1000    0.0001
##     80        0.6316             nan     0.1000    0.0005
##    100        0.6019             nan     0.1000    0.0004
##    120        0.5821             nan     0.1000   -0.0001
##    140        0.5698             nan     0.1000    0.0003
##    150        0.5636             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2953             nan     0.1000    0.0425
##      2        1.2220             nan     0.1000    0.0340
##      3        1.1653             nan     0.1000    0.0254
##      4        1.1082             nan     0.1000    0.0266
##      5        1.0663             nan     0.1000    0.0204
##      6        1.0289             nan     0.1000    0.0196
##      7        0.9925             nan     0.1000    0.0151
##      8        0.9609             nan     0.1000    0.0128
##      9        0.9363             nan     0.1000    0.0098
##     10        0.9130             nan     0.1000    0.0109
##     20        0.7581             nan     0.1000    0.0050
##     40        0.6265             nan     0.1000    0.0012
##     60        0.5701             nan     0.1000   -0.0003
##     80        0.5327             nan     0.1000   -0.0001
##    100        0.5103             nan     0.1000   -0.0006
##    120        0.4859             nan     0.1000   -0.0009
##    140        0.4697             nan     0.1000   -0.0006
##    150        0.4609             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2888             nan     0.1000    0.0476
##      2        1.2054             nan     0.1000    0.0400
##      3        1.1419             nan     0.1000    0.0288
##      4        1.0853             nan     0.1000    0.0262
##      5        1.0381             nan     0.1000    0.0227
##      6        0.9929             nan     0.1000    0.0209
##      7        0.9569             nan     0.1000    0.0166
##      8        0.9189             nan     0.1000    0.0191
##      9        0.8911             nan     0.1000    0.0129
##     10        0.8580             nan     0.1000    0.0145
##     20        0.6892             nan     0.1000    0.0071
##     40        0.5668             nan     0.1000    0.0005
##     60        0.5157             nan     0.1000   -0.0004
##     80        0.4782             nan     0.1000   -0.0009
##    100        0.4464             nan     0.1000   -0.0014
##    120        0.4187             nan     0.1000   -0.0004
##    140        0.3985             nan     0.1000   -0.0005
##    150        0.3890             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3188             nan     0.1000    0.0313
##      2        1.2666             nan     0.1000    0.0255
##      3        1.2200             nan     0.1000    0.0210
##      4        1.1852             nan     0.1000    0.0173
##      5        1.1558             nan     0.1000    0.0146
##      6        1.1371             nan     0.1000    0.0065
##      7        1.1108             nan     0.1000    0.0116
##      8        1.0864             nan     0.1000    0.0099
##      9        1.0707             nan     0.1000    0.0059
##     10        1.0547             nan     0.1000    0.0070
##     20        0.9288             nan     0.1000    0.0030
##     40        0.8015             nan     0.1000    0.0022
##     60        0.7343             nan     0.1000    0.0013
##     80        0.6943             nan     0.1000   -0.0001
##    100        0.6667             nan     0.1000   -0.0004
##    120        0.6487             nan     0.1000   -0.0000
##    140        0.6353             nan     0.1000   -0.0005
##    150        0.6292             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3018             nan     0.1000    0.0414
##      2        1.2397             nan     0.1000    0.0315
##      3        1.1830             nan     0.1000    0.0279
##      4        1.1348             nan     0.1000    0.0233
##      5        1.0924             nan     0.1000    0.0190
##      6        1.0604             nan     0.1000    0.0148
##      7        1.0339             nan     0.1000    0.0126
##      8        1.0040             nan     0.1000    0.0126
##      9        0.9797             nan     0.1000    0.0099
##     10        0.9533             nan     0.1000    0.0108
##     20        0.8078             nan     0.1000    0.0028
##     40        0.6853             nan     0.1000    0.0013
##     60        0.6329             nan     0.1000   -0.0001
##     80        0.5996             nan     0.1000   -0.0008
##    100        0.5712             nan     0.1000    0.0004
##    120        0.5509             nan     0.1000   -0.0009
##    140        0.5364             nan     0.1000   -0.0006
##    150        0.5283             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3019             nan     0.1000    0.0424
##      2        1.2336             nan     0.1000    0.0341
##      3        1.1723             nan     0.1000    0.0292
##      4        1.1180             nan     0.1000    0.0246
##      5        1.0691             nan     0.1000    0.0235
##      6        1.0315             nan     0.1000    0.0175
##      7        0.9928             nan     0.1000    0.0179
##      8        0.9627             nan     0.1000    0.0129
##      9        0.9354             nan     0.1000    0.0111
##     10        0.9130             nan     0.1000    0.0103
##     20        0.7382             nan     0.1000    0.0045
##     40        0.6337             nan     0.1000   -0.0001
##     60        0.5869             nan     0.1000   -0.0009
##     80        0.5469             nan     0.1000   -0.0002
##    100        0.5200             nan     0.1000   -0.0008
##    120        0.4965             nan     0.1000   -0.0008
##    140        0.4752             nan     0.1000   -0.0004
##    150        0.4624             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3240             nan     0.1000    0.0289
##      2        1.2696             nan     0.1000    0.0239
##      3        1.2263             nan     0.1000    0.0209
##      4        1.1945             nan     0.1000    0.0163
##      5        1.1629             nan     0.1000    0.0142
##      6        1.1412             nan     0.1000    0.0101
##      7        1.1217             nan     0.1000    0.0085
##      8        1.0979             nan     0.1000    0.0104
##      9        1.0778             nan     0.1000    0.0081
##     10        1.0615             nan     0.1000    0.0067
##     20        0.9385             nan     0.1000    0.0047
##     40        0.8103             nan     0.1000    0.0010
##     60        0.7390             nan     0.1000    0.0007
##     80        0.6964             nan     0.1000   -0.0003
##    100        0.6692             nan     0.1000   -0.0002
##    120        0.6480             nan     0.1000    0.0001
##    140        0.6356             nan     0.1000    0.0003
##    150        0.6317             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3102             nan     0.1000    0.0400
##      2        1.2414             nan     0.1000    0.0322
##      3        1.1900             nan     0.1000    0.0267
##      4        1.1447             nan     0.1000    0.0205
##      5        1.1023             nan     0.1000    0.0193
##      6        1.0674             nan     0.1000    0.0168
##      7        1.0381             nan     0.1000    0.0122
##      8        1.0129             nan     0.1000    0.0129
##      9        0.9873             nan     0.1000    0.0125
##     10        0.9672             nan     0.1000    0.0088
##     20        0.8096             nan     0.1000    0.0035
##     40        0.6811             nan     0.1000    0.0006
##     60        0.6288             nan     0.1000   -0.0002
##     80        0.5982             nan     0.1000   -0.0006
##    100        0.5793             nan     0.1000   -0.0007
##    120        0.5589             nan     0.1000   -0.0009
##    140        0.5393             nan     0.1000   -0.0007
##    150        0.5301             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3031             nan     0.1000    0.0403
##      2        1.2262             nan     0.1000    0.0344
##      3        1.1637             nan     0.1000    0.0292
##      4        1.1159             nan     0.1000    0.0211
##      5        1.0724             nan     0.1000    0.0207
##      6        1.0354             nan     0.1000    0.0185
##      7        1.0040             nan     0.1000    0.0142
##      8        0.9752             nan     0.1000    0.0128
##      9        0.9509             nan     0.1000    0.0111
##     10        0.9162             nan     0.1000    0.0146
##     20        0.7515             nan     0.1000    0.0056
##     40        0.6301             nan     0.1000    0.0022
##     60        0.5846             nan     0.1000   -0.0016
##     80        0.5476             nan     0.1000   -0.0006
##    100        0.5158             nan     0.1000   -0.0008
##    120        0.4905             nan     0.1000   -0.0005
##    140        0.4688             nan     0.1000   -0.0008
##    150        0.4589             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3170             nan     0.1000    0.0291
##      2        1.2658             nan     0.1000    0.0251
##      3        1.2219             nan     0.1000    0.0231
##      4        1.1842             nan     0.1000    0.0192
##      5        1.1485             nan     0.1000    0.0155
##      6        1.1212             nan     0.1000    0.0146
##      7        1.0958             nan     0.1000    0.0117
##      8        1.0741             nan     0.1000    0.0098
##      9        1.0522             nan     0.1000    0.0104
##     10        1.0351             nan     0.1000    0.0068
##     20        0.9070             nan     0.1000    0.0040
##     40        0.7857             nan     0.1000    0.0017
##     60        0.7236             nan     0.1000    0.0008
##     80        0.6851             nan     0.1000   -0.0006
##    100        0.6577             nan     0.1000    0.0003
##    120        0.6446             nan     0.1000   -0.0003
##    140        0.6327             nan     0.1000   -0.0004
##    150        0.6276             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2996             nan     0.1000    0.0412
##      2        1.2294             nan     0.1000    0.0335
##      3        1.1703             nan     0.1000    0.0274
##      4        1.1238             nan     0.1000    0.0227
##      5        1.0838             nan     0.1000    0.0187
##      6        1.0475             nan     0.1000    0.0158
##      7        1.0169             nan     0.1000    0.0131
##      8        0.9863             nan     0.1000    0.0146
##      9        0.9623             nan     0.1000    0.0112
##     10        0.9417             nan     0.1000    0.0108
##     20        0.7997             nan     0.1000    0.0048
##     40        0.6800             nan     0.1000    0.0005
##     60        0.6364             nan     0.1000   -0.0009
##     80        0.6059             nan     0.1000    0.0002
##    100        0.5861             nan     0.1000   -0.0006
##    120        0.5680             nan     0.1000   -0.0013
##    140        0.5480             nan     0.1000   -0.0002
##    150        0.5401             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2902             nan     0.1000    0.0420
##      2        1.2183             nan     0.1000    0.0325
##      3        1.1583             nan     0.1000    0.0266
##      4        1.1018             nan     0.1000    0.0253
##      5        1.0481             nan     0.1000    0.0214
##      6        1.0081             nan     0.1000    0.0173
##      7        0.9717             nan     0.1000    0.0143
##      8        0.9428             nan     0.1000    0.0134
##      9        0.9146             nan     0.1000    0.0126
##     10        0.8876             nan     0.1000    0.0085
##     20        0.7364             nan     0.1000    0.0040
##     40        0.6382             nan     0.1000   -0.0006
##     60        0.5903             nan     0.1000   -0.0004
##     80        0.5526             nan     0.1000    0.0000
##    100        0.5285             nan     0.1000   -0.0028
##    120        0.5028             nan     0.1000   -0.0002
##    140        0.4817             nan     0.1000   -0.0006
##    150        0.4700             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3222             nan     0.1000    0.0263
##      2        1.2680             nan     0.1000    0.0251
##      3        1.2253             nan     0.1000    0.0203
##      4        1.1914             nan     0.1000    0.0155
##      5        1.1640             nan     0.1000    0.0144
##      6        1.1397             nan     0.1000    0.0116
##      7        1.1238             nan     0.1000    0.0062
##      8        1.1017             nan     0.1000    0.0091
##      9        1.0832             nan     0.1000    0.0077
##     10        1.0629             nan     0.1000    0.0083
##     20        0.9452             nan     0.1000    0.0034
##     40        0.8189             nan     0.1000    0.0017
##     60        0.7488             nan     0.1000    0.0010
##     80        0.7058             nan     0.1000   -0.0001
##    100        0.6768             nan     0.1000   -0.0004
##    120        0.6586             nan     0.1000   -0.0002
##    140        0.6457             nan     0.1000   -0.0009
##    150        0.6397             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2978             nan     0.1000    0.0366
##      2        1.2383             nan     0.1000    0.0301
##      3        1.1899             nan     0.1000    0.0227
##      4        1.1478             nan     0.1000    0.0160
##      5        1.1125             nan     0.1000    0.0168
##      6        1.0819             nan     0.1000    0.0138
##      7        1.0557             nan     0.1000    0.0110
##      8        1.0275             nan     0.1000    0.0121
##      9        1.0005             nan     0.1000    0.0109
##     10        0.9769             nan     0.1000    0.0099
##     20        0.8244             nan     0.1000    0.0026
##     40        0.6952             nan     0.1000   -0.0008
##     60        0.6508             nan     0.1000    0.0004
##     80        0.6239             nan     0.1000   -0.0015
##    100        0.5985             nan     0.1000   -0.0019
##    120        0.5792             nan     0.1000   -0.0013
##    140        0.5633             nan     0.1000   -0.0003
##    150        0.5543             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2946             nan     0.1000    0.0391
##      2        1.2305             nan     0.1000    0.0311
##      3        1.1719             nan     0.1000    0.0274
##      4        1.1145             nan     0.1000    0.0274
##      5        1.0700             nan     0.1000    0.0209
##      6        1.0355             nan     0.1000    0.0146
##      7        1.0055             nan     0.1000    0.0149
##      8        0.9782             nan     0.1000    0.0125
##      9        0.9498             nan     0.1000    0.0137
##     10        0.9229             nan     0.1000    0.0124
##     20        0.7602             nan     0.1000    0.0030
##     40        0.6494             nan     0.1000    0.0004
##     60        0.6009             nan     0.1000    0.0004
##     80        0.5660             nan     0.1000   -0.0002
##    100        0.5391             nan     0.1000   -0.0013
##    120        0.5160             nan     0.1000   -0.0010
##    140        0.4927             nan     0.1000   -0.0005
##    150        0.4813             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3139             nan     0.1000    0.0332
##      2        1.2622             nan     0.1000    0.0278
##      3        1.2137             nan     0.1000    0.0247
##      4        1.1724             nan     0.1000    0.0203
##      5        1.1345             nan     0.1000    0.0170
##      6        1.1075             nan     0.1000    0.0143
##      7        1.0815             nan     0.1000    0.0114
##      8        1.0575             nan     0.1000    0.0123
##      9        1.0338             nan     0.1000    0.0098
##     10        1.0154             nan     0.1000    0.0076
##     20        0.8823             nan     0.1000    0.0039
##     40        0.7486             nan     0.1000    0.0013
##     60        0.6858             nan     0.1000   -0.0000
##     80        0.6486             nan     0.1000   -0.0003
##    100        0.6283             nan     0.1000   -0.0004
##    120        0.6148             nan     0.1000   -0.0005
##    140        0.6043             nan     0.1000   -0.0002
##    150        0.6000             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2949             nan     0.1000    0.0415
##      2        1.2279             nan     0.1000    0.0342
##      3        1.1721             nan     0.1000    0.0278
##      4        1.1207             nan     0.1000    0.0246
##      5        1.0779             nan     0.1000    0.0211
##      6        1.0410             nan     0.1000    0.0178
##      7        1.0093             nan     0.1000    0.0154
##      8        0.9822             nan     0.1000    0.0125
##      9        0.9547             nan     0.1000    0.0139
##     10        0.9267             nan     0.1000    0.0120
##     20        0.7719             nan     0.1000    0.0019
##     40        0.6528             nan     0.1000    0.0003
##     60        0.6098             nan     0.1000   -0.0001
##     80        0.5862             nan     0.1000   -0.0001
##    100        0.5667             nan     0.1000   -0.0015
##    120        0.5496             nan     0.1000   -0.0015
##    140        0.5330             nan     0.1000   -0.0002
##    150        0.5248             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2890             nan     0.1000    0.0460
##      2        1.2133             nan     0.1000    0.0386
##      3        1.1428             nan     0.1000    0.0348
##      4        1.0908             nan     0.1000    0.0254
##      5        1.0450             nan     0.1000    0.0203
##      6        1.0040             nan     0.1000    0.0184
##      7        0.9643             nan     0.1000    0.0202
##      8        0.9342             nan     0.1000    0.0154
##      9        0.9061             nan     0.1000    0.0126
##     10        0.8795             nan     0.1000    0.0105
##     20        0.7190             nan     0.1000    0.0021
##     40        0.6125             nan     0.1000   -0.0003
##     60        0.5728             nan     0.1000   -0.0008
##     80        0.5428             nan     0.1000   -0.0015
##    100        0.5137             nan     0.1000   -0.0015
##    120        0.4914             nan     0.1000   -0.0017
##    140        0.4705             nan     0.1000   -0.0013
##    150        0.4619             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3251             nan     0.1000    0.0317
##      2        1.2714             nan     0.1000    0.0260
##      3        1.2244             nan     0.1000    0.0225
##      4        1.1864             nan     0.1000    0.0180
##      5        1.1566             nan     0.1000    0.0146
##      6        1.1276             nan     0.1000    0.0138
##      7        1.1021             nan     0.1000    0.0109
##      8        1.0797             nan     0.1000    0.0084
##      9        1.0593             nan     0.1000    0.0076
##     10        1.0424             nan     0.1000    0.0067
##     20        0.9243             nan     0.1000    0.0042
##     40        0.7999             nan     0.1000    0.0017
##     60        0.7330             nan     0.1000    0.0009
##     80        0.6893             nan     0.1000   -0.0002
##    100        0.6650             nan     0.1000    0.0003
##    120        0.6479             nan     0.1000    0.0001
##    140        0.6350             nan     0.1000    0.0002
##    150        0.6295             nan     0.1000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3116             nan     0.1000    0.0352
##      2        1.2471             nan     0.1000    0.0312
##      3        1.1882             nan     0.1000    0.0250
##      4        1.1419             nan     0.1000    0.0197
##      5        1.1034             nan     0.1000    0.0174
##      6        1.0677             nan     0.1000    0.0171
##      7        1.0381             nan     0.1000    0.0123
##      8        1.0124             nan     0.1000    0.0121
##      9        0.9876             nan     0.1000    0.0105
##     10        0.9648             nan     0.1000    0.0106
##     20        0.8082             nan     0.1000    0.0050
##     40        0.6820             nan     0.1000    0.0010
##     60        0.6288             nan     0.1000    0.0003
##     80        0.5957             nan     0.1000   -0.0002
##    100        0.5664             nan     0.1000   -0.0002
##    120        0.5437             nan     0.1000   -0.0010
##    140        0.5263             nan     0.1000   -0.0001
##    150        0.5168             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2978             nan     0.1000    0.0418
##      2        1.2269             nan     0.1000    0.0330
##      3        1.1651             nan     0.1000    0.0277
##      4        1.1191             nan     0.1000    0.0210
##      5        1.0700             nan     0.1000    0.0208
##      6        1.0283             nan     0.1000    0.0201
##      7        0.9885             nan     0.1000    0.0168
##      8        0.9567             nan     0.1000    0.0117
##      9        0.9249             nan     0.1000    0.0134
##     10        0.8999             nan     0.1000    0.0103
##     20        0.7405             nan     0.1000    0.0041
##     40        0.6317             nan     0.1000   -0.0009
##     60        0.5812             nan     0.1000    0.0002
##     80        0.5397             nan     0.1000    0.0004
##    100        0.5047             nan     0.1000   -0.0008
##    120        0.4759             nan     0.1000   -0.0004
##    140        0.4537             nan     0.1000   -0.0000
##    150        0.4454             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3264             nan     0.1000    0.0304
##      2        1.2747             nan     0.1000    0.0255
##      3        1.2332             nan     0.1000    0.0195
##      4        1.1925             nan     0.1000    0.0170
##      5        1.1598             nan     0.1000    0.0154
##      6        1.1355             nan     0.1000    0.0129
##      7        1.1090             nan     0.1000    0.0101
##      8        1.0891             nan     0.1000    0.0083
##      9        1.0697             nan     0.1000    0.0091
##     10        1.0539             nan     0.1000    0.0064
##     20        0.9321             nan     0.1000    0.0045
##     40        0.7960             nan     0.1000    0.0016
##     60        0.7240             nan     0.1000    0.0004
##     80        0.6790             nan     0.1000    0.0005
##    100        0.6490             nan     0.1000   -0.0006
##    120        0.6314             nan     0.1000   -0.0004
##    140        0.6180             nan     0.1000    0.0002
##    150        0.6129             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3116             nan     0.1000    0.0332
##      2        1.2470             nan     0.1000    0.0312
##      3        1.1968             nan     0.1000    0.0238
##      4        1.1523             nan     0.1000    0.0219
##      5        1.1133             nan     0.1000    0.0165
##      6        1.0699             nan     0.1000    0.0178
##      7        1.0407             nan     0.1000    0.0130
##      8        1.0144             nan     0.1000    0.0122
##      9        0.9906             nan     0.1000    0.0100
##     10        0.9627             nan     0.1000    0.0120
##     20        0.8002             nan     0.1000    0.0025
##     40        0.6732             nan     0.1000    0.0006
##     60        0.6203             nan     0.1000    0.0001
##     80        0.5931             nan     0.1000   -0.0017
##    100        0.5706             nan     0.1000   -0.0003
##    120        0.5495             nan     0.1000   -0.0009
##    140        0.5317             nan     0.1000   -0.0012
##    150        0.5225             nan     0.1000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3006             nan     0.1000    0.0350
##      2        1.2308             nan     0.1000    0.0341
##      3        1.1645             nan     0.1000    0.0316
##      4        1.1170             nan     0.1000    0.0235
##      5        1.0699             nan     0.1000    0.0219
##      6        1.0237             nan     0.1000    0.0194
##      7        0.9867             nan     0.1000    0.0165
##      8        0.9524             nan     0.1000    0.0145
##      9        0.9288             nan     0.1000    0.0099
##     10        0.8993             nan     0.1000    0.0132
##     20        0.7317             nan     0.1000    0.0026
##     40        0.6147             nan     0.1000    0.0013
##     60        0.5643             nan     0.1000   -0.0007
##     80        0.5312             nan     0.1000   -0.0014
##    100        0.5066             nan     0.1000   -0.0008
##    120        0.4811             nan     0.1000   -0.0008
##    140        0.4616             nan     0.1000   -0.0012
##    150        0.4516             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3211             nan     0.1000    0.0294
##      2        1.2714             nan     0.1000    0.0242
##      3        1.2285             nan     0.1000    0.0205
##      4        1.1851             nan     0.1000    0.0200
##      5        1.1568             nan     0.1000    0.0139
##      6        1.1262             nan     0.1000    0.0150
##      7        1.1031             nan     0.1000    0.0114
##      8        1.0799             nan     0.1000    0.0086
##      9        1.0611             nan     0.1000    0.0087
##     10        1.0404             nan     0.1000    0.0099
##     20        0.9060             nan     0.1000    0.0030
##     40        0.7588             nan     0.1000    0.0008
##     60        0.6824             nan     0.1000    0.0005
##     80        0.6411             nan     0.1000    0.0004
##    100        0.6123             nan     0.1000   -0.0003
##    120        0.5947             nan     0.1000   -0.0008
##    140        0.5813             nan     0.1000   -0.0004
##    150        0.5754             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3039             nan     0.1000    0.0416
##      2        1.2391             nan     0.1000    0.0313
##      3        1.1867             nan     0.1000    0.0251
##      4        1.1360             nan     0.1000    0.0232
##      5        1.0858             nan     0.1000    0.0226
##      6        1.0436             nan     0.1000    0.0194
##      7        1.0133             nan     0.1000    0.0131
##      8        0.9836             nan     0.1000    0.0126
##      9        0.9563             nan     0.1000    0.0120
##     10        0.9328             nan     0.1000    0.0079
##     20        0.7571             nan     0.1000    0.0039
##     40        0.6290             nan     0.1000    0.0012
##     60        0.5748             nan     0.1000    0.0004
##     80        0.5468             nan     0.1000   -0.0010
##    100        0.5269             nan     0.1000   -0.0017
##    120        0.5083             nan     0.1000   -0.0007
##    140        0.4908             nan     0.1000   -0.0010
##    150        0.4818             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2938             nan     0.1000    0.0441
##      2        1.2186             nan     0.1000    0.0363
##      3        1.1491             nan     0.1000    0.0336
##      4        1.0884             nan     0.1000    0.0255
##      5        1.0439             nan     0.1000    0.0225
##      6        1.0040             nan     0.1000    0.0175
##      7        0.9648             nan     0.1000    0.0166
##      8        0.9332             nan     0.1000    0.0144
##      9        0.9000             nan     0.1000    0.0153
##     10        0.8733             nan     0.1000    0.0106
##     20        0.6952             nan     0.1000    0.0015
##     40        0.5781             nan     0.1000    0.0015
##     60        0.5309             nan     0.1000   -0.0009
##     80        0.4968             nan     0.1000   -0.0000
##    100        0.4708             nan     0.1000   -0.0008
##    120        0.4511             nan     0.1000   -0.0020
##    140        0.4323             nan     0.1000   -0.0017
##    150        0.4229             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3084             nan     0.1000    0.0352
##      2        1.2550             nan     0.1000    0.0222
##      3        1.2035             nan     0.1000    0.0249
##      4        1.1628             nan     0.1000    0.0206
##      5        1.1269             nan     0.1000    0.0172
##      6        1.0965             nan     0.1000    0.0138
##      7        1.0702             nan     0.1000    0.0117
##      8        1.0485             nan     0.1000    0.0104
##      9        1.0291             nan     0.1000    0.0084
##     10        1.0128             nan     0.1000    0.0077
##     20        0.9011             nan     0.1000    0.0029
##     40        0.7758             nan     0.1000    0.0018
##     60        0.7128             nan     0.1000    0.0008
##     80        0.6810             nan     0.1000   -0.0001
##    100        0.6590             nan     0.1000    0.0003
##    120        0.6435             nan     0.1000   -0.0007
##    140        0.6312             nan     0.1000   -0.0003
##    150        0.6271             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3027             nan     0.1000    0.0390
##      2        1.2310             nan     0.1000    0.0329
##      3        1.1728             nan     0.1000    0.0260
##      4        1.1279             nan     0.1000    0.0232
##      5        1.0862             nan     0.1000    0.0195
##      6        1.0533             nan     0.1000    0.0138
##      7        1.0260             nan     0.1000    0.0137
##      8        1.0001             nan     0.1000    0.0116
##      9        0.9720             nan     0.1000    0.0091
##     10        0.9422             nan     0.1000    0.0122
##     20        0.7924             nan     0.1000    0.0053
##     40        0.6731             nan     0.1000    0.0009
##     60        0.6205             nan     0.1000   -0.0004
##     80        0.5958             nan     0.1000    0.0006
##    100        0.5758             nan     0.1000   -0.0015
##    120        0.5555             nan     0.1000   -0.0004
##    140        0.5375             nan     0.1000   -0.0001
##    150        0.5290             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2970             nan     0.1000    0.0386
##      2        1.2207             nan     0.1000    0.0362
##      3        1.1555             nan     0.1000    0.0315
##      4        1.0963             nan     0.1000    0.0273
##      5        1.0445             nan     0.1000    0.0215
##      6        1.0038             nan     0.1000    0.0184
##      7        0.9727             nan     0.1000    0.0141
##      8        0.9368             nan     0.1000    0.0145
##      9        0.9063             nan     0.1000    0.0142
##     10        0.8819             nan     0.1000    0.0106
##     20        0.7197             nan     0.1000    0.0019
##     40        0.6131             nan     0.1000   -0.0001
##     60        0.5671             nan     0.1000   -0.0002
##     80        0.5318             nan     0.1000   -0.0006
##    100        0.5040             nan     0.1000   -0.0013
##    120        0.4798             nan     0.1000   -0.0009
##    140        0.4583             nan     0.1000   -0.0012
##    150        0.4457             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3176             nan     0.1000    0.0318
##      2        1.2598             nan     0.1000    0.0243
##      3        1.2136             nan     0.1000    0.0218
##      4        1.1721             nan     0.1000    0.0192
##      5        1.1403             nan     0.1000    0.0159
##      6        1.1129             nan     0.1000    0.0136
##      7        1.0940             nan     0.1000    0.0091
##      8        1.0732             nan     0.1000    0.0094
##      9        1.0548             nan     0.1000    0.0092
##     10        1.0375             nan     0.1000    0.0075
##     20        0.9092             nan     0.1000    0.0042
##     40        0.7861             nan     0.1000    0.0008
##     60        0.7219             nan     0.1000    0.0002
##     80        0.6781             nan     0.1000   -0.0006
##    100        0.6464             nan     0.1000   -0.0004
##    120        0.6276             nan     0.1000   -0.0002
##    140        0.6159             nan     0.1000   -0.0006
##    150        0.6106             nan     0.1000    0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3042             nan     0.1000    0.0397
##      2        1.2385             nan     0.1000    0.0340
##      3        1.1823             nan     0.1000    0.0256
##      4        1.1322             nan     0.1000    0.0212
##      5        1.0874             nan     0.1000    0.0204
##      6        1.0494             nan     0.1000    0.0176
##      7        1.0197             nan     0.1000    0.0120
##      8        0.9947             nan     0.1000    0.0107
##      9        0.9677             nan     0.1000    0.0111
##     10        0.9450             nan     0.1000    0.0109
##     20        0.7950             nan     0.1000    0.0043
##     40        0.6615             nan     0.1000    0.0015
##     60        0.6098             nan     0.1000   -0.0007
##     80        0.5768             nan     0.1000   -0.0005
##    100        0.5512             nan     0.1000   -0.0012
##    120        0.5302             nan     0.1000   -0.0007
##    140        0.5156             nan     0.1000    0.0004
##    150        0.5077             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2932             nan     0.1000    0.0438
##      2        1.2217             nan     0.1000    0.0352
##      3        1.1593             nan     0.1000    0.0310
##      4        1.1093             nan     0.1000    0.0250
##      5        1.0630             nan     0.1000    0.0206
##      6        1.0225             nan     0.1000    0.0176
##      7        0.9867             nan     0.1000    0.0166
##      8        0.9536             nan     0.1000    0.0146
##      9        0.9236             nan     0.1000    0.0128
##     10        0.8978             nan     0.1000    0.0117
##     20        0.7239             nan     0.1000    0.0048
##     40        0.6093             nan     0.1000   -0.0015
##     60        0.5584             nan     0.1000   -0.0004
##     80        0.5188             nan     0.1000   -0.0010
##    100        0.4913             nan     0.1000   -0.0004
##    120        0.4665             nan     0.1000   -0.0006
##    140        0.4357             nan     0.1000   -0.0003
##    150        0.4238             nan     0.1000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3223             nan     0.1000    0.0288
##      2        1.2727             nan     0.1000    0.0232
##      3        1.2323             nan     0.1000    0.0198
##      4        1.1976             nan     0.1000    0.0160
##      5        1.1689             nan     0.1000    0.0129
##      6        1.1405             nan     0.1000    0.0126
##      7        1.1164             nan     0.1000    0.0103
##      8        1.0957             nan     0.1000    0.0081
##      9        1.0775             nan     0.1000    0.0092
##     10        1.0616             nan     0.1000    0.0069
##     20        0.9290             nan     0.1000    0.0042
##     40        0.7840             nan     0.1000    0.0016
##     60        0.6997             nan     0.1000    0.0008
##     80        0.6545             nan     0.1000   -0.0000
##    100        0.6280             nan     0.1000    0.0004
##    120        0.6086             nan     0.1000   -0.0005
##    140        0.5965             nan     0.1000   -0.0003
##    150        0.5902             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3133             nan     0.1000    0.0336
##      2        1.2514             nan     0.1000    0.0292
##      3        1.1895             nan     0.1000    0.0284
##      4        1.1450             nan     0.1000    0.0213
##      5        1.0971             nan     0.1000    0.0206
##      6        1.0649             nan     0.1000    0.0152
##      7        1.0313             nan     0.1000    0.0159
##      8        1.0026             nan     0.1000    0.0133
##      9        0.9766             nan     0.1000    0.0100
##     10        0.9528             nan     0.1000    0.0108
##     20        0.7767             nan     0.1000    0.0046
##     40        0.6437             nan     0.1000    0.0014
##     60        0.5926             nan     0.1000   -0.0000
##     80        0.5649             nan     0.1000   -0.0013
##    100        0.5385             nan     0.1000    0.0000
##    120        0.5221             nan     0.1000    0.0000
##    140        0.5027             nan     0.1000   -0.0004
##    150        0.4934             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3005             nan     0.1000    0.0370
##      2        1.2320             nan     0.1000    0.0305
##      3        1.1705             nan     0.1000    0.0271
##      4        1.1086             nan     0.1000    0.0306
##      5        1.0618             nan     0.1000    0.0219
##      6        1.0189             nan     0.1000    0.0191
##      7        0.9766             nan     0.1000    0.0188
##      8        0.9329             nan     0.1000    0.0196
##      9        0.8956             nan     0.1000    0.0156
##     10        0.8648             nan     0.1000    0.0139
##     20        0.7012             nan     0.1000    0.0039
##     40        0.5853             nan     0.1000    0.0000
##     60        0.5414             nan     0.1000    0.0005
##     80        0.5040             nan     0.1000   -0.0005
##    100        0.4717             nan     0.1000   -0.0010
##    120        0.4471             nan     0.1000   -0.0007
##    140        0.4250             nan     0.1000   -0.0001
##    150        0.4136             nan     0.1000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3181             nan     0.1000    0.0326
##      2        1.2640             nan     0.1000    0.0253
##      3        1.2204             nan     0.1000    0.0217
##      4        1.1844             nan     0.1000    0.0171
##      5        1.1664             nan     0.1000    0.0077
##      6        1.1327             nan     0.1000    0.0138
##      7        1.1085             nan     0.1000    0.0103
##      8        1.0865             nan     0.1000    0.0117
##      9        1.0656             nan     0.1000    0.0099
##     10        1.0492             nan     0.1000    0.0074
##     20        0.9328             nan     0.1000    0.0036
##     40        0.8075             nan     0.1000    0.0013
##     60        0.7426             nan     0.1000    0.0005
##     80        0.7075             nan     0.1000    0.0000
##    100        0.6882             nan     0.1000   -0.0005
##    120        0.6736             nan     0.1000   -0.0007
##    140        0.6605             nan     0.1000   -0.0001
##    150        0.6541             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3004             nan     0.1000    0.0430
##      2        1.2292             nan     0.1000    0.0346
##      3        1.1715             nan     0.1000    0.0271
##      4        1.1282             nan     0.1000    0.0191
##      5        1.0861             nan     0.1000    0.0220
##      6        1.0467             nan     0.1000    0.0175
##      7        1.0157             nan     0.1000    0.0149
##      8        0.9910             nan     0.1000    0.0108
##      9        0.9722             nan     0.1000    0.0078
##     10        0.9493             nan     0.1000    0.0091
##     20        0.8168             nan     0.1000    0.0042
##     40        0.7112             nan     0.1000   -0.0017
##     60        0.6597             nan     0.1000   -0.0005
##     80        0.6302             nan     0.1000   -0.0025
##    100        0.5962             nan     0.1000   -0.0006
##    120        0.5712             nan     0.1000   -0.0006
##    140        0.5514             nan     0.1000   -0.0006
##    150        0.5418             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2998             nan     0.1000    0.0416
##      2        1.2254             nan     0.1000    0.0354
##      3        1.1591             nan     0.1000    0.0304
##      4        1.1062             nan     0.1000    0.0258
##      5        1.0612             nan     0.1000    0.0208
##      6        1.0256             nan     0.1000    0.0175
##      7        0.9873             nan     0.1000    0.0162
##      8        0.9524             nan     0.1000    0.0151
##      9        0.9282             nan     0.1000    0.0117
##     10        0.9098             nan     0.1000    0.0062
##     20        0.7586             nan     0.1000    0.0028
##     40        0.6501             nan     0.1000   -0.0001
##     60        0.5922             nan     0.1000   -0.0009
##     80        0.5534             nan     0.1000   -0.0001
##    100        0.5209             nan     0.1000   -0.0008
##    120        0.4939             nan     0.1000   -0.0001
##    140        0.4702             nan     0.1000   -0.0010
##    150        0.4607             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3271             nan     0.1000    0.0274
##      2        1.2811             nan     0.1000    0.0233
##      3        1.2386             nan     0.1000    0.0190
##      4        1.2057             nan     0.1000    0.0146
##      5        1.1791             nan     0.1000    0.0126
##      6        1.1544             nan     0.1000    0.0121
##      7        1.1382             nan     0.1000    0.0066
##      8        1.1192             nan     0.1000    0.0088
##      9        1.0988             nan     0.1000    0.0091
##     10        1.0829             nan     0.1000    0.0069
##     20        0.9521             nan     0.1000    0.0038
##     40        0.8169             nan     0.1000    0.0028
##     60        0.7413             nan     0.1000    0.0010
##     80        0.6980             nan     0.1000    0.0009
##    100        0.6719             nan     0.1000   -0.0008
##    120        0.6540             nan     0.1000    0.0000
##    140        0.6403             nan     0.1000   -0.0007
##    150        0.6349             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3078             nan     0.1000    0.0362
##      2        1.2544             nan     0.1000    0.0250
##      3        1.1990             nan     0.1000    0.0258
##      4        1.1484             nan     0.1000    0.0229
##      5        1.1092             nan     0.1000    0.0171
##      6        1.0754             nan     0.1000    0.0144
##      7        1.0464             nan     0.1000    0.0141
##      8        1.0198             nan     0.1000    0.0131
##      9        0.9915             nan     0.1000    0.0134
##     10        0.9707             nan     0.1000    0.0097
##     20        0.8185             nan     0.1000    0.0045
##     40        0.6891             nan     0.1000    0.0005
##     60        0.6370             nan     0.1000   -0.0003
##     80        0.6002             nan     0.1000    0.0003
##    100        0.5704             nan     0.1000   -0.0006
##    120        0.5507             nan     0.1000   -0.0016
##    140        0.5319             nan     0.1000   -0.0012
##    150        0.5220             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3040             nan     0.1000    0.0352
##      2        1.2362             nan     0.1000    0.0320
##      3        1.1796             nan     0.1000    0.0277
##      4        1.1281             nan     0.1000    0.0244
##      5        1.0877             nan     0.1000    0.0160
##      6        1.0445             nan     0.1000    0.0198
##      7        1.0147             nan     0.1000    0.0115
##      8        0.9843             nan     0.1000    0.0122
##      9        0.9559             nan     0.1000    0.0123
##     10        0.9338             nan     0.1000    0.0082
##     20        0.7602             nan     0.1000    0.0008
##     40        0.6410             nan     0.1000    0.0003
##     60        0.5914             nan     0.1000    0.0001
##     80        0.5495             nan     0.1000   -0.0013
##    100        0.5185             nan     0.1000   -0.0008
##    120        0.4918             nan     0.1000   -0.0012
##    140        0.4682             nan     0.1000   -0.0015
##    150        0.4594             nan     0.1000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3236             nan     0.1000    0.0301
##      2        1.2744             nan     0.1000    0.0244
##      3        1.2337             nan     0.1000    0.0179
##      4        1.1959             nan     0.1000    0.0180
##      5        1.1639             nan     0.1000    0.0146
##      6        1.1398             nan     0.1000    0.0119
##      7        1.1180             nan     0.1000    0.0095
##      8        1.0981             nan     0.1000    0.0067
##      9        1.0803             nan     0.1000    0.0070
##     10        1.0663             nan     0.1000    0.0049
##     20        0.9484             nan     0.1000    0.0041
##     40        0.8242             nan     0.1000    0.0011
##     60        0.7558             nan     0.1000   -0.0003
##     80        0.7128             nan     0.1000   -0.0003
##    100        0.6859             nan     0.1000    0.0004
##    120        0.6701             nan     0.1000   -0.0009
##    140        0.6587             nan     0.1000   -0.0005
##    150        0.6536             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3089             nan     0.1000    0.0370
##      2        1.2499             nan     0.1000    0.0256
##      3        1.1983             nan     0.1000    0.0247
##      4        1.1523             nan     0.1000    0.0203
##      5        1.1145             nan     0.1000    0.0180
##      6        1.0755             nan     0.1000    0.0167
##      7        1.0419             nan     0.1000    0.0119
##      8        1.0116             nan     0.1000    0.0138
##      9        0.9858             nan     0.1000    0.0114
##     10        0.9656             nan     0.1000    0.0086
##     20        0.8250             nan     0.1000    0.0042
##     40        0.7081             nan     0.1000    0.0011
##     60        0.6514             nan     0.1000    0.0003
##     80        0.6225             nan     0.1000   -0.0013
##    100        0.5996             nan     0.1000   -0.0005
##    120        0.5760             nan     0.1000   -0.0011
##    140        0.5546             nan     0.1000   -0.0006
##    150        0.5473             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3052             nan     0.1000    0.0377
##      2        1.2361             nan     0.1000    0.0312
##      3        1.1788             nan     0.1000    0.0260
##      4        1.1322             nan     0.1000    0.0232
##      5        1.0829             nan     0.1000    0.0230
##      6        1.0478             nan     0.1000    0.0148
##      7        1.0088             nan     0.1000    0.0179
##      8        0.9742             nan     0.1000    0.0155
##      9        0.9476             nan     0.1000    0.0113
##     10        0.9206             nan     0.1000    0.0116
##     20        0.7667             nan     0.1000    0.0035
##     40        0.6583             nan     0.1000   -0.0015
##     60        0.6044             nan     0.1000   -0.0001
##     80        0.5624             nan     0.1000   -0.0014
##    100        0.5327             nan     0.1000   -0.0005
##    120        0.5066             nan     0.1000   -0.0006
##    140        0.4799             nan     0.1000   -0.0014
##    150        0.4653             nan     0.1000    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3189             nan     0.1000    0.0334
##      2        1.2657             nan     0.1000    0.0246
##      3        1.2187             nan     0.1000    0.0201
##      4        1.1798             nan     0.1000    0.0176
##      5        1.1479             nan     0.1000    0.0141
##      6        1.1217             nan     0.1000    0.0128
##      7        1.0990             nan     0.1000    0.0113
##      8        1.0855             nan     0.1000    0.0056
##      9        1.0725             nan     0.1000    0.0060
##     10        1.0517             nan     0.1000    0.0085
##     20        0.9304             nan     0.1000    0.0036
##     40        0.7992             nan     0.1000    0.0018
##     60        0.7232             nan     0.1000    0.0012
##     80        0.6745             nan     0.1000    0.0003
##    100        0.6456             nan     0.1000    0.0000
##    120        0.6250             nan     0.1000   -0.0009
##    140        0.6120             nan     0.1000   -0.0005
##    150        0.6073             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3046             nan     0.1000    0.0398
##      2        1.2360             nan     0.1000    0.0330
##      3        1.1763             nan     0.1000    0.0263
##      4        1.1304             nan     0.1000    0.0223
##      5        1.0919             nan     0.1000    0.0167
##      6        1.0606             nan     0.1000    0.0154
##      7        1.0330             nan     0.1000    0.0112
##      8        1.0053             nan     0.1000    0.0115
##      9        0.9837             nan     0.1000    0.0084
##     10        0.9630             nan     0.1000    0.0086
##     20        0.8019             nan     0.1000    0.0027
##     40        0.6677             nan     0.1000    0.0014
##     60        0.6199             nan     0.1000    0.0001
##     80        0.5944             nan     0.1000   -0.0006
##    100        0.5739             nan     0.1000   -0.0009
##    120        0.5593             nan     0.1000   -0.0006
##    140        0.5428             nan     0.1000   -0.0010
##    150        0.5352             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2974             nan     0.1000    0.0418
##      2        1.2293             nan     0.1000    0.0346
##      3        1.1697             nan     0.1000    0.0284
##      4        1.1211             nan     0.1000    0.0239
##      5        1.0779             nan     0.1000    0.0227
##      6        1.0388             nan     0.1000    0.0182
##      7        0.9998             nan     0.1000    0.0180
##      8        0.9672             nan     0.1000    0.0150
##      9        0.9417             nan     0.1000    0.0106
##     10        0.9192             nan     0.1000    0.0105
##     20        0.7377             nan     0.1000    0.0036
##     40        0.6180             nan     0.1000   -0.0005
##     60        0.5785             nan     0.1000   -0.0011
##     80        0.5488             nan     0.1000   -0.0009
##    100        0.5260             nan     0.1000   -0.0016
##    120        0.5008             nan     0.1000   -0.0006
##    140        0.4790             nan     0.1000   -0.0010
##    150        0.4671             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3271             nan     0.1000    0.0296
##      2        1.2749             nan     0.1000    0.0244
##      3        1.2361             nan     0.1000    0.0178
##      4        1.2020             nan     0.1000    0.0164
##      5        1.1761             nan     0.1000    0.0126
##      6        1.1499             nan     0.1000    0.0102
##      7        1.1273             nan     0.1000    0.0086
##      8        1.1083             nan     0.1000    0.0076
##      9        1.0891             nan     0.1000    0.0072
##     10        1.0727             nan     0.1000    0.0062
##     20        0.9456             nan     0.1000    0.0038
##     40        0.8105             nan     0.1000    0.0016
##     60        0.7409             nan     0.1000    0.0015
##     80        0.7017             nan     0.1000   -0.0015
##    100        0.6756             nan     0.1000   -0.0006
##    120        0.6525             nan     0.1000    0.0006
##    140        0.6386             nan     0.1000   -0.0003
##    150        0.6342             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3066             nan     0.1000    0.0395
##      2        1.2436             nan     0.1000    0.0312
##      3        1.1983             nan     0.1000    0.0212
##      4        1.1500             nan     0.1000    0.0241
##      5        1.1086             nan     0.1000    0.0199
##      6        1.0690             nan     0.1000    0.0162
##      7        1.0380             nan     0.1000    0.0138
##      8        1.0108             nan     0.1000    0.0118
##      9        0.9867             nan     0.1000    0.0106
##     10        0.9681             nan     0.1000    0.0082
##     20        0.8157             nan     0.1000    0.0054
##     40        0.6921             nan     0.1000    0.0017
##     60        0.6354             nan     0.1000    0.0011
##     80        0.6046             nan     0.1000   -0.0011
##    100        0.5806             nan     0.1000   -0.0003
##    120        0.5565             nan     0.1000   -0.0002
##    140        0.5310             nan     0.1000   -0.0005
##    150        0.5228             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3044             nan     0.1000    0.0420
##      2        1.2378             nan     0.1000    0.0324
##      3        1.1812             nan     0.1000    0.0267
##      4        1.1293             nan     0.1000    0.0225
##      5        1.0809             nan     0.1000    0.0230
##      6        1.0419             nan     0.1000    0.0158
##      7        1.0034             nan     0.1000    0.0177
##      8        0.9752             nan     0.1000    0.0121
##      9        0.9468             nan     0.1000    0.0132
##     10        0.9251             nan     0.1000    0.0102
##     20        0.7451             nan     0.1000    0.0048
##     40        0.6328             nan     0.1000   -0.0004
##     60        0.5785             nan     0.1000   -0.0003
##     80        0.5383             nan     0.1000   -0.0010
##    100        0.5090             nan     0.1000   -0.0011
##    120        0.4809             nan     0.1000   -0.0010
##    140        0.4563             nan     0.1000   -0.0014
##    150        0.4468             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3193             nan     0.1000    0.0325
##      2        1.2653             nan     0.1000    0.0272
##      3        1.2212             nan     0.1000    0.0222
##      4        1.1851             nan     0.1000    0.0180
##      5        1.1524             nan     0.1000    0.0154
##      6        1.1231             nan     0.1000    0.0141
##      7        1.0999             nan     0.1000    0.0113
##      8        1.0801             nan     0.1000    0.0096
##      9        1.0647             nan     0.1000    0.0066
##     10        1.0479             nan     0.1000    0.0071
##     20        0.9156             nan     0.1000    0.0041
##     40        0.7693             nan     0.1000    0.0027
##     60        0.6910             nan     0.1000    0.0005
##     80        0.6435             nan     0.1000   -0.0005
##    100        0.6119             nan     0.1000   -0.0002
##    120        0.5897             nan     0.1000   -0.0004
##    140        0.5767             nan     0.1000   -0.0001
##    150        0.5715             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2943             nan     0.1000    0.0412
##      2        1.2353             nan     0.1000    0.0293
##      3        1.1738             nan     0.1000    0.0294
##      4        1.1306             nan     0.1000    0.0195
##      5        1.0929             nan     0.1000    0.0190
##      6        1.0489             nan     0.1000    0.0200
##      7        1.0158             nan     0.1000    0.0160
##      8        0.9872             nan     0.1000    0.0130
##      9        0.9621             nan     0.1000    0.0135
##     10        0.9337             nan     0.1000    0.0132
##     20        0.7680             nan     0.1000    0.0067
##     40        0.6228             nan     0.1000    0.0002
##     60        0.5680             nan     0.1000    0.0005
##     80        0.5379             nan     0.1000   -0.0012
##    100        0.5199             nan     0.1000   -0.0006
##    120        0.4972             nan     0.1000   -0.0013
##    140        0.4787             nan     0.1000   -0.0006
##    150        0.4693             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2913             nan     0.1000    0.0461
##      2        1.2141             nan     0.1000    0.0358
##      3        1.1515             nan     0.1000    0.0313
##      4        1.0971             nan     0.1000    0.0253
##      5        1.0438             nan     0.1000    0.0257
##      6        1.0006             nan     0.1000    0.0193
##      7        0.9644             nan     0.1000    0.0165
##      8        0.9250             nan     0.1000    0.0167
##      9        0.8975             nan     0.1000    0.0121
##     10        0.8697             nan     0.1000    0.0119
##     20        0.6851             nan     0.1000    0.0057
##     40        0.5667             nan     0.1000   -0.0002
##     60        0.5147             nan     0.1000   -0.0001
##     80        0.4839             nan     0.1000    0.0005
##    100        0.4564             nan     0.1000   -0.0027
##    120        0.4318             nan     0.1000   -0.0008
##    140        0.4126             nan     0.1000   -0.0011
##    150        0.4055             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3196             nan     0.1000    0.0326
##      2        1.2679             nan     0.1000    0.0262
##      3        1.2229             nan     0.1000    0.0226
##      4        1.1857             nan     0.1000    0.0188
##      5        1.1501             nan     0.1000    0.0157
##      6        1.1208             nan     0.1000    0.0123
##      7        1.0984             nan     0.1000    0.0102
##      8        1.0809             nan     0.1000    0.0063
##      9        1.0603             nan     0.1000    0.0086
##     10        1.0408             nan     0.1000    0.0087
##     20        0.9191             nan     0.1000    0.0047
##     40        0.7926             nan     0.1000    0.0014
##     60        0.7283             nan     0.1000    0.0007
##     80        0.6879             nan     0.1000   -0.0001
##    100        0.6628             nan     0.1000   -0.0008
##    120        0.6486             nan     0.1000   -0.0000
##    140        0.6359             nan     0.1000   -0.0010
##    150        0.6316             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3039             nan     0.1000    0.0385
##      2        1.2389             nan     0.1000    0.0331
##      3        1.1834             nan     0.1000    0.0244
##      4        1.1385             nan     0.1000    0.0218
##      5        1.0949             nan     0.1000    0.0198
##      6        1.0588             nan     0.1000    0.0154
##      7        1.0281             nan     0.1000    0.0130
##      8        1.0005             nan     0.1000    0.0105
##      9        0.9765             nan     0.1000    0.0116
##     10        0.9490             nan     0.1000    0.0127
##     20        0.8043             nan     0.1000    0.0023
##     40        0.6887             nan     0.1000    0.0001
##     60        0.6382             nan     0.1000   -0.0002
##     80        0.6050             nan     0.1000    0.0009
##    100        0.5788             nan     0.1000   -0.0003
##    120        0.5583             nan     0.1000   -0.0008
##    140        0.5415             nan     0.1000   -0.0013
##    150        0.5345             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.1000    0.0446
##      2        1.2158             nan     0.1000    0.0362
##      3        1.1576             nan     0.1000    0.0279
##      4        1.1122             nan     0.1000    0.0214
##      5        1.0709             nan     0.1000    0.0196
##      6        1.0286             nan     0.1000    0.0179
##      7        0.9881             nan     0.1000    0.0156
##      8        0.9536             nan     0.1000    0.0155
##      9        0.9264             nan     0.1000    0.0117
##     10        0.9023             nan     0.1000    0.0073
##     20        0.7412             nan     0.1000    0.0043
##     40        0.6384             nan     0.1000   -0.0004
##     60        0.5932             nan     0.1000   -0.0007
##     80        0.5537             nan     0.1000   -0.0002
##    100        0.5218             nan     0.1000   -0.0009
##    120        0.4936             nan     0.1000   -0.0013
##    140        0.4689             nan     0.1000   -0.0005
##    150        0.4573             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3184             nan     0.1000    0.0284
##      2        1.2703             nan     0.1000    0.0246
##      3        1.2279             nan     0.1000    0.0197
##      4        1.1922             nan     0.1000    0.0157
##      5        1.1620             nan     0.1000    0.0140
##      6        1.1352             nan     0.1000    0.0097
##      7        1.1153             nan     0.1000    0.0106
##      8        1.0951             nan     0.1000    0.0094
##      9        1.0777             nan     0.1000    0.0075
##     10        1.0610             nan     0.1000    0.0075
##     20        0.9365             nan     0.1000    0.0030
##     40        0.8075             nan     0.1000    0.0018
##     60        0.7447             nan     0.1000   -0.0001
##     80        0.7091             nan     0.1000    0.0004
##    100        0.6861             nan     0.1000   -0.0002
##    120        0.6723             nan     0.1000   -0.0015
##    140        0.6612             nan     0.1000   -0.0006
##    150        0.6563             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2997             nan     0.1000    0.0385
##      2        1.2334             nan     0.1000    0.0303
##      3        1.1791             nan     0.1000    0.0264
##      4        1.1329             nan     0.1000    0.0225
##      5        1.0953             nan     0.1000    0.0177
##      6        1.0610             nan     0.1000    0.0159
##      7        1.0351             nan     0.1000    0.0116
##      8        1.0111             nan     0.1000    0.0099
##      9        0.9851             nan     0.1000    0.0112
##     10        0.9646             nan     0.1000    0.0086
##     20        0.8207             nan     0.1000    0.0040
##     40        0.7036             nan     0.1000    0.0003
##     60        0.6604             nan     0.1000   -0.0015
##     80        0.6289             nan     0.1000   -0.0005
##    100        0.6026             nan     0.1000   -0.0003
##    120        0.5783             nan     0.1000   -0.0022
##    140        0.5605             nan     0.1000   -0.0004
##    150        0.5521             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2972             nan     0.1000    0.0395
##      2        1.2237             nan     0.1000    0.0371
##      3        1.1623             nan     0.1000    0.0290
##      4        1.1083             nan     0.1000    0.0272
##      5        1.0653             nan     0.1000    0.0195
##      6        1.0267             nan     0.1000    0.0180
##      7        0.9938             nan     0.1000    0.0150
##      8        0.9671             nan     0.1000    0.0119
##      9        0.9371             nan     0.1000    0.0143
##     10        0.9114             nan     0.1000    0.0104
##     20        0.7536             nan     0.1000    0.0040
##     40        0.6542             nan     0.1000   -0.0009
##     60        0.6067             nan     0.1000    0.0002
##     80        0.5703             nan     0.1000   -0.0020
##    100        0.5392             nan     0.1000   -0.0011
##    120        0.5148             nan     0.1000   -0.0012
##    140        0.4924             nan     0.1000   -0.0011
##    150        0.4819             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3213             nan     0.1000    0.0306
##      2        1.2724             nan     0.1000    0.0236
##      3        1.2313             nan     0.1000    0.0209
##      4        1.1955             nan     0.1000    0.0175
##      5        1.1651             nan     0.1000    0.0134
##      6        1.1386             nan     0.1000    0.0123
##      7        1.1172             nan     0.1000    0.0103
##      8        1.0971             nan     0.1000    0.0085
##      9        1.0806             nan     0.1000    0.0061
##     10        1.0640             nan     0.1000    0.0076
##     20        0.9503             nan     0.1000    0.0041
##     40        0.8226             nan     0.1000    0.0006
##     60        0.7537             nan     0.1000    0.0004
##     80        0.7128             nan     0.1000    0.0007
##    100        0.6878             nan     0.1000   -0.0002
##    120        0.6686             nan     0.1000   -0.0003
##    140        0.6533             nan     0.1000   -0.0003
##    150        0.6474             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3027             nan     0.1000    0.0387
##      2        1.2411             nan     0.1000    0.0280
##      3        1.1834             nan     0.1000    0.0260
##      4        1.1391             nan     0.1000    0.0210
##      5        1.1036             nan     0.1000    0.0175
##      6        1.0738             nan     0.1000    0.0162
##      7        1.0472             nan     0.1000    0.0129
##      8        1.0222             nan     0.1000    0.0100
##      9        0.9979             nan     0.1000    0.0107
##     10        0.9769             nan     0.1000    0.0089
##     20        0.8318             nan     0.1000    0.0055
##     40        0.6960             nan     0.1000    0.0009
##     60        0.6419             nan     0.1000    0.0000
##     80        0.6075             nan     0.1000   -0.0005
##    100        0.5845             nan     0.1000   -0.0001
##    120        0.5600             nan     0.1000    0.0001
##    140        0.5426             nan     0.1000   -0.0003
##    150        0.5336             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2979             nan     0.1000    0.0406
##      2        1.2238             nan     0.1000    0.0319
##      3        1.1657             nan     0.1000    0.0280
##      4        1.1155             nan     0.1000    0.0237
##      5        1.0694             nan     0.1000    0.0187
##      6        1.0349             nan     0.1000    0.0161
##      7        1.0049             nan     0.1000    0.0143
##      8        0.9749             nan     0.1000    0.0135
##      9        0.9501             nan     0.1000    0.0113
##     10        0.9261             nan     0.1000    0.0108
##     20        0.7660             nan     0.1000    0.0054
##     40        0.6521             nan     0.1000    0.0006
##     60        0.5998             nan     0.1000   -0.0015
##     80        0.5589             nan     0.1000   -0.0010
##    100        0.5260             nan     0.1000   -0.0010
##    120        0.4977             nan     0.1000   -0.0006
##    140        0.4733             nan     0.1000   -0.0004
##    150        0.4594             nan     0.1000    0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3104             nan     0.1000    0.0343
##      2        1.2561             nan     0.1000    0.0277
##      3        1.2100             nan     0.1000    0.0220
##      4        1.1724             nan     0.1000    0.0173
##      5        1.1399             nan     0.1000    0.0165
##      6        1.1117             nan     0.1000    0.0140
##      7        1.0901             nan     0.1000    0.0105
##      8        1.0672             nan     0.1000    0.0086
##      9        1.0494             nan     0.1000    0.0086
##     10        1.0327             nan     0.1000    0.0075
##     20        0.9184             nan     0.1000    0.0016
##     40        0.7957             nan     0.1000    0.0018
##     60        0.7389             nan     0.1000    0.0002
##     80        0.7049             nan     0.1000   -0.0005
##    100        0.6889             nan     0.1000    0.0003
##    120        0.6759             nan     0.1000    0.0000
##    140        0.6661             nan     0.1000   -0.0006
##    150        0.6622             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3043             nan     0.1000    0.0393
##      2        1.2377             nan     0.1000    0.0299
##      3        1.1840             nan     0.1000    0.0256
##      4        1.1326             nan     0.1000    0.0222
##      5        1.0954             nan     0.1000    0.0165
##      6        1.0609             nan     0.1000    0.0160
##      7        1.0297             nan     0.1000    0.0141
##      8        1.0047             nan     0.1000    0.0113
##      9        0.9835             nan     0.1000    0.0108
##     10        0.9614             nan     0.1000    0.0081
##     20        0.8146             nan     0.1000    0.0035
##     40        0.6987             nan     0.1000    0.0006
##     60        0.6562             nan     0.1000   -0.0017
##     80        0.6282             nan     0.1000   -0.0008
##    100        0.6050             nan     0.1000   -0.0012
##    120        0.5820             nan     0.1000   -0.0010
##    140        0.5605             nan     0.1000   -0.0007
##    150        0.5522             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2967             nan     0.1000    0.0404
##      2        1.2294             nan     0.1000    0.0338
##      3        1.1676             nan     0.1000    0.0290
##      4        1.1117             nan     0.1000    0.0258
##      5        1.0694             nan     0.1000    0.0196
##      6        1.0315             nan     0.1000    0.0167
##      7        0.9970             nan     0.1000    0.0152
##      8        0.9627             nan     0.1000    0.0150
##      9        0.9397             nan     0.1000    0.0099
##     10        0.9118             nan     0.1000    0.0133
##     20        0.7600             nan     0.1000    0.0047
##     40        0.6488             nan     0.1000   -0.0017
##     60        0.5998             nan     0.1000   -0.0001
##     80        0.5633             nan     0.1000   -0.0011
##    100        0.5317             nan     0.1000   -0.0007
##    120        0.5046             nan     0.1000   -0.0003
##    140        0.4821             nan     0.1000   -0.0012
##    150        0.4727             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3016             nan     0.1000    0.0384
##      2        1.2490             nan     0.1000    0.0251
##      3        1.1930             nan     0.1000    0.0281
##      4        1.1436             nan     0.1000    0.0218
##      5        1.1045             nan     0.1000    0.0173
##      6        1.0685             nan     0.1000    0.0168
##      7        1.0370             nan     0.1000    0.0142
##      8        1.0125             nan     0.1000    0.0101
##      9        0.9897             nan     0.1000    0.0097
##     10        0.9720             nan     0.1000    0.0087
##     20        0.8233             nan     0.1000    0.0031
##     40        0.7088             nan     0.1000   -0.0007
##     60        0.6670             nan     0.1000   -0.0010
##     80        0.6445             nan     0.1000   -0.0007
##    100        0.6310             nan     0.1000   -0.0010
## 
## [1] "svmRadial"
## [1] "svmRadialCost"
## [1] "svmRadialSigma"

names(fits) <- models

Did you train all of the models? Yes

Now that you have all the trained models in a list, use sapply or map to create a matrix of predictions for the test set. You should end up with a matrix with length(mnist_27$test$y) rows and length(models).

What are the dimensions of the matrix of predictions? - Number of rows: 200 - Number of columns: 23

pred <- sapply(fits, function(object) 
    predict(object, newdata = mnist_27$test))
dim(pred)

## [1] 200  23

Now compute accuracy for each model on the test set. Report the mean accuracy across all models.

0.8065217

acc <- colMeans(pred == mnist_27$test$y)
acc

##            glm            lda    naive_bayes      svmLinear       gamboost 
##          0.750          0.750          0.795          0.755          0.825 
##       gamLoess            qda            knn           kknn         loclda 
##          0.845          0.820          0.840          0.790          0.850 
##            gam             rf         ranger           wsrf        Rborist 
##          0.845          0.780          0.785          0.795          0.775 
##         avNNet            mlp         monmlp       adaboost            gbm 
##          0.840          0.755          0.825          0.785          0.835 
##      svmRadial  svmRadialCost svmRadialSigma 
##          0.845          0.845          0.820

mean(acc)

## [1] 0.8065217

Next, build an ensemble prediction by majority vote and compute the accuracy of the ensemble.

What is the accuracy of the ensemble? - 0.845

votes <- rowMeans(pred == "7")
y_hat <- ifelse(votes > 0.5, "7", "2")
mean(y_hat == mnist_27$test$y)

## [1] 0.84

In Q3, we computed the accuracy of each method on the training set and noticed that the individual accuracies varied.

How many of the individual methods do better than the ensemble? 1

Which individual methods perform better than the ensemble?

glm
lda
naive_bayes
svmLinear
gamboost
gamLoess
qda
knn
kknn
loclda
gam
rf
ranger
wsrf
Rborist
avNNet
mlp
monmlp
adaboost
gbm
svmRadial
svmRadialCost
svmRadialSigma

ind <- acc > mean(y_hat == mnist_27$test$y)
sum(ind)

## [1] 5

models[ind]

## [1] "gamLoess"      "loclda"        "gam"           "svmRadial"    
## [5] "svmRadialCost"

It is tempting to remove the methods that do not perform well and re-do the ensemble. The problem with this approach is that we are using the test data to make a decision. However, we could use the accuracy estimates obtained from cross validation with the training data. Obtain these estimates and save them in an object. Report the mean accuracy of the new estimates.

What is the mean accuracy of the new estimates? - 0.8123296

acc_hat <- sapply(fits, function(fit) min(fit$results$Accuracy))
mean(acc_hat)

## [1] 0.8123296

Now let’s only consider the methods with an estimated accuracy of greater than or equal to 0.8 when constructing the ensemble.

What is the accuracy of the ensemble now? - 0.85

ind <- acc_hat >= 0.8
votes <- rowMeans(pred[,ind] == "7")
y_hat <- ifelse(votes>=0.5, 7, 2)
mean(y_hat == mnist_27$test$y)

## [1] 0.85

Assessment 2- Dimension Reduction

We want to explore the tissue_gene_expression predictors by plotting them.

library(dplyr)
data("tissue_gene_expression")
dim(tissue_gene_expression$x)

## [1] 189 500

We want to get an idea of which observations are close to each other, but, as you can see from the dimensions, the predictors are 500-dimensional, making plotting difficult. Plot the first two principal components with color representing tissue type.

Which tissue is in a cluster by itself?

cerebellum
colon
endometrium
hippocampus
kidney
liver
placenta

pc <- prcomp(tissue_gene_expression$x)
data.frame(pc_1 = pc$x[,1], pc_2 = pc$x[,2], 
            tissue = tissue_gene_expression$y) %>%
    ggplot(aes(pc_1, pc_2, color = tissue)) +
    geom_point()

The predictors for each observation are measured using the same device and experimental procedure. This introduces biases that can affect all the predictors from one observation. For each observation, compute the average across all predictors, and then plot this against the first PC with color representing tissue. Report the correlation.

What is the correlation? - 0.5969088

avgs <- rowMeans(tissue_gene_expression$x)
data.frame(pc_1 = pc$x[,1], avg = avgs, 
            tissue = tissue_gene_expression$y) %>%
ggplot(aes(avgs, pc_1, color = tissue)) +
    geom_point()

cor(avgs, pc$x[,1])

## [1] 0.5969088

We see an association with the first PC and the observation averages. Redo the PCA but only after removing the center. Part of the code is provided for you.

#BLANK
x <- with(tissue_gene_expression, sweep(x, 1, rowMeans(x)))

pc <- prcomp(x)
data.frame(pc_1 = pc$x[,1], pc_2 = pc$x[,2], 
            tissue = tissue_gene_expression$y) %>%
    ggplot(aes(pc_1, pc_2, color = tissue)) +
    geom_point()

Which line of code should be used to replace #BLANK in the code block above?

A. x <- with(tissue_gene_expression, sweep(x, 1, mean(x)))
B. x <- sweep(x, 1, rowMeans(tissue_gene_expression$x))
C. x <- tissue_gene_expression$x - mean(tissue_gene_expression$x)
D. x <- with(tissue_gene_expression, sweep(x, 1, rowMeans(x)))

For the first 10 PCs, make a boxplot showing the values for each tissue.

For the 7th PC, which two tissues have the greatest median difference?

cerebellum
colon
endometrium
hippocampus
kidney
liver
placenta

for(i in 1:10){
    boxplot(pc$x[,i] ~ tissue_gene_expression$y, main = paste("PC", i))
}

Plot the percent variance explained by PC number. Hint: use the summary function.

How many PCs are required to reach a cumulative percent variance explained greater than 50%? - 3

plot(summary(pc)$importance[3,])

Assessment 3- Recommendation Systems

The following exercises all work with the movielens data, which can be loaded using the following code:

library(dslabs)
data("movielens")

Q1. Compute the number of ratings for each movie and then plot it against the year the movie came out. Use the square root transformation on the counts.

What year has the highest median number of ratings? - 1995

movielens %>% group_by(movieId) %>%
    summarize(n = n(), year = as.character(first(year))) %>%
    qplot(year, n, data = ., geom = "boxplot") +
    coord_trans(y = "sqrt") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))

Q2. We see that, on average, movies that came out after 1993 get more ratings. We also see that with newer movies, starting in 1993, the number of ratings decreases with year: the more recent a movie is, the less time users have had to rate it.

Among movies that came out in 1993 or later, what are the 25 movies with the most ratings per year, and what is the average rating of each of the top 25 movies?

What is the average rating for the movie The Shawshank Redemption? - 4.49

What is the average number of ratings per year for the movie Forrest Gump? - 14.2

movielens %>% 
    filter(year >= 1993) %>%
    group_by(movieId) %>%
    summarize(n = n(), years = 2018 - first(year),
                title = title[1],
                rating = mean(rating)) %>%
    mutate(rate = n/years) %>%
    top_n(25, rate) %>%
    arrange(desc(rate))

Q3. From the table constructed in Q2, we can see that the most frequently rated movies tend to have above average ratings. This is not surprising: more people watch popular movies. To confirm this, stratify the post-1993 movies by ratings per year and compute their average ratings. Make a plot of average rating versus ratings per year and show an estimate of the trend.

What type of trend do you observe?

A. There is no relationship between how often a movie is rated and its average rating.
B. Movies with very few and very many ratings have the highest average ratings.
C. The more often a movie is rated, the higher its average rating.
D. The more often a movie is rated, the lower its average rating.

movielens %>% 
    filter(year >= 1993) %>%
    group_by(movieId) %>%
    summarize(n = n(), years = 2017 - first(year),
                title = title[1],
                rating = mean(rating)) %>%
    mutate(rate = n/years) %>%
    ggplot(aes(rate, rating)) +
    geom_point() +
    geom_smooth()

Q4. Suppose you are doing a predictive analysis in which you need to fill in the missing ratings with some value.

Given your observations in the exercise in Q3, which of the following strategies would be most appropriate?

A. Fill in the missing values with the average rating across all movies.
B. Fill in the missing values with 0.
C. Fill in the missing values with a lower value than the average rating across all movies.
D. Fill in the value with a higher value than the average rating across all movies.
E. None of the above.

Explanation: Because a lack of ratings is associated with lower ratings, it would be most appropriate to fill in the missing value with a lower value than the average. You should try out different values to fill in the missing value and evaluate prediction in a test set.

Q5. The movielens dataset also includes a time stamp. This variable represents the time and data in which the rating was provided. The units are seconds since January 1, 1970. Create a new column date with the date.

Which code correctly creates this new column?

A. movielens <- mutate(movielens, date = as.date(timestamp))
B. movielens <- mutate(movielens, date = as_datetime(timestamp))
C. movielens <- mutate(movielens, date = as.data(timestamp))
D. movielens <- mutate(movielens, date = timestamp)

library(lubridate)
movielens <- mutate(movielens, date = as_datetime(timestamp))

Q6. Compute the average rating for each week and plot this average against day. Hint: use the round_date function before you group_by.

What type of trend do you observe?

A. There is strong evidence of a time effect on average rating.
B. There is some evidence of a time effect on average rating.
C. There is no evidence of a time effect on average rating.

movielens %>% mutate(date = round_date(date, unit = "week")) %>%
    group_by(date) %>%
    summarize(rating = mean(rating)) %>%
    ggplot(aes(date, rating)) +
    geom_point() +
    geom_smooth()

Q7. Consider again the plot you generated in Q6.

If we define $\ d_{u,i}$ as the day for user’s $\ u$ rating of movie $\ i$, which of the following models is most appropriate?

A. $\ Y_{u,i} = \mu + b_i + b_u + d_{u,i} + \varepsilon_{u,i}$
B. $\ Y_{u,i} = \mu + b_i + b_u + d_{u,i}\beta + \varepsilon_{u,i}$
C. $\ Y_{u,i} = \mu + b_i + b_u + d_{u,i}\beta_i + \varepsilon_{u,i}$
D. $\ Y_{u,i} = \mu + b_i + b_u + f(d_{u,i}) + \varepsilon_{u,i}$, with $\ f$ a smooth function of $\ d_{u,i}$

Q8. The movielens data also has a genres column. This column includes every genre that applies to the movie. Some movies fall under several genres. Define a category as whatever combination appears in this column. Keep only categories with more than 1,000 ratings. Then compute the average and standard error for each category. Plot these as error bar plots.

Which genre has the lowest average rating? - Comedy

movielens %>% group_by(genres) %>%
    summarize(n = n(), avg = mean(rating), se = sd(rating)/sqrt(n())) %>%
    filter(n >= 1000) %>% 
    mutate(genres = reorder(genres, avg)) %>%
    ggplot(aes(x = genres, y = avg, ymin = avg - 2*se, ymax = avg + 2*se)) + 
    geom_point() +
    geom_errorbar() + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1))

Q9. The plot you generated in Q8 shows strong evidence of a genre effect. Consider this plot as you answer the following question.

If we define $\ g_{u,i}$ as the day for user’s $\ u$ rating of movie $\ i$, which of the following models is most appropriate?

$\ Y_{u,i} = \mu + b_i + b_u + g_{u,i} + \varepsilon_{u,i}$
$\ Y_{u,i} = \mu + b_i + b_u + g_{u,i}\beta + \varepsilon_{u,i}$
C. $\ Y_{u,i} = \mu + b_i + b_u + \sum{k=1}^K x_{u,i} \beta_k + \varepsilon_{u,i}$, with x^k_{u,i}$ = 1 if $\ g_{u,i}$ is genre $\ k$
$\ Y_{u,i} = \mu + b_i + b_u + f(g_{u,i}) + \varepsilon_{u,i}$, with $\ f$ a smooth function of $\ g_{u,i}$

Assessment 4- Regularization

The exercises in Q1-Q8 work with a simulated dataset for 100 schools. This pre-exercise setup walks you through the code needed to simulate the dataset.

An education expert is advocating for smaller schools. The expert bases this recommendation on the fact that among the best performing schools, many are small schools. Let’s simulate a dataset for 100 schools. First, let’s simulate the number of students in each school, using the following code:

set.seed(1986)
n <- round(2^rnorm(1000, 8, 1))

Now let’s assign a true quality for each school that is completely independent from size. This is the parameter we want to estimate in our analysis. The true quality can be assigned using the following code:

set.seed(1)
mu <- round(80 + 2*rt(1000, 5))
range(mu)

## [1] 67 94

schools <- data.frame(id = paste("PS",1:100),
                      size = n,
                      quality = mu,
                      rank = rank(-mu))

We can see the top 10 schools using this code:

schools %>% top_n(10, quality) %>% arrange(desc(quality))

Now let’s have the students in the school take a test. There is random variability in test taking, so we will simulate the test scores as normally distributed with the average determined by the school quality with a standard deviation of 30 percentage points. This code will simulate the test scores:

set.seed(1)
scores <- sapply(1:nrow(schools), function(i){
       scores <- rnorm(schools$size[i], schools$quality[i], 30)
       scores
})
schools <- schools %>% mutate(score = sapply(scores, mean))

Q1. What are the top schools based on the average score? Show just the ID, size, and the average score.

Report the ID of the top school and average score of the 10th school.

What is the ID of the top school? - 67

What is the average score of the 10th school? - 88.09490

schools %>% top_n(10, score) %>% arrange(desc(score)) %>% select(id, size, score)

Q2. Compare the median school size to the median school size of the top 10 schools based on the score.

What is the median school size overall? - 261 - What is the median school size of the of the top 10 schools based on the score? - 136

median(schools$size)

## [1] 261

schools %>% top_n(10, score) %>% .$size %>% median()

## [1] 136

Q3. According to this analysis, it appears that small schools produce better test scores than large schools. Four out of the top 10 schools have 100 or fewer students. But how can this be? We constructed the simulation so that quality and size were independent. Repeat the exercise for the worst 10 schools.

What is the median school size of the bottom 10 schools based on the score? - 146

median(schools$size)

## [1] 261

schools %>% top_n(-10, score) %>% .$size %>% median()

## [1] 146

Q4. From this analysis, we see that the worst schools are also small. Plot the average score versus school size to see what’s going on. Highlight the top 10 schools based on the true quality. Use a log scale to transform for the size.

What do you observe?

There is no difference in the standard error of the score based on school size; there must be an error in how we generated our data.
The standard error of the score has larger variability when the school is smaller, which is why both the best and the worst schools are more likely to be small.
The standard error of the score has smaller variability when the school is smaller, which is why both the best and the worst schools are more likely to be small.
The standard error of the score has larger variability when the school is very small or very large, which is why both the best and the worst schools are more likely to be small.
The standard error of the score has smaller variability when the school is very small or very large, which is why both the best and the worst schools are more likely to be small.

schools %>% ggplot(aes(size, score)) +
    geom_point(alpha = 0.5) +
    geom_point(data = filter(schools, rank<=10), col = 2)

Q5. Let’s use regularization to pick the best schools. Remember regularization shrinks deviations from the average towards 0. To apply regularization here, we first need to define the overall average for all schools, using the following code:

overall <- mean(sapply(scores, mean))

Then, we need to define, for each school, how it deviates from that average.

Write code that estimates the score above the average for each school but dividing by $\ n + \alpha$ instead of $\ n$, with $\ n$ the schools size and $\ \alpha$ a regularization parameters. Try $\ \alpha = 25$.

What is the ID of the top school with regularization? - 91

What is the regularized score of the 10th school? - 86.90070

alpha <- 25
score_reg <- sapply(scores, function(x)  overall + sum(x-overall)/(length(x)+alpha))
schools %>% mutate(score_reg = score_reg) %>%
    top_n(10, score_reg) %>% arrange(desc(score_reg))

Q6. Notice that this improves things a bit. The number of small schools that are not highly ranked is now lower. Is there a better $\ \alpha$? Find the $\ \alpha$ that minimizes the RMSE = $\ \frac{1}{100} \sum_{i=1}^{100} (\mbox{quality} - \mbox{estimate})^2$.

What value of gives the minimum RMSE? - 128

alphas <- seq(10,250)
rmse <- sapply(alphas, function(alpha){
    score_reg <- sapply(scores, function(x) overall+sum(x-overall)/(length(x)+alpha))
    mean((score_reg - schools$quality)^2)
})
plot(alphas, rmse)

alphas[which.min(rmse)]

## [1] 128

Q7. Rank the schools based on the average obtained with the best $\ \alpha$. Note that no small school is incorrectly included.

What is the ID of the top school now? - 91

What is the regularized average score of the 10th school now? - 85.35335

alpha <- alphas[which.min(rmse)]  
score_reg <- sapply(scores, function(x)
    overall+sum(x-overall)/(length(x)+alpha))
schools %>% mutate(score_reg = score_reg) %>%
    top_n(10, score_reg) %>% arrange(desc(score_reg))

Q8. A common mistake made when using regularization is shrinking values towards 0 that are not centered around 0. For example, if we don’t subtract the overall average before shrinking, we actually obtain a very similar result. Confirm this by re-running the code from the exercise in Q6 but without removing the overall mean.

What value of $\ \alpha$ gives the minimum RMSE here? - 10

alphas <- seq(10,250)
rmse <- sapply(alphas, function(alpha){
    score_reg <- sapply(scores, function(x) sum(x)/(length(x)+alpha))
    mean((score_reg - schools$quality)^2)
})
plot(alphas, rmse)

alphas[which.min(rmse)]

## [1] 10

Assessment 5- Matrix Factorization

In this exercise set, we will be covering a topic useful for understanding matrix factorization: the singular value decomposition (SVD). SVD is a mathematical result that is widely used in machine learning, both in practice and to understand the mathematical properties of some algorithms. This is a rather advanced topic and to complete this exercise set you will have to be familiar with linear algebra concepts such as matrix multiplication, orthogonal matrices, and diagonal matrices.

The SVD tells us that we can decompose an $\ N\times p$ matrix $\ Y$ with $\ p < N$ as $\ Y = U D V^{\top}$

with $\ U$ and $\ V$ orthogonal of dimensions $\ N\times p$ and $\ p\times p$ respectively and$\ D a$ pp$ diagonal matrix with the values of the diagonal decreasing: $\ d_{1,1} \geq d_{2,2} \geq \dots d_{p,p}$

In this exercise, we will see one of the ways that this decomposition can be useful. To do this, we will construct a dataset that represents grade scores for 100 students in 24 different subjects. The overall average has been removed so this data represents the percentage point each student received above or below the average test score. So a 0 represents an average grade (C), a 25 is a high grade (A+), and a -25 represents a low grade (F). You can simulate the data like this:

set.seed(1987)
n <- 100
k <- 8
Sigma <- 64  * matrix(c(1, .75, .5, .75, 1, .5, .5, .5, 1), 3, 3) 
m <- MASS::mvrnorm(n, rep(0, 3), Sigma)
m <- m[order(rowMeans(m), decreasing = TRUE),]
y <- m %x% matrix(rep(1, k), nrow = 1) + matrix(rnorm(matrix(n*k*3)), n, k*3)
colnames(y) <- c(paste(rep("Math",k), 1:k, sep="_"),
                 paste(rep("Science",k), 1:k, sep="_"),
                 paste(rep("Arts",k), 1:k, sep="_"))

Our goal is to describe the student performances as succinctly as possible. For example, we want to know if these test results are all just a random independent numbers. Are all students just about as good? Does being good in one subject imply you will be good in another? How does the SVD help with all this? We will go step by step to show that with just three relatively small pairs of vectors we can explain much of the variability in this 100 x 24 dataset.

Q1. You can visualize the 24 test scores for the 100 students by plotting an image:

my_image <- function(x, zlim = range(x), ...){
    colors = rev(RColorBrewer::brewer.pal(9, "RdBu"))
    cols <- 1:ncol(x)
    rows <- 1:nrow(x)
    image(cols, rows, t(x[rev(rows),,drop=FALSE]), xaxt = "n", yaxt = "n",
            xlab="", ylab="",  col = colors, zlim = zlim, ...)
    abline(h=rows + 0.5, v = cols + 0.5)
    axis(side = 1, cols, colnames(x), las = 2)
}

my_image(y)

How would you describe the data based on this figure?

The test scores are all independent of each other.
The students that are good at math are not good at science.
The students that are good at math are not good at arts.
The students that test well are at the top of the image and there seem to be three groupings by subject.
The students that test well are at the bottom of the image and there seem to be three groupings by subject.

Q2. You can examine the correlation between the test scores directly like this:

my_image(cor(y), zlim = c(-1,1))
range(cor(y))

## [1] 0.4855371 1.0000000

axis(side = 2, 1:ncol(y), rev(colnames(y)), las = 2)

Which of the following best describes what you see?

The test scores are independent.
Test scores in math and science are highly correlated but scores in arts are not.
There is high correlation between tests in the same subject but no correlation across subjects.
There is correlation among all tests, but higher if the tests are in science and math and even higher within each subject.

Q3. Remember that orthogonality means that $\ U^{\top}U$ and $\ V^{\top}V$ are equal to the identity matrix. This implies that we can also rewrite the decomposition as

$\ Y V = U D \mbox{ or } U^{\top}Y = D V^{\top}$

We can think of $\ YV$ and $ U^{}V as two transformations of $\ Y$ that preserve the total variability of $\ Y$ since $\ U$ and $\ V$ are orthogonal.

Use the function svd`` to compute the SVD ofy```. This function will return $\ U, V$, and the diagonal entries of $\ D$.

s <- svd(y)
names(s)

## [1] "d" "u" "v"

You can check that the SVD works by typing:

y_svd <- s$u %*% diag(s$d) %*% t(s$v)
max(abs(y - y_svd))

## [1] 5.329071e-14

Compute the sum of squares of the columns of $\ Y$ and store them in ss_y. Then compute the sum of squares of columns of the transformed YV and store them in ss_yv. Confirm that sum(ss_y) is equal to sum(ss_yv).

y_sq <- y*y 
ss_y <- colSums(y_sq)
sum(ss_y)

## [1] 175434.6

y_svd_sq <- y_svd*y_svd 
ss_yv <- colSums(y_svd_sq)
sum(ss_yv)

## [1] 175434.6

What is the value of sum(ss_y) (and also the value of sum(ss_yv))? - 175435

Q4. We see that the total sum of squares is preserved. This is because $\ V$ is orthogonal. Now to start understanding how $\ YV$ is useful, plot ss_y against the column number and then do the same for ss_yv.

What do you observe?

plot(ss_y)

plot(ss_yv)

A. ss_y and ss_yv are decreasing and close to 0 for the 4th column and beyond.
B. ss_yv is decreasing and close to 0 for the 4th column and beyond.
C. ss_y is decreasing and close to 0 for the 4th column and beyond.
D. There is no discernible pattern to either ss_y or ss_yv.

Q5. Note that we didn’t have to compute ss_yv because we already have the answer. How? Remember that $\ YV = UD$ and because $\ U$ is orthogonal, we know that the sum of squares of the columns of $\ UD$ are the diagonal entries of $\ D$ squared. Confirm this by plotting the square root of ss_yv versus the diagonal entries of $\ D$.

What else is equal to $\ YV$?

D
U
UD
VUD

plot(sqrt(ss_yv), s$d)
abline(0,1)

Q6. So from the above we know that the sum of squares of the columns of $\ Y$ (the total sum of squares) adds up to the sum of s$d^2 and that the transformation $\ YV$ gives us columns with sums of squares equal to s$d^2. Now compute the percent of the total variability that is explained by just the first three columns of $\ YV$.

What proportion of the total variability is explained by the first three columns of $\ YV$? - 0.988

sum(s$d[1:3]^2) / sum(s$d^2)

## [1] 0.9877922

Q7. Before we continue, let’s show a useful computational trick to avoid creating the matrix diag(s$d). To motivate this, we note that if we write out in its columns $\ [U_1, U_2, \dots, U_p]$ then $\ UD$ is equal to $\ UD = [ U_1 d_{1,1}, U_2 d_{2,2}, \dots, U_p d_{p,p}]$

Use the sweep function to compute $\ UD$ without constructing diag(s$d) or using matrix multiplication.

Which code is correct?

identical(s$u %*% diag(s$d), sweep(s$u, 2, s$d, FUN = "*"))

## [1] TRUE

A. identical(t(s$u %*% diag(s$d)), sweep(s$u, 2, s$d, FUN = "*"))
B. identical(s$u %*% diag(s$d), sweep(s$u, 2, s$d, FUN = "*"))
C. identical(s$u %*% t(diag(s$d)), sweep(s$u, 2, s$d, FUN = "*"))
D. identical(s$u %*% diag(s$d), sweep(s$u, 2, s, FUN = "*"))

Q8. We know that $\ U_1 d_{1,1}$, the first column of $\ UD$, has the most variability of all the columns of $\ UD$. Earlier we looked at an image of $\ Y$ using my_image(y), in which we saw that the student to student variability is quite large and that students that are good in one subject tend to be good in all. This implies that the average (across all subjects) for each student should explain a lot of the variability. Compute the average score for each student, plot it against $\ U_1 d_{1,1}$, and describe what you find.

What do you observe?

A. There is no relationship between the average score for each student and .
B. There is a linearly decreasing relationship between the average score for each student and $\ U_1 d_{1,1}$.
C. There is a linearly increasing relationship between the average score for each student and $\ U_1 d_{1,1}$.
D. There is an exponentially increasing relationship between the average score for each student and $\ U_1 d_{1,1}$.
E. There is an exponentially decreasing relationship between the average score for each student and $\ U_1 d_{1,1}$.

plot(-s$u[,1]*s$d[1], rowMeans(y))

Q9. We note that the signs in SVD are arbitrary because:

$\ U D V^{\top} = (-U) D (-V)^{\top}$

With this in mind we see that the first column of $\ UD$ is almost identical to the average score for each student except for the sign.

This implies that multiplying $\ Y$ by the first column of $\ V$ must be performing a similar operation to taking the average. Make an image plot of $\ V$ and describe the first column relative to others and how this relates to taking an average.

How does the first column relate to the others, and how does this relate to taking an average?

A. The first column is very variable, which implies that the first column of YV is the sum of the rows of Y multiplied by some non-constant function, and is thus not proportional to an average.

B. The first column is very variable, which implies that the first column of YV is the sum of the rows of Y multiplied by some non-constant function, and is thus proportional to an average.

C. The first column is very close to being a constant, which implies that the - first column of YV is the sum of the rows of Y multiplied by some constant, and is thus proportional to an average.

D. The first three columns are all very close to being a constant, which implies that these columns are the sum of the rows of Y multiplied by some constant, and are thus proportional to an average.

my_image(s$v)

Q10. We already saw that we can rewrite $\ UD$ as $\ U_1 d_{1,1} + U_2 d_{2,2} + \dots + U_p d_{p,p}$

with $\ U_j$ the j-th column of $\ U$. This implies that we can rewrite the entire SVD as:

$\ Y = U_1 d_{1,1} V_1 ^{\top} + U_2 d_{2,2} V_2 ^{\top} + \dots + U_p d_{p,p} V_p ^{\top}$

with $ V_j $the jth column of $\ V$. Plot $\ U_1$, then plot $\ V_1^{\top}$ using the same range for the y-axis limits, then make an image of $\ U_1 d_{1,1} V_1 ^{\top}$ and compare it to the image of $\ Y$. Hint: use the my_image function defined above. Use the drop=FALSE argument to assure the subsets of matrices are matrices.

plot(s$u[,1], ylim = c(-0.25, 0.25))

plot(s$v[,1], ylim = c(-0.25, 0.25))

with(s, my_image((u[, 1, drop=FALSE]*d[1]) %*% t(v[, 1, drop=FALSE])))

my_image(y)

Q11. We see that with just a vector of length 100, a scalar, and a vector of length 24, we can actually come close to reconstructing the a 100 x 24 matrix. This is our first matrix factorization:

$\ Y \approx d_{1,1} U_1 V_1^{\top}$

In the exercise in Q6, we saw how to calculate the percent of total variability explained. However, our approximation only explains the observation that good students tend to be good in all subjects. Another aspect of the original data that our approximation does not explain was the higher similarity we observed within subjects. We can see this by computing the difference between our approximation and original data and then computing the correlations. You can see this by running this code:

resid <- y - with(s,(u[, 1, drop=FALSE]*d[1]) %*% t(v[, 1, drop=FALSE]))
my_image(cor(resid), zlim = c(-1,1))
axis(side = 2, 1:ncol(y), rev(colnames(y)), las = 2)

Now that we have removed the overall student effect, the correlation plot reveals that we have not yet explained the within subject correlation nor the fact that math and science are closer to each other than to the arts. So let’s explore the second column of the SVD.

Repeat the previous exercise (Q10) but for the second column: Plot $\ U_2$, then plot $\ V_2^{\top}$ using the same range for the y-axis limits, then make an image of $\ U_2 d_{2,2} V_2 ^{\top}$ and compare it to the image of `resid .

plot(s$u[,2], ylim = c(-0.5, 0.5))

plot(s$v[,2], ylim = c(-0.5, 0.5))

with(s, my_image((u[, 2, drop=FALSE]*d[2]) %*% t(v[, 2, drop=FALSE])))

my_image(resid)

Q12. The second column clearly relates to a student’s difference in ability in math/science versus the arts. We can see this most clearly from the plot of s$v[,2]. Adding the matrix we obtain with these two columns will help with our approximation:

$\ Y \approx d_{1,1} U_1 V_1^{\top} + d_{2,2} U_2 V_2^{\top}$

We know it will explain sum(s$d[1:2]^2)/sum(s$d^2) * 100 percent of the total variability. We can compute new residuals like this:

resid <- y - with(s,sweep(u[, 1:2], 2, d[1:2], FUN="*") %*% t(v[, 1:2]))
my_image(cor(resid), zlim = c(-1,1))
axis(side = 2, 1:ncol(y), rev(colnames(y)), las = 2)

and see that the structure that is left is driven by the differences between math and science. Confirm this by first plotting $\ U_3$, then plotting $\ V_3^{\top}$ using the same range for the y-axis limits, then making an image of $\ U_3 d_{3,3} V_3 ^{\top}$ and comparing it to the image of resid.

plot(s$u[,3], ylim = c(-0.5, 0.5))

plot(s$v[,3], ylim = c(-0.5, 0.5))

with(s, my_image((u[, 3, drop=FALSE]*d[3]) %*% t(v[, 3, drop=FALSE])))

my_image(resid)

Q13. The third column clearly relates to a student’s difference in ability in math and science. We can see this most clearly from the plot of s$v[,3]. Adding the matrix we obtain with these two columns will help with our approximation:

$\ Y \approx d_{1,1} U_1 V_1^{\top} + d_{2,2} U_2 V_2^{\top} + d_{3,3} U_3 V_3^{\top}$

We know it will explain: sum(s$d[1:3]^2)/sum(s$d^2) * 100 percent of the total variability. We can compute new residuals like this:

resid <- y - with(s,sweep(u[, 1:3], 2, d[1:3], FUN="*") %*% t(v[, 1:3]))
my_image(cor(resid), zlim = c(-1,1))
axis(side = 2, 1:ncol(y), rev(colnames(y)), las = 2)

We no longer see structure in the residuals: they seem to be independent of each other. This implies that we can describe the data with the following model:

$\ Y = d_{1,1} U_1 V_1^{\top} + d_{2,2} U_2 V_2^{\top} + d_{3,3} U_3 V_3^{\top} + \varepsilon$

with $\ \varepsilon$ a matrix of independent identically distributed errors. This model is useful because we summarize of 100 x 24 observations with 3 X (100+24+1) = 375 numbers.

Furthermore, the three components of the model have useful interpretations:

1 - the overall ability of a student 2 - the difference in ability between the math/sciences and arts 3 - the remaining differences between the three subjects.

The sizes $\ d_{1,1}, d_{2,2}$ and $\ d_{3,3$} tell us the variability explained by each component. Finally, note that the components $\ d_{j,j} U_j V_j^{\top}$ are equivalent to the jth principal component.

Finish the exercise by plotting an image of $\ Y$, an image of $\ d_{1,1} U_1 V_1^{\top} + d_{2,2} U_2 V_2^{\top} + d_{3,3} U_3 V_3^{\top}$ and an image of the residuals, all with the same zlim.

y_hat <- with(s,sweep(u[, 1:3], 2, d[1:3], FUN="*") %*% t(v[, 1:3]))
my_image(y, zlim = range(y))

my_image(y_hat, zlim = range(y))

my_image(y - y_hat, zlim = range(y))

Assessment 6- Clustering

These exercises will work with the tissue_gene_expression dataset, which is part of the dslabs package.

Q1. Load the tissue_gene_expression dataset. Remove the row means and compute the distance between each observation. Store the result in d.

Which of the following lines of code correctly does this computation?

A. d <- dist(tissue_gene_expression$x)
B. d <- dist(rowMeans(tissue_gene_expression$x))
C. d <- dist(rowMeans(tissue_gene_expression$y))
D. d <- dist(tissue_gene_expression$x - rowMeans(tissue_gene_expression$x))

d <- dist(tissue_gene_expression$x - rowMeans(tissue_gene_expression$x))

Q2. Make a hierarchical clustering plot and add the tissue types as labels.

You will observe multiple branches.

Which tissue type is in the branch farthest to the left?

cerebellum
colon
endometrium
hippocampus
kidney
liver
placenta

h <- hclust(d)
plot(h)

Q3. Run a k-means clustering on the data with $\ K = 7$. Make a table comparing the identified clusters to the actual tissue types. Run the algorithm several times to see how the answer changes.

What do you observe for the clustering of the liver tissue?

A. Liver is always classified in a single cluster.
B. Liver is never classified in a single cluster.
C. Liver is classified in a single cluster roughly 20% of the time and in more than one cluster roughly 80% of the time.
D. Liver is classified in a single cluster roughly 80% of the time and in more than one cluster roughly 20% of the time.

cl <- kmeans(tissue_gene_expression$x, centers = 7)
table(cl$cluster, tissue_gene_expression$y)

##    
##     cerebellum colon endometrium hippocampus kidney liver placenta
##   1          0    34           1           0      0     0        6
##   2          0     0           0           0      0     6        0
##   3          0     0          14           0     36     0        0
##   4         36     0           0          29      0     0        0
##   5          0     0           0           0      0    14        0
##   6          0     0           0           0      0     6        0
##   7          2     0           0           2      3     0        0

Q4. Select the 50 most variable genes. Make sure the observations show up in the columns, that the predictor are centered, and add a color bar to show the different tissue types. Hint: use the ColSideColors argument to assign colors. Also, use col = RColorBrewer::brewer.pal(11, “RdBu”) for a better use of colors.

Part of the code is provided for you here:

Which line of code should replace #BLANK in the code above?

library(RColorBrewer)
library(matrixStats)
sds <- matrixStats::colSds(tissue_gene_expression$x)
ind <- order(sds, decreasing = TRUE)[1:50]
colors <- brewer.pal(7, "Dark2")[as.numeric(tissue_gene_expression$y)]
#BLANK:
heatmap(t(tissue_gene_expression$x[,ind]), col = brewer.pal(11, "RdBu"), scale = "row", ColSideColors = colors)

A. heatmap(t(tissue_gene_expression$x[,ind]), col = brewer.pal(11, "RdBu"), scale = "row", ColSideColors = colors)
B. heatmap(t(tissue_gene_expression$x[,ind]), col = brewer.pal(11, "RdBu"), scale = "row", ColSideColors = rev(colors))
C. heatmap(t(tissue_gene_expression$x[,ind]), col = brewer.pal(11, "RdBu"), scale = "row", ColSideColors = sample(colors))
D. heatmap(t(tissue_gene_expression$x[,ind]), col = brewer.pal(11, "RdBu"), scale = "row", ColSideColors = sample(colors))

HarvardX: PH125.8x | Data Science: Machine Learning

Md Faisal Akbar | dsfaisal.com

2019-04-25

Data Science: Machine Learning

Abstract

Learning Objectives

Course Overview

Introduction to Machine Learning Overview

Assessment 1- Introduction to Machine Learning

Machine Learning Basics Overview

Assessment 1- Basics of Evaluating Machine Learning Algorithms

Assessment 2- Confusion Matrix

Assessment 3- Practice with Machine Learning

Assessment 4- Conditional Probabilities Review

Assessment 5- Conditional Probabilities Practice

Linear Regression for Prediction, Smoothing, and Working with Matrices Overview

Assessment 1- Linear Regression

Assessment 2- Logistic Regression

Assessment 3- Smoothing

Assessment 4- Working with Matrices

Distance, Knn, Cross Validation, and Generative Models

Assessment 1- Distance

Assessment 2- Nearest Neighbors

Assessment 3- Cross-validation

Assessment 4- Bootstrap

Assessment 5- Generative Models

Classification with More than Two Classes and the Caret Package

Assessment 1- Trees and Random Forests

Assessment 2- Caret Package

Model Fitting and Recommendation Systems Overview

Assessment 1- Ensembles

Assessment 2- Dimension Reduction

Assessment 3- Recommendation Systems

Assessment 4- Regularization

Assessment 5- Matrix Factorization

Assessment 6- Clustering