R Notebook

Libraries that is being used

library(tidyverse)

package 㤼㸱tidyverse㤼㸲 was built under R version 4.1.2Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.1 --
v ggplot2 3.3.5     v purrr   0.3.4
v tibble  3.1.5     v dplyr   1.0.7
v tidyr   1.1.4     v stringr 1.4.0
v readr   2.1.0     v forcats 0.5.1
package 㤼㸱ggplot2㤼㸲 was built under R version 4.1.2package 㤼㸱readr㤼㸲 was built under R version 4.1.2-- Conflicts ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(caret)

package 㤼㸱caret㤼㸲 was built under R version 4.1.2Loading required package: lattice
package 㤼㸱lattice㤼㸲 was built under R version 4.1.2Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

Attaching package: 㤼㸱caret㤼㸲

The following object is masked from 㤼㸱package:purrr㤼㸲:

    lift

library(ggplot2)
library(dplyr)
library(mice)

package 㤼㸱mice㤼㸲 was built under R version 4.1.2
Attaching package: 㤼㸱mice㤼㸲

The following object is masked from 㤼㸱package:stats㤼㸲:

    filter

The following objects are masked from 㤼㸱package:base㤼㸲:

    cbind, rbind

library('randomForest')

package 㤼㸱randomForest㤼㸲 was built under R version 4.1.2randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.

Attaching package: 㤼㸱randomForest㤼㸲

The following object is masked from 㤼㸱package:dplyr㤼㸲:

    combine

The following object is masked from 㤼㸱package:ggplot2㤼㸲:

    margin

train <- read.csv("train.csv")
test <- read.csv("test.csv")
df <- bind_rows(train,test)

A) Four ways to get initial understanding of the data

Print the head of the dataset

head(df)

What are the columns actually means

Variable Name	Description
Survived	Survived (1) or died (0)
Pclass	Passenger’s class
Name	Passenger’s name
Sex	Passenger’s sex
Age	Passenger’s age
SibSp	Number of siblings/spouses aboard
Parch	Number of parents/children aboard
Ticket	Ticket number
Fare	Fare
Cabin	Cabin
Embarked	Port of embarkation

dim() returns the dimension of the matrix, array, or data frame

dim(df)

[1] 1309   12

str() used for compactly displaying the internal structure of a R object

str(df)

'data.frame':   1309 obs. of  12 variables:
 $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
 $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
 $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
 $ Name       : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
 $ Sex        : chr  "male" "female" "female" "female" ...
 

Error in gregexpr(calltext, singleline, fixed = TRUE) : 
  regular expression is invalid UTF-8
Error in gregexpr(calltext, singleline, fixed = TRUE) : 
  regular expression is invalid UTF-8
Error in gregexpr(calltext, singleline, fixed = TRUE) : 
  regular expression is invalid UTF-8

$ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
 $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
 $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
 $ Ticket     : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
 $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
 $ Cabin      : chr  "" "C85" "" "C123" ...
 $ Embarked   : chr  "S" "C" "S" "S" ...

Summary() command provides summary data related to the individual object that was fed into it

summary(df)

  PassengerId      Survived          Pclass          Name               Sex                 Age            SibSp            Parch          Ticket               Fare            Cabin             Embarked        
 Min.   :   1   Min.   :0.0000   Min.   :1.000   Length:1309        Length:1309        Min.   : 0.17   Min.   :0.0000   Min.   :0.000   Length:1309        Min.   :  0.000   Length:1309        Length:1309       
 1st Qu.: 328   1st Qu.:0.0000   1st Qu.:2.000   Class :character   Class :character   1st Qu.:21.00   1st Qu.:0.0000   1st Qu.:0.000   Class :character   1st Qu.:  7.896   Class :character   Class :character  
 Median : 655   Median :0.0000   Median :3.000   Mode  :character   Mode  :character   Median :28.00   Median :0.0000   Median :0.000   Mode  :character   Median : 14.454   Mode  :character   Mode  :character  
 Mean   : 655   Mean   :0.3838   Mean   :2.295                                         Mean   :29.88   Mean   :0.4989   Mean   :0.385                      Mean   : 33.295                                        
 3rd Qu.: 982   3rd Qu.:1.0000   3rd Qu.:3.000                                         3rd Qu.:39.00   3rd Qu.:1.0000   3rd Qu.:0.000                      3rd Qu.: 31.275                                        
 Max.   :1309   Max.   :1.0000   Max.   :3.000                                         Max.   :80.00   Max.   :8.0000   Max.   :9.000                      Max.   :512.329                                        
                NA's   :418                                                            NA's   :263                                                         NA's   :1                                              

Checking any na collumns

colSums(is.na(df))

PassengerId    Survived      Pclass        Name         Sex         Age       SibSp       Parch      Ticket        Fare       Cabin    Embarked 
          0         418           0           0           0         263           0           0           0           1           0           0 

Checking any missing values in the collumns

colSums(df == "")

PassengerId    Survived      Pclass        Name         Sex         Age       SibSp       Parch      Ticket        Fare       Cabin    Embarked 
          0          NA           0           0           0          NA           0           0           0          NA        1014           2 

NA is the value where nothing was provided or value is assigned. "" empty is a string value. that means there is an empty string.

B) Four ways of subsetting / choosing row or columns

1. Subset using brackets by extracting the rows and columns we want.

df_1 <- df[1:20,]
df_1

NA

2. Subsetting using conditional

df_2 <- df[df$Age > 50, ]
head(df_2)

3. Using subset()

df_3 <- subset(df, Sex == "male")
df_3

4. Using the Select and Filter

df_4 <- select(filter(df, Age < 20),c("Name","Sex","Fare"))
df_4

C) Four ways to Preprocess data

mutate() adds new variables and preserves existing ones while

Factors are used to work with categorical variables, variables that have a fixed and known set of possible values. They are also useful when you want to display character vectors in a non-alphabetical order.

df <- df %>% mutate(Survived = factor(Survived),
               Pclass = factor(Pclass),
               Sex = factor(Sex),
               Embarked = factor(Embarked))

1. Standardizing the Titles

showing a tibble of the titles and the count for each of #them

df$Title <- gsub('(.*, )|(\\..*)', '', df$Name)


df %>% group_by(Title)%>%
  summarize(count = n())

NA

catogorizing the various titles which has the same meaning into 1

df$Title[df$Title == 'Mlle'] <- 'Miss'
df$Title[df$Title == 'Ms'] <- 'Miss'
df$Title[df$Title == 'Mme'] <- 'Mrs'
other <- c('Capt','Col','Don','Dona','Jonkheer','Lady','Major','Rev','Sir','the Countess')
df$Title[df$Title %in% other]  <- 'Other'
df$Title <- factor(df$Title)

df %>% group_by(Title)%>%
  summarize(count = n())

NA
NA

2. Creating a new variable as Family_size which takes into account the passenger,parents and siblings

df$Family_size <- df$SibSp + df$Parch + 1
df$Family_size <- factor(df$Family_size)

3. replacing the values for embarked

which(df$Embarked == "")

[1]  62 830

Both of these observations look like they should be Embarked from C

df[c(62,830),]

df$Embarked[c(62,830)] <- "C"
df[c(62,830),]

4 . dropping the column cabin as it has too many missing values

df_drop <- c("cabin")
df = df[,!(names(df) %in% df_drop)]
head(df)

5. replacing the the values in Fare with the average value

which(is.na(df$Fare))

[1] 1044

df[1044,]

df <- df %>%
    mutate(Fare = ifelse(is.na(Fare),median(Fare, na.rm = TRUE),Fare))

df[1044,]

6. replacing the the values using predictions

temp <- df %>% select(Pclass,Sex,Age)
set.seed(1)
mice_input <- mice(temp, method = 'rf')

 iter imp variable
  1   1  Age
  1   2  Age
  1   3  Age
  1   4  Age
  1   5  Age
  2   1  Age
  2   2  Age
  2   3  Age
  2   4  Age
  2   5  Age
  3   1  Age
  3   2  Age
  3   3  Age
  3   4  Age
  3   5  Age
  4   1  Age
  4   2  Age
  4   3  Age
  4   4  Age
  4   5  Age
  5   1  Age
  5   2  Age
  5   3  Age
  5   4  Age
  5   5  Age

mice_output <- complete(mice_input)

Using histograms to make sure the new predictions match the distribution of all

hist(df$Age)

Predicted age histogram using mice

hist(mice_output$Age)

replacing age variable with new age predictions

df$Age <- mice_output$Age
sum(is.na(df$Age))

[1] 0

Final check to see any misisng values are still in the data set

colSums(is.na(df))

PassengerId    Survived      Pclass        Name         Sex         Age       SibSp       Parch      Ticket        Fare       Cabin    Embarked       Title Family_size 
          0         418           0           0           0           0           0           0           0           0           0           0           0           0 

Exploratory Data Analysis

Percentage of Gender

write.csv(df, file = 'main.csv', row.names = FALSE)
round(mean(df$Sex == "male")*100,2)

[1] 64.4

round(mean(df$Sex == "female")*100,2)

[1] 35.6

Percentage of Survived vs Died

round(mean(train$Survived == 1)*100,2)

[1] 38.38

round(mean(train$Survived == 0)*100,2)

[1] 61.62

train %>% ggplot(aes(factor(Survived))) +
  facet_grid(.~Sex) +
    geom_bar(aes(fill=factor(Survived))) +
  ggtitle("Amount that Survived and Did Not Survived by Sex") +
  scale_fill_discrete(name = "Survivial Status",
                      labels = c("Did Not Survive", "Survived"))

df %>% ggplot(aes(Age)) +
  geom_histogram(fill = "pink") +
  ggtitle("Age distribution")

train %>% ggplot(aes(factor(Survived),Fare)) +
  geom_boxplot(color = "blue") +
  ggtitle("Survival and ticket price (Survived = 1)")

df %>% ggplot(aes(Age,Fare)) +
  geom_point(color = "blue") +
  ggtitle("Scatter Plot with Age and Fare") +
  xlab("Age") +
  ylab("Fare") 

train %>% ggplot(aes(factor(Survived))) +
  facet_grid(.~Pclass) +
  geom_bar(aes(fill=factor(Survived))) +
  ggtitle("Amount Survived and Not Survived, Split by Pclass") +
  scale_fill_discrete(name = "survival status", 
                      labels = c("Did Not Survive","Survived"))

Shiny requires much more manual labor to produce great-looking dashboards. Hence to come up with fast interecative dashboard PowerBi is the tools i use for most of my EDA

Neural Network

splitting the df dataset back into train and test

train <- df[1:891,]
test <- df[892:1309,]

Will be using k-fold cross validation on all the algorithms creating the k-fold parameters, k is 10

set.seed(1, sample.kind = "Rounding")

non-uniform 'Rounding' sampler used

control <- trainControl(method = "cv", number = 10, p = .9)

setting the parameters for the neural network

tuning <- data.frame(size = seq(100), decay = seq(.01,1,.1))

creating the x and y for the model. X is the data that will be used as input. Y is what we will be trying to predict as the output.

train_x <- train %>% select(Pclass, Sex, Age, SibSp, Parch, Fare, Embarked,Title,Family_size)

train_y <- train$Survived

set.seed(1, sample.kind = "Rounding")

non-uniform 'Rounding' sampler used

train_nn <- train(train_x, train_y,method = "nnet",tuneGrid = tuning,trControl = control)

non-uniform 'Rounding' sampler used

# weights:  26
initial  value 563.356146 
iter  10 value 534.158866
iter  20 value 457.956583
iter  30 value 366.754245
iter  40 value 333.869718
iter  50 value 332.300033
iter  60 value 331.586753
iter  70 value 324.478468
iter  80 value 317.892422
iter  90 value 317.747708
iter 100 value 317.747123
final  value 317.747123 
stopped after 100 iterations
# weights:  51
initial  value 552.058409 
iter  10 value 490.106189
iter  20 value 409.807056
iter  30 value 356.445504
iter  40 value 327.502472
iter  50 value 323.728838
iter  60 value 321.792075
iter  70 value 321.507247
iter  80 value 321.323004
iter  90 value 321.318117
final  value 321.318029 
converged
# weights:  76
initial  value 532.515087 
iter  10 value 485.936776
iter  20 value 453.452084
iter  30 value 375.129588
iter  40 value 364.457125
iter  50 value 337.408644
iter  60 value 327.787108
iter  70 value 324.493599
iter  80 value 321.128576
iter  90 value 319.541479
iter 100 value 318.884459
final  value 318.884459 
stopped after 100 iterations
# weights:  101
initial  value 729.023397 
iter  10 value 485.874570
iter  20 value 462.729999
iter  30 value 380.662727
iter  40 value 344.516652
iter  50 value 334.549368
iter  60 value 332.562148
iter  70 value 331.543189
iter  80 value 329.830242
iter  90 value 329.762147
iter 100 value 329.756776
final  value 329.756776 
stopped after 100 iterations
# weights:  126
initial  value 558.974501 
iter  10 value 478.589283
iter  20 value 422.820709
iter  30 value 380.255975
iter  40 value 351.000935
iter  50 value 338.237564
iter  60 value 333.987001
iter  70 value 332.831143
iter  80 value 332.482662
iter  90 value 332.428947
iter 100 value 332.427478
final  value 332.427478 
stopped after 100 iterations
# weights:  151
initial  value 717.353845 
iter  10 value 494.522029
iter  20 value 438.714841
iter  30 value 375.671163
iter  40 value 356.821806
iter  50 value 351.797381
iter  60 value 349.608384
iter  70 value 348.079551
iter  80 value 344.889959
iter  90 value 340.910810
iter 100 value 339.839476
final  value 339.839476 
stopped after 100 iterations
# weights:  176
initial  value 609.845324 
iter  10 value 465.120811
iter  20 value 387.814609
iter  30 value 361.410496
iter  40 value 350.829108
iter  50 value 348.678118
iter  60 value 347.185736
iter  70 value 346.209825
iter  80 value 345.624929
iter  90 value 345.302940
iter 100 value 345.145625
final  value 345.145625 
stopped after 100 iterations
# weights:  201
initial  value 548.822028 
iter  10 value 481.359917
iter  20 value 432.175330
iter  30 value 370.924273
iter  40 value 357.686246
iter  50 value 353.607813
iter  60 value 351.087213
iter  70 value 350.053702
iter  80 value 349.532337
iter  90 value 349.370757
iter 100 value 349.227831
final  value 349.227831 
stopped after 100 iterations
# weights:  226
initial  value 579.164439 
iter  10 value 492.409784
iter  20 value 416.571051
iter  30 value 392.376603
iter  40 value 366.483648
iter  50 value 361.554756
iter  60 value 359.370760
iter  70 value 355.125686
iter  80 value 353.508812
iter  90 value 352.755612
iter 100 value 352.682825
final  value 352.682825 
stopped after 100 iterations
# weights:  251
initial  value 641.129001 
iter  10 value 509.382356
iter  20 value 447.474993
iter  30 value 416.633052
iter  40 value 381.047671
iter  50 value 365.263160
iter  60 value 362.635731
iter  70 value 359.438391
iter  80 value 357.751049
iter  90 value 356.889730
iter 100 value 356.399752
final  value 356.399752 
stopped after 100 iterations
# weights:  276
initial  value 750.161861 
iter  10 value 498.193200
iter  20 value 433.982928
iter  30 value 386.271945
iter  40 value 361.350985
iter  50 value 355.565248
iter  60 value 345.292986
iter  70 value 321.881750
iter  80 value 308.895510
iter  90 value 299.672806
iter 100 value 290.920322
final  value 290.920322 
stopped after 100 iterations
# weights:  301
initial  value 659.056208 
iter  10 value 466.833472
iter  20 value 420.929288
iter  30 value 392.362303
iter  40 value 350.721679
iter  50 value 328.924054
iter  60 value 317.948936
iter  70 value 313.073778
iter  80 value 309.589524
iter  90 value 304.288143
iter 100 value 298.336372
final  value 298.336372 
stopped after 100 iterations
# weights:  326
initial  value 528.854344 
iter  10 value 457.617692
iter  20 value 380.583449
iter  30 value 361.654655
iter  40 value 340.446429
iter  50 value 330.549463
iter  60 value 322.686015
iter  70 value 318.984403
iter  80 value 316.444027
iter  90 value 314.735277
iter 100 value 313.369351
final  value 313.369351 
stopped after 100 iterations
# weights:  351
initial  value 786.040483 
iter  10 value 484.148103
iter  20 value 401.502931
iter  30 value 361.406786
iter  40 value 348.326823
iter  50 value 344.498996
iter  60 value 334.276296
iter  70 value 329.989699
iter  80 value 326.509019
iter  90 value 323.762173
iter 100 value 322.851543
final  value 322.851543 
stopped after 100 iterations
# weights:  376
initial  value 601.133984 
iter  10 value 488.659955
iter  20 value 425.954082
iter  30 value 389.874699
iter  40 value 366.880249
iter  50 value 357.537625
iter  60 value 344.932531
iter  70 value 339.561379
iter  80 value 335.716182
iter  90 value 333.517931
iter 100 value 331.887555
final  value 331.887555 
stopped after 100 iterations
# weights:  401
initial  value 750.295719 
iter  10 value 521.978513
iter  20 value 456.985905
iter  30 value 382.778139
iter  40 value 363.015278
iter  50 value 343.092018
iter  60 value 340.738684
iter  70 value 339.863473
iter  80 value 338.988082
iter  90 value 338.568030
iter 100 value 338.210032
final  value 338.210032 
stopped after 100 iterations
# weights:  426
initial  value 834.988626 
iter  10 value 506.523323
iter  20 value 449.257315
iter  30 value 423.723676
iter  40 value 386.026873
iter  50 value 368.576865
iter  60 value 353.911330
iter  70 value 347.778934
iter  80 value 345.589468
iter  90 value 343.992730
iter 100 value 343.297450
final  value 343.297450 
stopped after 100 iterations
# weights:  451
initial  value 598.853155 
iter  10 value 511.305882
iter  20 value 441.981694
iter  30 value 405.371322
iter  40 value 388.608974
iter  50 value 358.957869
iter  60 value 354.081364
iter  70 value 352.089360
iter  80 value 350.802204
iter  90 value 349.835006
iter 100 value 349.147756
final  value 349.147756 
stopped after 100 iterations
# weights:  476
initial  value 738.170127 
iter  10 value 526.047908
iter  20 value 466.667549
iter  30 value 416.839158
iter  40 value 385.499599
iter  50 value 363.890555
iter  60 value 359.012694
iter  70 value 356.605008
iter  80 value 355.010346
iter  90 value 353.961134
iter 100 value 353.282174
final  value 353.282174 
stopped after 100 iterations
# weights:  501
initial  value 767.358587 
iter  10 value 536.547196
iter  20 value 461.603782
iter  30 value 401.546880
iter  40 value 377.445429
iter  50 value 364.045183
iter  60 value 360.361047
iter  70 value 358.539167
iter  80 value 357.734730
iter  90 value 356.788652
iter 100 value 355.894707
final  value 355.894707 
stopped after 100 iterations
# weights:  526
initial  value 624.988359 
iter  10 value 457.471647
iter  20 value 436.326742
iter  30 value 418.784787
iter  40 value 394.225662
iter  50 value 368.937729
iter  60 value 328.672290
iter  70 value 312.778270
iter  80 value 305.013216
iter  90 value 297.105216
iter 100 value 284.850532
final  value 284.850532 
stopped after 100 iterations
# weights:  551
initial  value 725.057010 
iter  10 value 472.911412
iter  20 value 383.773534
iter  30 value 357.794047
iter  40 value 328.270549
iter  50 value 319.223644
iter  60 value 312.515997
iter  70 value 305.869853
iter  80 value 301.413015
iter  90 value 297.059218
iter 100 value 294.517182
final  value 294.517182 
stopped after 100 iterations
# weights:  576
initial  value 571.052944 
iter  10 value 488.810998
iter  20 value 442.991025
iter  30 value 403.283286
iter  40 value 370.957611
iter  50 value 339.281661
iter  60 value 324.880841
iter  70 value 320.275978
iter  80 value 317.004454
iter  90 value 315.422393
iter 100 value 313.763495
final  value 313.763495 
stopped after 100 iterations
# weights:  601
initial  value 943.312700 
iter  10 value 507.586865
iter  20 value 470.492830
iter  30 value 407.819718
iter  40 value 380.121064
iter  50 value 361.201474
iter  60 value 345.034777
iter  70 value 336.633988
iter  80 value 331.543934
iter  90 value 328.289037
iter 100 value 325.902162
final  value 325.902162 
stopped after 100 iterations
# weights:  626
initial  value 657.893776 
iter  10 value 511.464265
iter  20 value 457.650144
iter  30 value 408.682472
iter  40 value 372.424006
iter  50 value 354.842219
iter  60 value 347.018291
iter  70 value 340.677472
iter  80 value 337.554115
iter  90 value 335.577154
iter 100 value 333.532884
final  value 333.532884 
stopped after 100 iterations
# weights:  651
initial  value 696.554500 
iter  10 value 518.245801
iter  20 value 495.767631
iter  30 value 399.751415
iter  40 value 373.909739
iter  50 value 356.171651
iter  60 value 348.430880
iter  70 value 344.671556
iter  80 value 343.191583
iter  90 value 341.823908
iter 100 value 339.739480
final  value 339.739480 
stopped after 100 iterations
# weights:  676
initial  value 846.036948 
iter  10 value 529.925036
iter  20 value 449.865582
iter  30 value 411.291235
iter  40 value 380.176825
iter  50 value 362.516076
iter  60 value 355.934910
iter  70 value 351.189809
iter  80 value 348.512801
iter  90 value 346.405045
iter 100 value 344.502301
final  value 344.502301 
stopped after 100 iterations
# weights:  701
initial  value 745.948475 
iter  10 value 535.334078
iter  20 value 476.454442
iter  30 value 411.049106
iter  40 value 387.668497
iter  50 value 369.716808
iter  60 value 360.302425
iter  70 value 354.954456
iter  80 value 351.714283
iter  90 value 350.197297
iter 100 value 349.113019
final  value 349.113019 
stopped after 100 iterations
# weights:  726
initial  value 891.139677 
iter  10 value 547.744133
iter  20 value 458.718607
iter  30 value 430.649277
iter  40 value 391.123248
iter  50 value 376.196028
iter  60 value 365.868540
iter  70 value 360.575965
iter  80 value 357.251689
iter  90 value 354.921900
iter 100 value 353.596938
final  value 353.596938 
stopped after 100 iterations
# weights:  751
initial  value 757.604185 
iter  10 value 569.070007
iter  20 value 505.622954
iter  30 value 461.273071
iter  40 value 397.440469
iter  50 value 381.576891
iter  60 value 369.622793
iter  70 value 362.879968
iter  80 value 360.609784
iter  90 value 358.927979
iter 100 value 357.644226
final  value 357.644226 
stopped after 100 iterations
# weights:  776
initial  value 1047.075031 
iter  10 value 477.221865
iter  20 value 441.777990
iter  30 value 411.940331
iter  40 value 368.085091
iter  50 value 305.866259
iter  60 value 279.172637
iter  70 value 260.012683
iter  80 value 248.769194
iter  90 value 240.304153
iter 100 value 235.225908
final  value 235.225908 
stopped after 100 iterations
# weights:  801
initial  value 746.575748 
iter  10 value 497.559812
iter  20 value 463.238778
iter  30 value 427.772973
iter  40 value 399.291294
iter  50 value 352.534213
iter  60 value 336.060016
iter  70 value 315.335481
iter  80 value 305.421703
iter  90 value 299.263297
iter 100 value 294.977477
final  value 294.977477 
stopped after 100 iterations
# weights:  826
initial  value 546.594287 
iter  10 value 493.819027
iter  20 value 418.441795
iter  30 value 378.705105
iter  40 value 352.863407
iter  50 value 338.140083
iter  60 value 330.147619
iter  70 value 325.224220
iter  80 value 320.980953
iter  90 value 316.256584
iter 100 value 312.902713
final  value 312.902713 
stopped after 100 iterations
# weights:  851
initial  value 1640.832375 
iter  10 value 525.143945
iter  20 value 458.044957
iter  30 value 440.743748
iter  40 value 419.256882
iter  50 value 391.155937
iter  60 value 366.661297
iter  70 value 347.464103
iter  80 value 336.830916
iter  90 value 327.999749
iter 100 value 324.360181
final  value 324.360181 
stopped after 100 iterations
# weights:  876
initial  value 649.374638 
iter  10 value 516.994531
iter  20 value 423.168816
iter  30 value 385.832146
iter  40 value 361.635459
iter  50 value 353.836356
iter  60 value 342.801404
iter  70 value 337.605878
iter  80 value 334.678060
iter  90 value 332.690756
iter 100 value 331.563852
final  value 331.563852 
stopped after 100 iterations
# weights:  901
initial  value 973.496007 
iter  10 value 539.825966
iter  20 value 446.313489
iter  30 value 397.522600
iter  40 value 372.553102
iter  50 value 358.194807
iter  60 value 348.131263
iter  70 value 344.117745
iter  80 value 340.248962
iter  90 value 338.590608
iter 100 value 337.876744
final  value 337.876744 
stopped after 100 iterations
# weights:  926
initial  value 602.791558 
iter  10 value 540.468735
iter  20 value 447.241249
iter  30 value 410.606832
iter  40 value 388.390166
iter  50 value 369.113073
iter  60 value 358.565799
iter  70 value 352.739754
iter  80 value 349.919899
iter  90 value 348.121852
iter 100 value 345.751403
final  value 345.751403 
stopped after 100 iterations
# weights:  951
initial  value 1561.099069 
iter  10 value 566.250621
iter  20 value 452.742718
iter  30 value 430.224074
iter  40 value 399.166985
iter  50 value 367.914463
iter  60 value 358.825498
iter  70 value 354.077631
iter  80 value 351.021308
iter  90 value 349.650146
iter 100 value 348.476823
final  value 348.476823 
stopped after 100 iterations
# weights:  976
initial  value 767.159365 
iter  10 value 569.806705
iter  20 value 515.932997
iter  30 value 483.608749
iter  40 value 405.436011
iter  50 value 371.723697
iter  60 value 363.032987
iter  70 value 358.355532
iter  80 value 355.545626
iter  90 value 354.065561
iter 100 value 353.058831
final  value 353.058831 
stopped after 100 iterations

model fit failed for Fold01: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold01: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold01: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold01: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold01: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold01: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold01: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold01: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold01: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold01: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold01: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold01: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold01: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold01: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold01: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold01: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold01: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold01: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold01: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold01: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold01: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold01: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold01: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold01: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold01: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold01: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold01: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold01: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold01: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold01: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold01: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold01: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold01: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold01: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold01: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold01: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold01: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold01: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold01: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold01: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold01: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold01: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold01: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold01: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold01: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold01: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold01: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold01: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold01: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold01: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold01: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold01: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold01: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold01: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold01: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold01: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold01: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold01: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold01: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold01: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold01: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 554.758857 
iter  10 value 483.674541
iter  20 value 391.994220
iter  30 value 312.470134
iter  40 value 300.897541
iter  50 value 300.498324
iter  60 value 300.490147
final  value 300.489681 
converged
# weights:  51
initial  value 554.153548 
iter  10 value 451.441057
iter  20 value 415.015224
iter  30 value 357.918227
iter  40 value 318.970945
iter  50 value 311.003330
iter  60 value 309.566293
iter  70 value 309.541485
final  value 309.541317 
converged
# weights:  76
initial  value 738.233270 
iter  10 value 487.978369
iter  20 value 409.749635
iter  30 value 335.267368
iter  40 value 327.502160
iter  50 value 318.789582
iter  60 value 318.002268
iter  70 value 317.773352
iter  80 value 317.350132
final  value 317.337024 
converged
# weights:  101
initial  value 602.779827 
iter  10 value 451.729488
iter  20 value 379.070902
iter  30 value 341.236472
iter  40 value 325.085076
iter  50 value 318.303791
iter  60 value 312.494369
iter  70 value 310.404045
iter  80 value 309.803164
iter  90 value 309.620511
iter 100 value 309.468184
final  value 309.468184 
stopped after 100 iterations
# weights:  126
initial  value 600.800527 
iter  10 value 478.191176
iter  20 value 400.730067
iter  30 value 366.118698
iter  40 value 336.348542
iter  50 value 325.370330
iter  60 value 322.202005
iter  70 value 319.474884
iter  80 value 315.004213
iter  90 value 314.184592
iter 100 value 314.157253
final  value 314.157253 
stopped after 100 iterations
# weights:  151
initial  value 615.827287 
iter  10 value 456.573160
iter  20 value 364.976332
iter  30 value 335.513954
iter  40 value 325.951292
iter  50 value 324.829788
iter  60 value 323.634869
iter  70 value 322.294120
iter  80 value 321.328401
iter  90 value 320.741061
iter 100 value 319.760599
final  value 319.760599 
stopped after 100 iterations
# weights:  176
initial  value 638.527241 
iter  10 value 500.015886
iter  20 value 472.535451
iter  30 value 419.439017
iter  40 value 373.306166
iter  50 value 340.313711
iter  60 value 332.899946
iter  70 value 329.483047
iter  80 value 326.472077
iter  90 value 325.854527
iter 100 value 325.343309
final  value 325.343309 
stopped after 100 iterations
# weights:  201
initial  value 582.662461 
iter  10 value 485.628188
iter  20 value 416.714259
iter  30 value 366.179569
iter  40 value 346.544846
iter  50 value 336.663495
iter  60 value 332.583211
iter  70 value 330.837973
iter  80 value 330.508098
iter  90 value 330.379915
iter 100 value 330.125472
final  value 330.125472 
stopped after 100 iterations
# weights:  226
initial  value 716.287316 
iter  10 value 485.089169
iter  20 value 431.859305
iter  30 value 385.150802
iter  40 value 356.964061
iter  50 value 342.528591
iter  60 value 337.629646
iter  70 value 336.417262
iter  80 value 335.509758
iter  90 value 334.867902
iter 100 value 333.787539
final  value 333.787539 
stopped after 100 iterations
# weights:  251
initial  value 691.928656 
iter  10 value 471.714303
iter  20 value 402.352156
iter  30 value 365.493364
iter  40 value 350.239161
iter  50 value 342.450212
iter  60 value 340.315815
iter  70 value 339.205821
iter  80 value 338.454889
iter  90 value 337.338261
iter 100 value 336.497378
final  value 336.497378 
stopped after 100 iterations
# weights:  276
initial  value 584.174795 
iter  10 value 473.566685
iter  20 value 422.931282
iter  30 value 335.354395
iter  40 value 324.985634
iter  50 value 307.077665
iter  60 value 286.857448
iter  70 value 264.326105
iter  80 value 257.426420
iter  90 value 255.653179
iter 100 value 254.463938
final  value 254.463938 
stopped after 100 iterations
# weights:  301
initial  value 858.362163 
iter  10 value 479.637019
iter  20 value 367.302087
iter  30 value 329.370338
iter  40 value 322.584097
iter  50 value 307.456783
iter  60 value 294.903025
iter  70 value 287.929240
iter  80 value 283.944264
iter  90 value 279.711710
iter 100 value 272.765762
final  value 272.765762 
stopped after 100 iterations
# weights:  326
initial  value 650.886283 
iter  10 value 468.123796
iter  20 value 428.007374
iter  30 value 387.207633
iter  40 value 347.324965
iter  50 value 324.338870
iter  60 value 314.821609
iter  70 value 306.006265
iter  80 value 299.683743
iter  90 value 294.883407
iter 100 value 291.551769
final  value 291.551769 
stopped after 100 iterations
# weights:  351
initial  value 855.645219 
iter  10 value 489.971344
iter  20 value 420.829206
iter  30 value 368.216725
iter  40 value 330.140933
iter  50 value 321.966104
iter  60 value 316.334738
iter  70 value 312.236821
iter  80 value 311.252317
iter  90 value 307.115223
iter 100 value 303.598263
final  value 303.598263 
stopped after 100 iterations
# weights:  376
initial  value 524.202159 
iter  10 value 475.164568
iter  20 value 391.291380
iter  30 value 342.635073
iter  40 value 325.917664
iter  50 value 321.442427
iter  60 value 316.118809
iter  70 value 313.680085
iter  80 value 312.350553
iter  90 value 311.703928
iter 100 value 310.785216
final  value 310.785216 
stopped after 100 iterations
# weights:  401
initial  value 595.293758 
iter  10 value 494.748936
iter  20 value 449.577945
iter  30 value 417.321846
iter  40 value 388.951428
iter  50 value 350.547766
iter  60 value 335.526248
iter  70 value 328.859227
iter  80 value 321.854796
iter  90 value 320.293558
iter 100 value 319.553493
final  value 319.553493 
stopped after 100 iterations
# weights:  426
initial  value 641.814870 
iter  10 value 505.999710
iter  20 value 406.904298
iter  30 value 353.457933
iter  40 value 347.168856
iter  50 value 339.468259
iter  60 value 331.631440
iter  70 value 327.063179
iter  80 value 325.193883
iter  90 value 324.292803
iter 100 value 323.865646
final  value 323.865646 
stopped after 100 iterations
# weights:  451
initial  value 1183.824277 
iter  10 value 519.675360
iter  20 value 461.141557
iter  30 value 394.975213
iter  40 value 365.261126
iter  50 value 342.984309
iter  60 value 337.924943
iter  70 value 333.162580
iter  80 value 330.334954
iter  90 value 328.730653
iter 100 value 328.022020
final  value 328.022020 
stopped after 100 iterations
# weights:  476
initial  value 594.938532 
iter  10 value 514.682159
iter  20 value 445.295713
iter  30 value 383.399909
iter  40 value 357.754267
iter  50 value 344.516657
iter  60 value 338.784903
iter  70 value 335.723912
iter  80 value 333.846115
iter  90 value 333.193355
iter 100 value 332.368910
final  value 332.368910 
stopped after 100 iterations
# weights:  501
initial  value 724.536430 
iter  10 value 525.406626
iter  20 value 475.279163
iter  30 value 400.477237
iter  40 value 369.325863
iter  50 value 358.684215
iter  60 value 347.785444
iter  70 value 343.644940
iter  80 value 341.417494
iter  90 value 339.609842
iter 100 value 338.048864
final  value 338.048864 
stopped after 100 iterations
# weights:  526
initial  value 896.843470 
iter  10 value 454.000055
iter  20 value 396.183007
iter  30 value 343.957428
iter  40 value 314.758184
iter  50 value 300.775419
iter  60 value 293.533340
iter  70 value 283.378154
iter  80 value 265.424714
iter  90 value 256.767692
iter 100 value 250.211220
final  value 250.211220 
stopped after 100 iterations
# weights:  551
initial  value 675.117327 
iter  10 value 480.986192
iter  20 value 430.332645
iter  30 value 399.307319
iter  40 value 353.069660
iter  50 value 318.720214
iter  60 value 295.987232
iter  70 value 284.002786
iter  80 value 276.883817
iter  90 value 271.242148
iter 100 value 267.457855
final  value 267.457855 
stopped after 100 iterations
# weights:  576
initial  value 972.336554 
iter  10 value 491.281072
iter  20 value 399.002773
iter  30 value 356.483165
iter  40 value 329.629526
iter  50 value 318.302816
iter  60 value 306.528024
iter  70 value 300.818970
iter  80 value 297.425690
iter  90 value 293.823201
iter 100 value 290.933660
final  value 290.933660 
stopped after 100 iterations
# weights:  601
initial  value 1212.810201 
iter  10 value 485.811078
iter  20 value 417.585041
iter  30 value 378.204219
iter  40 value 346.219725
iter  50 value 325.588036
iter  60 value 316.605531
iter  70 value 310.420732
iter  80 value 306.007987
iter  90 value 302.729060
iter 100 value 299.956330
final  value 299.956330 
stopped after 100 iterations
# weights:  626
initial  value 1207.270202 
iter  10 value 490.232460
iter  20 value 403.006319
iter  30 value 375.829544
iter  40 value 348.442890
iter  50 value 327.668831
iter  60 value 322.224560
iter  70 value 317.377579
iter  80 value 314.378750
iter  90 value 312.891602
iter 100 value 311.639424
final  value 311.639424 
stopped after 100 iterations
# weights:  651
initial  value 1112.508958 
iter  10 value 521.484078
iter  20 value 471.215558
iter  30 value 447.699447
iter  40 value 382.883382
iter  50 value 355.356374
iter  60 value 339.899725
iter  70 value 329.648651
iter  80 value 322.571040
iter  90 value 320.763269
iter 100 value 319.265221
final  value 319.265221 
stopped after 100 iterations
# weights:  676
initial  value 671.596662 
iter  10 value 523.802182
iter  20 value 493.392710
iter  30 value 442.602626
iter  40 value 363.262855
iter  50 value 344.746578
iter  60 value 339.809119
iter  70 value 334.192297
iter  80 value 330.897215
iter  90 value 328.272788
iter 100 value 326.455839
final  value 326.455839 
stopped after 100 iterations
# weights:  701
initial  value 652.118467 
iter  10 value 510.861479
iter  20 value 451.690030
iter  30 value 392.183784
iter  40 value 352.047013
iter  50 value 344.079784
iter  60 value 338.291930
iter  70 value 334.725816
iter  80 value 333.103503
iter  90 value 330.809841
iter 100 value 329.247098
final  value 329.247098 
stopped after 100 iterations
# weights:  726
initial  value 828.539681 
iter  10 value 511.031512
iter  20 value 406.594667
iter  30 value 379.386673
iter  40 value 369.100524
iter  50 value 355.178831
iter  60 value 347.327426
iter  70 value 340.188748
iter  80 value 336.489860
iter  90 value 334.843326
iter 100 value 333.435433
final  value 333.435433 
stopped after 100 iterations
# weights:  751
initial  value 1419.533302 
iter  10 value 566.490673
iter  20 value 477.574573
iter  30 value 406.685464
iter  40 value 367.250623
iter  50 value 354.833893
iter  60 value 345.282852
iter  70 value 342.815688
iter  80 value 340.659733
iter  90 value 339.091414
iter 100 value 337.253804
final  value 337.253804 
stopped after 100 iterations
# weights:  776
initial  value 1199.750971 
iter  10 value 431.842693
iter  20 value 335.860988
iter  30 value 315.364928
iter  40 value 297.582793
iter  50 value 281.706035
iter  60 value 256.681838
iter  70 value 246.117910
iter  80 value 232.466931
iter  90 value 224.035294
iter 100 value 211.892366
final  value 211.892366 
stopped after 100 iterations
# weights:  801
initial  value 539.818923 
iter  10 value 474.823361
iter  20 value 420.324053
iter  30 value 349.311557
iter  40 value 323.194253
iter  50 value 316.302609
iter  60 value 303.738534
iter  70 value 292.608597
iter  80 value 286.076072
iter  90 value 280.140663
iter 100 value 276.057860
final  value 276.057860 
stopped after 100 iterations
# weights:  826
initial  value 581.410204 
iter  10 value 495.318543
iter  20 value 420.776964
iter  30 value 385.752638
iter  40 value 363.766294
iter  50 value 336.901355
iter  60 value 316.452288
iter  70 value 300.641169
iter  80 value 295.565059
iter  90 value 292.183497
iter 100 value 289.456057
final  value 289.456057 
stopped after 100 iterations
# weights:  851
initial  value 701.677889 
iter  10 value 511.431134
iter  20 value 463.181775
iter  30 value 413.708203
iter  40 value 353.598397
iter  50 value 334.264767
iter  60 value 323.808007
iter  70 value 316.978933
iter  80 value 312.920698
iter  90 value 308.668165
iter 100 value 305.369309
final  value 305.369309 
stopped after 100 iterations
# weights:  876
initial  value 593.397095 
iter  10 value 500.601722
iter  20 value 444.497888
iter  30 value 406.134888
iter  40 value 374.990161
iter  50 value 349.206503
iter  60 value 331.006093
iter  70 value 320.353679
iter  80 value 316.136771
iter  90 value 312.467577
iter 100 value 310.772858
final  value 310.772858 
stopped after 100 iterations
# weights:  901
initial  value 620.387539 
iter  10 value 519.264751
iter  20 value 430.543735
iter  30 value 395.307853
iter  40 value 366.201104
iter  50 value 339.859824
iter  60 value 330.246649
iter  70 value 324.166362
iter  80 value 321.103471
iter  90 value 319.463130
iter 100 value 318.222855
final  value 318.222855 
stopped after 100 iterations
# weights:  926
initial  value 592.079550 
iter  10 value 528.862590
iter  20 value 447.866204
iter  30 value 405.411429
iter  40 value 370.429794
iter  50 value 348.147834
iter  60 value 339.758699
iter  70 value 333.269054
iter  80 value 330.698376
iter  90 value 327.519220
iter 100 value 325.576476
final  value 325.576476 
stopped after 100 iterations
# weights:  951
initial  value 888.949730 
iter  10 value 542.247464
iter  20 value 489.273822
iter  30 value 412.267889
iter  40 value 385.665861
iter  50 value 361.991577
iter  60 value 344.218207
iter  70 value 338.247301
iter  80 value 333.996542
iter  90 value 332.109499
iter 100 value 330.830344
final  value 330.830344 
stopped after 100 iterations
# weights:  976
initial  value 1427.349439 
iter  10 value 549.624810
iter  20 value 453.816323
iter  30 value 419.975578
iter  40 value 377.094471
iter  50 value 357.710511
iter  60 value 346.005115
iter  70 value 341.258631
iter  80 value 339.174196
iter  90 value 337.591270
iter 100 value 335.638512
final  value 335.638512 
stopped after 100 iterations

model fit failed for Fold02: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold02: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold02: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold02: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold02: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold02: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold02: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold02: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold02: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold02: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold02: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold02: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold02: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold02: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold02: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold02: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold02: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold02: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold02: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold02: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold02: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold02: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold02: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold02: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold02: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold02: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold02: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold02: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold02: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold02: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold02: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold02: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold02: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold02: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold02: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold02: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold02: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold02: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold02: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold02: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold02: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold02: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold02: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold02: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold02: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold02: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold02: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold02: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold02: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold02: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold02: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold02: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold02: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold02: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold02: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold02: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold02: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold02: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold02: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold02: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold02: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 536.317689 
iter  10 value 487.069207
iter  20 value 473.217510
iter  30 value 406.353977
iter  40 value 335.436001
iter  50 value 319.997213
iter  60 value 318.890403
iter  70 value 310.132500
iter  80 value 301.686971
iter  90 value 301.223076
iter 100 value 300.992280
final  value 300.992280 
stopped after 100 iterations
# weights:  51
initial  value 538.894660 
iter  10 value 472.474193
iter  20 value 358.715881
iter  30 value 318.606526
iter  40 value 305.512515
iter  50 value 302.901311
iter  60 value 302.193764
iter  70 value 302.164616
iter  80 value 302.157984
iter  80 value 302.157983
iter  80 value 302.157983
final  value 302.157983 
converged
# weights:  76
initial  value 585.749925 
iter  10 value 482.677003
iter  20 value 357.048314
iter  30 value 333.074227
iter  40 value 321.352343
iter  50 value 310.427938
iter  60 value 308.269796
iter  70 value 305.199723
iter  80 value 304.668618
iter  90 value 304.039676
iter 100 value 303.948481
final  value 303.948481 
stopped after 100 iterations
# weights:  101
initial  value 771.638005 
iter  10 value 497.196399
iter  20 value 456.105443
iter  30 value 405.787710
iter  40 value 345.841422
iter  50 value 328.068494
iter  60 value 324.392013
iter  70 value 323.377778
iter  80 value 319.987326
iter  90 value 312.433894
iter 100 value 310.598869
final  value 310.598869 
stopped after 100 iterations
# weights:  126
initial  value 888.934757 
iter  10 value 480.908490
iter  20 value 400.722937
iter  30 value 352.169149
iter  40 value 333.543533
iter  50 value 326.752567
iter  60 value 323.794741
iter  70 value 322.372623
iter  80 value 321.641895
iter  90 value 319.608957
iter 100 value 317.020334
final  value 317.020334 
stopped after 100 iterations
# weights:  151
initial  value 514.491621 
iter  10 value 466.293531
iter  20 value 366.723563
iter  30 value 338.942341
iter  40 value 328.979332
iter  50 value 323.468352
iter  60 value 321.938235
iter  70 value 321.524468
iter  80 value 321.373225
iter  90 value 321.287054
iter 100 value 321.189232
final  value 321.189232 
stopped after 100 iterations
# weights:  176
initial  value 596.664931 
iter  10 value 490.574918
iter  20 value 435.345004
iter  30 value 375.497665
iter  40 value 343.619431
iter  50 value 336.847312
iter  60 value 333.240353
iter  70 value 331.930311
iter  80 value 330.730028
iter  90 value 328.364543
iter 100 value 327.597521
final  value 327.597521 
stopped after 100 iterations
# weights:  201
initial  value 644.366927 
iter  10 value 496.506579
iter  20 value 471.978317
iter  30 value 450.130200
iter  40 value 387.147160
iter  50 value 357.535712
iter  60 value 345.259631
iter  70 value 342.027774
iter  80 value 339.597276
iter  90 value 336.633632
iter 100 value 333.369900
final  value 333.369900 
stopped after 100 iterations
# weights:  226
initial  value 698.655450 
iter  10 value 515.533422
iter  20 value 446.795511
iter  30 value 392.471250
iter  40 value 356.464430
iter  50 value 347.235246
iter  60 value 343.193322
iter  70 value 341.681128
iter  80 value 340.847663
iter  90 value 339.601829
iter 100 value 338.047822
final  value 338.047822 
stopped after 100 iterations
# weights:  251
initial  value 688.413856 
iter  10 value 492.136771
iter  20 value 451.093765
iter  30 value 397.920224
iter  40 value 375.331176
iter  50 value 358.134741
iter  60 value 349.946491
iter  70 value 345.938187
iter  80 value 344.515143
iter  90 value 343.706485
iter 100 value 341.733460
final  value 341.733460 
stopped after 100 iterations
# weights:  276
initial  value 710.012195 
iter  10 value 482.996486
iter  20 value 419.174636
iter  30 value 322.987650
iter  40 value 312.311372
iter  50 value 301.803781
iter  60 value 294.022508
iter  70 value 280.700796
iter  80 value 270.865773
iter  90 value 269.287246
iter 100 value 262.355705
final  value 262.355705 
stopped after 100 iterations
# weights:  301
initial  value 619.689895 
iter  10 value 468.153148
iter  20 value 382.560114
iter  30 value 335.635407
iter  40 value 310.393684
iter  50 value 301.504313
iter  60 value 296.518890
iter  70 value 290.467383
iter  80 value 286.015765
iter  90 value 281.869116
iter 100 value 278.427575
final  value 278.427575 
stopped after 100 iterations
# weights:  326
initial  value 697.675423 
iter  10 value 486.980680
iter  20 value 388.212355
iter  30 value 348.177718
iter  40 value 323.666966
iter  50 value 314.527235
iter  60 value 306.071484
iter  70 value 302.847232
iter  80 value 300.023901
iter  90 value 297.227858
iter 100 value 295.469982
final  value 295.469982 
stopped after 100 iterations
# weights:  351
initial  value 605.102414 
iter  10 value 457.011918
iter  20 value 433.836748
iter  30 value 397.190106
iter  40 value 357.377637
iter  50 value 331.125857
iter  60 value 323.271672
iter  70 value 318.888000
iter  80 value 313.327871
iter  90 value 308.213760
iter 100 value 305.996247
final  value 305.996247 
stopped after 100 iterations
# weights:  376
initial  value 753.400372 
iter  10 value 492.840676
iter  20 value 408.743993
iter  30 value 354.600506
iter  40 value 335.628472
iter  50 value 327.141166
iter  60 value 322.106484
iter  70 value 318.514845
iter  80 value 316.161547
iter  90 value 314.493104
iter 100 value 313.438480
final  value 313.438480 
stopped after 100 iterations
# weights:  401
initial  value 710.739663 
iter  10 value 495.799598
iter  20 value 426.851047
iter  30 value 368.524269
iter  40 value 347.484297
iter  50 value 337.784892
iter  60 value 327.887876
iter  70 value 324.147535
iter  80 value 322.044084
iter  90 value 321.363683
iter 100 value 320.606942
final  value 320.606942 
stopped after 100 iterations
# weights:  426
initial  value 571.154282 
iter  10 value 494.958176
iter  20 value 458.255609
iter  30 value 382.928040
iter  40 value 356.032912
iter  50 value 338.698897
iter  60 value 332.369961
iter  70 value 330.414755
iter  80 value 329.139740
iter  90 value 327.749066
iter 100 value 326.470486
final  value 326.470486 
stopped after 100 iterations
# weights:  451
initial  value 595.365068 
iter  10 value 493.643166
iter  20 value 445.432399
iter  30 value 404.828123
iter  40 value 383.073778
iter  50 value 354.862664
iter  60 value 342.767194
iter  70 value 338.157370
iter  80 value 334.227850
iter  90 value 332.017220
iter 100 value 330.786458
final  value 330.786458 
stopped after 100 iterations
# weights:  476
initial  value 565.653268 
iter  10 value 493.550320
iter  20 value 454.367767
iter  30 value 398.612728
iter  40 value 368.608142
iter  50 value 356.950448
iter  60 value 344.708888
iter  70 value 339.404192
iter  80 value 336.734351
iter  90 value 335.502218
iter 100 value 335.031397
final  value 335.031397 
stopped after 100 iterations
# weights:  501
initial  value 900.828508 
iter  10 value 513.833460
iter  20 value 437.269043
iter  30 value 402.294938
iter  40 value 370.837273
iter  50 value 351.538632
iter  60 value 345.379215
iter  70 value 343.585106
iter  80 value 341.482668
iter  90 value 340.063577
iter 100 value 339.143250
final  value 339.143250 
stopped after 100 iterations
# weights:  526
initial  value 601.584031 
iter  10 value 454.196594
iter  20 value 360.929025
iter  30 value 311.593092
iter  40 value 286.621965
iter  50 value 271.235440
iter  60 value 261.525215
iter  70 value 253.165428
iter  80 value 246.128744
iter  90 value 237.052733
iter 100 value 227.296418
final  value 227.296418 
stopped after 100 iterations
# weights:  551
initial  value 752.290603 
iter  10 value 488.433778
iter  20 value 393.752595
iter  30 value 350.949551
iter  40 value 316.619266
iter  50 value 303.374305
iter  60 value 294.574621
iter  70 value 286.104602
iter  80 value 281.467456
iter  90 value 277.090922
iter 100 value 273.262903
final  value 273.262903 
stopped after 100 iterations
# weights:  576
initial  value 1082.213903 
iter  10 value 484.434621
iter  20 value 395.444255
iter  30 value 360.660389
iter  40 value 342.579971
iter  50 value 322.608024
iter  60 value 315.213125
iter  70 value 307.857571
iter  80 value 304.272225
iter  90 value 301.121334
iter 100 value 296.677311
final  value 296.677311 
stopped after 100 iterations
# weights:  601
initial  value 644.308341 
iter  10 value 480.757371
iter  20 value 411.922093
iter  30 value 381.302090
iter  40 value 345.194658
iter  50 value 328.400749
iter  60 value 318.021172
iter  70 value 309.577768
iter  80 value 306.441417
iter  90 value 304.743978
iter 100 value 303.234460
final  value 303.234460 
stopped after 100 iterations
# weights:  626
initial  value 1386.851104 
iter  10 value 494.517851
iter  20 value 412.485816
iter  30 value 380.010931
iter  40 value 344.590085
iter  50 value 329.591674
iter  60 value 323.230175
iter  70 value 319.357210
iter  80 value 315.256390
iter  90 value 314.270523
iter 100 value 313.093678
final  value 313.093678 
stopped after 100 iterations
# weights:  651
initial  value 543.465251 
iter  10 value 466.539159
iter  20 value 422.729440
iter  30 value 380.209767
iter  40 value 345.643863
iter  50 value 329.941328
iter  60 value 323.559386
iter  70 value 320.564682
iter  80 value 319.029090
iter  90 value 318.156479
iter 100 value 317.703808
final  value 317.703808 
stopped after 100 iterations
# weights:  676
initial  value 565.742137 
iter  10 value 513.554433
iter  20 value 420.854586
iter  30 value 365.754321
iter  40 value 349.956087
iter  50 value 338.727485
iter  60 value 332.476462
iter  70 value 330.234308
iter  80 value 328.124929
iter  90 value 325.905788
iter 100 value 324.939391
final  value 324.939391 
stopped after 100 iterations
# weights:  701
initial  value 729.085877 
iter  10 value 506.028981
iter  20 value 435.018037
iter  30 value 405.287295
iter  40 value 362.316500
iter  50 value 346.149543
iter  60 value 341.368297
iter  70 value 337.917991
iter  80 value 335.721591
iter  90 value 333.698467
iter 100 value 332.190134
final  value 332.190134 
stopped after 100 iterations
# weights:  726
initial  value 774.941095 
iter  10 value 514.324104
iter  20 value 452.126407
iter  30 value 370.293220
iter  40 value 355.176705
iter  50 value 349.005240
iter  60 value 342.671747
iter  70 value 339.121555
iter  80 value 337.718179
iter  90 value 336.430479
iter 100 value 335.403953
final  value 335.403953 
stopped after 100 iterations
# weights:  751
initial  value 1016.466446 
iter  10 value 545.576834
iter  20 value 475.477064
iter  30 value 424.435349
iter  40 value 394.436001
iter  50 value 367.667146
iter  60 value 357.053060
iter  70 value 350.234134
iter  80 value 346.193085
iter  90 value 343.720945
iter 100 value 341.532468
final  value 341.532468 
stopped after 100 iterations
# weights:  776
initial  value 555.613219 
iter  10 value 471.575218
iter  20 value 373.557982
iter  30 value 341.841386
iter  40 value 315.855445
iter  50 value 297.377623
iter  60 value 279.112212
iter  70 value 273.615456
iter  80 value 269.656963
iter  90 value 261.462792
iter 100 value 241.227557
final  value 241.227557 
stopped after 100 iterations
# weights:  801
initial  value 526.076835 
iter  10 value 473.593904
iter  20 value 396.266479
iter  30 value 337.183420
iter  40 value 318.783055
iter  50 value 306.335908
iter  60 value 294.161774
iter  70 value 288.631419
iter  80 value 283.037293
iter  90 value 279.638944
iter 100 value 276.241747
final  value 276.241747 
stopped after 100 iterations
# weights:  826
initial  value 957.395916 
iter  10 value 517.937411
iter  20 value 480.601245
iter  30 value 446.927221
iter  40 value 406.461950
iter  50 value 365.792049
iter  60 value 332.031287
iter  70 value 321.412047
iter  80 value 310.570467
iter  90 value 306.224960
iter 100 value 302.489532
final  value 302.489532 
stopped after 100 iterations
# weights:  851
initial  value 664.088777 
iter  10 value 518.554307
iter  20 value 447.577462
iter  30 value 399.980108
iter  40 value 365.496625
iter  50 value 350.918161
iter  60 value 340.123767
iter  70 value 327.384406
iter  80 value 317.692911
iter  90 value 311.996477
iter 100 value 307.265393
final  value 307.265393 
stopped after 100 iterations
# weights:  876
initial  value 615.712282 
iter  10 value 507.032113
iter  20 value 440.967424
iter  30 value 409.361897
iter  40 value 372.402587
iter  50 value 347.521443
iter  60 value 337.049675
iter  70 value 329.512271
iter  80 value 323.431745
iter  90 value 320.390273
iter 100 value 316.821445
final  value 316.821445 
stopped after 100 iterations
# weights:  901
initial  value 897.994963 
iter  10 value 503.387047
iter  20 value 457.401837
iter  30 value 414.762845
iter  40 value 375.990991
iter  50 value 353.238003
iter  60 value 339.982100
iter  70 value 333.130221
iter  80 value 328.213181
iter  90 value 325.198405
iter 100 value 323.209604
final  value 323.209604 
stopped after 100 iterations
# weights:  926
initial  value 577.043023 
iter  10 value 516.377335
iter  20 value 443.977102
iter  30 value 403.236433
iter  40 value 374.802782
iter  50 value 350.889160
iter  60 value 339.032767
iter  70 value 333.963549
iter  80 value 329.698004
iter  90 value 327.634362
iter 100 value 326.060076
final  value 326.060076 
stopped after 100 iterations
# weights:  951
initial  value 837.605803 
iter  10 value 556.239968
iter  20 value 458.511459
iter  30 value 419.281269
iter  40 value 381.063045
iter  50 value 358.792322
iter  60 value 348.755400
iter  70 value 341.562722
iter  80 value 338.997830
iter  90 value 335.864669
iter 100 value 333.031667
final  value 333.031667 
stopped after 100 iterations
# weights:  976
initial  value 1035.219894 
iter  10 value 524.938664
iter  20 value 467.522178
iter  30 value 386.715121
iter  40 value 367.320745
iter  50 value 352.974134
iter  60 value 346.419272
iter  70 value 342.248020
iter  80 value 339.282778
iter  90 value 337.833560
iter 100 value 336.567287
final  value 336.567287 
stopped after 100 iterations

model fit failed for Fold03: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold03: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold03: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold03: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold03: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold03: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold03: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold03: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold03: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold03: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold03: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold03: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold03: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold03: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold03: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold03: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold03: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold03: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold03: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold03: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold03: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold03: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold03: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold03: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold03: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold03: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold03: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold03: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold03: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold03: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold03: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold03: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold03: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold03: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold03: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold03: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold03: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold03: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold03: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold03: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold03: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold03: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold03: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold03: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold03: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold03: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold03: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold03: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold03: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold03: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold03: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold03: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold03: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold03: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold03: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold03: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold03: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold03: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold03: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold03: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold03: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 532.495098 
iter  10 value 420.681514
iter  20 value 358.184575
iter  30 value 319.055907
iter  40 value 312.894571
iter  50 value 310.620445
iter  60 value 309.448657
iter  70 value 306.062964
iter  80 value 298.036379
iter  90 value 297.491796
iter 100 value 297.490226
final  value 297.490226 
stopped after 100 iterations
# weights:  51
initial  value 624.022740 
iter  10 value 458.460515
iter  20 value 383.689470
iter  30 value 327.502509
iter  40 value 312.469105
iter  50 value 308.686562
iter  60 value 307.370416
iter  70 value 307.302658
iter  80 value 307.300734
iter  90 value 307.299548
final  value 307.299432 
converged
# weights:  76
initial  value 578.043946 
iter  10 value 480.993647
iter  20 value 422.176553
iter  30 value 351.820298
iter  40 value 318.911539
iter  50 value 310.534861
iter  60 value 309.275304
iter  70 value 308.907760
iter  80 value 308.881276
iter  90 value 308.429080
iter 100 value 305.848466
final  value 305.848466 
stopped after 100 iterations
# weights:  101
initial  value 556.528768 
iter  10 value 471.459793
iter  20 value 409.243558
iter  30 value 327.133381
iter  40 value 315.622911
iter  50 value 314.156220
iter  60 value 313.393117
iter  70 value 313.170292
iter  80 value 313.071940
final  value 313.071317 
converged
# weights:  126
initial  value 603.861748 
iter  10 value 481.010176
iter  20 value 416.092643
iter  30 value 363.964590
iter  40 value 332.211917
iter  50 value 320.898931
iter  60 value 317.601518
iter  70 value 316.812657
iter  80 value 313.444634
iter  90 value 312.347273
iter 100 value 311.932607
final  value 311.932607 
stopped after 100 iterations
# weights:  151
initial  value 567.757935 
iter  10 value 466.083960
iter  20 value 433.147770
iter  30 value 395.284034
iter  40 value 355.259625
iter  50 value 331.150990
iter  60 value 325.325733
iter  70 value 320.804575
iter  80 value 320.214780
iter  90 value 319.906452
iter 100 value 319.445594
final  value 319.445594 
stopped after 100 iterations
# weights:  176
initial  value 553.805582 
iter  10 value 440.627683
iter  20 value 389.016563
iter  30 value 353.503508
iter  40 value 339.367897
iter  50 value 332.235465
iter  60 value 328.829859
iter  70 value 326.829222
iter  80 value 324.803549
iter  90 value 324.175897
iter 100 value 324.074818
final  value 324.074818 
stopped after 100 iterations
# weights:  201
initial  value 749.974830 
iter  10 value 494.827058
iter  20 value 428.140682
iter  30 value 359.300263
iter  40 value 342.174237
iter  50 value 333.499755
iter  60 value 330.443411
iter  70 value 328.916706
iter  80 value 328.050967
iter  90 value 327.678046
iter 100 value 327.178407
final  value 327.178407 
stopped after 100 iterations
# weights:  226
initial  value 796.349692 
iter  10 value 492.485680
iter  20 value 393.660328
iter  30 value 355.187523
iter  40 value 339.705892
iter  50 value 335.171723
iter  60 value 333.638810
iter  70 value 332.833808
iter  80 value 332.509173
iter  90 value 331.784945
iter 100 value 331.161350
final  value 331.161350 
stopped after 100 iterations
# weights:  251
initial  value 767.675732 
iter  10 value 503.552901
iter  20 value 455.461493
iter  30 value 414.863586
iter  40 value 368.512746
iter  50 value 346.740304
iter  60 value 343.191395
iter  70 value 341.369999
iter  80 value 340.402487
iter  90 value 339.824647
iter 100 value 338.593426
final  value 338.593426 
stopped after 100 iterations
# weights:  276
initial  value 735.310563 
iter  10 value 467.683518
iter  20 value 391.852934
iter  30 value 336.510309
iter  40 value 309.180564
iter  50 value 299.610652
iter  60 value 293.371838
iter  70 value 279.280215
iter  80 value 271.540989
iter  90 value 268.208664
iter 100 value 260.149714
final  value 260.149714 
stopped after 100 iterations
# weights:  301
initial  value 598.677505 
iter  10 value 466.301115
iter  20 value 395.730090
iter  30 value 354.360193
iter  40 value 317.116055
iter  50 value 306.115908
iter  60 value 298.048831
iter  70 value 288.445783
iter  80 value 280.747698
iter  90 value 275.811653
iter 100 value 273.510793
final  value 273.510793 
stopped after 100 iterations
# weights:  326
initial  value 502.734031 
iter  10 value 477.527004
iter  20 value 403.785978
iter  30 value 354.043550
iter  40 value 327.869711
iter  50 value 315.880476
iter  60 value 309.131586
iter  70 value 299.656755
iter  80 value 295.187530
iter  90 value 293.551074
iter 100 value 292.705233
final  value 292.705233 
stopped after 100 iterations
# weights:  351
initial  value 562.503729 
iter  10 value 471.978209
iter  20 value 386.217007
iter  30 value 361.628447
iter  40 value 344.053599
iter  50 value 321.345733
iter  60 value 309.491076
iter  70 value 305.376863
iter  80 value 303.465992
iter  90 value 302.382154
iter 100 value 301.820967
final  value 301.820967 
stopped after 100 iterations
# weights:  376
initial  value 701.128777 
iter  10 value 493.881542
iter  20 value 433.934183
iter  30 value 376.919123
iter  40 value 342.282048
iter  50 value 328.768598
iter  60 value 320.280116
iter  70 value 314.047909
iter  80 value 311.750974
iter  90 value 310.196731
iter 100 value 309.857632
final  value 309.857632 
stopped after 100 iterations
# weights:  401
initial  value 607.924697 
iter  10 value 492.320163
iter  20 value 455.087217
iter  30 value 410.016383
iter  40 value 360.675550
iter  50 value 344.918066
iter  60 value 329.943435
iter  70 value 322.337482
iter  80 value 319.875276
iter  90 value 318.209816
iter 100 value 316.959702
final  value 316.959702 
stopped after 100 iterations
# weights:  426
initial  value 756.933760 
iter  10 value 523.774083
iter  20 value 458.607892
iter  30 value 389.938697
iter  40 value 353.905815
iter  50 value 338.046886
iter  60 value 329.605146
iter  70 value 325.789134
iter  80 value 324.512458
iter  90 value 323.177369
iter 100 value 322.445607
final  value 322.445607 
stopped after 100 iterations
# weights:  451
initial  value 1323.707018 
iter  10 value 530.877494
iter  20 value 458.028258
iter  30 value 378.221142
iter  40 value 355.890179
iter  50 value 346.660107
iter  60 value 338.138301
iter  70 value 331.822804
iter  80 value 329.266613
iter  90 value 327.491000
iter 100 value 326.950147
final  value 326.950147 
stopped after 100 iterations
# weights:  476
initial  value 704.688510 
iter  10 value 523.562311
iter  20 value 425.453959
iter  30 value 374.001919
iter  40 value 353.273206
iter  50 value 345.052102
iter  60 value 339.545519
iter  70 value 335.310726
iter  80 value 332.625609
iter  90 value 331.651916
iter 100 value 330.949234
final  value 330.949234 
stopped after 100 iterations
# weights:  501
initial  value 730.481524 
iter  10 value 527.099235
iter  20 value 446.510030
iter  30 value 386.057413
iter  40 value 363.354357
iter  50 value 350.203307
iter  60 value 346.241288
iter  70 value 342.692351
iter  80 value 339.947363
iter  90 value 337.367090
iter 100 value 335.877981
final  value 335.877981 
stopped after 100 iterations
# weights:  526
initial  value 806.866843 
iter  10 value 454.140353
iter  20 value 401.334256
iter  30 value 355.214860
iter  40 value 300.650849
iter  50 value 293.622661
iter  60 value 278.941245
iter  70 value 252.317270
iter  80 value 235.228989
iter  90 value 221.506506
iter 100 value 207.227469
final  value 207.227469 
stopped after 100 iterations
# weights:  551
initial  value 636.188003 
iter  10 value 485.410606
iter  20 value 385.313290
iter  30 value 347.962924
iter  40 value 327.161863
iter  50 value 312.802710
iter  60 value 306.027520
iter  70 value 302.851814
iter  80 value 295.780437
iter  90 value 288.695301
iter 100 value 284.689956
final  value 284.689956 
stopped after 100 iterations
# weights:  576
initial  value 766.154555 
iter  10 value 473.704081
iter  20 value 378.243807
iter  30 value 343.875287
iter  40 value 323.491561
iter  50 value 311.944898
iter  60 value 303.792896
iter  70 value 298.565748
iter  80 value 295.526843
iter  90 value 291.614460
iter 100 value 287.670593
final  value 287.670593 
stopped after 100 iterations
# weights:  601
initial  value 767.150792 
iter  10 value 499.638845
iter  20 value 447.955706
iter  30 value 382.886745
iter  40 value 345.678266
iter  50 value 323.004508
iter  60 value 314.199384
iter  70 value 309.446116
iter  80 value 306.151077
iter  90 value 302.454311
iter 100 value 300.658376
final  value 300.658376 
stopped after 100 iterations
# weights:  626
initial  value 635.204270 
iter  10 value 512.304337
iter  20 value 424.799275
iter  30 value 401.260477
iter  40 value 376.823921
iter  50 value 342.618227
iter  60 value 330.247350
iter  70 value 323.674631
iter  80 value 318.143049
iter  90 value 314.918914
iter 100 value 312.411099
final  value 312.411099 
stopped after 100 iterations
# weights:  651
initial  value 907.273735 
iter  10 value 504.797318
iter  20 value 426.415305
iter  30 value 377.386220
iter  40 value 352.139235
iter  50 value 344.106584
iter  60 value 332.151844
iter  70 value 325.685703
iter  80 value 322.821901
iter  90 value 320.536894
iter 100 value 318.758691
final  value 318.758691 
stopped after 100 iterations
# weights:  676
initial  value 731.672257 
iter  10 value 528.718728
iter  20 value 469.324178
iter  30 value 428.248810
iter  40 value 368.270889
iter  50 value 352.896708
iter  60 value 337.153130
iter  70 value 328.515477
iter  80 value 325.264219
iter  90 value 323.534237
iter 100 value 322.623551
final  value 322.623551 
stopped after 100 iterations
# weights:  701
initial  value 1113.919276 
iter  10 value 497.449804
iter  20 value 437.798674
iter  30 value 387.978201
iter  40 value 359.054415
iter  50 value 343.157704
iter  60 value 336.590456
iter  70 value 331.282040
iter  80 value 329.788822
iter  90 value 328.015093
iter 100 value 327.037166
final  value 327.037166 
stopped after 100 iterations
# weights:  726
initial  value 630.841522 
iter  10 value 535.026890
iter  20 value 463.399137
iter  30 value 409.282921
iter  40 value 373.206141
iter  50 value 358.626228
iter  60 value 349.289221
iter  70 value 344.305973
iter  80 value 341.058341
iter  90 value 337.912249
iter 100 value 336.200288
final  value 336.200288 
stopped after 100 iterations
# weights:  751
initial  value 675.916046 
iter  10 value 575.325611
iter  20 value 468.452380
iter  30 value 388.931081
iter  40 value 357.487293
iter  50 value 347.966226
iter  60 value 342.884782
iter  70 value 339.493353
iter  80 value 337.451973
iter  90 value 335.936774
iter 100 value 334.419477
final  value 334.419477 
stopped after 100 iterations
# weights:  776
initial  value 613.044036 
iter  10 value 457.080455
iter  20 value 386.728875
iter  30 value 331.240212
iter  40 value 307.097404
iter  50 value 287.745621
iter  60 value 276.624245
iter  70 value 259.613461
iter  80 value 250.330030
iter  90 value 241.500225
iter 100 value 232.742272
final  value 232.742272 
stopped after 100 iterations
# weights:  801
initial  value 774.437764 
iter  10 value 476.826967
iter  20 value 429.844411
iter  30 value 372.523096
iter  40 value 348.491896
iter  50 value 324.639529
iter  60 value 313.127162
iter  70 value 303.067654
iter  80 value 291.834549
iter  90 value 281.055805
iter 100 value 276.462697
final  value 276.462697 
stopped after 100 iterations
# weights:  826
initial  value 578.491072 
iter  10 value 499.888410
iter  20 value 441.811121
iter  30 value 406.100540
iter  40 value 359.341231
iter  50 value 318.626558
iter  60 value 305.622685
iter  70 value 299.660280
iter  80 value 295.766984
iter  90 value 293.216843
iter 100 value 291.082080
final  value 291.082080 
stopped after 100 iterations
# weights:  851
initial  value 579.975837 
iter  10 value 503.088860
iter  20 value 435.597370
iter  30 value 381.619889
iter  40 value 335.685596
iter  50 value 321.245388
iter  60 value 312.416673
iter  70 value 306.945403
iter  80 value 304.480517
iter  90 value 302.749570
iter 100 value 301.612309
final  value 301.612309 
stopped after 100 iterations
# weights:  876
initial  value 1021.501778 
iter  10 value 512.766245
iter  20 value 451.228309
iter  30 value 380.682960
iter  40 value 357.389427
iter  50 value 342.958133
iter  60 value 330.525901
iter  70 value 321.099812
iter  80 value 316.770846
iter  90 value 312.575678
iter 100 value 310.140707
final  value 310.140707 
stopped after 100 iterations
# weights:  901
initial  value 662.910337 
iter  10 value 498.041084
iter  20 value 407.643583
iter  30 value 368.353355
iter  40 value 348.329018
iter  50 value 337.028712
iter  60 value 330.821619
iter  70 value 322.919334
iter  80 value 318.589439
iter  90 value 317.013171
iter 100 value 315.943208
final  value 315.943208 
stopped after 100 iterations
# weights:  926
initial  value 599.217526 
iter  10 value 531.225471
iter  20 value 489.770401
iter  30 value 417.524258
iter  40 value 389.849925
iter  50 value 361.938311
iter  60 value 354.583265
iter  70 value 338.488064
iter  80 value 328.790265
iter  90 value 325.324385
iter 100 value 323.631742
final  value 323.631742 
stopped after 100 iterations
# weights:  951
initial  value 639.281594 
iter  10 value 543.671588
iter  20 value 418.303181
iter  30 value 364.110506
iter  40 value 348.059247
iter  50 value 338.525585
iter  60 value 334.393027
iter  70 value 331.530652
iter  80 value 329.850159
iter  90 value 328.518946
iter 100 value 327.345473
final  value 327.345473 
stopped after 100 iterations
# weights:  976
initial  value 817.646828 
iter  10 value 566.748969
iter  20 value 473.380356
iter  30 value 393.314022
iter  40 value 366.200897
iter  50 value 354.115797
iter  60 value 342.323509
iter  70 value 338.803431
iter  80 value 335.770781
iter  90 value 333.539818
iter 100 value 331.631064
final  value 331.631064 
stopped after 100 iterations

model fit failed for Fold04: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold04: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold04: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold04: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold04: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold04: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold04: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold04: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold04: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold04: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold04: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold04: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold04: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold04: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold04: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold04: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold04: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold04: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold04: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold04: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold04: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold04: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold04: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold04: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold04: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold04: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold04: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold04: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold04: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold04: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold04: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold04: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold04: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold04: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold04: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold04: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold04: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold04: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold04: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold04: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold04: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold04: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold04: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold04: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold04: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold04: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold04: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold04: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold04: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold04: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold04: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold04: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold04: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold04: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold04: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold04: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold04: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold04: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold04: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold04: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold04: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 585.360737 
iter  10 value 422.504914
iter  20 value 368.682505
iter  30 value 319.467550
iter  40 value 315.083175
iter  50 value 313.439822
iter  60 value 306.759015
iter  70 value 304.502385
iter  80 value 304.478879
final  value 304.478873 
converged
# weights:  51
initial  value 520.456495 
iter  10 value 430.035424
iter  20 value 340.400982
iter  30 value 315.755125
iter  40 value 308.578630
iter  50 value 307.217127
iter  60 value 306.013049
iter  70 value 305.338747
iter  80 value 305.315426
final  value 305.315415 
converged
# weights:  76
initial  value 556.717031 
iter  10 value 474.458853
iter  20 value 425.100714
iter  30 value 345.553929
iter  40 value 325.765975
iter  50 value 320.313977
iter  60 value 314.554249
iter  70 value 313.359162
iter  80 value 312.297091
iter  90 value 311.664363
iter 100 value 311.182461
final  value 311.182461 
stopped after 100 iterations
# weights:  101
initial  value 540.588563 
iter  10 value 481.850475
iter  20 value 443.181747
iter  30 value 367.154487
iter  40 value 343.874481
iter  50 value 327.508635
iter  60 value 323.688987
iter  70 value 319.491033
iter  80 value 316.349934
iter  90 value 315.241678
iter 100 value 315.104894
final  value 315.104894 
stopped after 100 iterations
# weights:  126
initial  value 608.764531 
iter  10 value 482.042052
iter  20 value 423.627218
iter  30 value 350.184924
iter  40 value 334.895501
iter  50 value 330.301128
iter  60 value 329.095812
iter  70 value 328.065579
iter  80 value 327.728612
iter  90 value 327.064364
iter 100 value 325.592628
final  value 325.592628 
stopped after 100 iterations
# weights:  151
initial  value 620.442513 
iter  10 value 484.977592
iter  20 value 450.628008
iter  30 value 407.531850
iter  40 value 361.398045
iter  50 value 346.070905
iter  60 value 338.867799
iter  70 value 334.467447
iter  80 value 332.340054
iter  90 value 328.711145
iter 100 value 327.075318
final  value 327.075318 
stopped after 100 iterations
# weights:  176
initial  value 623.151456 
iter  10 value 489.160493
iter  20 value 444.534760
iter  30 value 383.820639
iter  40 value 357.951466
iter  50 value 343.047091
iter  60 value 337.848582
iter  70 value 334.080183
iter  80 value 332.305644
iter  90 value 331.710934
iter 100 value 331.369485
final  value 331.369485 
stopped after 100 iterations
# weights:  201
initial  value 688.450413 
iter  10 value 488.019695
iter  20 value 443.052859
iter  30 value 400.023149
iter  40 value 362.826767
iter  50 value 341.674381
iter  60 value 336.750168
iter  70 value 335.569422
iter  80 value 334.733170
iter  90 value 334.310326
iter 100 value 333.934912
final  value 333.934912 
stopped after 100 iterations
# weights:  226
initial  value 617.667550 
iter  10 value 476.645606
iter  20 value 403.496197
iter  30 value 376.423788
iter  40 value 354.983354
iter  50 value 348.580577
iter  60 value 344.330546
iter  70 value 342.044689
iter  80 value 339.863227
iter  90 value 339.316254
iter 100 value 338.822233
final  value 338.822233 
stopped after 100 iterations
# weights:  251
initial  value 615.973034 
iter  10 value 502.426241
iter  20 value 449.152237
iter  30 value 372.924513
iter  40 value 356.856133
iter  50 value 348.695916
iter  60 value 344.918494
iter  70 value 343.702042
iter  80 value 342.796532
iter  90 value 341.651341
iter 100 value 341.409363
final  value 341.409363 
stopped after 100 iterations
# weights:  276
initial  value 541.222674 
iter  10 value 455.045638
iter  20 value 421.610950
iter  30 value 322.914813
iter  40 value 298.815988
iter  50 value 294.844635
iter  60 value 280.302589
iter  70 value 265.252979
iter  80 value 254.031261
iter  90 value 244.736554
iter 100 value 237.766385
final  value 237.766385 
stopped after 100 iterations
# weights:  301
initial  value 488.428671 
iter  10 value 457.658997
iter  20 value 416.710667
iter  30 value 363.726270
iter  40 value 321.742352
iter  50 value 308.581723
iter  60 value 305.189207
iter  70 value 301.736912
iter  80 value 293.671073
iter  90 value 289.323001
iter 100 value 283.689980
final  value 283.689980 
stopped after 100 iterations
# weights:  326
initial  value 663.153619 
iter  10 value 486.304947
iter  20 value 430.845897
iter  30 value 382.661176
iter  40 value 353.847924
iter  50 value 319.197549
iter  60 value 310.997012
iter  70 value 306.667085
iter  80 value 302.408701
iter  90 value 298.173541
iter 100 value 295.271128
final  value 295.271128 
stopped after 100 iterations
# weights:  351
initial  value 692.940407 
iter  10 value 461.590356
iter  20 value 407.055844
iter  30 value 357.481389
iter  40 value 350.781506
iter  50 value 336.175307
iter  60 value 320.361375
iter  70 value 312.823274
iter  80 value 309.795468
iter  90 value 307.912910
iter 100 value 307.143057
final  value 307.143057 
stopped after 100 iterations
# weights:  376
initial  value 636.215174 
iter  10 value 505.311139
iter  20 value 469.166148
iter  30 value 392.946718
iter  40 value 354.811959
iter  50 value 340.340324
iter  60 value 326.919564
iter  70 value 320.557087
iter  80 value 317.882700
iter  90 value 316.141924
iter 100 value 314.997302
final  value 314.997302 
stopped after 100 iterations
# weights:  401
initial  value 625.427343 
iter  10 value 488.569758
iter  20 value 385.568449
iter  30 value 353.173850
iter  40 value 342.997793
iter  50 value 337.162782
iter  60 value 333.325331
iter  70 value 329.578697
iter  80 value 327.148781
iter  90 value 324.536953
iter 100 value 322.748263
final  value 322.748263 
stopped after 100 iterations
# weights:  426
initial  value 752.638094 
iter  10 value 499.077660
iter  20 value 431.837583
iter  30 value 359.046118
iter  40 value 343.310883
iter  50 value 335.531828
iter  60 value 331.363430
iter  70 value 329.890364
iter  80 value 329.008492
iter  90 value 328.481949
iter 100 value 327.949487
final  value 327.949487 
stopped after 100 iterations
# weights:  451
initial  value 645.059280 
iter  10 value 471.225561
iter  20 value 395.371308
iter  30 value 367.620460
iter  40 value 355.569445
iter  50 value 344.498378
iter  60 value 338.926945
iter  70 value 336.383222
iter  80 value 335.285218
iter  90 value 333.728917
iter 100 value 332.701656
final  value 332.701656 
stopped after 100 iterations
# weights:  476
initial  value 676.719078 
iter  10 value 509.578376
iter  20 value 419.619422
iter  30 value 373.503222
iter  40 value 356.443146
iter  50 value 348.531341
iter  60 value 341.853830
iter  70 value 339.701247
iter  80 value 338.878613
iter  90 value 338.226287
iter 100 value 337.727201
final  value 337.727201 
stopped after 100 iterations
# weights:  501
initial  value 682.639376 
iter  10 value 530.242359
iter  20 value 490.481165
iter  30 value 425.277103
iter  40 value 376.397335
iter  50 value 361.379384
iter  60 value 354.494873
iter  70 value 348.058932
iter  80 value 345.487874
iter  90 value 343.883140
iter 100 value 342.559731
final  value 342.559731 
stopped after 100 iterations
# weights:  526
initial  value 664.068088 
iter  10 value 433.082788
iter  20 value 335.338097
iter  30 value 293.518712
iter  40 value 283.047718
iter  50 value 269.651972
iter  60 value 266.798045
iter  70 value 264.441273
iter  80 value 263.489158
iter  90 value 261.942645
iter 100 value 255.509067
final  value 255.509067 
stopped after 100 iterations
# weights:  551
initial  value 556.154067 
iter  10 value 465.745538
iter  20 value 389.310648
iter  30 value 334.762620
iter  40 value 314.454158
iter  50 value 305.784067
iter  60 value 300.670358
iter  70 value 292.965511
iter  80 value 286.753627
iter  90 value 284.172430
iter 100 value 281.719218
final  value 281.719218 
stopped after 100 iterations
# weights:  576
initial  value 857.261854 
iter  10 value 489.709272
iter  20 value 438.082790
iter  30 value 376.565992
iter  40 value 361.939830
iter  50 value 330.507623
iter  60 value 312.520372
iter  70 value 306.738946
iter  80 value 303.935080
iter  90 value 301.392464
iter 100 value 298.920870
final  value 298.920870 
stopped after 100 iterations
# weights:  601
initial  value 660.301148 
iter  10 value 486.622808
iter  20 value 426.739180
iter  30 value 371.201846
iter  40 value 332.871591
iter  50 value 318.122216
iter  60 value 314.423457
iter  70 value 311.760433
iter  80 value 310.424053
iter  90 value 308.395244
iter 100 value 307.072756
final  value 307.072756 
stopped after 100 iterations
# weights:  626
initial  value 755.567780 
iter  10 value 494.387994
iter  20 value 433.790368
iter  30 value 398.288148
iter  40 value 354.857226
iter  50 value 334.959461
iter  60 value 323.509071
iter  70 value 320.251235
iter  80 value 318.131129
iter  90 value 316.728500
iter 100 value 315.915784
final  value 315.915784 
stopped after 100 iterations
# weights:  651
initial  value 639.804362 
iter  10 value 492.243070
iter  20 value 405.798222
iter  30 value 389.099282
iter  40 value 376.316187
iter  50 value 353.397980
iter  60 value 336.552608
iter  70 value 328.431111
iter  80 value 325.385219
iter  90 value 324.644083
iter 100 value 323.803211
final  value 323.803211 
stopped after 100 iterations
# weights:  676
initial  value 812.518015 
iter  10 value 527.753641
iter  20 value 436.234344
iter  30 value 387.838378
iter  40 value 364.416585
iter  50 value 348.134710
iter  60 value 339.747676
iter  70 value 336.930307
iter  80 value 333.920958
iter  90 value 331.014167
iter 100 value 329.625743
final  value 329.625743 
stopped after 100 iterations
# weights:  701
initial  value 747.047902 
iter  10 value 524.246278
iter  20 value 464.838985
iter  30 value 407.283045
iter  40 value 377.670631
iter  50 value 359.094019
iter  60 value 350.426328
iter  70 value 343.363732
iter  80 value 339.218437
iter  90 value 336.830442
iter 100 value 334.521625
final  value 334.521625 
stopped after 100 iterations
# weights:  726
initial  value 793.670366 
iter  10 value 554.874231
iter  20 value 459.680123
iter  30 value 395.841425
iter  40 value 374.944413
iter  50 value 353.424107
iter  60 value 344.022693
iter  70 value 340.278914
iter  80 value 339.365680
iter  90 value 338.135185
iter 100 value 337.234533
final  value 337.234533 
stopped after 100 iterations
# weights:  751
initial  value 886.865770 
iter  10 value 541.518689
iter  20 value 453.576468
iter  30 value 418.214174
iter  40 value 383.881283
iter  50 value 358.250011
iter  60 value 347.692757
iter  70 value 344.953179
iter  80 value 343.171908
iter  90 value 342.064174
iter 100 value 341.160227
final  value 341.160227 
stopped after 100 iterations
# weights:  776
initial  value 809.622659 
iter  10 value 456.881330
iter  20 value 381.430245
iter  30 value 327.476915
iter  40 value 298.605745
iter  50 value 283.116719
iter  60 value 267.839863
iter  70 value 252.142491
iter  80 value 243.237963
iter  90 value 233.446008
iter 100 value 224.354248
final  value 224.354248 
stopped after 100 iterations
# weights:  801
initial  value 579.033729 
iter  10 value 468.678742
iter  20 value 394.022414
iter  30 value 352.635936
iter  40 value 332.283886
iter  50 value 302.382867
iter  60 value 290.608729
iter  70 value 282.260897
iter  80 value 277.754826
iter  90 value 273.685786
iter 100 value 270.384627
final  value 270.384627 
stopped after 100 iterations
# weights:  826
initial  value 718.266532 
iter  10 value 504.158489
iter  20 value 456.715521
iter  30 value 399.240902
iter  40 value 361.840467
iter  50 value 329.126311
iter  60 value 313.298875
iter  70 value 309.298302
iter  80 value 304.464640
iter  90 value 301.640524
iter 100 value 298.587872
final  value 298.587872 
stopped after 100 iterations
# weights:  851
initial  value 542.488053 
iter  10 value 501.219697
iter  20 value 415.791177
iter  30 value 376.848566
iter  40 value 348.422901
iter  50 value 327.279813
iter  60 value 315.675985
iter  70 value 311.468841
iter  80 value 308.648417
iter  90 value 306.888228
iter 100 value 305.210712
final  value 305.210712 
stopped after 100 iterations
# weights:  876
initial  value 753.354029 
iter  10 value 503.828152
iter  20 value 452.322555
iter  30 value 418.634665
iter  40 value 363.673748
iter  50 value 336.272683
iter  60 value 324.966953
iter  70 value 320.804602
iter  80 value 319.417003
iter  90 value 317.923709
iter 100 value 316.780640
final  value 316.780640 
stopped after 100 iterations
# weights:  901
initial  value 633.321421 
iter  10 value 511.683726
iter  20 value 446.983189
iter  30 value 400.744378
iter  40 value 359.135502
iter  50 value 346.304715
iter  60 value 338.224021
iter  70 value 332.367392
iter  80 value 328.880845
iter  90 value 325.778861
iter 100 value 323.840865
final  value 323.840865 
stopped after 100 iterations
# weights:  926
initial  value 593.056844 
iter  10 value 529.196059
iter  20 value 480.493474
iter  30 value 442.226795
iter  40 value 392.477245
iter  50 value 352.830806
iter  60 value 342.793100
iter  70 value 336.199054
iter  80 value 332.535663
iter  90 value 330.764349
iter 100 value 329.109436
final  value 329.109436 
stopped after 100 iterations
# weights:  951
initial  value 1176.702337 
iter  10 value 547.907890
iter  20 value 465.246116
iter  30 value 394.129017
iter  40 value 365.575232
iter  50 value 359.283963
iter  60 value 352.529755
iter  70 value 347.532562
iter  80 value 342.110063
iter  90 value 338.333386
iter 100 value 336.272667
final  value 336.272667 
stopped after 100 iterations
# weights:  976
initial  value 1364.042007 
iter  10 value 563.696689
iter  20 value 445.513945
iter  30 value 401.496651
iter  40 value 384.381150
iter  50 value 365.053250
iter  60 value 353.817768
iter  70 value 347.576205
iter  80 value 344.361572
iter  90 value 342.318733
iter 100 value 339.950402
final  value 339.950402 
stopped after 100 iterations

model fit failed for Fold05: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold05: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold05: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold05: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold05: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold05: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold05: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold05: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold05: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold05: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold05: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold05: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold05: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold05: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold05: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold05: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold05: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold05: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold05: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold05: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold05: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold05: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold05: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold05: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold05: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold05: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold05: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold05: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold05: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold05: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold05: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold05: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold05: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold05: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold05: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold05: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold05: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold05: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold05: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold05: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold05: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold05: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold05: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold05: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold05: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold05: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold05: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold05: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold05: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold05: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold05: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold05: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold05: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold05: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold05: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold05: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold05: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold05: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold05: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold05: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold05: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 550.105725 
iter  10 value 533.860500
iter  20 value 430.783385
iter  30 value 367.452527
iter  40 value 350.714592
iter  50 value 317.050904
iter  60 value 311.694374
iter  70 value 310.652792
iter  80 value 309.425590
iter  90 value 308.336274
iter 100 value 307.986063
final  value 307.986063 
stopped after 100 iterations
# weights:  51
initial  value 604.815208 
iter  10 value 487.937115
iter  20 value 414.471593
iter  30 value 350.892508
iter  40 value 327.715328
iter  50 value 312.408620
iter  60 value 311.123253
iter  70 value 311.056507
iter  80 value 310.642713
iter  90 value 310.573677
final  value 310.571969 
converged
# weights:  76
initial  value 551.946263 
iter  10 value 495.415544
iter  20 value 428.048487
iter  30 value 346.449792
iter  40 value 327.161108
iter  50 value 320.680086
iter  60 value 319.329562
iter  70 value 316.778877
iter  80 value 315.565603
iter  90 value 314.179911
iter 100 value 312.963416
final  value 312.963416 
stopped after 100 iterations
# weights:  101
initial  value 553.139621 
iter  10 value 484.837527
iter  20 value 433.891518
iter  30 value 357.195759
iter  40 value 340.578957
iter  50 value 327.505524
iter  60 value 323.384825
iter  70 value 321.771265
iter  80 value 320.834621
iter  90 value 319.436779
iter 100 value 317.905918
final  value 317.905918 
stopped after 100 iterations
# weights:  126
initial  value 576.680435 
iter  10 value 488.180398
iter  20 value 440.818519
iter  30 value 383.244958
iter  40 value 355.621110
iter  50 value 336.267772
iter  60 value 332.783274
iter  70 value 327.225220
iter  80 value 324.611503
iter  90 value 323.685342
iter 100 value 323.663813
final  value 323.663813 
stopped after 100 iterations
# weights:  151
initial  value 562.734216 
iter  10 value 484.031484
iter  20 value 407.986880
iter  30 value 368.066663
iter  40 value 343.060859
iter  50 value 335.485188
iter  60 value 334.108359
iter  70 value 330.549882
iter  80 value 330.164115
iter  90 value 330.056084
iter 100 value 329.904364
final  value 329.904364 
stopped after 100 iterations
# weights:  176
initial  value 550.974306 
iter  10 value 486.560040
iter  20 value 449.034183
iter  30 value 382.599129
iter  40 value 356.372805
iter  50 value 344.945343
iter  60 value 339.811394
iter  70 value 337.937677
iter  80 value 336.495592
iter  90 value 336.017258
iter 100 value 335.517109
final  value 335.517109 
stopped after 100 iterations
# weights:  201
initial  value 559.583061 
iter  10 value 494.089832
iter  20 value 441.855639
iter  30 value 377.797319
iter  40 value 353.600624
iter  50 value 348.082991
iter  60 value 344.806070
iter  70 value 343.023124
iter  80 value 341.547712
iter  90 value 340.976947
iter 100 value 340.297722
final  value 340.297722 
stopped after 100 iterations
# weights:  226
initial  value 627.796905 
iter  10 value 522.219066
iter  20 value 464.690306
iter  30 value 429.670562
iter  40 value 399.407693
iter  50 value 367.903115
iter  60 value 351.058472
iter  70 value 348.157764
iter  80 value 345.768736
iter  90 value 344.705572
iter 100 value 344.468422
final  value 344.468422 
stopped after 100 iterations
# weights:  251
initial  value 605.102254 
iter  10 value 517.430214
iter  20 value 419.852012
iter  30 value 373.435113
iter  40 value 362.645602
iter  50 value 350.959753
iter  60 value 349.418615
iter  70 value 348.972898
iter  80 value 348.760206
iter  90 value 348.649406
iter 100 value 348.382908
final  value 348.382908 
stopped after 100 iterations
# weights:  276
initial  value 601.695489 
iter  10 value 454.907647
iter  20 value 384.381586
iter  30 value 337.715094
iter  40 value 324.369197
iter  50 value 310.012950
iter  60 value 292.625482
iter  70 value 284.761580
iter  80 value 279.132683
iter  90 value 270.272674
iter 100 value 262.437968
final  value 262.437968 
stopped after 100 iterations
# weights:  301
initial  value 523.850916 
iter  10 value 479.024228
iter  20 value 433.134447
iter  30 value 373.150907
iter  40 value 322.997277
iter  50 value 308.267981
iter  60 value 303.241155
iter  70 value 298.325374
iter  80 value 295.082809
iter  90 value 293.613277
iter 100 value 292.130024
final  value 292.130024 
stopped after 100 iterations
# weights:  326
initial  value 596.695404 
iter  10 value 490.212431
iter  20 value 392.386510
iter  30 value 356.081635
iter  40 value 332.892734
iter  50 value 324.163319
iter  60 value 314.206091
iter  70 value 310.320655
iter  80 value 308.082038
iter  90 value 306.425565
iter 100 value 303.277325
final  value 303.277325 
stopped after 100 iterations
# weights:  351
initial  value 576.310097 
iter  10 value 480.859468
iter  20 value 430.862216
iter  30 value 403.685071
iter  40 value 354.259681
iter  50 value 335.562709
iter  60 value 323.600806
iter  70 value 316.817188
iter  80 value 314.151634
iter  90 value 313.156932
iter 100 value 312.186121
final  value 312.186121 
stopped after 100 iterations
# weights:  376
initial  value 691.798119 
iter  10 value 494.007060
iter  20 value 435.289161
iter  30 value 402.949182
iter  40 value 368.848916
iter  50 value 348.044254
iter  60 value 337.613221
iter  70 value 330.186451
iter  80 value 326.975506
iter  90 value 324.205326
iter 100 value 322.258839
final  value 322.258839 
stopped after 100 iterations
# weights:  401
initial  value 582.716363 
iter  10 value 457.915871
iter  20 value 397.548808
iter  30 value 358.634550
iter  40 value 348.179009
iter  50 value 340.062862
iter  60 value 337.044079
iter  70 value 333.954205
iter  80 value 331.746867
iter  90 value 329.806336
iter 100 value 328.806622
final  value 328.806622 
stopped after 100 iterations
# weights:  426
initial  value 630.746953 
iter  10 value 513.659421
iter  20 value 413.739688
iter  30 value 394.906586
iter  40 value 360.959044
iter  50 value 346.683675
iter  60 value 340.414505
iter  70 value 336.532450
iter  80 value 335.327924
iter  90 value 334.673672
iter 100 value 333.959065
final  value 333.959065 
stopped after 100 iterations
# weights:  451
initial  value 698.373627 
iter  10 value 496.041809
iter  20 value 454.769890
iter  30 value 412.417819
iter  40 value 365.126946
iter  50 value 348.085558
iter  60 value 345.410497
iter  70 value 342.011886
iter  80 value 340.636687
iter  90 value 339.737114
iter 100 value 338.921190
final  value 338.921190 
stopped after 100 iterations
# weights:  476
initial  value 564.968668 
iter  10 value 456.967126
iter  20 value 394.826003
iter  30 value 376.165461
iter  40 value 365.529601
iter  50 value 358.500335
iter  60 value 352.034633
iter  70 value 347.176392
iter  80 value 345.488431
iter  90 value 344.636499
iter 100 value 343.649815
final  value 343.649815 
stopped after 100 iterations
# weights:  501
initial  value 617.420131 
iter  10 value 510.894581
iter  20 value 434.831110
iter  30 value 388.219327
iter  40 value 365.471635
iter  50 value 352.068799
iter  60 value 349.536773
iter  70 value 348.255112
iter  80 value 347.491548
iter  90 value 347.085183
iter 100 value 346.705487
final  value 346.705487 
stopped after 100 iterations
# weights:  526
initial  value 735.424903 
iter  10 value 476.795559
iter  20 value 406.717166
iter  30 value 355.386102
iter  40 value 328.345062
iter  50 value 296.744715
iter  60 value 277.812443
iter  70 value 262.532765
iter  80 value 254.409318
iter  90 value 253.830234
iter 100 value 251.856421
final  value 251.856421 
stopped after 100 iterations
# weights:  551
initial  value 1734.734629 
iter  10 value 498.584834
iter  20 value 429.509306
iter  30 value 371.720606
iter  40 value 341.225705
iter  50 value 317.548652
iter  60 value 313.819311
iter  70 value 309.633672
iter  80 value 303.162948
iter  90 value 294.200205
iter 100 value 288.630828
final  value 288.630828 
stopped after 100 iterations
# weights:  576
initial  value 555.128974 
iter  10 value 483.961065
iter  20 value 414.226506
iter  30 value 371.488613
iter  40 value 352.357859
iter  50 value 342.330683
iter  60 value 327.634047
iter  70 value 314.391024
iter  80 value 309.081358
iter  90 value 304.568252
iter 100 value 301.315933
final  value 301.315933 
stopped after 100 iterations
# weights:  601
initial  value 802.034465 
iter  10 value 501.549669
iter  20 value 410.736666
iter  30 value 371.807384
iter  40 value 347.621240
iter  50 value 337.107695
iter  60 value 328.640580
iter  70 value 324.302635
iter  80 value 320.531854
iter  90 value 317.725875
iter 100 value 316.419432
final  value 316.419432 
stopped after 100 iterations
# weights:  626
initial  value 603.486335 
iter  10 value 513.301728
iter  20 value 437.294730
iter  30 value 373.922982
iter  40 value 346.998182
iter  50 value 338.725928
iter  60 value 330.302116
iter  70 value 329.202044
iter  80 value 325.983789
iter  90 value 323.519242
iter 100 value 321.744273
final  value 321.744273 
stopped after 100 iterations
# weights:  651
initial  value 905.811311 
iter  10 value 488.490946
iter  20 value 435.083756
iter  30 value 389.549684
iter  40 value 359.276459
iter  50 value 345.602935
iter  60 value 336.246899
iter  70 value 332.473805
iter  80 value 330.529825
iter  90 value 329.529378
iter 100 value 328.923016
final  value 328.923016 
stopped after 100 iterations
# weights:  676
initial  value 602.380969 
iter  10 value 525.034409
iter  20 value 449.071499
iter  30 value 394.583136
iter  40 value 363.280635
iter  50 value 349.276640
iter  60 value 343.434668
iter  70 value 340.103124
iter  80 value 338.644687
iter  90 value 336.818422
iter 100 value 335.580720
final  value 335.580720 
stopped after 100 iterations
# weights:  701
initial  value 690.235125 
iter  10 value 542.627835
iter  20 value 485.244372
iter  30 value 436.813324
iter  40 value 411.183701
iter  50 value 390.656783
iter  60 value 366.949826
iter  70 value 356.178104
iter  80 value 349.494411
iter  90 value 345.730766
iter 100 value 343.874228
final  value 343.874228 
stopped after 100 iterations
# weights:  726
initial  value 1497.615018 
iter  10 value 581.526786
iter  20 value 468.828222
iter  30 value 410.162670
iter  40 value 388.286227
iter  50 value 371.242758
iter  60 value 357.576414
iter  70 value 352.454468
iter  80 value 350.584924
iter  90 value 348.808573
iter 100 value 345.525326
final  value 345.525326 
stopped after 100 iterations
# weights:  751
initial  value 842.107758 
iter  10 value 519.512694
iter  20 value 443.439444
iter  30 value 387.488440
iter  40 value 362.390360
iter  50 value 355.158711
iter  60 value 352.493048
iter  70 value 350.764007
iter  80 value 349.588587
iter  90 value 348.662283
iter 100 value 347.311405
final  value 347.311405 
stopped after 100 iterations
# weights:  776
initial  value 647.341282 
iter  10 value 460.434740
iter  20 value 393.975858
iter  30 value 349.755512
iter  40 value 327.503750
iter  50 value 302.015091
iter  60 value 284.620019
iter  70 value 270.801403
iter  80 value 257.171773
iter  90 value 244.794640
iter 100 value 239.495586
final  value 239.495586 
stopped after 100 iterations
# weights:  801
initial  value 963.861577 
iter  10 value 503.148367
iter  20 value 431.966769
iter  30 value 380.006948
iter  40 value 336.431063
iter  50 value 325.739460
iter  60 value 317.579561
iter  70 value 311.579196
iter  80 value 305.606208
iter  90 value 297.766238
iter 100 value 292.441209
final  value 292.441209 
stopped after 100 iterations
# weights:  826
initial  value 692.969619 
iter  10 value 494.948821
iter  20 value 435.872752
iter  30 value 393.146309
iter  40 value 355.633239
iter  50 value 350.225841
iter  60 value 333.948679
iter  70 value 316.161248
iter  80 value 306.816826
iter  90 value 304.703801
iter 100 value 304.281448
final  value 304.281448 
stopped after 100 iterations
# weights:  851
initial  value 681.163191 
iter  10 value 515.498930
iter  20 value 449.291071
iter  30 value 410.965061
iter  40 value 374.277787
iter  50 value 351.490368
iter  60 value 331.186981
iter  70 value 320.105647
iter  80 value 316.182195
iter  90 value 313.981077
iter 100 value 312.929910
final  value 312.929910 
stopped after 100 iterations
# weights:  876
initial  value 707.135952 
iter  10 value 511.204538
iter  20 value 443.270131
iter  30 value 390.491300
iter  40 value 350.831278
iter  50 value 337.783814
iter  60 value 330.653813
iter  70 value 326.998996
iter  80 value 325.089698
iter  90 value 323.432900
iter 100 value 322.076788
final  value 322.076788 
stopped after 100 iterations
# weights:  901
initial  value 637.822674 
iter  10 value 531.373072
iter  20 value 452.148709
iter  30 value 413.226866
iter  40 value 367.228851
iter  50 value 346.648894
iter  60 value 341.040656
iter  70 value 336.460977
iter  80 value 333.078849
iter  90 value 331.726943
iter 100 value 330.847622
final  value 330.847622 
stopped after 100 iterations
# weights:  926
initial  value 865.108552 
iter  10 value 535.249303
iter  20 value 444.572993
iter  30 value 412.011246
iter  40 value 391.539201
iter  50 value 367.514687
iter  60 value 355.534169
iter  70 value 349.638881
iter  80 value 344.381441
iter  90 value 340.510576
iter 100 value 336.870786
final  value 336.870786 
stopped after 100 iterations
# weights:  951
initial  value 621.568796 
iter  10 value 572.045411
iter  20 value 460.339671
iter  30 value 418.375293
iter  40 value 375.806509
iter  50 value 358.150835
iter  60 value 351.196791
iter  70 value 346.518129
iter  80 value 343.619621
iter  90 value 341.536241
iter 100 value 340.084416
final  value 340.084416 
stopped after 100 iterations
# weights:  976
initial  value 890.755685 
iter  10 value 545.649338
iter  20 value 461.585397
iter  30 value 400.714625
iter  40 value 368.588907
iter  50 value 359.008781
iter  60 value 351.492549
iter  70 value 348.304446
iter  80 value 346.230861
iter  90 value 344.739969
iter 100 value 343.727415
final  value 343.727415 
stopped after 100 iterations

model fit failed for Fold06: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold06: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold06: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold06: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold06: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold06: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold06: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold06: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold06: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold06: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold06: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold06: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold06: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold06: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold06: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold06: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold06: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold06: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold06: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold06: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold06: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold06: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold06: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold06: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold06: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold06: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold06: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold06: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold06: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold06: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold06: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold06: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold06: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold06: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold06: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold06: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold06: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold06: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold06: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold06: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold06: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold06: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold06: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold06: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold06: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold06: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold06: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold06: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold06: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold06: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold06: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold06: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold06: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold06: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold06: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold06: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold06: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold06: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold06: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold06: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold06: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 653.763409 
iter  10 value 476.421606
iter  20 value 360.422047
iter  30 value 353.811731
iter  40 value 344.720813
iter  50 value 333.804467
iter  60 value 314.640679
iter  70 value 304.768987
iter  80 value 302.642361
iter  90 value 302.426768
final  value 302.424886 
converged
# weights:  51
initial  value 551.803276 
iter  10 value 458.825722
iter  20 value 384.841853
iter  30 value 336.124208
iter  40 value 315.289272
iter  50 value 313.667022
iter  60 value 313.633868
iter  70 value 313.610159
final  value 313.610145 
converged
# weights:  76
initial  value 624.068322 
iter  10 value 482.104984
iter  20 value 437.813409
iter  30 value 360.105425
iter  40 value 322.891597
iter  50 value 312.954172
iter  60 value 308.555630
iter  70 value 307.185542
iter  80 value 306.238266
iter  90 value 305.931299
iter 100 value 305.205268
final  value 305.205268 
stopped after 100 iterations
# weights:  101
initial  value 616.206291 
iter  10 value 482.141090
iter  20 value 385.976961
iter  30 value 345.461427
iter  40 value 328.989337
iter  50 value 326.567228
iter  60 value 326.261059
iter  70 value 325.701644
iter  80 value 323.891844
iter  90 value 320.579963
iter 100 value 317.067735
final  value 317.067735 
stopped after 100 iterations
# weights:  126
initial  value 629.650148 
iter  10 value 476.281992
iter  20 value 406.423197
iter  30 value 359.166424
iter  40 value 340.514422
iter  50 value 332.924440
iter  60 value 330.675561
iter  70 value 326.896420
iter  80 value 324.265364
iter  90 value 320.422007
iter 100 value 319.348135
final  value 319.348135 
stopped after 100 iterations
# weights:  151
initial  value 555.235755 
iter  10 value 482.715007
iter  20 value 410.463534
iter  30 value 373.595602
iter  40 value 346.998049
iter  50 value 333.108756
iter  60 value 330.267681
iter  70 value 328.483462
iter  80 value 325.576093
iter  90 value 325.124601
iter 100 value 325.006495
final  value 325.006495 
stopped after 100 iterations
# weights:  176
initial  value 541.774307 
iter  10 value 489.306023
iter  20 value 454.017865
iter  30 value 401.463943
iter  40 value 363.614857
iter  50 value 341.463824
iter  60 value 336.154196
iter  70 value 333.392396
iter  80 value 330.641261
iter  90 value 330.170856
iter 100 value 329.898362
final  value 329.898362 
stopped after 100 iterations
# weights:  201
initial  value 689.455941 
iter  10 value 499.179114
iter  20 value 432.111087
iter  30 value 389.288215
iter  40 value 353.885403
iter  50 value 344.243161
iter  60 value 341.195998
iter  70 value 339.353050
iter  80 value 336.443255
iter  90 value 335.765395
iter 100 value 335.336874
final  value 335.336874 
stopped after 100 iterations
# weights:  226
initial  value 753.615062 
iter  10 value 498.932450
iter  20 value 427.094499
iter  30 value 376.440479
iter  40 value 361.898055
iter  50 value 350.216166
iter  60 value 345.530218
iter  70 value 342.473517
iter  80 value 340.610484
iter  90 value 339.914858
iter 100 value 339.640360
final  value 339.640360 
stopped after 100 iterations
# weights:  251
initial  value 655.024055 
iter  10 value 484.814718
iter  20 value 399.052316
iter  30 value 373.136304
iter  40 value 359.482141
iter  50 value 348.130033
iter  60 value 345.365068
iter  70 value 343.394079
iter  80 value 343.114303
iter  90 value 342.900147
iter 100 value 342.573798
final  value 342.573798 
stopped after 100 iterations
# weights:  276
initial  value 544.971395 
iter  10 value 476.512747
iter  20 value 383.326488
iter  30 value 331.741882
iter  40 value 303.262368
iter  50 value 287.316890
iter  60 value 286.030115
iter  70 value 284.054495
iter  80 value 276.875811
iter  90 value 264.799142
iter 100 value 248.232380
final  value 248.232380 
stopped after 100 iterations
# weights:  301
initial  value 690.837254 
iter  10 value 490.683654
iter  20 value 436.986557
iter  30 value 389.081572
iter  40 value 352.062480
iter  50 value 327.480277
iter  60 value 307.936913
iter  70 value 297.015691
iter  80 value 288.283590
iter  90 value 281.080013
iter 100 value 276.652214
final  value 276.652214 
stopped after 100 iterations
# weights:  326
initial  value 641.262384 
iter  10 value 468.587218
iter  20 value 382.678898
iter  30 value 360.407123
iter  40 value 336.681280
iter  50 value 318.513751
iter  60 value 313.570720
iter  70 value 311.296169
iter  80 value 306.886339
iter  90 value 301.646880
iter 100 value 296.783023
final  value 296.783023 
stopped after 100 iterations
# weights:  351
initial  value 524.287497 
iter  10 value 487.481853
iter  20 value 408.231256
iter  30 value 361.083511
iter  40 value 320.605585
iter  50 value 316.938314
iter  60 value 313.930304
iter  70 value 312.065256
iter  80 value 310.366596
iter  90 value 309.793524
iter 100 value 309.558333
final  value 309.558333 
stopped after 100 iterations
# weights:  376
initial  value 629.473323 
iter  10 value 505.133368
iter  20 value 476.958334
iter  30 value 447.579474
iter  40 value 415.189815
iter  50 value 361.862141
iter  60 value 342.204210
iter  70 value 331.529993
iter  80 value 325.633749
iter  90 value 321.827578
iter 100 value 319.417137
final  value 319.417137 
stopped after 100 iterations
# weights:  401
initial  value 968.006469 
iter  10 value 487.627478
iter  20 value 430.985081
iter  30 value 374.385605
iter  40 value 356.780297
iter  50 value 345.825536
iter  60 value 335.602253
iter  70 value 331.456735
iter  80 value 329.115211
iter  90 value 327.507530
iter 100 value 325.170966
final  value 325.170966 
stopped after 100 iterations
# weights:  426
initial  value 884.023563 
iter  10 value 504.202929
iter  20 value 467.183527
iter  30 value 386.073302
iter  40 value 363.414008
iter  50 value 348.217252
iter  60 value 339.863332
iter  70 value 336.351262
iter  80 value 333.773865
iter  90 value 331.816281
iter 100 value 331.003718
final  value 331.003718 
stopped after 100 iterations
# weights:  451
initial  value 759.161032 
iter  10 value 512.361307
iter  20 value 489.319163
iter  30 value 403.159382
iter  40 value 365.221562
iter  50 value 352.475744
iter  60 value 344.657650
iter  70 value 340.865271
iter  80 value 339.167026
iter  90 value 337.846115
iter 100 value 334.628515
final  value 334.628515 
stopped after 100 iterations
# weights:  476
initial  value 614.502435 
iter  10 value 498.497270
iter  20 value 426.115079
iter  30 value 379.927083
iter  40 value 359.351231
iter  50 value 349.087557
iter  60 value 344.246349
iter  70 value 341.887461
iter  80 value 340.072340
iter  90 value 339.083762
iter 100 value 338.289504
final  value 338.289504 
stopped after 100 iterations
# weights:  501
initial  value 581.721242 
iter  10 value 512.614171
iter  20 value 428.692595
iter  30 value 381.757148
iter  40 value 360.184369
iter  50 value 352.611429
iter  60 value 347.604416
iter  70 value 344.879797
iter  80 value 343.788067
iter  90 value 343.429402
iter 100 value 342.944931
final  value 342.944931 
stopped after 100 iterations
# weights:  526
initial  value 572.881111 
iter  10 value 475.352279
iter  20 value 399.148057
iter  30 value 326.120901
iter  40 value 296.304535
iter  50 value 277.853757
iter  60 value 268.281771
iter  70 value 257.576764
iter  80 value 250.059560
iter  90 value 238.271487
iter 100 value 229.134593
final  value 229.134593 
stopped after 100 iterations
# weights:  551
initial  value 773.060800 
iter  10 value 489.892960
iter  20 value 447.148717
iter  30 value 379.957128
iter  40 value 348.721705
iter  50 value 323.398047
iter  60 value 308.772040
iter  70 value 298.520462
iter  80 value 292.804303
iter  90 value 291.597846
iter 100 value 289.541416
final  value 289.541416 
stopped after 100 iterations
# weights:  576
initial  value 542.121521 
iter  10 value 479.792840
iter  20 value 427.682753
iter  30 value 368.958417
iter  40 value 347.679868
iter  50 value 323.002771
iter  60 value 312.390719
iter  70 value 305.728249
iter  80 value 303.229503
iter  90 value 299.752952
iter 100 value 296.126353
final  value 296.126353 
stopped after 100 iterations
# weights:  601
initial  value 574.936380 
iter  10 value 502.031195
iter  20 value 433.380684
iter  30 value 404.996696
iter  40 value 364.515003
iter  50 value 341.468110
iter  60 value 325.887078
iter  70 value 317.902096
iter  80 value 312.846425
iter  90 value 308.460388
iter 100 value 306.145060
final  value 306.145060 
stopped after 100 iterations
# weights:  626
initial  value 672.831092 
iter  10 value 507.505385
iter  20 value 482.253491
iter  30 value 410.612760
iter  40 value 386.399005
iter  50 value 361.495865
iter  60 value 337.036648
iter  70 value 325.795408
iter  80 value 321.843308
iter  90 value 319.331427
iter 100 value 317.926517
final  value 317.926517 
stopped after 100 iterations
# weights:  651
initial  value 1599.353213 
iter  10 value 513.862567
iter  20 value 442.246111
iter  30 value 403.777856
iter  40 value 375.507645
iter  50 value 357.141464
iter  60 value 343.389003
iter  70 value 334.517341
iter  80 value 328.751788
iter  90 value 325.778066
iter 100 value 324.283954
final  value 324.283954 
stopped after 100 iterations
# weights:  676
initial  value 600.526265 
iter  10 value 529.921677
iter  20 value 429.709462
iter  30 value 406.548965
iter  40 value 366.566025
iter  50 value 349.244651
iter  60 value 339.469340
iter  70 value 336.352450
iter  80 value 332.975110
iter  90 value 330.694895
iter 100 value 328.922546
final  value 328.922546 
stopped after 100 iterations
# weights:  701
initial  value 705.865157 
iter  10 value 509.010029
iter  20 value 422.961195
iter  30 value 380.289032
iter  40 value 358.186299
iter  50 value 344.829441
iter  60 value 338.297058
iter  70 value 336.592709
iter  80 value 335.282168
iter  90 value 334.207706
iter 100 value 333.676770
final  value 333.676770 
stopped after 100 iterations
# weights:  726
initial  value 800.806426 
iter  10 value 516.115207
iter  20 value 471.136126
iter  30 value 424.064812
iter  40 value 397.563529
iter  50 value 372.745374
iter  60 value 349.817049
iter  70 value 342.510598
iter  80 value 340.214976
iter  90 value 339.190405
iter 100 value 338.516360
final  value 338.516360 
stopped after 100 iterations
# weights:  751
initial  value 702.327933 
iter  10 value 531.015851
iter  20 value 452.068829
iter  30 value 398.167141
iter  40 value 370.834050
iter  50 value 360.293001
iter  60 value 349.162961
iter  70 value 345.950250
iter  80 value 344.206050
iter  90 value 343.569917
iter 100 value 342.781455
final  value 342.781455 
stopped after 100 iterations
# weights:  776
initial  value 1202.163869 
iter  10 value 479.562991
iter  20 value 448.520214
iter  30 value 380.854324
iter  40 value 347.907163
iter  50 value 304.866565
iter  60 value 284.250199
iter  70 value 265.520087
iter  80 value 254.066743
iter  90 value 250.027767
iter 100 value 247.244374
final  value 247.244374 
stopped after 100 iterations
# weights:  801
initial  value 722.637598 
iter  10 value 485.750726
iter  20 value 408.444237
iter  30 value 379.603851
iter  40 value 348.797490
iter  50 value 310.085970
iter  60 value 295.532787
iter  70 value 282.677589
iter  80 value 275.668717
iter  90 value 270.835782
iter 100 value 266.907961
final  value 266.907961 
stopped after 100 iterations
# weights:  826
initial  value 543.997405 
iter  10 value 462.050899
iter  20 value 385.482445
iter  30 value 359.361621
iter  40 value 344.085210
iter  50 value 337.927035
iter  60 value 325.245568
iter  70 value 313.426226
iter  80 value 308.515809
iter  90 value 304.776251
iter 100 value 301.102538
final  value 301.102538 
stopped after 100 iterations
# weights:  851
initial  value 617.543711 
iter  10 value 502.104331
iter  20 value 434.737277
iter  30 value 403.084603
iter  40 value 376.539976
iter  50 value 347.592954
iter  60 value 332.941416
iter  70 value 321.135062
iter  80 value 316.465396
iter  90 value 314.923790
iter 100 value 311.834711
final  value 311.834711 
stopped after 100 iterations
# weights:  876
initial  value 598.615135 
iter  10 value 514.118613
iter  20 value 455.732570
iter  30 value 397.531681
iter  40 value 369.115877
iter  50 value 345.129650
iter  60 value 332.684591
iter  70 value 326.502952
iter  80 value 322.006043
iter  90 value 319.430126
iter 100 value 317.403292
final  value 317.403292 
stopped after 100 iterations
# weights:  901
initial  value 754.804540 
iter  10 value 502.224115
iter  20 value 438.736768
iter  30 value 404.290000
iter  40 value 374.073507
iter  50 value 355.957141
iter  60 value 342.097959
iter  70 value 334.623027
iter  80 value 330.228539
iter  90 value 326.507740
iter 100 value 323.794364
final  value 323.794364 
stopped after 100 iterations
# weights:  926
initial  value 1154.902332 
iter  10 value 538.417940
iter  20 value 481.550039
iter  30 value 429.596461
iter  40 value 398.138736
iter  50 value 365.768544
iter  60 value 348.415911
iter  70 value 338.684758
iter  80 value 333.439401
iter  90 value 331.581044
iter 100 value 330.313645
final  value 330.313645 
stopped after 100 iterations
# weights:  951
initial  value 1317.650733 
iter  10 value 572.932413
iter  20 value 483.680723
iter  30 value 442.649355
iter  40 value 399.195236
iter  50 value 366.494983
iter  60 value 352.708811
iter  70 value 345.324921
iter  80 value 340.909892
iter  90 value 337.476672
iter 100 value 335.852708
final  value 335.852708 
stopped after 100 iterations
# weights:  976
initial  value 808.634708 
iter  10 value 578.105862
iter  20 value 507.710537
iter  30 value 437.668102
iter  40 value 402.932806
iter  50 value 364.403304
iter  60 value 356.236978
iter  70 value 349.870087
iter  80 value 344.932320
iter  90 value 342.392067
iter 100 value 340.406929
final  value 340.406929 
stopped after 100 iterations

model fit failed for Fold07: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold07: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold07: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold07: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold07: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold07: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold07: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold07: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold07: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold07: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold07: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold07: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold07: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold07: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold07: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold07: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold07: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold07: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold07: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold07: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold07: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold07: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold07: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold07: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold07: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold07: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold07: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold07: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold07: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold07: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold07: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold07: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold07: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold07: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold07: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold07: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold07: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold07: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold07: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold07: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold07: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold07: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold07: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold07: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold07: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold07: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold07: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold07: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold07: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold07: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold07: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold07: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold07: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold07: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold07: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold07: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold07: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold07: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold07: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold07: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold07: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 539.061013 
iter  10 value 494.107966
iter  20 value 458.342475
iter  30 value 412.072773
iter  40 value 382.112142
iter  50 value 330.400092
iter  60 value 320.640360
iter  70 value 319.320622
iter  80 value 318.983473
final  value 318.978642 
converged
# weights:  51
initial  value 673.538243 
iter  10 value 476.253134
iter  20 value 371.335397
iter  30 value 339.385339
iter  40 value 330.240909
iter  50 value 329.139113
iter  60 value 328.969240
iter  70 value 328.751586
iter  80 value 324.943639
iter  90 value 323.631122
iter 100 value 322.620084
final  value 322.620084 
stopped after 100 iterations
# weights:  76
initial  value 542.312488 
iter  10 value 462.224326
iter  20 value 385.066936
iter  30 value 352.566139
iter  40 value 332.850841
iter  50 value 329.677480
iter  60 value 328.831048
iter  70 value 327.474031
iter  80 value 326.402264
iter  90 value 324.182115
iter 100 value 322.632991
final  value 322.632991 
stopped after 100 iterations
# weights:  101
initial  value 526.848010 
iter  10 value 478.411283
iter  20 value 440.819431
iter  30 value 395.113835
iter  40 value 355.231602
iter  50 value 337.335204
iter  60 value 329.081163
iter  70 value 327.535775
iter  80 value 327.105708
iter  90 value 326.991620
iter 100 value 326.971021
final  value 326.971021 
stopped after 100 iterations
# weights:  126
initial  value 546.752675 
iter  10 value 491.986286
iter  20 value 429.397454
iter  30 value 377.109726
iter  40 value 353.624490
iter  50 value 341.246950
iter  60 value 338.364685
iter  70 value 337.790791
iter  80 value 337.547537
iter  90 value 335.496364
iter 100 value 333.551911
final  value 333.551911 
stopped after 100 iterations
# weights:  151
initial  value 859.428631 
iter  10 value 464.959762
iter  20 value 438.371260
iter  30 value 373.632965
iter  40 value 354.846161
iter  50 value 345.878294
iter  60 value 342.721471
iter  70 value 342.227850
iter  80 value 342.015891
iter  90 value 341.626853
iter 100 value 341.295734
final  value 341.295734 
stopped after 100 iterations
# weights:  176
initial  value 802.142680 
iter  10 value 489.376206
iter  20 value 437.506636
iter  30 value 390.583270
iter  40 value 357.829343
iter  50 value 351.375902
iter  60 value 350.725999
iter  70 value 349.023960
iter  80 value 346.486201
iter  90 value 344.960166
iter 100 value 344.480030
final  value 344.480030 
stopped after 100 iterations
# weights:  201
initial  value 595.477392 
iter  10 value 468.049066
iter  20 value 409.036794
iter  30 value 365.646824
iter  40 value 355.189840
iter  50 value 352.594550
iter  60 value 351.766661
iter  70 value 351.362666
iter  80 value 350.973917
iter  90 value 350.413558
iter 100 value 348.673905
final  value 348.673905 
stopped after 100 iterations
# weights:  226
initial  value 665.327094 
iter  10 value 493.986458
iter  20 value 433.127563
iter  30 value 387.784261
iter  40 value 365.963041
iter  50 value 360.692524
iter  60 value 355.894873
iter  70 value 353.686556
iter  80 value 352.735450
iter  90 value 351.951157
iter 100 value 351.570667
final  value 351.570667 
stopped after 100 iterations
# weights:  251
initial  value 605.643451 
iter  10 value 467.475782
iter  20 value 439.644567
iter  30 value 386.678204
iter  40 value 369.966109
iter  50 value 360.698908
iter  60 value 357.379183
iter  70 value 356.598084
iter  80 value 356.217217
iter  90 value 356.071845
iter 100 value 355.833276
final  value 355.833276 
stopped after 100 iterations
# weights:  276
initial  value 563.225225 
iter  10 value 448.287431
iter  20 value 379.769929
iter  30 value 353.391746
iter  40 value 326.435541
iter  50 value 311.157475
iter  60 value 309.198371
iter  70 value 295.981124
iter  80 value 286.689579
iter  90 value 279.829566
iter 100 value 277.004218
final  value 277.004218 
stopped after 100 iterations
# weights:  301
initial  value 593.151016 
iter  10 value 466.310774
iter  20 value 417.079030
iter  30 value 361.457638
iter  40 value 346.250309
iter  50 value 334.931550
iter  60 value 326.862580
iter  70 value 311.148077
iter  80 value 302.490848
iter  90 value 295.973794
iter 100 value 292.260820
final  value 292.260820 
stopped after 100 iterations
# weights:  326
initial  value 565.846070 
iter  10 value 491.048361
iter  20 value 437.563152
iter  30 value 409.640394
iter  40 value 363.184751
iter  50 value 331.025767
iter  60 value 321.674998
iter  70 value 316.649227
iter  80 value 313.427632
iter  90 value 310.904849
iter 100 value 307.692688
final  value 307.692688 
stopped after 100 iterations
# weights:  351
initial  value 862.195846 
iter  10 value 509.162673
iter  20 value 485.978733
iter  30 value 416.423511
iter  40 value 367.005784
iter  50 value 354.619263
iter  60 value 340.487925
iter  70 value 334.495215
iter  80 value 331.428122
iter  90 value 328.607856
iter 100 value 324.986612
final  value 324.986612 
stopped after 100 iterations
# weights:  376
initial  value 707.257250 
iter  10 value 501.581609
iter  20 value 456.641392
iter  30 value 411.205393
iter  40 value 367.635889
iter  50 value 351.382203
iter  60 value 339.130238
iter  70 value 334.911732
iter  80 value 333.602943
iter  90 value 332.380469
iter 100 value 331.332212
final  value 331.332212 
stopped after 100 iterations
# weights:  401
initial  value 544.182909 
iter  10 value 472.629606
iter  20 value 448.596799
iter  30 value 427.229496
iter  40 value 378.561954
iter  50 value 355.459967
iter  60 value 350.574021
iter  70 value 346.257791
iter  80 value 343.929535
iter  90 value 341.416326
iter 100 value 338.900997
final  value 338.900997 
stopped after 100 iterations
# weights:  426
initial  value 788.633055 
iter  10 value 515.136672
iter  20 value 439.255586
iter  30 value 391.857096
iter  40 value 373.565233
iter  50 value 356.601969
iter  60 value 349.845517
iter  70 value 347.421635
iter  80 value 345.489750
iter  90 value 343.932241
iter 100 value 343.031264
final  value 343.031264 
stopped after 100 iterations
# weights:  451
initial  value 586.939785 
iter  10 value 517.484246
iter  20 value 451.453663
iter  30 value 407.392019
iter  40 value 376.906357
iter  50 value 360.167790
iter  60 value 354.181343
iter  70 value 351.007476
iter  80 value 349.116353
iter  90 value 348.584572
iter 100 value 347.983667
final  value 347.983667 
stopped after 100 iterations
# weights:  476
initial  value 794.331600 
iter  10 value 542.660852
iter  20 value 481.967177
iter  30 value 434.813666
iter  40 value 379.142492
iter  50 value 363.579187
iter  60 value 358.379578
iter  70 value 355.950559
iter  80 value 353.757684
iter  90 value 352.610760
iter 100 value 351.714189
final  value 351.714189 
stopped after 100 iterations
# weights:  501
initial  value 612.558864 
iter  10 value 518.275192
iter  20 value 437.862728
iter  30 value 404.868647
iter  40 value 377.695310
iter  50 value 365.350970
iter  60 value 361.851529
iter  70 value 359.422733
iter  80 value 357.976204
iter  90 value 356.400865
iter 100 value 354.800631
final  value 354.800631 
stopped after 100 iterations
# weights:  526
initial  value 682.105509 
iter  10 value 446.713919
iter  20 value 361.343163
iter  30 value 350.847544
iter  40 value 335.085362
iter  50 value 316.915254
iter  60 value 303.047326
iter  70 value 285.799041
iter  80 value 274.868450
iter  90 value 264.546951
iter 100 value 262.893235
final  value 262.893235 
stopped after 100 iterations
# weights:  551
initial  value 551.728399 
iter  10 value 479.895166
iter  20 value 454.883888
iter  30 value 370.533573
iter  40 value 334.740434
iter  50 value 317.762362
iter  60 value 312.135617
iter  70 value 305.757195
iter  80 value 299.303967
iter  90 value 292.865604
iter 100 value 289.254872
final  value 289.254872 
stopped after 100 iterations
# weights:  576
initial  value 651.897976 
iter  10 value 488.159535
iter  20 value 413.792809
iter  30 value 379.050628
iter  40 value 348.679730
iter  50 value 335.116055
iter  60 value 328.058032
iter  70 value 321.084706
iter  80 value 314.637870
iter  90 value 309.036265
iter 100 value 306.203540
final  value 306.203540 
stopped after 100 iterations
# weights:  601
initial  value 732.700274 
iter  10 value 506.860251
iter  20 value 431.682515
iter  30 value 393.208111
iter  40 value 379.941927
iter  50 value 360.079631
iter  60 value 333.972340
iter  70 value 329.894309
iter  80 value 328.432962
iter  90 value 325.995287
iter 100 value 323.969950
final  value 323.969950 
stopped after 100 iterations
# weights:  626
initial  value 1009.029361 
iter  10 value 505.179778
iter  20 value 416.951073
iter  30 value 385.657723
iter  40 value 366.682659
iter  50 value 350.532783
iter  60 value 341.440388
iter  70 value 336.984687
iter  80 value 334.641679
iter  90 value 332.907270
iter 100 value 331.503132
final  value 331.503132 
stopped after 100 iterations
# weights:  651
initial  value 567.037375 
iter  10 value 498.565527
iter  20 value 455.734609
iter  30 value 407.039584
iter  40 value 378.176360
iter  50 value 355.995742
iter  60 value 345.087433
iter  70 value 343.222007
iter  80 value 341.073120
iter  90 value 339.147162
iter 100 value 338.053632
final  value 338.053632 
stopped after 100 iterations
# weights:  676
initial  value 585.048172 
iter  10 value 519.546811
iter  20 value 445.911920
iter  30 value 412.433685
iter  40 value 394.241666
iter  50 value 374.959256
iter  60 value 359.052918
iter  70 value 348.920215
iter  80 value 345.746619
iter  90 value 344.115106
iter 100 value 343.188513
final  value 343.188513 
stopped after 100 iterations
# weights:  701
initial  value 723.569088 
iter  10 value 506.649285
iter  20 value 426.759645
iter  30 value 395.719084
iter  40 value 370.782761
iter  50 value 360.858179
iter  60 value 354.045197
iter  70 value 352.045063
iter  80 value 349.887955
iter  90 value 348.557933
iter 100 value 347.291549
final  value 347.291549 
stopped after 100 iterations
# weights:  726
initial  value 758.690266 
iter  10 value 565.965748
iter  20 value 485.060869
iter  30 value 444.090857
iter  40 value 395.753719
iter  50 value 371.047079
iter  60 value 364.038069
iter  70 value 357.753122
iter  80 value 354.158754
iter  90 value 352.680908
iter 100 value 351.922416
final  value 351.922416 
stopped after 100 iterations
# weights:  751
initial  value 808.741847 
iter  10 value 533.757589
iter  20 value 463.162580
iter  30 value 416.840181
iter  40 value 391.031909
iter  50 value 378.879229
iter  60 value 371.275656
iter  70 value 365.694957
iter  80 value 362.520852
iter  90 value 360.081630
iter 100 value 357.927564
final  value 357.927564 
stopped after 100 iterations
# weights:  776
initial  value 1272.875971 
iter  10 value 469.849683
iter  20 value 442.832532
iter  30 value 373.507193
iter  40 value 334.988083
iter  50 value 316.901185
iter  60 value 307.255395
iter  70 value 305.265480
iter  80 value 302.711772
iter  90 value 293.693684
iter 100 value 292.191267
final  value 292.191267 
stopped after 100 iterations
# weights:  801
initial  value 613.384325 
iter  10 value 488.452530
iter  20 value 463.034052
iter  30 value 375.435080
iter  40 value 349.683468
iter  50 value 336.850450
iter  60 value 324.265387
iter  70 value 316.078993
iter  80 value 310.603067
iter  90 value 305.522986
iter 100 value 300.889081
final  value 300.889081 
stopped after 100 iterations
# weights:  826
initial  value 875.309981 
iter  10 value 484.561153
iter  20 value 400.768562
iter  30 value 379.847076
iter  40 value 362.740780
iter  50 value 334.841250
iter  60 value 323.303236
iter  70 value 319.227647
iter  80 value 314.724102
iter  90 value 310.834114
iter 100 value 306.245049
final  value 306.245049 
stopped after 100 iterations
# weights:  851
initial  value 1132.041691 
iter  10 value 509.935452
iter  20 value 433.392830
iter  30 value 383.561263
iter  40 value 362.369075
iter  50 value 345.123092
iter  60 value 333.595795
iter  70 value 329.536054
iter  80 value 327.135083
iter  90 value 324.047740
iter 100 value 321.430304
final  value 321.430304 
stopped after 100 iterations
# weights:  876
initial  value 762.121800 
iter  10 value 499.808219
iter  20 value 483.369632
iter  30 value 411.742314
iter  40 value 385.188100
iter  50 value 362.726773
iter  60 value 354.348655
iter  70 value 344.392313
iter  80 value 339.071828
iter  90 value 334.723294
iter 100 value 332.415702
final  value 332.415702 
stopped after 100 iterations
# weights:  901
initial  value 955.485962 
iter  10 value 533.065209
iter  20 value 431.280646
iter  30 value 386.027790
iter  40 value 362.062509
iter  50 value 350.843009
iter  60 value 344.769196
iter  70 value 341.254156
iter  80 value 339.401660
iter  90 value 337.671675
iter 100 value 336.672572
final  value 336.672572 
stopped after 100 iterations
# weights:  926
initial  value 1112.433183 
iter  10 value 559.201529
iter  20 value 473.036519
iter  30 value 422.819614
iter  40 value 403.856960
iter  50 value 375.073167
iter  60 value 357.948524
iter  70 value 349.501705
iter  80 value 346.423136
iter  90 value 344.709845
iter 100 value 343.650633
final  value 343.650633 
stopped after 100 iterations
# weights:  951
initial  value 745.424553 
iter  10 value 573.527208
iter  20 value 519.242329
iter  30 value 481.618906
iter  40 value 412.776479
iter  50 value 368.812662
iter  60 value 359.553968
iter  70 value 353.563385
iter  80 value 350.578815
iter  90 value 349.510589
iter 100 value 348.591070
final  value 348.591070 
stopped after 100 iterations
# weights:  976
initial  value 883.770368 
iter  10 value 570.325935
iter  20 value 502.470503
iter  30 value 452.629507
iter  40 value 395.854977
iter  50 value 377.105968
iter  60 value 367.444127
iter  70 value 361.420954
iter  80 value 357.641213
iter  90 value 354.594867
iter 100 value 353.221132
final  value 353.221132 
stopped after 100 iterations

model fit failed for Fold08: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold08: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold08: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold08: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold08: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold08: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold08: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold08: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold08: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold08: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold08: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold08: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold08: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold08: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold08: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold08: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold08: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold08: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold08: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold08: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold08: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold08: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold08: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold08: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold08: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold08: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold08: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold08: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold08: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold08: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold08: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold08: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold08: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold08: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold08: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold08: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold08: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold08: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold08: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold08: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold08: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold08: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold08: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold08: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold08: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold08: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold08: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold08: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold08: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold08: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold08: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold08: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold08: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold08: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold08: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold08: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold08: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold08: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold08: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold08: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold08: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 555.055709 
iter  10 value 508.160706
iter  20 value 430.955230
iter  30 value 350.764249
iter  40 value 316.818808
iter  50 value 312.396546
iter  60 value 312.029269
iter  70 value 311.357105
iter  80 value 309.972206
iter  90 value 309.203645
iter 100 value 299.648778
final  value 299.648778 
stopped after 100 iterations
# weights:  51
initial  value 640.488323 
iter  10 value 480.515165
iter  20 value 425.762429
iter  30 value 400.279649
iter  40 value 367.211805
iter  50 value 315.027487
iter  60 value 306.655135
iter  70 value 305.299893
iter  80 value 302.244345
iter  90 value 301.453922
iter 100 value 301.376544
final  value 301.376544 
stopped after 100 iterations
# weights:  76
initial  value 560.532908 
iter  10 value 486.020119
iter  20 value 405.544686
iter  30 value 336.058957
iter  40 value 317.053657
iter  50 value 312.909632
iter  60 value 310.804322
iter  70 value 308.879645
iter  80 value 306.884643
iter  90 value 303.674051
iter 100 value 301.009227
final  value 301.009227 
stopped after 100 iterations
# weights:  101
initial  value 556.771150 
iter  10 value 486.898415
iter  20 value 451.891205
iter  30 value 397.105237
iter  40 value 339.015277
iter  50 value 317.856413
iter  60 value 314.082905
iter  70 value 311.935357
iter  80 value 309.749496
iter  90 value 308.464232
iter 100 value 308.073967
final  value 308.073967 
stopped after 100 iterations
# weights:  126
initial  value 542.115106 
iter  10 value 487.550037
iter  20 value 428.755840
iter  30 value 394.164643
iter  40 value 337.590869
iter  50 value 321.813038
iter  60 value 320.935819
iter  70 value 320.755446
iter  80 value 320.669740
iter  90 value 320.570210
final  value 320.570119 
converged
# weights:  151
initial  value 532.041713 
iter  10 value 463.183214
iter  20 value 399.313434
iter  30 value 341.166476
iter  40 value 326.984920
iter  50 value 324.387006
iter  60 value 322.923446
iter  70 value 322.116284
iter  80 value 320.048929
iter  90 value 318.989809
iter 100 value 318.878847
final  value 318.878847 
stopped after 100 iterations
# weights:  176
initial  value 745.517290 
iter  10 value 489.789336
iter  20 value 431.373429
iter  30 value 370.363623
iter  40 value 335.924735
iter  50 value 329.409919
iter  60 value 327.835192
iter  70 value 326.633165
iter  80 value 325.234680
iter  90 value 324.737124
iter 100 value 324.324603
final  value 324.324603 
stopped after 100 iterations
# weights:  201
initial  value 696.828392 
iter  10 value 494.134658
iter  20 value 430.377798
iter  30 value 368.458047
iter  40 value 343.615843
iter  50 value 334.462575
iter  60 value 331.709265
iter  70 value 330.434716
iter  80 value 330.000683
iter  90 value 329.895428
iter 100 value 329.867318
final  value 329.867318 
stopped after 100 iterations
# weights:  226
initial  value 611.613896 
iter  10 value 483.120090
iter  20 value 416.986480
iter  30 value 393.659302
iter  40 value 358.745525
iter  50 value 344.450192
iter  60 value 341.322088
iter  70 value 339.908527
iter  80 value 337.451271
iter  90 value 335.045806
iter 100 value 333.235045
final  value 333.235045 
stopped after 100 iterations
# weights:  251
initial  value 591.449535 
iter  10 value 532.952720
iter  20 value 454.823558
iter  30 value 384.620706
iter  40 value 355.380769
iter  50 value 347.059160
iter  60 value 343.462782
iter  70 value 341.225603
iter  80 value 339.799724
iter  90 value 338.529492
iter 100 value 337.473551
final  value 337.473551 
stopped after 100 iterations
# weights:  276
initial  value 643.666276 
iter  10 value 469.475534
iter  20 value 447.971800
iter  30 value 373.227865
iter  40 value 313.784674
iter  50 value 290.085929
iter  60 value 275.744401
iter  70 value 268.490307
iter  80 value 266.202662
iter  90 value 265.272745
iter 100 value 260.634333
final  value 260.634333 
stopped after 100 iterations
# weights:  301
initial  value 597.101397 
iter  10 value 457.170933
iter  20 value 374.565076
iter  30 value 329.666274
iter  40 value 303.594973
iter  50 value 292.093087
iter  60 value 287.651834
iter  70 value 285.342060
iter  80 value 282.517799
iter  90 value 279.194323
iter 100 value 276.290125
final  value 276.290125 
stopped after 100 iterations
# weights:  326
initial  value 692.053222 
iter  10 value 452.646089
iter  20 value 365.681957
iter  30 value 332.268977
iter  40 value 315.814459
iter  50 value 304.848963
iter  60 value 300.600384
iter  70 value 297.233394
iter  80 value 295.180260
iter  90 value 294.024517
iter 100 value 292.455367
final  value 292.455367 
stopped after 100 iterations
# weights:  351
initial  value 653.918883 
iter  10 value 454.353608
iter  20 value 388.889023
iter  30 value 356.190182
iter  40 value 325.639076
iter  50 value 314.616700
iter  60 value 309.567607
iter  70 value 306.225106
iter  80 value 302.952130
iter  90 value 301.237251
iter 100 value 300.592835
final  value 300.592835 
stopped after 100 iterations
# weights:  376
initial  value 794.783546 
iter  10 value 462.250421
iter  20 value 406.257980
iter  30 value 360.992887
iter  40 value 329.996882
iter  50 value 318.533989
iter  60 value 315.617952
iter  70 value 313.739421
iter  80 value 312.338786
iter  90 value 311.923415
iter 100 value 311.672874
final  value 311.672874 
stopped after 100 iterations
# weights:  401
initial  value 623.612949 
iter  10 value 497.804146
iter  20 value 428.385462
iter  30 value 384.129267
iter  40 value 344.724218
iter  50 value 329.980756
iter  60 value 325.156461
iter  70 value 323.023663
iter  80 value 321.899989
iter  90 value 321.091756
iter 100 value 320.407343
final  value 320.407343 
stopped after 100 iterations
# weights:  426
initial  value 663.884182 
iter  10 value 512.884865
iter  20 value 428.523079
iter  30 value 373.708412
iter  40 value 352.892765
iter  50 value 338.826447
iter  60 value 331.868806
iter  70 value 328.627492
iter  80 value 326.396877
iter  90 value 324.874548
iter 100 value 323.969066
final  value 323.969066 
stopped after 100 iterations
# weights:  451
initial  value 608.688198 
iter  10 value 532.088414
iter  20 value 486.170137
iter  30 value 437.280910
iter  40 value 411.750641
iter  50 value 375.310243
iter  60 value 355.688139
iter  70 value 339.411632
iter  80 value 333.289401
iter  90 value 332.083322
iter 100 value 330.854060
final  value 330.854060 
stopped after 100 iterations
# weights:  476
initial  value 1170.279479 
iter  10 value 503.067546
iter  20 value 455.406796
iter  30 value 407.232832
iter  40 value 366.985247
iter  50 value 348.868477
iter  60 value 340.599759
iter  70 value 336.107754
iter  80 value 334.192061
iter  90 value 333.169028
iter 100 value 332.466879
final  value 332.466879 
stopped after 100 iterations
# weights:  501
initial  value 807.979611 
iter  10 value 501.243276
iter  20 value 403.023656
iter  30 value 372.293251
iter  40 value 358.099720
iter  50 value 348.495518
iter  60 value 346.024399
iter  70 value 342.990979
iter  80 value 340.692708
iter  90 value 338.961335
iter 100 value 337.131673
final  value 337.131673 
stopped after 100 iterations
# weights:  526
initial  value 934.340275 
iter  10 value 467.999267
iter  20 value 382.287108
iter  30 value 339.046591
iter  40 value 313.118100
iter  50 value 296.143135
iter  60 value 269.960794
iter  70 value 256.811762
iter  80 value 247.905345
iter  90 value 244.439791
iter 100 value 238.399172
final  value 238.399172 
stopped after 100 iterations
# weights:  551
initial  value 997.631596 
iter  10 value 482.313535
iter  20 value 428.598956
iter  30 value 399.572068
iter  40 value 362.309743
iter  50 value 331.510897
iter  60 value 312.745051
iter  70 value 298.046075
iter  80 value 287.158358
iter  90 value 280.942653
iter 100 value 275.379015
final  value 275.379015 
stopped after 100 iterations
# weights:  576
initial  value 1071.487142 
iter  10 value 483.028604
iter  20 value 444.243876
iter  30 value 368.743702
iter  40 value 341.199013
iter  50 value 321.849446
iter  60 value 312.856070
iter  70 value 308.998091
iter  80 value 302.961703
iter  90 value 296.573718
iter 100 value 292.822092
final  value 292.822092 
stopped after 100 iterations
# weights:  601
initial  value 915.841251 
iter  10 value 502.741492
iter  20 value 456.635228
iter  30 value 429.606017
iter  40 value 398.291831
iter  50 value 357.314351
iter  60 value 326.261530
iter  70 value 314.605152
iter  80 value 309.479692
iter  90 value 306.609799
iter 100 value 304.914308
final  value 304.914308 
stopped after 100 iterations
# weights:  626
initial  value 688.233194 
iter  10 value 515.202008
iter  20 value 462.850728
iter  30 value 432.193179
iter  40 value 373.445574
iter  50 value 337.111957
iter  60 value 332.817198
iter  70 value 325.020501
iter  80 value 319.654701
iter  90 value 315.535091
iter 100 value 311.939628
final  value 311.939628 
stopped after 100 iterations
# weights:  651
initial  value 727.018847 
iter  10 value 515.126609
iter  20 value 412.881954
iter  30 value 373.435215
iter  40 value 348.359519
iter  50 value 336.160571
iter  60 value 328.693041
iter  70 value 324.591698
iter  80 value 321.734638
iter  90 value 319.924644
iter 100 value 318.333836
final  value 318.333836 
stopped after 100 iterations
# weights:  676
initial  value 842.073833 
iter  10 value 498.692838
iter  20 value 469.251313
iter  30 value 422.026274
iter  40 value 398.799493
iter  50 value 352.080837
iter  60 value 339.873977
iter  70 value 332.650399
iter  80 value 330.496712
iter  90 value 326.471652
iter 100 value 324.061259
final  value 324.061259 
stopped after 100 iterations
# weights:  701
initial  value 1068.879240 
iter  10 value 518.423185
iter  20 value 465.836783
iter  30 value 419.566794
iter  40 value 369.017143
iter  50 value 350.011307
iter  60 value 338.689309
iter  70 value 334.370047
iter  80 value 332.679999
iter  90 value 331.504019
iter 100 value 330.558014
final  value 330.558014 
stopped after 100 iterations
# weights:  726
initial  value 1437.035590 
iter  10 value 503.261414
iter  20 value 446.934579
iter  30 value 388.799214
iter  40 value 351.141696
iter  50 value 343.206707
iter  60 value 339.468807
iter  70 value 336.351895
iter  80 value 335.053008
iter  90 value 334.048984
iter 100 value 332.955656
final  value 332.955656 
stopped after 100 iterations
# weights:  751
initial  value 1002.568297 
iter  10 value 544.867643
iter  20 value 451.929725
iter  30 value 386.224584
iter  40 value 360.858364
iter  50 value 347.321190
iter  60 value 341.621162
iter  70 value 339.483270
iter  80 value 338.064588
iter  90 value 336.841914
iter 100 value 336.068643
final  value 336.068643 
stopped after 100 iterations
# weights:  776
initial  value 506.490479 
iter  10 value 459.350112
iter  20 value 373.359764
iter  30 value 328.812796
iter  40 value 297.747353
iter  50 value 274.453130
iter  60 value 258.963672
iter  70 value 237.491084
iter  80 value 230.953605
iter  90 value 223.071539
iter 100 value 216.079535
final  value 216.079535 
stopped after 100 iterations
# weights:  801
initial  value 664.166388 
iter  10 value 490.180095
iter  20 value 420.380165
iter  30 value 388.869632
iter  40 value 354.116358
iter  50 value 327.707328
iter  60 value 301.844441
iter  70 value 290.432853
iter  80 value 283.234706
iter  90 value 277.100005
iter 100 value 271.853606
final  value 271.853606 
stopped after 100 iterations
# weights:  826
initial  value 562.450179 
iter  10 value 493.209125
iter  20 value 426.065483
iter  30 value 415.329907
iter  40 value 399.398383
iter  50 value 380.901100
iter  60 value 361.815997
iter  70 value 328.856279
iter  80 value 311.038933
iter  90 value 297.776303
iter 100 value 292.559999
final  value 292.559999 
stopped after 100 iterations
# weights:  851
initial  value 651.065037 
iter  10 value 509.756652
iter  20 value 444.725079
iter  30 value 377.769843
iter  40 value 348.126630
iter  50 value 328.857962
iter  60 value 315.434030
iter  70 value 310.872738
iter  80 value 308.440070
iter  90 value 306.789819
iter 100 value 304.806405
final  value 304.806405 
stopped after 100 iterations
# weights:  876
initial  value 763.432938 
iter  10 value 507.428583
iter  20 value 410.947048
iter  30 value 346.709267
iter  40 value 329.161998
iter  50 value 325.148892
iter  60 value 321.843288
iter  70 value 319.097653
iter  80 value 315.410825
iter  90 value 313.897163
iter 100 value 312.071216
final  value 312.071216 
stopped after 100 iterations
# weights:  901
initial  value 799.679181 
iter  10 value 526.482127
iter  20 value 464.256874
iter  30 value 395.865924
iter  40 value 346.266885
iter  50 value 330.619958
iter  60 value 326.422987
iter  70 value 323.901299
iter  80 value 322.033862
iter  90 value 320.847800
iter 100 value 319.160694
final  value 319.160694 
stopped after 100 iterations
# weights:  926
initial  value 833.452754 
iter  10 value 534.436580
iter  20 value 450.453171
iter  30 value 410.670934
iter  40 value 380.533863
iter  50 value 356.217175
iter  60 value 339.643916
iter  70 value 332.915618
iter  80 value 328.031277
iter  90 value 325.965720
iter 100 value 324.714972
final  value 324.714972 
stopped after 100 iterations
# weights:  951
initial  value 842.883678 
iter  10 value 543.042659
iter  20 value 484.986603
iter  30 value 443.531173
iter  40 value 408.455827
iter  50 value 371.172301
iter  60 value 354.135945
iter  70 value 346.089327
iter  80 value 339.257870
iter  90 value 333.110908
iter 100 value 331.161494
final  value 331.161494 
stopped after 100 iterations
# weights:  976
initial  value 632.017741 
iter  10 value 536.931878
iter  20 value 452.143813
iter  30 value 389.416656
iter  40 value 359.017152
iter  50 value 344.959298
iter  60 value 337.250496
iter  70 value 334.848675
iter  80 value 333.681851
iter  90 value 332.948078
iter 100 value 332.093985
final  value 332.093985 
stopped after 100 iterations

model fit failed for Fold09: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold09: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold09: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold09: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold09: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold09: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold09: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold09: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold09: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold09: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold09: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold09: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold09: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold09: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold09: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold09: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold09: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold09: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold09: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold09: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold09: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold09: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold09: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold09: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold09: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold09: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold09: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold09: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold09: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold09: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold09: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold09: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold09: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold09: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold09: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold09: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold09: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold09: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold09: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold09: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold09: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold09: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold09: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold09: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold09: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold09: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold09: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold09: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold09: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold09: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold09: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold09: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold09: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold09: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold09: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold09: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold09: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold09: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold09: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold09: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold09: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights

# weights:  26
initial  value 705.934549 
iter  10 value 534.253201
iter  20 value 474.900900
iter  30 value 367.460667
iter  40 value 321.505638
iter  50 value 312.570299
iter  60 value 309.902484
iter  70 value 309.855339
final  value 309.846866 
converged
# weights:  51
initial  value 572.131657 
iter  10 value 475.780745
iter  20 value 418.866922
iter  30 value 348.452824
iter  40 value 323.904225
iter  50 value 319.543897
iter  60 value 319.175871
iter  70 value 319.050839
iter  80 value 318.860118
final  value 318.841323 
converged
# weights:  76
initial  value 602.946407 
iter  10 value 483.377840
iter  20 value 440.078081
iter  30 value 410.240629
iter  40 value 344.064149
iter  50 value 329.045900
iter  60 value 325.954503
iter  70 value 319.657462
iter  80 value 312.717259
iter  90 value 311.284294
iter 100 value 311.091857
final  value 311.091857 
stopped after 100 iterations
# weights:  101
initial  value 547.809030 
iter  10 value 501.533703
iter  20 value 454.056690
iter  30 value 375.189926
iter  40 value 338.660202
iter  50 value 334.107397
iter  60 value 332.309077
iter  70 value 331.612131
iter  80 value 331.406830
iter  90 value 331.374781
iter 100 value 331.358795
final  value 331.358795 
stopped after 100 iterations
# weights:  126
initial  value 666.265352 
iter  10 value 496.527972
iter  20 value 451.614745
iter  30 value 403.838591
iter  40 value 356.061967
iter  50 value 338.720863
iter  60 value 334.082934
iter  70 value 326.350558
iter  80 value 324.561297
iter  90 value 323.127731
iter 100 value 322.583074
final  value 322.583074 
stopped after 100 iterations
# weights:  151
initial  value 561.250254 
iter  10 value 474.548388
iter  20 value 428.283966
iter  30 value 356.433880
iter  40 value 335.114261
iter  50 value 331.188637
iter  60 value 330.209752
iter  70 value 329.063037
iter  80 value 328.807248
iter  90 value 328.574786
iter 100 value 328.512917
final  value 328.512917 
stopped after 100 iterations
# weights:  176
initial  value 718.808805 
iter  10 value 508.154276
iter  20 value 468.927007
iter  30 value 392.378365
iter  40 value 361.507200
iter  50 value 345.367200
iter  60 value 337.920154
iter  70 value 337.348734
iter  80 value 337.202692
iter  90 value 336.732461
iter 100 value 336.032979
final  value 336.032979 
stopped after 100 iterations
# weights:  201
initial  value 628.595894 
iter  10 value 484.917333
iter  20 value 429.040633
iter  30 value 370.932585
iter  40 value 356.442825
iter  50 value 350.529170
iter  60 value 345.732888
iter  70 value 341.111419
iter  80 value 339.890414
iter  90 value 339.561209
iter 100 value 339.363164
final  value 339.363164 
stopped after 100 iterations
# weights:  226
initial  value 879.045524 
iter  10 value 500.081862
iter  20 value 476.331178
iter  30 value 428.029615
iter  40 value 372.544303
iter  50 value 362.316000
iter  60 value 353.281694
iter  70 value 348.386528
iter  80 value 346.275717
iter  90 value 345.147493
iter 100 value 344.453760
final  value 344.453760 
stopped after 100 iterations
# weights:  251
initial  value 699.315453 
iter  10 value 512.767058
iter  20 value 486.667904
iter  30 value 417.359675
iter  40 value 370.544565
iter  50 value 356.222208
iter  60 value 351.888788
iter  70 value 349.366002
iter  80 value 348.087391
iter  90 value 347.746605
iter 100 value 347.418579
final  value 347.418579 
stopped after 100 iterations
# weights:  276
initial  value 531.825110 
iter  10 value 479.013797
iter  20 value 373.512004
iter  30 value 331.336964
iter  40 value 298.027499
iter  50 value 290.158808
iter  60 value 285.691360
iter  70 value 280.386075
iter  80 value 270.973004
iter  90 value 266.014595
iter 100 value 264.154246
final  value 264.154246 
stopped after 100 iterations
# weights:  301
initial  value 525.165971 
iter  10 value 465.599123
iter  20 value 417.664893
iter  30 value 365.156016
iter  40 value 335.698333
iter  50 value 327.329759
iter  60 value 316.270581
iter  70 value 307.620156
iter  80 value 300.934958
iter  90 value 295.378571
iter 100 value 293.773958
final  value 293.773958 
stopped after 100 iterations
# weights:  326
initial  value 744.619738 
iter  10 value 490.685407
iter  20 value 435.454927
iter  30 value 353.351618
iter  40 value 330.505259
iter  50 value 319.195554
iter  60 value 313.745558
iter  70 value 309.230657
iter  80 value 305.123352
iter  90 value 302.381935
iter 100 value 300.406399
final  value 300.406399 
stopped after 100 iterations
# weights:  351
initial  value 605.071458 
iter  10 value 489.646529
iter  20 value 444.186176
iter  30 value 378.677010
iter  40 value 346.443841
iter  50 value 333.311926
iter  60 value 323.729656
iter  70 value 321.210418
iter  80 value 319.072934
iter  90 value 316.984184
iter 100 value 315.294973
final  value 315.294973 
stopped after 100 iterations
# weights:  376
initial  value 743.947147 
iter  10 value 481.209867
iter  20 value 459.077485
iter  30 value 417.584927
iter  40 value 378.834299
iter  50 value 353.900904
iter  60 value 339.954922
iter  70 value 331.743065
iter  80 value 325.937776
iter  90 value 323.633632
iter 100 value 321.964388
final  value 321.964388 
stopped after 100 iterations
# weights:  401
initial  value 682.194257 
iter  10 value 521.386597
iter  20 value 482.523039
iter  30 value 453.898323
iter  40 value 426.583066
iter  50 value 378.577963
iter  60 value 345.647118
iter  70 value 336.204688
iter  80 value 331.452857
iter  90 value 329.263328
iter 100 value 328.308556
final  value 328.308556 
stopped after 100 iterations
# weights:  426
initial  value 557.910816 
iter  10 value 486.883446
iter  20 value 409.276513
iter  30 value 384.661614
iter  40 value 362.896215
iter  50 value 347.144332
iter  60 value 341.615192
iter  70 value 338.427341
iter  80 value 335.801639
iter  90 value 334.369598
iter 100 value 333.729415
final  value 333.729415 
stopped after 100 iterations
# weights:  451
initial  value 1008.751143 
iter  10 value 516.457623
iter  20 value 449.195742
iter  30 value 373.425540
iter  40 value 354.285655
iter  50 value 347.378951
iter  60 value 342.068603
iter  70 value 340.266159
iter  80 value 339.502142
iter  90 value 338.649299
iter 100 value 337.831948
final  value 337.831948 
stopped after 100 iterations
# weights:  476
initial  value 694.122426 
iter  10 value 537.800219
iter  20 value 402.828818
iter  30 value 374.881708
iter  40 value 365.512939
iter  50 value 353.844451
iter  60 value 349.078405
iter  70 value 347.341752
iter  80 value 345.431350
iter  90 value 343.962214
iter 100 value 343.649353
final  value 343.649353 
stopped after 100 iterations
# weights:  501
initial  value 914.093960 
iter  10 value 532.891104
iter  20 value 419.701110
iter  30 value 371.012668
iter  40 value 356.849451
iter  50 value 354.322328
iter  60 value 351.533276
iter  70 value 349.424757
iter  80 value 348.615531
iter  90 value 347.935668
iter 100 value 346.969002
final  value 346.969002 
stopped after 100 iterations
# weights:  526
initial  value 635.310341 
iter  10 value 482.608605
iter  20 value 439.334746
iter  30 value 400.196146
iter  40 value 361.529939
iter  50 value 316.422806
iter  60 value 288.281944
iter  70 value 270.422654
iter  80 value 250.342377
iter  90 value 245.395712
iter 100 value 241.752856
final  value 241.752856 
stopped after 100 iterations
# weights:  551
initial  value 886.056287 
iter  10 value 465.395279
iter  20 value 426.854947
iter  30 value 378.044985
iter  40 value 351.900581
iter  50 value 331.789625
iter  60 value 311.740785
iter  70 value 306.020200
iter  80 value 302.001811
iter  90 value 300.972062
iter 100 value 298.757864
final  value 298.757864 
stopped after 100 iterations
# weights:  576
initial  value 673.234623 
iter  10 value 470.465343
iter  20 value 429.231827
iter  30 value 367.214755
iter  40 value 342.539372
iter  50 value 331.862046
iter  60 value 324.037565
iter  70 value 315.120701
iter  80 value 307.699858
iter  90 value 303.001697
iter 100 value 301.073456
final  value 301.073456 
stopped after 100 iterations
# weights:  601
initial  value 1310.684151 
iter  10 value 489.359129
iter  20 value 425.562320
iter  30 value 386.592118
iter  40 value 369.769498
iter  50 value 342.963414
iter  60 value 322.112561
iter  70 value 316.894161
iter  80 value 315.378526
iter  90 value 313.786746
iter 100 value 313.090139
final  value 313.090139 
stopped after 100 iterations
# weights:  626
initial  value 595.918758 
iter  10 value 497.967367
iter  20 value 451.722454
iter  30 value 410.432833
iter  40 value 370.467953
iter  50 value 359.132814
iter  60 value 340.830882
iter  70 value 335.171287
iter  80 value 330.648116
iter  90 value 327.803968
iter 100 value 326.201208
final  value 326.201208 
stopped after 100 iterations
# weights:  651
initial  value 634.993022 
iter  10 value 486.415236
iter  20 value 433.425815
iter  30 value 378.106411
iter  40 value 369.445231
iter  50 value 357.442900
iter  60 value 347.158130
iter  70 value 339.020357
iter  80 value 333.235641
iter  90 value 330.702796
iter 100 value 328.851236
final  value 328.851236 
stopped after 100 iterations
# weights:  676
initial  value 814.763922 
iter  10 value 518.370405
iter  20 value 451.304524
iter  30 value 406.739628
iter  40 value 367.764141
iter  50 value 350.371592
iter  60 value 344.012399
iter  70 value 339.888976
iter  80 value 337.848354
iter  90 value 336.487807
iter 100 value 335.507696
final  value 335.507696 
stopped after 100 iterations
# weights:  701
initial  value 592.567159 
iter  10 value 526.703988
iter  20 value 472.893098
iter  30 value 429.155214
iter  40 value 397.078898
iter  50 value 367.568464
iter  60 value 349.835589
iter  70 value 343.353371
iter  80 value 340.616596
iter  90 value 339.488551
iter 100 value 337.875054
final  value 337.875054 
stopped after 100 iterations
# weights:  726
initial  value 898.470562 
iter  10 value 517.100148
iter  20 value 439.216336
iter  30 value 396.063092
iter  40 value 368.311842
iter  50 value 360.032665
iter  60 value 357.517982
iter  70 value 351.960800
iter  80 value 347.113499
iter  90 value 344.444645
iter 100 value 343.167302
final  value 343.167302 
stopped after 100 iterations
# weights:  751
initial  value 734.118823 
iter  10 value 555.803762
iter  20 value 473.461827
iter  30 value 411.293740
iter  40 value 386.573941
iter  50 value 366.245727
iter  60 value 360.876869
iter  70 value 356.701400
iter  80 value 352.401227
iter  90 value 349.388659
iter 100 value 348.075021
final  value 348.075021 
stopped after 100 iterations
# weights:  776
initial  value 900.283556 
iter  10 value 457.952903
iter  20 value 402.692486
iter  30 value 357.952290
iter  40 value 328.972657
iter  50 value 300.703011
iter  60 value 292.758841
iter  70 value 288.421027
iter  80 value 274.521453
iter  90 value 251.699200
iter 100 value 249.177090
final  value 249.177090 
stopped after 100 iterations
# weights:  801
initial  value 930.390272 
iter  10 value 476.806460
iter  20 value 406.877229
iter  30 value 352.284671
iter  40 value 329.664446
iter  50 value 316.263779
iter  60 value 307.062678
iter  70 value 298.706365
iter  80 value 291.909585
iter  90 value 286.996881
iter 100 value 281.357414
final  value 281.357414 
stopped after 100 iterations
# weights:  826
initial  value 529.337092 
iter  10 value 488.541087
iter  20 value 438.135304
iter  30 value 413.594917
iter  40 value 366.482628
iter  50 value 341.134089
iter  60 value 318.770397
iter  70 value 309.023561
iter  80 value 303.866937
iter  90 value 299.787330
iter 100 value 296.280150
final  value 296.280150 
stopped after 100 iterations
# weights:  851
initial  value 1364.519603 
iter  10 value 507.947587
iter  20 value 459.546474
iter  30 value 425.930298
iter  40 value 387.981827
iter  50 value 349.772203
iter  60 value 334.001470
iter  70 value 327.189605
iter  80 value 322.953904
iter  90 value 319.603908
iter 100 value 315.902142
final  value 315.902142 
stopped after 100 iterations
# weights:  876
initial  value 583.830613 
iter  10 value 519.236213
iter  20 value 471.716645
iter  30 value 419.842164
iter  40 value 399.933485
iter  50 value 361.355888
iter  60 value 342.143671
iter  70 value 335.014125
iter  80 value 331.624045
iter  90 value 326.645755
iter 100 value 323.046535
final  value 323.046535 
stopped after 100 iterations
# weights:  901
initial  value 1360.165123 
iter  10 value 535.618586
iter  20 value 451.322346
iter  30 value 422.765799
iter  40 value 406.083938
iter  50 value 377.938786
iter  60 value 355.221075
iter  70 value 342.214438
iter  80 value 337.476175
iter  90 value 334.846240
iter 100 value 332.605793
final  value 332.605793 
stopped after 100 iterations
# weights:  926
initial  value 643.994121 
iter  10 value 538.398021
iter  20 value 431.607237
iter  30 value 381.232333
iter  40 value 364.968027
iter  50 value 351.382613
iter  60 value 345.007582
iter  70 value 341.241316
iter  80 value 338.961321
iter  90 value 337.285761
iter 100 value 335.811031
final  value 335.811031 
stopped after 100 iterations
# weights:  951
initial  value 1378.895425 
iter  10 value 566.388384
iter  20 value 472.790197
iter  30 value 429.742662
iter  40 value 396.916575
iter  50 value 375.674589
iter  60 value 356.358582
iter  70 value 350.603681
iter  80 value 345.783697
iter  90 value 342.624606
iter 100 value 341.696768
final  value 341.696768 
stopped after 100 iterations
# weights:  976
initial  value 628.606804 
iter  10 value 549.292006
iter  20 value 419.733137
iter  30 value 375.017016
iter  40 value 364.981802
iter  50 value 359.519209
iter  60 value 354.738442
iter  70 value 352.047241
iter  80 value 349.407044
iter  90 value 346.740949
iter 100 value 344.325184
final  value 344.325184 
stopped after 100 iterations

model fit failed for Fold10: size= 40, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1001) weights
model fit failed for Fold10: size= 41, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1026) weights
model fit failed for Fold10: size= 42, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1051) weights
model fit failed for Fold10: size= 43, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1076) weights
model fit failed for Fold10: size= 44, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1101) weights
model fit failed for Fold10: size= 45, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1126) weights
model fit failed for Fold10: size= 46, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1151) weights
model fit failed for Fold10: size= 47, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1176) weights
model fit failed for Fold10: size= 48, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1201) weights
model fit failed for Fold10: size= 49, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1226) weights
model fit failed for Fold10: size= 50, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1251) weights
model fit failed for Fold10: size= 51, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1276) weights
model fit failed for Fold10: size= 52, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1301) weights
model fit failed for Fold10: size= 53, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1326) weights
model fit failed for Fold10: size= 54, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1351) weights
model fit failed for Fold10: size= 55, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1376) weights
model fit failed for Fold10: size= 56, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1401) weights
model fit failed for Fold10: size= 57, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1426) weights
model fit failed for Fold10: size= 58, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1451) weights
model fit failed for Fold10: size= 59, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1476) weights
model fit failed for Fold10: size= 60, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1501) weights
model fit failed for Fold10: size= 61, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1526) weights
model fit failed for Fold10: size= 62, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1551) weights
model fit failed for Fold10: size= 63, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1576) weights
model fit failed for Fold10: size= 64, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1601) weights
model fit failed for Fold10: size= 65, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1626) weights
model fit failed for Fold10: size= 66, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1651) weights
model fit failed for Fold10: size= 67, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1676) weights
model fit failed for Fold10: size= 68, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1701) weights
model fit failed for Fold10: size= 69, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1726) weights
model fit failed for Fold10: size= 70, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1751) weights
model fit failed for Fold10: size= 71, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1776) weights
model fit failed for Fold10: size= 72, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1801) weights
model fit failed for Fold10: size= 73, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1826) weights
model fit failed for Fold10: size= 74, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1851) weights
model fit failed for Fold10: size= 75, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1876) weights
model fit failed for Fold10: size= 76, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1901) weights
model fit failed for Fold10: size= 77, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1926) weights
model fit failed for Fold10: size= 78, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1951) weights
model fit failed for Fold10: size= 79, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (1976) weights
model fit failed for Fold10: size= 80, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2001) weights
model fit failed for Fold10: size= 81, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2026) weights
model fit failed for Fold10: size= 82, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2051) weights
model fit failed for Fold10: size= 83, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2076) weights
model fit failed for Fold10: size= 84, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2101) weights
model fit failed for Fold10: size= 85, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2126) weights
model fit failed for Fold10: size= 86, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2151) weights
model fit failed for Fold10: size= 87, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2176) weights
model fit failed for Fold10: size= 88, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2201) weights
model fit failed for Fold10: size= 89, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2226) weights
model fit failed for Fold10: size= 90, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2251) weights
model fit failed for Fold10: size= 91, decay=0.01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2276) weights
model fit failed for Fold10: size= 92, decay=0.11 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2301) weights
model fit failed for Fold10: size= 93, decay=0.21 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2326) weights
model fit failed for Fold10: size= 94, decay=0.31 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2351) weights
model fit failed for Fold10: size= 95, decay=0.41 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2376) weights
model fit failed for Fold10: size= 96, decay=0.51 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2401) weights
model fit failed for Fold10: size= 97, decay=0.61 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2426) weights
model fit failed for Fold10: size= 98, decay=0.71 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2451) weights
model fit failed for Fold10: size= 99, decay=0.81 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2476) weights
model fit failed for Fold10: size=100, decay=0.91 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
  too many (2501) weights
There were missing values in resampled performance measures.missing values found in aggregated results

# weights:  251
initial  value 672.190405 
iter  10 value 549.611792
iter  20 value 490.363061
iter  30 value 423.739736
iter  40 value 396.662851
iter  50 value 384.647318
iter  60 value 382.711173
iter  70 value 381.677349
iter  80 value 380.288314
iter  90 value 379.272059
iter 100 value 378.796717
final  value 378.796717 
stopped after 100 iterations

train_nn$bestTune

#Result

plot(train_nn)

rf_model <- randomForest(factor(Survived) ~ Pclass + Sex + Age + SibSp + Parch + 
                                            Fare + Embarked + Title + 
                                            Family_size,
                                            data = train)

plot(rf_model, ylim=c(0,0.36))
legend('topright', colnames(rf_model$err.rate), col=1:3, fill=1:3)

Let’s look at relative variable importance by plotting the mean decrease in Gini calculated across all trees.

importance    <- importance(rf_model)
varImportance <- data.frame(Variables = row.names(importance), 
                            Importance = round(importance[ ,'MeanDecreaseGini'],2))
rankImportance <- varImportance %>%
  mutate(Rank = paste0('#',dense_rank(desc(Importance))))


ggplot(rankImportance, aes(x = reorder(Variables, Importance), 
    y = Importance, fill = Importance)) +
  geom_bar(stat='identity') + 
  geom_text(aes(x = Variables, y = 0.5, label = Rank),
    hjust=0, vjust=0.55, size = 4, colour = 'red') +
  labs(x = 'Variables') +
  coord_flip()

prediction <- predict(rf_model, test)
solution <- data.frame(PassengerID = test$PassengerId, Survived = prediction)
write.csv(solution, file = 'rf_mod_Solution.csv', row.names = FALSE)

nn_preds <- predict(train_nn, test)

solution <- data.frame(PassengerID = test$PassengerId,
                       Survived = nn_preds)

write.csv(solution, file = 'nn.titanic.preds.csv', row.names = FALSE)