Mobile Price Classification using Tree-Based Algorithms - Bagging and Boosting
Introduction
As a mobile phone producer startup, one of the task at hand is to determine our brand’s price range. To complete this task, we are going to conduct a market price analysis. Using the data of competitor’s mobile phone specs and price, we
Using machine learning classification algorithms, we are going to clasify mobile phone price range according to its specs.
He does not know how to estimate price of mobiles his company creates. In this competitive mobile phone market you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.
Bob wants to find out some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.
In this problem you do not have to predict actual price but a price range indicating how high the price is
library(tidyverse)
library(GGally) # to make instant corr matrix in ggplot
library(caret) # classification and regression training
library(e1071) #naivebayes
library(xgboost)# read data Customer_Behaviour.csv
train <- read.csv("data_input/train.csv", stringsAsFactors = T)
head(train)## battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt
## 1 842 0 2.2 0 1 0 7 0.6 188
## 2 1021 1 0.5 1 0 1 53 0.7 136
## 3 563 1 0.5 1 2 1 41 0.9 145
## 4 615 1 2.5 0 0 0 10 0.8 131
## 5 1821 1 1.2 0 13 1 44 0.6 141
## 6 1859 0 0.5 1 3 0 22 0.7 164
## n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen
## 1 2 2 20 756 2549 9 7 19 0 0
## 2 3 6 905 1988 2631 17 3 7 1 1
## 3 5 6 1263 1716 2603 11 2 9 1 1
## 4 6 9 1216 1786 2769 16 8 11 1 0
## 5 2 14 1208 1212 1411 8 2 15 1 1
## 6 1 7 1004 1654 1067 17 1 10 1 0
## wifi price_range
## 1 1 1
## 2 0 2
## 3 0 2
## 4 0 2
## 5 0 1
## 6 0 1str(train)## 'data.frame': 2000 obs. of 21 variables:
## $ battery_power: int 842 1021 563 615 1821 1859 1821 1954 1445 509 ...
## $ blue : int 0 1 1 1 1 0 0 0 1 1 ...
## $ clock_speed : num 2.2 0.5 0.5 2.5 1.2 0.5 1.7 0.5 0.5 0.6 ...
## $ dual_sim : int 0 1 1 0 0 1 0 1 0 1 ...
## $ fc : int 1 0 2 0 13 3 4 0 0 2 ...
## $ four_g : int 0 1 1 0 1 0 1 0 0 1 ...
## $ int_memory : int 7 53 41 10 44 22 10 24 53 9 ...
## $ m_dep : num 0.6 0.7 0.9 0.8 0.6 0.7 0.8 0.8 0.7 0.1 ...
## $ mobile_wt : int 188 136 145 131 141 164 139 187 174 93 ...
## $ n_cores : int 2 3 5 6 2 1 8 4 7 5 ...
## $ pc : int 2 6 6 9 14 7 10 0 14 15 ...
## $ px_height : int 20 905 1263 1216 1208 1004 381 512 386 1137 ...
## $ px_width : int 756 1988 1716 1786 1212 1654 1018 1149 836 1224 ...
## $ ram : int 2549 2631 2603 2769 1411 1067 3220 700 1099 513 ...
## $ sc_h : int 9 17 11 16 8 17 13 16 17 19 ...
## $ sc_w : int 7 3 2 8 2 1 8 3 1 10 ...
## $ talk_time : int 19 7 9 11 15 10 18 5 20 12 ...
## $ three_g : int 0 1 1 1 1 1 1 1 1 1 ...
## $ touch_screen : int 0 1 1 0 1 0 0 1 0 0 ...
## $ wifi : int 1 0 0 0 0 0 1 1 0 0 ...
## $ price_range : int 1 2 2 2 1 1 3 0 0 0 ...Data Description
battery_power: total energy a battery can store in one time measured in mAhblue: has bluetooth or notclock_speed: speed at which microprocessor executes instructionsdual_sim: support dual sim or notfc: front camera megapixelfour_g: support 4G or notint_memory: Internal Memory in Gigabytesm_dep: Mobile Depth in cmmobile_wt: weight of mobile phonen_cores: number of core processorpc: primary camera megapixelspx_weight: pixel resolution heightpx_width: pixel resolution widthram: random access memory in megabytessc_h: screen height of mobile in cmsc_w: screen width of mobile in cmtalk_time: longest time that a single battery charge will last when you are on callthree_g: support 3G or nottouch_screen: has touch screen or notwifi: has wifi or notprice_range: this is the target variable with value of 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost).
train <- train %>%
mutate(across(c(blue, dual_sim, four_g, three_g, touch_screen, wifi, price_range), as.factor))colSums(is.na(train))## battery_power blue clock_speed dual_sim fc
## 0 0 0 0 0
## four_g int_memory m_dep mobile_wt n_cores
## 0 0 0 0 0
## pc px_height px_width ram sc_h
## 0 0 0 0 0
## sc_w talk_time three_g touch_screen wifi
## 0 0 0 0 0
## price_range
## 0EDA
- Target Class Distribution
ggplot(data = train, aes(x= price_range)) +
geom_bar() +
coord_flip() +
labs(title = 'Target Class Distribution') +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
attach(train)
par(mfrow = c(3,3))
hist(battery_power)
hist(clock_speed)
hist(fc)
hist(int_memory)
hist(m_dep)
hist(mobile_wt)
hist(n_cores)
hist(pc)
hist(ram)
attach(train)
par(mfrow = c(2,3))
plot(blue, main = "Bluetooth")
plot(wifi, main = "Wifi")
plot(dual_sim, main = "Dual SIM")
plot(four_g, main = "4G")
plot(three_g, main = "3G")
plot(touch_screen, main = "Touch Screen")
Training and Testing Model
Now we will make a Naive Bayes classsifier for our data as the benchmark model. We will make a 80/20 partitioning for the training and validation set. We used the createDataPartition from caret package to make a balanced partitioning. This is very important in this context because we are going to calculate the prior probabilities from the count of the training data. So, it should follow the proportion of the parent dataset.
# method 1: this method create a perfect partition of data
set.seed(7267166)
trainIndex <- createDataPartition(train$price_range, p=0.80)$Resample1
train <- train[trainIndex, ]
validation <- train[-trainIndex, ]# method 2
# RNGkind(sample.kind = "Rounding")
# set.seed(100)
# index <- sample(x = nrow(train), size= nrow(train)*0.80)
# train <- train[index,] # subsetting data berdasarkan index data yang ada di variabel index
# validation <- train[-index,] # Check the balance
print(table(train$price_range))##
## 0 1 2 3
## 400 400 400 400Naive Bayes
# Train Model
model_nb <- naiveBayes(formula = price_range~., data = train, laplace = 1)# predict
nb_predClass <- predict(object = model_nb, newdata = validation, type = "class")
head(nb_predClass)## [1] 1 0 0 2 3 1
## Levels: 0 1 2 3confusionMatrix(data = as.factor(nb_predClass), reference = as.factor(validation$price_range))## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3
## 0 71 9 0 0
## 1 4 62 12 0
## 2 0 20 53 13
## 3 0 0 6 65
##
## Overall Statistics
##
## Accuracy : 0.7968
## 95% CI : (0.7481, 0.8399)
## No Information Rate : 0.2889
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7293
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3
## Sensitivity 0.9467 0.6813 0.7465 0.8333
## Specificity 0.9625 0.9286 0.8648 0.9747
## Pos Pred Value 0.8875 0.7949 0.6163 0.9155
## Neg Pred Value 0.9830 0.8776 0.9214 0.9467
## Prevalence 0.2381 0.2889 0.2254 0.2476
## Detection Rate 0.2254 0.1968 0.1683 0.2063
## Detection Prevalence 0.2540 0.2476 0.2730 0.2254
## Balanced Accuracy 0.9546 0.8049 0.8056 0.9040Decision Tree
library(partykit)## Loading required package: grid## Loading required package: libcoin## Loading required package: mvtnormmodel_dctree <- ctree(formula = price_range~., data = train)model_dctree##
## Model formula:
## price_range ~ battery_power + blue + clock_speed + dual_sim +
## fc + four_g + int_memory + m_dep + mobile_wt + n_cores +
## pc + px_height + px_width + ram + sc_h + sc_w + talk_time +
## three_g + touch_screen + wifi
##
## Fitted party:
## [1] root
## | [2] ram <= 2235
## | | [3] ram <= 1105
## | | | [4] px_height <= 1446
## | | | | [5] battery_power <= 1802
## | | | | | [6] px_height <= 1222: 0 (n = 285, err = 2.5%)
## | | | | | [7] px_height > 1222: 0 (n = 16, err = 31.2%)
## | | | | [8] battery_power > 1802
## | | | | | [9] px_width <= 1532: 0 (n = 29, err = 10.3%)
## | | | | | [10] px_width > 1532: 1 (n = 14, err = 21.4%)
## | | | [11] px_height > 1446: 1 (n = 15, err = 20.0%)
## | | [12] ram > 1105
## | | | [13] battery_power <= 1083
## | | | | [14] ram <= 1609
## | | | | | [15] px_height <= 730
## | | | | | | [16] px_width <= 1643: 0 (n = 50, err = 0.0%)
## | | | | | | [17] px_width > 1643: 1 (n = 7, err = 42.9%)
## | | | | | [18] px_height > 730
## | | | | | | [19] px_width <= 1164: 0 (n = 7, err = 14.3%)
## | | | | | | [20] px_width > 1164: 1 (n = 18, err = 11.1%)
## | | | | [21] ram > 1609
## | | | | | [22] px_width <= 621: 0 (n = 7, err = 28.6%)
## | | | | | [23] px_width > 621
## | | | | | | [24] px_height <= 1185
## | | | | | | | [25] ram <= 1663: 1 (n = 9, err = 33.3%)
## | | | | | | | [26] ram > 1663: 1 (n = 90, err = 4.4%)
## | | | | | | [27] px_height > 1185: 1 (n = 9, err = 44.4%)
## | | | [28] battery_power > 1083
## | | | | [29] ram <= 1896
## | | | | | [30] px_height <= 849
## | | | | | | [31] ram <= 1175: 1 (n = 11, err = 45.5%)
## | | | | | | [32] ram > 1175: 1 (n = 128, err = 7.0%)
## | | | | | [33] px_height > 849
## | | | | | | [34] ram <= 1480: 1 (n = 38, err = 15.8%)
## | | | | | | [35] ram > 1480
## | | | | | | | [36] battery_power <= 1656: 1 (n = 21, err = 47.6%)
## | | | | | | | [37] battery_power > 1656: 2 (n = 13, err = 0.0%)
## | | | | [38] ram > 1896
## | | | | | [39] px_width <= 1051
## | | | | | | [40] battery_power <= 1449: 1 (n = 17, err = 0.0%)
## | | | | | | [41] battery_power > 1449: 2 (n = 13, err = 46.2%)
## | | | | | [42] px_width > 1051: 2 (n = 41, err = 4.9%)
## | [43] ram > 2235
## | | [44] ram <= 3012
## | | | [45] battery_power <= 1350
## | | | | [46] px_width <= 1351
## | | | | | [47] ram <= 2651
## | | | | | | [48] battery_power <= 930: 1 (n = 31, err = 3.2%)
## | | | | | | [49] battery_power > 930
## | | | | | | | [50] px_width <= 800: 1 (n = 14, err = 21.4%)
## | | | | | | | [51] px_width > 800: 2 (n = 18, err = 0.0%)
## | | | | | [52] ram > 2651: 2 (n = 52, err = 5.8%)
## | | | | [53] px_width > 1351
## | | | | | [54] px_height <= 356: 2 (n = 22, err = 13.6%)
## | | | | | [55] px_height > 356
## | | | | | | [56] battery_power <= 1021: 2 (n = 51, err = 3.9%)
## | | | | | | [57] battery_power > 1021: 2 (n = 17, err = 41.2%)
## | | | [58] battery_power > 1350
## | | | | [59] px_height <= 642
## | | | | | [60] battery_power <= 1441: 2 (n = 7, err = 28.6%)
## | | | | | [61] battery_power > 1441
## | | | | | | [62] battery_power <= 1851: 2 (n = 58, err = 3.4%)
## | | | | | | [63] battery_power > 1851: 2 (n = 22, err = 36.4%)
## | | | | [64] px_height > 642
## | | | | | [65] ram <= 2677: 2 (n = 34, err = 38.2%)
## | | | | | [66] ram > 2677: 3 (n = 19, err = 0.0%)
## | | [67] ram > 3012
## | | | [68] battery_power <= 909
## | | | | [69] px_height <= 575
## | | | | | [70] ram <= 3731: 2 (n = 48, err = 16.7%)
## | | | | | [71] ram > 3731: 3 (n = 19, err = 21.1%)
## | | | | [72] px_height > 575
## | | | | | [73] ram <= 3187: 2 (n = 7, err = 42.9%)
## | | | | | [74] ram > 3187: 3 (n = 49, err = 6.1%)
## | | | [75] battery_power > 909
## | | | | [76] ram <= 3291
## | | | | | [77] px_width <= 839: 2 (n = 17, err = 35.3%)
## | | | | | [78] px_width > 839: 3 (n = 57, err = 10.5%)
## | | | | [79] ram > 3291: 3 (n = 220, err = 0.0%)
##
## Number of inner nodes: 39
## Number of terminal nodes: 40# prediksi kelas di data train
dctree_predClass_train <- predict(object = model_dctree, newdata = train, type = "response")
head(dctree_predClass_train)## 1 2 3 5 6 7
## 1 2 2 1 1 3
## Levels: 0 1 2 3# confusion matrix data train
confusionMatrix(data = as.factor(dctree_predClass_train),
reference = train$price_range) ## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3
## 0 376 18 0 0
## 1 24 366 32 0
## 2 0 16 355 49
## 3 0 0 13 351
##
## Overall Statistics
##
## Accuracy : 0.905
## 95% CI : (0.8896, 0.9189)
## No Information Rate : 0.25
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8733
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3
## Sensitivity 0.9400 0.9150 0.8875 0.8775
## Specificity 0.9850 0.9533 0.9458 0.9892
## Pos Pred Value 0.9543 0.8673 0.8452 0.9643
## Neg Pred Value 0.9801 0.9711 0.9619 0.9604
## Prevalence 0.2500 0.2500 0.2500 0.2500
## Detection Rate 0.2350 0.2288 0.2219 0.2194
## Detection Prevalence 0.2462 0.2637 0.2625 0.2275
## Balanced Accuracy 0.9625 0.9342 0.9167 0.9333# prediksi kelas di data test
dctree_predClass <- predict(object = model_dctree, newdata = validation, type = "response")
head(dctree_predClass)## 5 10 16 25 34 40
## 1 0 0 1 3 1
## Levels: 0 1 2 3# confusion matrix data test
confusionMatrix(data = as.factor(dctree_predClass),
reference = validation$price_range) ## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3
## 0 72 2 0 0
## 1 3 86 7 0
## 2 0 3 63 9
## 3 0 0 1 69
##
## Overall Statistics
##
## Accuracy : 0.9206
## 95% CI : (0.8851, 0.948)
## No Information Rate : 0.2889
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8938
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3
## Sensitivity 0.9600 0.9451 0.8873 0.8846
## Specificity 0.9917 0.9554 0.9508 0.9958
## Pos Pred Value 0.9730 0.8958 0.8400 0.9857
## Neg Pred Value 0.9876 0.9772 0.9667 0.9633
## Prevalence 0.2381 0.2889 0.2254 0.2476
## Detection Rate 0.2286 0.2730 0.2000 0.2190
## Detection Prevalence 0.2349 0.3048 0.2381 0.2222
## Balanced Accuracy 0.9758 0.9502 0.9191 0.9402Pruning
Pruning paramater:
- mincriterion: Nilai 1-\(\alpha\). Saat mincriterion 0.95, P-value harus < 0.05 untuk suatu node dapat membuat cabang. (default: 0.95)
- minsplit: Jumlah minimal observasi di tiap cabang setelah pemisahan. Bila tidak terpenuhi, tidak dilakukan percabangan. (default: 20)
- minbucket: Jumlah minimal observasi di terminal node. Bila tidak terpenuhi, tidak dilakukan percabangan. (default: 7)
# # parameter pruning
# model_dctree_new <- ctree(formula = price_range~., data = train,
# control = ctree_control(mincriterion = 0.07,
# minsplit = 25,
# minbucket = 7))Random Forest
Random Forest is an ensemble technique that is a tree-based algorithm. It is a bagging technique that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. Instead of relying on one decision tree, the random forest takes the prediction from each tree and based on the majority votes of predictions, and it predicts the final output. The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting.
library(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## marginBy default, the number of decision trees in the forest is 500 and the number of features used as potential candidates for each split is 3. The model will automatically attempt to classify each of the samples in the Out-Of-Bag dataset and display a confusion matrix with the results.
Using tuneRF from randomForest package, we can find the best mtry, the number of variables randomly sampled
rownames(train) <- NULLset.seed(123)
mtry <- tuneRF(train[,-21], train[,21], stepFactor=1.5)## mtry = 4 OOB error = 17.5%
## Searching left ...
## mtry = 3 OOB error = 23.69%
## -0.3535714 0.05
## Searching right ...
## mtry = 6 OOB error = 13.44%
## 0.2321429 0.05
## mtry = 9 OOB error = 11.38%
## 0.1534884 0.05
## mtry = 13 OOB error = 10.69%
## 0.06043956 0.05
## mtry = 19 OOB error = 11.5%
## -0.07602339 0.05
# stepFactor is a magnitude by which the chosen mtry gets deflated or inflated.
best.m <- mtry[mtry[, 2] == min(mtry[, 2]), 1]
print(mtry)## mtry OOBError
## 3.OOB 3 0.236875
## 4.OOB 4 0.175000
## 6.OOB 6 0.134375
## 9.OOB 9 0.113750
## 13.OOB 13 0.106875
## 19.OOB 19 0.115000print(best.m)## [1] 13The optimum mtry is 9. Now, we can proceed to model fitting.
set.seed(123)
model_rf <- randomForest(
price_range ~ .,
data=train,
mtry = 9
)model_rf##
## Call:
## randomForest(formula = price_range ~ ., data = train, mtry = 9)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 9
##
## OOB estimate of error rate: 9.75%
## Confusion matrix:
## 0 1 2 3 class.error
## 0 376 24 0 0 0.0600
## 1 27 346 27 0 0.1350
## 2 0 27 349 24 0.1275
## 3 0 0 27 373 0.0675\[Accuracy = 100 - 9.62 = 90.38 \%\]
# prediksi kelas di data test
rf_predClass <- predict(object = model_rf, newdata = validation, type = "response")
head(rf_predClass)## 5 10 16 25 34 40
## 1 0 0 1 3 2
## Levels: 0 1 2 3# confusion matrix data test
confusionMatrix(data = as.factor(rf_predClass),
reference = validation$price_range) ## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3
## 0 75 0 0 0
## 1 0 91 0 0
## 2 0 0 71 0
## 3 0 0 0 78
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9884, 1)
## No Information Rate : 0.2889
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3
## Sensitivity 1.0000 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000 1.0000
## Prevalence 0.2381 0.2889 0.2254 0.2476
## Detection Rate 0.2381 0.2889 0.2254 0.2476
## Detection Prevalence 0.2381 0.2889 0.2254 0.2476
## Balanced Accuracy 1.0000 1.0000 1.0000 1.0000plot(model_rf)
importance(model_rf)## MeanDecreaseGini
## battery_power 115.389001
## blue 1.760175
## clock_speed 14.425589
## dual_sim 1.765545
## fc 14.772111
## four_g 2.200327
## int_memory 22.546449
## m_dep 14.042289
## mobile_wt 26.963784
## n_cores 12.686672
## pc 17.702943
## px_height 69.430194
## px_width 69.487765
## ram 759.647748
## sc_h 16.336939
## sc_w 16.534080
## talk_time 17.686105
## three_g 1.844758
## touch_screen 2.495804
## wifi 1.515420LEast important features: blue, dual_sim, three_g, wifi, four_g, touch_screen.
partialPlot(model_rf, train, ram, "3")
partialPlot(model_rf, train, battery_power, "3")
The inference should be:
- if the RAM of a mobile phone is more than 1.25GB, then it has a higher chance of classifying into class 3 (very high cost).
- if the battery power is more than 1,250 mAH, then it has a higher chance of classifying into class 3 very high cost).
train_reduced <- train %>%
select(-c(blue, dual_sim, three_g, wifi, four_g, touch_screen))
validation_reduced <- validation %>%
select(-c(blue, dual_sim, three_g, wifi, four_g, touch_screen))set.seed(123)
model_rf_2 <- randomForest(
price_range ~ .,
data=train_reduced,
mtry = 9
)set.seed(123)
mtry <- tuneRF(train_reduced[,-15], train_reduced[,15], stepFactor=1.5)## mtry = 3 OOB error = 15.69%
## Searching left ...
## mtry = 2 OOB error = 21.44%
## -0.3665339 0.05
## Searching right ...
## mtry = 4 OOB error = 12.81%
## 0.1832669 0.05
## mtry = 6 OOB error = 10.88%
## 0.1512195 0.05
## mtry = 9 OOB error = 10.56%
## 0.02873563 0.05
best.m <- mtry[mtry[, 2] == min(mtry[, 2]), 1]
print(mtry)## mtry OOBError
## 2.OOB 2 0.214375
## 3.OOB 3 0.156875
## 4.OOB 4 0.128125
## 6.OOB 6 0.108750
## 9.OOB 9 0.105625print(best.m)## [1] 9model_rf_2##
## Call:
## randomForest(formula = price_range ~ ., data = train_reduced, mtry = 9)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 9
##
## OOB estimate of error rate: 9.62%
## Confusion matrix:
## 0 1 2 3 class.error
## 0 382 18 0 0 0.045
## 1 28 348 24 0 0.130
## 2 0 28 348 24 0.130
## 3 0 0 32 368 0.080importance(model_rf_2)## MeanDecreaseGini
## battery_power 132.366696
## clock_speed 8.941181
## fc 10.786739
## int_memory 16.061521
## m_dep 10.638421
## mobile_wt 20.812637
## n_cores 8.403486
## pc 12.671719
## px_height 81.013497
## px_width 80.097889
## ram 781.310306
## sc_h 11.447211
## sc_w 11.681159
## talk_time 13.008850XGBoost Classification
The XGBoost library implements the gradient boosting decision tree algorithm. This algorithm goes by lots of different names such as gradient boosting, multiple additive regression trees, stochastic gradient boosting or gradient boosting machines. Boosting means combining a learning algorithm in series to achieve a strong learner from many sequentially connected weak learners. In case of gradient boosted decision trees algorithm, the weak learners are decision trees. Each tree attempts to minimize the errors of previous tree.
Gradient boosting re-defines boosting as a numerical optimization problem where the objective is to minimize the loss function of the model by adding weak learners using gradient descent. Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. As gradient boosting is based on minimizing a loss function, different types of loss functions can be used resulting in a flexible technique that can be applied to regression, multi-class classification, etc.
The name XGBoost (Extreme Gradient Boosting) actually refers to the engineering goal to push the limit of computations resources for boosted tree algorithms. Which is the reason why many people use xgboost. It uses sequentially-built shallow decision trees to provide accurate results and a highly-scalable training method that avoids overfitting.
The advantage of XGBoost compared to Random Forest:
rownames(train_reduced) <- NULL
train_reduced## battery_power clock_speed fc int_memory m_dep mobile_wt n_cores pc
## 1 842 2.2 1 7 0.6 188 2 2
## 2 1021 0.5 0 53 0.7 136 3 6
## 3 563 0.5 2 41 0.9 145 5 6
## 4 1821 1.2 13 44 0.6 141 2 14
## 5 1859 0.5 3 22 0.7 164 1 7
## 6 1821 1.7 4 10 0.8 139 8 10
## 7 1445 0.5 0 53 0.7 174 7 14
## 8 509 0.6 2 9 0.1 93 5 15
## 9 769 2.9 0 9 0.1 182 5 1
## 10 1520 2.2 5 33 0.5 177 8 18
## 11 803 2.1 7 17 1.0 198 4 11
## 12 1866 0.5 13 52 0.7 185 1 17
## 13 775 1.0 3 46 0.7 159 2 16
## 14 838 0.5 1 13 0.1 196 8 4
## 15 595 0.9 7 23 0.1 121 3 17
## 16 1131 0.5 11 49 0.6 101 5 18
## 17 682 0.5 4 19 1.0 121 4 11
## 18 1709 2.1 1 13 1.0 156 2 2
## 19 1949 2.6 4 47 0.3 199 4 7
## 20 1602 2.8 4 38 0.7 114 3 20
## 21 503 1.2 5 8 0.4 111 3 13
## 22 519 1.6 7 51 0.3 132 4 19
## 23 956 0.5 1 41 1.0 143 7 6
## 24 1453 1.6 12 52 0.3 96 2 18
## 25 1319 0.9 3 41 0.9 107 1 18
## 26 1310 2.2 0 51 0.6 100 4 0
## 27 725 1.3 16 60 0.4 160 8 17
## 28 1725 1.6 6 6 0.5 119 2 18
## 29 790 2.0 16 11 0.3 87 6 17
## 30 560 0.5 15 50 0.3 159 2 20
## 31 1347 2.9 5 44 0.6 132 1 14
## 32 1253 0.5 5 5 0.2 152 2 19
## 33 1656 1.0 5 34 0.1 166 3 7
## 34 1195 2.8 1 20 0.8 110 2 14
## 35 1514 2.9 0 27 0.2 118 3 1
## 36 1723 1.1 1 42 1.0 164 8 14
## 37 1054 1.8 3 40 0.8 196 7 10
## 38 596 2.1 9 64 0.8 111 8 15
## 39 1547 3.0 2 14 0.7 198 3 19
## 40 1760 1.4 5 63 0.8 127 8 19
## 41 1654 1.5 0 43 0.3 109 2 0
## 42 1073 0.5 0 51 0.5 145 7 0
## 43 823 2.7 13 60 0.5 148 8 19
## 44 987 1.3 0 61 0.4 107 3 9
## 45 1757 0.5 8 49 0.5 180 6 14
## 46 799 2.3 1 63 0.8 144 8 6
## 47 1156 1.2 0 50 0.8 159 2 0
## 48 702 2.6 2 9 0.7 141 3 3
## 49 616 1.9 13 44 0.8 81 3 17
## 50 1866 1.4 0 30 0.5 182 3 0
## 51 1242 1.1 0 10 0.6 165 2 1
## 52 1166 1.5 0 43 0.8 80 4 1
## 53 1448 0.5 6 45 0.8 138 7 11
## 54 1407 2.4 1 22 0.7 104 4 4
## 55 605 1.0 8 9 0.1 142 3 16
## 56 1038 1.2 3 43 0.7 141 1 5
## 57 797 2.9 4 38 0.5 90 4 6
## 58 819 0.6 8 42 0.9 188 6 20
## 59 1114 2.8 4 9 0.4 197 3 15
## 60 1589 0.6 0 58 0.9 85 7 7
## 61 1510 0.9 2 45 0.9 180 5 3
## 62 1008 0.8 11 61 1.0 114 3 15
## 63 1127 2.9 5 57 0.8 163 1 13
## 64 1412 2.4 5 25 0.8 96 8 17
## 65 1496 2.0 4 42 0.5 182 5 5
## 66 1309 1.1 0 33 0.5 100 4 14
## 67 1724 2.0 2 57 0.5 177 3 5
## 68 1977 2.0 7 54 1.0 171 7 18
## 69 885 2.3 0 15 0.4 103 7 0
## 70 1137 1.0 18 7 1.0 196 3 19
## 71 1355 2.3 10 23 0.2 132 5 16
## 72 1665 0.5 3 60 0.2 194 6 18
## 73 1883 0.5 3 10 0.3 146 6 13
## 74 1732 1.1 4 12 0.8 119 8 9
## 75 543 0.5 0 57 0.7 192 5 4
## 76 1939 2.8 0 55 0.6 199 4 10
## 77 553 2.4 1 8 0.5 121 7 3
## 78 832 0.7 2 39 0.7 103 4 3
## 79 1661 1.9 5 23 1.0 172 5 10
## 80 1135 2.0 0 46 0.3 83 1 4
## 81 617 2.3 0 32 0.5 199 7 20
## 82 867 1.4 0 4 0.7 135 6 1
## 83 1310 1.1 13 7 1.0 194 5 14
## 84 1804 0.5 9 22 0.6 153 7 19
## 85 1900 2.3 1 18 0.2 89 7 9
## 86 909 1.4 0 53 0.3 82 8 0
## 87 1084 2.6 3 40 0.9 107 3 11
## 88 1308 1.3 8 46 0.9 199 1 12
## 89 1778 0.5 0 15 0.5 102 3 0
## 90 1701 1.6 3 10 0.3 194 8 7
## 91 772 2.4 1 10 0.5 80 4 2
## 92 825 0.5 7 52 0.9 130 3 10
## 93 1166 2.0 3 25 0.8 198 5 4
## 94 1659 2.8 5 16 0.6 89 1 20
## 95 826 2.4 0 58 0.3 88 4 1
## 96 1957 0.9 11 31 0.6 181 3 16
## 97 1571 1.4 0 19 0.1 100 6 1
## 98 1790 2.5 9 2 0.1 184 1 14
## 99 668 2.9 7 30 0.2 195 8 12
## 100 1652 1.1 1 36 0.7 89 4 5
## 101 1213 0.5 0 16 0.5 108 5 0
## 102 1272 2.5 0 22 0.9 133 6 8
## 103 866 0.5 5 46 0.5 179 8 7
## 104 523 2.6 1 14 0.1 155 8 2
## 105 1753 0.5 0 31 0.1 134 1 18
## 106 1218 1.8 1 14 0.5 147 3 4
## 107 1537 2.5 4 43 0.3 137 1 5
## 108 1678 2.1 3 64 0.9 134 3 11
## 109 1101 1.8 0 31 0.2 148 2 7
## 110 1461 2.4 3 40 0.5 141 7 4
## 111 1216 3.0 4 47 0.4 199 3 7
## 112 506 1.6 6 41 0.8 159 1 7
## 113 742 2.2 4 56 0.2 190 7 14
## 114 1692 2.1 3 61 0.9 176 6 12
## 115 1485 1.0 2 32 0.7 84 2 9
## 116 1692 2.3 4 46 0.9 139 1 6
## 117 637 2.3 3 60 0.8 97 4 4
## 118 1224 0.5 16 6 0.4 109 6 18
## 119 1356 2.8 0 29 0.5 198 6 0
## 120 1840 0.5 12 34 0.7 142 1 16
## 121 1481 0.7 4 37 0.6 96 5 13
## 122 961 1.5 10 51 0.3 152 6 16
## 123 1193 0.9 11 26 0.5 166 1 20
## 124 1441 2.1 11 3 0.1 118 8 17
## 125 748 1.7 0 27 0.9 150 8 4
## 126 1569 2.8 17 44 0.3 110 4 20
## 127 1270 0.6 2 32 0.5 101 7 6
## 128 1854 3.0 2 8 0.1 101 5 5
## 129 625 1.2 5 10 0.2 183 4 8
## 130 946 1.7 8 25 0.8 143 6 15
## 131 685 0.5 2 29 0.8 93 7 10
## 132 1949 1.3 0 23 0.1 101 5 1
## 133 801 2.2 2 63 0.4 165 7 4
## 134 703 2.7 12 30 0.3 113 1 20
## 135 1165 2.4 11 41 0.2 178 6 12
## 136 1082 0.8 0 38 0.1 198 8 5
## 137 959 2.6 15 20 1.0 84 4 19
## 138 1502 1.2 0 54 0.2 138 5 12
## 139 1380 2.1 1 4 0.8 92 3 13
## 140 1266 0.5 0 5 0.1 95 6 9
## 141 1934 2.6 0 46 0.1 103 1 17
## 142 1905 0.5 3 6 0.5 151 1 5
## 143 1831 1.4 2 43 0.9 150 5 5
## 144 596 1.4 8 46 0.6 165 1 18
## 145 1562 1.3 1 7 0.2 190 5 15
## 146 1490 0.5 4 64 0.3 150 8 8
## 147 1523 2.4 5 57 0.2 117 2 6
## 148 640 0.6 0 20 0.1 163 4 15
## 149 1526 2.1 1 23 0.2 117 7 8
## 150 1989 2.5 0 41 0.8 94 3 13
## 151 1308 1.9 0 61 0.7 106 3 7
## 152 1703 0.5 0 22 0.6 192 1 0
## 153 1445 2.4 7 20 0.4 173 7 15
## 154 1087 1.3 0 16 0.3 166 3 9
## 155 671 0.9 10 30 0.7 105 7 17
## 156 1472 2.3 6 61 0.1 168 6 19
## 157 765 0.5 0 47 0.3 115 8 1
## 158 1642 0.5 16 8 0.3 171 6 17
## 159 1265 1.5 7 49 0.7 182 5 16
## 160 664 1.1 1 51 0.1 110 7 4
## 161 1277 0.5 0 35 0.4 142 6 6
## 162 1539 2.6 0 41 0.1 159 8 0
## 163 1827 1.7 4 39 0.5 128 5 16
## 164 1987 0.5 3 53 1.0 181 3 7
## 165 1154 1.2 0 49 0.2 181 8 2
## 166 1886 0.5 0 24 0.4 112 3 1
## 167 1551 1.1 4 51 0.1 88 5 6
## 168 850 1.6 6 29 0.5 133 5 19
## 169 1225 2.1 8 13 0.5 116 4 12
## 170 1452 0.5 1 25 0.9 123 6 11
## 171 1708 2.4 18 49 0.1 109 1 19
## 172 1151 2.9 0 31 0.6 128 8 1
## 173 1578 1.9 2 37 0.1 138 4 19
## 174 1689 1.8 17 24 0.3 127 3 18
## 175 1715 1.0 4 31 0.5 83 8 17
## 176 1523 1.8 6 11 0.1 129 1 8
## 177 1133 1.4 3 42 0.1 199 5 12
## 178 1330 0.5 0 38 1.0 183 7 1
## 179 1799 0.5 0 49 0.4 107 4 2
## 180 633 2.2 0 49 0.1 139 8 1
## 181 724 2.7 0 44 0.5 187 7 0
## 182 822 0.5 0 51 0.3 157 6 20
## 183 1918 1.9 2 10 0.6 110 5 10
## 184 1891 2.8 7 37 0.4 129 7 14
## 185 1373 1.0 2 50 0.2 127 7 12
## 186 862 0.8 3 23 0.4 165 2 15
## 187 1273 1.0 3 48 0.4 99 4 15
## 188 1617 1.3 3 30 0.7 105 2 4
## 189 893 2.5 0 55 0.6 183 2 1
## 190 1210 0.5 0 63 0.1 93 5 0
## 191 708 2.7 0 63 1.0 195 8 7
## 192 835 1.1 2 7 0.2 101 4 7
## 193 1450 2.7 9 19 0.6 131 1 17
## 194 973 1.5 5 57 0.2 190 7 16
## 195 601 1.4 4 4 0.1 154 4 13
## 196 507 0.5 1 32 0.5 141 7 11
## 197 564 1.8 0 51 0.5 168 3 1
## 198 1559 1.6 6 6 0.5 162 6 17
## 199 1431 0.5 0 56 0.6 191 1 1
## 200 1722 2.6 8 18 0.8 177 2 16
## 201 1992 1.6 5 57 1.0 100 2 15
## 202 1876 0.5 10 40 0.3 86 6 14
## 203 1054 1.1 10 38 0.4 136 6 12
## 204 1283 1.6 4 47 0.9 93 5 5
## 205 1066 0.6 2 7 0.9 114 7 18
## 206 1053 0.8 7 45 0.3 103 1 12
## 207 1611 0.5 11 3 0.6 98 3 14
## 208 793 1.0 5 30 0.9 133 2 15
## 209 1220 2.9 3 62 0.5 125 4 8
## 210 1281 2.8 3 8 0.3 144 4 19
## 211 538 0.8 12 2 0.8 177 7 13
## 212 823 0.5 0 39 0.4 187 8 4
## 213 1733 2.8 3 36 1.0 119 6 20
## 214 659 2.7 6 6 0.5 110 3 7
## 215 1172 1.9 0 27 0.7 164 1 3
## 216 615 0.5 0 42 0.6 163 6 4
## 217 1893 2.1 8 63 0.2 134 2 11
## 218 1563 1.7 10 16 0.1 151 7 13
## 219 1995 1.9 6 9 0.8 139 6 18
## 220 1517 0.5 6 48 0.6 143 5 14
## 221 832 1.6 1 34 0.3 108 5 5
## 222 1162 2.0 0 56 0.7 114 7 1
## 223 1329 1.0 1 52 0.9 181 6 2
## 224 928 0.5 11 56 0.7 80 8 13
## 225 1656 0.5 8 38 0.1 105 7 17
## 226 1937 1.7 17 58 0.6 189 1 19
## 227 977 1.1 2 57 0.6 181 6 7
## 228 768 0.9 16 56 0.1 113 1 20
## 229 1464 0.5 11 12 0.9 117 2 14
## 230 1177 0.9 1 29 0.4 191 2 8
## 231 1348 2.0 18 52 0.3 98 3 20
## 232 1956 2.2 0 37 0.5 87 3 2
## 233 1751 0.5 6 8 0.6 178 6 20
## 234 1530 2.0 11 42 0.8 177 6 13
## 235 1997 1.8 1 40 0.5 185 4 8
## 236 1414 1.5 5 44 0.5 180 2 6
## 237 894 0.5 1 50 0.5 199 3 14
## 238 645 1.2 6 41 0.7 184 8 17
## 239 1163 2.8 0 49 0.6 155 2 0
## 240 1126 0.7 3 49 0.9 165 8 17
## 241 1648 0.7 6 64 0.8 170 4 9
## 242 508 0.8 7 42 0.3 94 1 8
## 243 728 1.7 0 5 0.6 101 2 20
## 244 1379 2.1 14 63 1.0 83 1 17
## 245 1698 2.1 5 18 0.9 160 6 20
## 246 920 2.1 5 25 0.4 115 7 14
## 247 1995 0.5 6 26 0.2 129 1 15
## 248 662 0.5 5 20 0.8 138 1 10
## 249 1867 2.3 0 9 0.1 191 6 3
## 250 1730 2.0 0 20 0.1 143 4 10
## 251 1723 1.0 1 7 0.6 126 3 17
## 252 1882 1.3 8 47 0.2 140 2 13
## 253 668 2.3 2 48 0.3 114 1 19
## 254 1391 1.4 2 36 0.8 182 1 6
## 255 1560 2.3 0 22 0.8 169 7 1
## 256 821 1.8 5 54 0.1 109 3 9
## 257 811 2.4 5 2 0.3 106 6 13
## 258 1989 2.9 8 17 0.1 193 8 15
## 259 1034 2.7 6 37 0.7 120 7 20
## 260 618 2.1 2 6 0.3 86 1 4
## 261 667 0.6 6 3 0.7 185 6 13
## 262 869 1.0 7 57 0.6 118 1 15
## 263 635 0.5 2 42 0.4 149 3 4
## 264 609 3.0 15 44 0.3 117 1 16
## 265 1604 1.7 2 33 1.0 191 8 5
## 266 1182 0.5 7 8 0.5 138 8 16
## 267 1610 2.9 7 64 0.9 181 4 12
## 268 1828 2.1 0 17 0.9 199 1 1
## 269 880 2.0 1 50 0.5 194 7 3
## 270 1394 0.9 10 62 0.3 175 5 13
## 271 1960 2.3 1 61 0.3 86 7 3
## 272 809 0.8 10 21 0.9 91 7 19
## 273 899 0.5 13 57 0.9 172 2 18
## 274 1976 0.7 4 32 0.8 171 2 7
## 275 879 1.5 0 21 0.8 187 4 2
## 276 916 2.6 0 36 0.3 195 4 12
## 277 763 1.7 8 2 0.5 92 8 14
## 278 508 1.6 0 9 0.4 162 6 2
## 279 825 2.9 0 34 0.8 99 8 17
## 280 1864 1.4 8 46 0.7 178 1 12
## 281 1108 2.9 10 52 0.1 183 2 12
## 282 1011 1.1 4 36 0.2 131 7 18
## 283 1703 1.5 17 55 0.7 138 5 19
## 284 1067 1.7 0 58 0.5 135 7 0
## 285 1334 2.4 5 42 0.6 115 5 14
## 286 775 2.6 0 30 0.4 189 7 10
## 287 1899 0.6 0 26 0.1 151 2 0
## 288 930 1.3 1 14 0.4 97 6 10
## 289 1874 2.7 10 8 0.9 166 7 13
## 290 1482 0.5 2 48 0.2 142 7 10
## 291 864 3.0 0 54 0.7 187 2 1
## 292 625 1.9 12 33 0.2 191 1 20
## 293 1880 1.8 4 18 0.7 138 3 5
## 294 1138 1.4 12 44 0.2 187 8 17
## 295 560 0.5 14 23 1.0 146 8 15
## 296 1836 1.7 3 26 0.1 134 6 16
## 297 1036 2.4 0 39 0.2 119 7 3
## 298 1860 2.3 15 23 0.6 162 4 16
## 299 1375 1.1 3 9 0.9 145 7 19
## 300 1945 1.6 0 57 0.3 195 4 1
## 301 1896 0.5 8 7 0.4 141 2 13
## 302 788 2.5 4 57 0.9 91 8 12
## 303 880 1.7 0 33 0.7 173 2 2
## 304 1323 2.5 10 28 0.2 131 4 12
## 305 1868 2.0 0 42 0.2 169 1 4
## 306 1266 1.9 0 7 0.2 184 3 12
## 307 1042 2.7 16 50 0.8 93 7 18
## 308 1479 0.5 8 19 0.6 156 3 10
## 309 1476 1.2 0 55 0.1 171 4 5
## 310 1552 2.2 6 13 0.2 123 6 9
## 311 1007 2.0 0 45 0.1 95 5 2
## 312 652 2.4 9 17 0.7 173 5 11
## 313 1726 0.9 2 51 0.6 193 4 3
## 314 1029 1.9 5 5 0.2 171 5 8
## 315 1582 2.8 2 44 0.5 112 6 13
## 316 1478 0.8 12 48 0.7 99 5 17
## 317 707 2.1 2 25 0.8 131 3 17
## 318 912 2.0 1 54 0.3 161 8 5
## 319 1483 2.1 1 54 0.2 176 7 2
## 320 914 0.9 2 4 0.8 100 2 12
## 321 1178 2.2 6 57 0.3 84 3 17
## 322 1503 0.7 10 43 0.2 166 4 14
## 323 1681 2.5 2 11 0.4 158 2 13
## 324 715 2.3 4 48 0.1 160 4 6
## 325 1190 2.2 9 47 0.3 186 6 10
## 326 1755 1.3 0 24 0.1 134 7 2
## 327 1048 0.9 10 28 0.8 194 2 19
## 328 1887 3.0 6 17 0.2 126 8 7
## 329 1772 0.5 3 64 0.7 116 8 15
## 330 1421 2.8 0 57 0.4 165 1 2
## 331 1464 0.5 0 17 0.7 153 6 8
## 332 728 2.3 4 23 0.8 111 4 5
## 333 954 0.5 0 50 0.3 134 2 11
## 334 685 0.5 8 13 0.8 82 7 15
## 335 853 2.0 10 3 0.6 200 2 16
## 336 1469 2.1 0 8 0.7 123 7 0
## 337 1310 2.3 6 57 0.9 135 8 9
## 338 1448 1.4 13 26 1.0 156 3 16
## 339 1631 0.9 2 3 0.1 198 7 12
## 340 1608 1.8 7 28 0.4 183 2 13
## 341 1991 2.7 5 64 0.9 145 2 13
## 342 1349 2.0 15 62 0.3 114 5 16
## 343 1589 2.5 5 51 0.8 195 1 18
## 344 1844 2.3 1 51 0.7 158 8 3
## 345 712 0.5 3 35 0.2 84 2 10
## 346 1406 0.5 2 34 0.8 98 3 5
## 347 1289 0.9 10 25 1.0 178 6 12
## 348 932 1.3 1 64 0.7 154 7 2
## 349 1747 0.9 6 48 0.7 94 3 19
## 350 1172 2.1 4 41 0.1 95 3 7
## 351 1128 1.4 12 53 0.4 84 5 14
## 352 664 1.2 0 28 0.9 106 5 5
## 353 513 0.5 6 44 0.1 186 4 12
## 354 1742 2.3 9 43 0.4 170 7 12
## 355 1512 1.7 12 6 0.7 162 7 17
## 356 965 1.5 5 13 1.0 109 2 14
## 357 1067 2.4 3 40 0.6 122 8 15
## 358 1583 1.6 5 42 0.8 118 3 10
## 359 1653 0.5 2 37 0.9 176 4 6
## 360 765 2.9 0 18 0.1 153 7 0
## 361 1845 0.5 10 61 0.3 96 1 12
## 362 752 0.5 1 48 0.7 87 7 13
## 363 1948 2.8 8 16 0.1 100 5 10
## 364 1077 2.5 0 45 1.0 174 3 4
## 365 932 0.5 1 60 0.7 104 4 2
## 366 1122 1.2 9 2 0.6 120 4 14
## 367 601 2.1 4 26 0.3 101 4 18
## 368 1615 0.5 14 15 0.1 132 4 16
## 369 1589 2.2 2 58 0.1 128 4 4
## 370 1417 2.7 2 56 0.9 114 6 4
## 371 1697 0.7 1 34 0.1 95 1 3
## 372 1330 1.3 14 3 0.3 83 5 17
## 373 1476 0.5 0 43 0.7 148 2 2
## 374 1579 1.5 0 27 0.2 142 6 18
## 375 1277 3.0 0 41 0.3 200 4 3
## 376 1089 0.9 12 2 0.7 145 5 15
## 377 1663 2.5 13 27 0.3 169 4 14
## 378 1138 1.1 5 48 0.6 109 3 14
## 379 685 2.7 7 17 0.3 174 6 20
## 380 857 2.0 3 17 0.2 195 7 9
## 381 1902 2.8 6 50 0.8 124 8 9
## 382 1225 2.4 0 15 0.1 155 5 0
## 383 1554 0.5 11 5 1.0 124 3 12
## 384 1337 0.5 13 31 0.8 186 2 20
## 385 1926 1.7 1 33 0.4 172 3 2
## 386 869 1.3 3 34 0.1 104 2 4
## 387 1278 0.5 1 54 0.1 104 8 16
## 388 1661 0.8 1 6 1.0 109 7 2
## 389 1438 2.2 0 32 0.6 176 7 6
## 390 1661 1.4 0 29 0.3 119 2 0
## 391 1846 0.6 1 19 0.7 159 1 2
## 392 512 0.5 7 15 0.9 83 3 15
## 393 581 2.2 0 59 0.7 185 5 6
## 394 1872 2.3 6 44 0.7 134 3 15
## 395 687 1.3 9 50 0.6 151 4 20
## 396 1062 2.5 6 10 0.9 160 1 13
## 397 1678 2.3 11 7 0.5 192 1 14
## 398 1417 0.5 10 45 0.9 166 4 19
## 399 1074 2.4 2 9 0.9 103 6 14
## 400 1832 0.7 2 2 0.6 90 7 6
## 401 1039 1.3 0 12 0.8 200 8 0
## 402 1059 2.4 8 61 0.1 134 3 18
## 403 1928 1.8 9 19 1.0 187 3 20
## 404 1875 0.5 11 55 0.7 91 1 17
## 405 1748 1.7 6 14 0.5 146 3 7
## 406 1413 0.5 5 51 0.3 117 5 11
## 407 1589 1.4 3 26 0.1 100 8 16
## 408 535 0.5 8 54 0.5 145 8 15
## 409 1780 0.5 6 27 0.1 190 4 11
## 410 1671 0.6 7 61 0.1 129 2 11
## 411 1821 0.9 9 12 0.3 114 1 18
## 412 1076 2.6 3 38 0.7 119 6 8
## 413 777 2.6 3 30 0.9 148 5 17
## 414 1217 0.5 3 17 0.5 182 4 13
## 415 1656 1.6 0 16 1.0 117 7 2
## 416 1185 1.9 0 31 0.4 152 8 7
## 417 577 2.4 8 37 0.9 161 6 20
## 418 737 2.4 7 49 0.6 193 4 16
## 419 704 2.2 4 53 0.2 186 6 6
## 420 525 0.5 5 51 0.5 137 8 11
## 421 504 0.5 5 16 0.1 166 1 9
## 422 793 1.2 1 38 0.6 176 4 7
## 423 569 0.6 2 39 0.1 184 8 9
## 424 1590 1.9 3 21 1.0 160 5 18
## 425 707 0.5 0 16 0.1 194 2 0
## 426 767 1.9 16 19 0.2 185 6 17
## 427 1350 0.5 0 50 0.8 181 8 4
## 428 1117 0.6 0 31 0.2 146 2 7
## 429 641 1.1 0 7 0.9 192 1 3
## 430 1408 2.1 10 45 0.9 89 5 14
## 431 1900 2.0 1 41 0.5 190 8 6
## 432 726 2.9 0 43 0.1 101 8 0
## 433 1544 2.3 12 45 0.1 113 8 20
## 434 1454 1.6 6 21 0.2 160 4 10
## 435 1519 2.1 0 32 0.7 200 1 10
## 436 1168 1.3 9 61 0.1 90 8 10
## 437 1229 0.5 15 14 0.8 191 2 16
## 438 1290 0.5 0 3 0.1 143 6 4
## 439 574 2.8 16 38 0.1 90 6 17
## 440 1271 0.5 12 32 0.1 113 7 15
## 441 1170 1.2 4 30 0.4 83 8 15
## 442 1269 1.3 14 52 0.8 118 2 15
## 443 1366 0.7 0 34 0.1 150 2 9
## 444 1572 0.8 7 8 0.8 169 5 8
## 445 1627 0.9 9 32 0.1 180 6 16
## 446 1701 0.5 13 46 0.9 115 2 15
## 447 1974 1.0 4 24 0.3 80 7 13
## 448 1197 0.5 9 20 0.1 129 5 10
## 449 587 2.3 4 6 0.7 170 1 16
## 450 934 1.1 7 24 0.8 112 5 12
## 451 1853 1.0 2 23 0.4 92 1 15
## 452 1164 1.0 4 19 0.9 95 8 18
## 453 1232 2.9 1 24 0.3 169 5 17
## 454 1813 0.6 0 5 0.2 198 3 8
## 455 739 2.3 2 58 0.1 88 3 4
## 456 1663 0.5 0 40 0.6 147 4 15
## 457 1966 1.4 2 40 0.9 185 8 18
## 458 1219 1.0 0 17 0.7 196 5 4
## 459 612 0.7 6 35 0.6 181 5 12
## 460 1658 1.4 0 39 0.7 182 1 5
## 461 1263 0.5 12 64 0.3 175 5 18
## 462 621 2.7 0 54 0.1 151 4 20
## 463 652 0.5 1 58 0.6 142 3 2
## 464 561 1.0 0 11 0.1 163 8 10
## 465 1137 2.2 6 3 0.9 173 5 7
## 466 1170 2.9 2 40 0.4 169 6 5
## 467 1595 0.8 0 44 0.7 85 4 20
## 468 1770 2.3 0 7 0.8 128 7 6
## 469 1878 1.5 5 56 0.3 139 5 13
## 470 1871 2.0 7 20 0.1 81 7 14
## 471 922 1.5 1 35 0.9 188 7 15
## 472 1975 1.5 6 16 0.9 128 8 11
## 473 1212 2.6 1 46 0.3 102 4 8
## 474 1958 1.9 0 4 0.2 190 2 3
## 475 1836 2.1 0 30 0.2 184 7 15
## 476 557 1.6 0 24 0.5 125 3 0
## 477 539 0.6 9 21 0.1 145 7 14
## 478 880 0.5 1 44 0.5 172 8 15
## 479 1369 1.8 6 34 0.5 162 1 7
## 480 1109 2.7 1 39 0.4 98 1 9
## 481 843 1.8 0 16 0.1 166 5 6
## 482 598 2.3 7 21 0.7 196 2 10
## 483 972 2.7 1 5 1.0 126 4 2
## 484 1944 0.5 5 36 0.1 197 6 18
## 485 1225 0.7 0 2 0.6 138 7 1
## 486 644 1.3 7 30 0.5 114 1 16
## 487 1919 1.5 5 48 0.8 150 4 6
## 488 501 2.3 12 54 0.3 131 4 19
## 489 1620 2.2 0 30 0.2 185 3 3
## 490 1359 0.5 1 58 0.5 107 6 3
## 491 1914 0.7 1 35 0.6 118 8 2
## 492 1645 1.8 1 21 0.7 123 7 2
## 493 1063 1.1 0 26 0.8 101 5 2
## 494 946 2.1 0 19 0.2 154 8 0
## 495 1231 1.2 5 54 0.8 185 3 10
## 496 1397 2.9 13 9 0.5 87 6 18
## 497 701 0.7 7 15 0.5 160 3 11
## 498 1993 0.5 10 27 0.3 147 8 11
## 499 955 0.5 4 43 0.9 143 5 8
## 500 924 0.5 0 47 0.3 167 8 6
## 501 600 0.5 0 14 0.8 112 5 2
## 502 518 0.8 0 3 0.1 178 7 1
## 503 920 0.5 2 46 0.8 149 3 3
## 504 1715 1.9 4 3 0.1 171 5 6
## 505 1841 1.3 6 14 0.6 167 8 8
## 506 543 0.5 4 60 0.7 132 5 7
## 507 1762 2.7 13 25 0.9 189 1 14
## 508 1112 0.5 1 53 0.6 121 4 16
## 509 709 2.0 0 35 0.1 136 1 0
## 510 1315 2.6 7 64 0.8 128 7 16
## 511 1762 2.5 10 15 0.3 107 8 11
## 512 1462 1.7 1 25 0.2 182 7 18
## 513 571 1.6 8 35 0.2 186 7 20
## 514 666 0.5 7 54 0.4 81 3 13
## 515 1994 1.5 2 7 0.5 146 8 7
## 516 1583 2.1 11 14 0.7 148 7 16
## 517 1778 1.4 3 45 1.0 132 4 17
## 518 1926 1.1 13 50 0.2 179 6 17
## 519 966 0.6 9 50 0.2 117 4 10
## 520 568 2.0 1 36 0.3 179 3 4
## 521 1695 0.5 6 46 0.7 200 3 9
## 522 914 0.5 0 26 0.7 147 4 0
## 523 1721 0.5 4 33 0.5 128 4 5
## 524 890 2.2 4 44 0.5 198 8 13
## 525 600 0.5 1 48 0.8 191 3 7
## 526 1677 1.8 4 11 0.7 124 6 8
## 527 723 0.5 0 3 0.2 168 4 3
## 528 638 1.7 11 27 0.5 102 4 17
## 529 1979 1.6 0 32 0.2 165 7 1
## 530 710 0.5 6 32 0.8 151 5 9
## 531 1034 2.6 2 45 0.3 190 3 4
## 532 600 2.5 8 22 0.1 145 1 11
## 533 1027 0.5 2 64 0.9 103 2 7
## 534 1260 0.6 5 13 1.0 106 6 6
## 535 1793 1.8 10 12 0.5 169 6 17
## 536 675 2.3 10 60 0.9 144 5 15
## 537 658 2.7 0 12 0.9 103 7 3
## 538 1694 0.6 0 34 0.3 102 7 7
## 539 1713 2.5 1 27 0.9 127 7 3
## 540 706 0.5 13 31 0.1 108 3 19
## 541 1362 0.5 3 25 0.8 131 7 9
## 542 1527 2.7 10 60 0.1 154 5 14
## 543 768 0.5 0 19 0.9 159 2 1
## 544 1314 2.9 8 41 0.1 155 4 12
## 545 781 1.1 2 38 0.4 198 5 7
## 546 986 1.5 1 23 0.8 183 8 3
## 547 1310 1.4 0 33 0.4 105 1 14
## 548 560 2.0 6 57 0.3 81 3 12
## 549 1348 1.3 0 43 0.4 116 5 6
## 550 1567 0.5 3 22 0.5 119 6 6
## 551 1940 0.9 4 17 0.7 93 5 7
## 552 1979 2.9 0 19 1.0 176 3 9
## 553 561 2.1 7 2 0.1 193 3 13
## 554 535 1.5 1 48 0.7 113 1 4
## 555 1413 0.5 4 39 0.1 185 5 12
## 556 1358 2.5 0 20 0.6 135 7 0
## 557 1519 2.9 2 30 0.7 112 7 8
## 558 1254 1.2 2 50 0.7 108 5 6
## 559 977 2.8 2 35 0.6 165 2 15
## 560 1640 0.7 10 21 0.8 169 7 14
## 561 663 0.8 0 25 0.6 183 2 1
## 562 1744 1.0 1 20 0.5 112 8 3
## 563 1624 1.6 8 28 0.4 150 2 10
## 564 1188 1.4 0 9 0.6 114 2 0
## 565 817 1.3 0 11 0.5 144 3 11
## 566 863 1.6 5 6 0.7 133 4 13
## 567 1397 2.4 5 3 0.2 121 3 15
## 568 1068 2.0 1 37 0.9 184 7 13
## 569 1702 2.3 12 52 0.5 145 2 15
## 570 1156 0.5 6 61 0.1 89 7 9
## 571 1271 0.5 2 54 0.1 159 8 3
## 572 696 0.5 6 51 0.3 197 3 8
## 573 614 1.7 0 58 0.2 128 7 2
## 574 1590 0.6 0 7 0.3 122 3 17
## 575 1945 1.6 4 21 0.7 113 3 10
## 576 1049 0.6 8 2 0.3 172 6 10
## 577 1807 2.1 2 49 0.8 125 1 10
## 578 984 0.5 13 42 0.8 98 7 17
## 579 720 1.4 0 61 0.6 96 7 8
## 580 1536 0.8 12 13 0.3 105 7 20
## 581 771 0.5 3 25 1.0 86 4 6
## 582 503 2.5 3 57 0.6 185 6 11
## 583 675 2.4 1 42 0.6 187 7 2
## 584 1303 1.0 0 15 0.1 138 3 10
## 585 1004 2.9 0 35 0.2 141 6 9
## 586 1972 1.2 0 37 0.7 102 2 1
## 587 1159 2.8 5 18 0.5 83 1 7
## 588 1782 2.8 6 58 0.8 157 1 13
## 589 894 2.1 16 16 0.7 168 8 20
## 590 1884 2.1 7 43 0.8 185 1 18
## 591 1648 0.5 1 17 0.9 142 2 3
## 592 798 2.4 5 4 0.2 96 3 9
## 593 802 2.4 1 45 0.8 157 3 9
## 594 1276 2.9 2 53 0.3 130 2 3
## 595 1331 1.6 0 16 0.5 165 2 7
## 596 1620 2.6 6 5 0.9 186 6 18
## 597 1996 2.5 5 51 0.4 122 4 9
## 598 1092 2.7 8 45 0.8 185 1 10
## 599 1018 0.7 7 63 0.1 155 5 18
## 600 545 0.5 0 11 0.3 131 3 1
## 601 554 1.3 0 12 0.1 105 6 10
## 602 1264 2.9 2 34 0.2 171 3 3
## 603 1030 1.6 3 58 0.2 138 6 20
## 604 1432 1.7 6 39 0.3 164 4 18
## 605 558 2.8 1 49 0.8 142 5 4
## 606 829 0.5 0 56 0.6 129 7 1
## 607 1741 1.8 0 14 0.7 143 8 0
## 608 1849 2.3 7 15 0.4 144 5 19
## 609 733 2.1 5 7 0.2 174 7 10
## 610 1872 0.9 0 16 0.5 80 2 2
## 611 1853 0.5 1 27 0.3 197 6 13
## 612 1149 2.2 4 30 0.2 106 6 5
## 613 891 0.5 9 25 0.3 196 2 19
## 614 1911 2.0 7 64 0.6 163 8 8
## 615 550 0.5 16 23 0.1 145 2 17
## 616 576 0.8 0 27 0.1 121 2 4
## 617 1760 1.9 1 50 0.8 198 1 2
## 618 969 2.4 0 45 0.3 124 7 0
## 619 1160 1.1 1 63 0.7 166 3 2
## 620 827 1.1 2 18 0.9 160 2 16
## 621 1786 0.5 9 58 0.7 161 7 10
## 622 774 0.6 2 8 0.4 136 1 4
## 623 819 2.5 0 11 0.2 83 8 1
## 624 1042 2.2 15 11 0.6 139 5 16
## 625 1592 2.1 6 50 0.1 181 2 17
## 626 1067 1.8 11 14 0.2 146 1 15
## 627 915 1.1 6 56 0.6 187 8 17
## 628 1614 1.9 8 55 0.3 188 3 17
## 629 911 2.9 0 46 0.1 181 5 1
## 630 1520 0.5 1 24 0.7 199 5 8
## 631 1647 1.4 3 38 0.3 102 6 15
## 632 1347 0.9 8 53 0.8 108 6 15
## 633 967 1.6 4 25 0.6 92 8 9
## 634 1442 2.2 0 56 0.7 145 5 3
## 635 1204 2.6 0 10 0.3 199 7 3
## 636 1320 2.1 0 41 0.6 144 7 2
## 637 1800 0.7 2 8 0.4 100 7 6
## 638 1567 2.1 3 22 0.8 153 3 19
## 639 1439 0.9 12 20 0.8 147 1 17
## 640 1422 1.0 15 3 0.1 112 1 17
## 641 1591 2.2 4 52 1.0 161 3 16
## 642 880 1.7 2 6 0.6 198 4 19
## 643 1929 0.5 0 19 1.0 163 6 2
## 644 1642 1.6 0 5 0.4 102 5 2
## 645 1511 2.5 3 21 0.2 114 3 18
## 646 1852 1.4 4 10 0.7 196 1 18
## 647 972 1.3 8 44 0.7 82 7 10
## 648 691 2.7 4 33 0.9 108 4 8
## 649 807 0.5 9 8 0.3 158 5 20
## 650 1113 0.6 0 25 0.3 145 1 10
## 651 721 1.3 10 16 0.9 125 1 18
## 652 1188 1.5 0 54 0.3 198 6 2
## 653 1512 0.5 1 52 0.1 131 1 14
## 654 918 0.9 1 8 0.1 123 7 5
## 655 1320 0.5 7 48 0.2 168 5 16
## 656 1236 0.9 2 57 0.1 188 1 14
## 657 1387 0.5 0 61 0.5 98 3 0
## 658 1883 2.7 6 30 0.5 95 2 10
## 659 839 2.0 0 14 0.4 175 7 3
## 660 1593 1.0 0 52 0.7 130 8 0
## 661 1722 1.0 7 25 0.8 88 6 19
## 662 1954 0.6 8 7 0.9 126 3 9
## 663 1628 0.5 5 33 0.9 101 6 8
## 664 1965 0.5 6 4 1.0 84 7 9
## 665 833 2.2 4 27 0.9 124 1 6
## 666 1808 0.5 1 31 0.5 92 2 16
## 667 1860 0.5 2 55 0.7 83 6 3
## 668 1368 1.6 4 9 0.1 115 7 7
## 669 1161 1.3 0 21 0.6 126 8 12
## 670 902 0.6 0 63 0.7 122 5 14
## 671 854 0.5 2 30 0.5 125 8 5
## 672 1184 1.5 1 48 0.6 191 1 6
## 673 1973 1.6 5 39 0.8 152 4 6
## 674 510 1.7 3 35 0.8 120 3 6
## 675 966 2.0 8 34 0.5 118 2 10
## 676 1438 1.8 3 16 0.6 169 8 7
## 677 1907 2.0 13 55 0.9 105 7 15
## 678 1489 2.4 8 32 0.6 200 1 16
## 679 1286 2.3 10 16 0.1 98 2 18
## 680 840 0.6 0 63 0.8 137 7 19
## 681 757 2.3 5 58 0.2 163 4 7
## 682 814 1.1 2 41 0.8 177 2 10
## 683 1195 1.1 2 8 0.9 80 6 6
## 684 767 0.8 7 10 0.5 107 4 12
## 685 1068 1.3 6 52 0.1 97 8 7
## 686 1550 2.5 6 21 0.8 133 2 11
## 687 1878 0.5 9 29 0.6 85 8 19
## 688 623 2.0 2 6 0.1 127 3 4
## 689 1829 0.5 0 15 0.4 160 5 7
## 690 525 2.4 11 35 0.8 190 7 20
## 691 1065 0.5 0 14 0.7 89 2 2
## 692 1425 1.2 9 35 0.7 127 8 14
## 693 642 2.8 3 34 0.8 197 2 15
## 694 1664 2.9 9 15 0.5 177 7 19
## 695 1498 0.7 3 8 0.1 170 7 4
## 696 1236 2.8 6 32 0.3 143 4 18
## 697 1673 2.8 7 9 0.1 187 3 10
## 698 712 1.6 6 9 0.8 172 2 14
## 699 603 0.7 0 64 0.2 156 6 0
## 700 1332 0.8 2 11 0.5 187 2 4
## 701 1366 1.7 5 53 0.2 161 5 9
## 702 906 2.1 7 18 0.1 136 2 11
## 703 1554 2.7 3 47 0.7 185 5 12
## 704 1187 2.3 0 21 0.2 122 6 6
## 705 1918 2.8 1 22 0.3 139 1 5
## 706 841 0.5 8 39 0.1 199 6 13
## 707 878 1.3 13 31 0.5 153 4 18
## 708 1578 2.9 8 3 0.9 200 1 20
## 709 1829 1.7 0 36 0.8 112 5 1
## 710 1652 0.6 4 40 1.0 198 7 6
## 711 912 0.5 0 58 0.3 107 5 1
## 712 1181 2.3 12 61 0.6 116 2 16
## 713 1497 0.7 5 32 0.7 92 3 9
## 714 1790 2.3 3 49 0.5 100 3 11
## 715 1742 1.6 3 49 0.8 100 1 9
## 716 1549 0.5 4 8 0.4 158 5 7
## 717 1372 2.7 7 34 0.4 193 4 17
## 718 1112 0.5 0 12 0.9 190 4 6
## 719 1005 0.5 0 46 0.4 133 8 17
## 720 783 1.7 9 27 0.3 150 6 11
## 721 1698 2.5 10 26 0.4 105 2 18
## 722 899 2.7 3 53 0.3 192 4 11
## 723 1062 1.5 3 45 0.2 151 3 4
## 724 989 2.0 4 17 0.2 166 3 19
## 725 1983 1.2 6 27 0.6 125 1 11
## 726 1800 3.0 5 53 0.3 87 1 12
## 727 1254 1.7 0 45 0.4 146 3 3
## 728 868 1.7 0 30 0.8 83 4 0
## 729 1205 0.5 3 12 0.6 175 4 6
## 730 1284 2.8 0 10 0.9 155 2 14
## 731 626 0.7 2 23 0.5 187 2 9
## 732 1763 2.9 1 53 0.2 101 4 20
## 733 1265 0.6 4 49 0.5 90 1 5
## 734 904 1.8 3 35 0.4 168 3 4
## 735 979 0.5 0 5 0.5 164 7 2
## 736 1531 2.0 0 60 0.4 162 6 1
## 737 934 2.0 2 30 0.7 182 8 15
## 738 1624 2.6 0 58 1.0 200 3 11
## 739 874 1.3 5 8 0.5 185 1 20
## 740 1027 2.2 0 63 0.8 102 5 4
## 741 1966 0.8 15 48 0.2 144 1 16
## 742 1048 1.5 2 29 1.0 182 1 4
## 743 1496 1.9 13 38 0.1 126 4 14
## 744 1349 2.2 5 55 0.1 105 7 7
## 745 1689 2.4 0 13 0.9 168 2 9
## 746 582 0.5 3 27 0.2 168 3 10
## 747 1485 2.8 3 44 0.1 138 4 14
## 748 1991 1.8 11 9 0.7 94 3 12
## 749 553 0.5 2 23 0.5 119 3 3
## 750 1231 0.7 2 7 0.9 113 1 7
## 751 764 1.2 1 13 1.0 152 8 2
## 752 1589 2.6 9 46 0.4 84 3 16
## 753 1456 1.6 9 39 0.1 80 8 10
## 754 1061 2.1 3 52 0.1 109 7 4
## 755 1428 1.6 0 7 0.1 153 4 2
## 756 674 2.8 0 8 0.2 93 3 0
## 757 832 1.1 0 46 0.3 93 6 0
## 758 1625 0.5 2 50 0.1 107 8 17
## 759 907 1.4 3 42 0.2 151 1 16
## 760 1631 0.5 2 13 0.5 166 2 16
## 761 1584 1.3 6 41 0.6 135 4 15
## 762 1382 0.5 7 13 0.3 199 2 18
## 763 633 1.4 0 46 0.7 102 6 10
## 764 852 1.6 6 54 0.4 147 5 7
## 765 922 0.7 0 47 1.0 157 8 14
## 766 1745 1.5 4 57 0.1 159 3 16
## 767 862 2.3 9 6 0.1 107 8 14
## 768 1754 2.1 1 52 1.0 101 4 3
## 769 1569 0.5 3 33 0.2 147 1 13
## 770 1596 2.9 7 7 0.7 149 2 9
## 771 1807 0.5 2 61 0.1 134 6 6
## 772 1660 2.8 4 7 0.8 184 1 6
## 773 648 1.9 4 8 1.0 91 5 19
## 774 1379 0.5 1 19 0.3 134 8 17
## 775 1807 2.0 0 13 0.5 155 8 0
## 776 1923 0.5 7 46 0.5 191 1 10
## 777 1345 3.0 7 38 0.1 162 3 11
## 778 904 1.6 5 14 0.2 102 8 9
## 779 1239 1.2 5 52 0.9 122 2 10
## 780 1330 0.9 6 52 0.8 84 4 12
## 781 516 0.7 1 30 0.9 138 1 12
## 782 892 0.5 0 47 0.4 94 1 18
## 783 1130 2.5 4 58 0.1 132 5 14
## 784 1359 0.6 4 59 0.3 86 2 6
## 785 1866 2.5 3 47 0.8 89 5 9
## 786 1597 2.3 11 34 0.4 89 1 14
## 787 1046 2.8 0 58 0.2 100 8 0
## 788 1035 0.6 2 44 0.5 99 3 3
## 789 1713 3.0 5 20 0.9 104 7 17
## 790 1688 2.5 0 21 0.2 170 7 0
## 791 559 0.5 10 11 0.4 82 5 14
## 792 1483 2.6 13 19 0.6 146 7 19
## 793 860 1.6 13 48 0.5 187 7 18
## 794 1090 1.3 5 27 0.1 91 4 16
## 795 686 0.5 11 3 0.3 91 6 15
## 796 1456 1.6 5 49 0.2 193 3 20
## 797 1068 0.5 0 19 0.9 197 8 9
## 798 1373 1.9 1 29 0.9 141 6 12
## 799 1402 0.6 3 46 0.3 87 7 12
## 800 965 0.5 15 53 1.0 131 8 19
## 801 1270 2.3 0 58 0.5 104 5 1
## 802 919 0.5 11 13 0.3 159 1 15
## 803 1963 1.0 16 34 0.1 157 2 17
## 804 1977 1.3 2 31 0.4 173 8 6
## 805 1881 2.2 4 51 0.3 158 5 7
## 806 772 2.6 7 6 0.1 100 2 10
## 807 536 1.4 0 53 0.7 135 3 0
## 808 1633 1.7 3 33 0.9 111 2 19
## 809 1371 0.5 4 52 0.5 167 4 19
## 810 1221 0.5 1 28 0.5 139 1 7
## 811 1000 0.5 12 63 0.7 179 8 18
## 812 1986 2.7 3 64 0.2 162 4 20
## 813 1699 0.5 1 59 0.5 97 4 3
## 814 1576 2.6 7 58 0.2 90 3 16
## 815 1081 2.5 13 49 1.0 99 3 17
## 816 606 2.5 1 53 0.2 146 4 2
## 817 502 1.5 7 37 0.2 199 2 12
## 818 957 0.7 0 4 0.1 160 2 0
## 819 1658 1.4 0 38 0.3 159 5 0
## 820 1681 2.5 6 18 0.3 167 1 7
## 821 1983 0.5 3 43 1.0 87 6 10
## 822 1753 1.2 8 12 0.9 113 7 17
## 823 1632 1.4 0 41 0.1 132 4 1
## 824 688 2.5 0 57 0.8 131 7 3
## 825 1104 1.7 1 60 0.4 199 2 13
## 826 1122 2.8 0 28 0.6 184 3 4
## 827 504 0.5 2 46 0.9 172 5 14
## 828 1674 2.8 0 52 0.2 89 3 7
## 829 1948 2.8 5 18 0.2 135 2 14
## 830 1218 2.8 3 39 0.8 150 7 14
## 831 531 1.1 10 63 0.7 189 7 14
## 832 1057 2.1 4 5 0.5 154 5 13
## 833 576 1.2 1 30 0.8 162 8 5
## 834 1960 2.1 1 7 0.2 165 7 9
## 835 673 1.7 3 22 0.1 88 1 5
## 836 718 0.7 0 21 0.8 88 1 7
## 837 864 1.7 3 35 0.3 103 3 7
## 838 547 1.9 1 37 0.4 154 5 4
## 839 623 0.9 1 3 0.1 102 8 7
## 840 1720 0.7 0 19 0.1 137 2 3
## 841 980 0.5 0 33 0.9 160 6 0
## 842 643 1.0 9 18 0.2 187 8 12
## 843 1925 3.0 0 16 0.8 175 8 11
## 844 1408 2.5 3 32 0.1 151 4 6
## 845 1522 0.7 4 28 0.2 124 1 5
## 846 587 2.5 16 51 0.6 111 1 17
## 847 1959 0.5 5 18 0.7 191 6 20
## 848 1872 0.5 3 17 0.7 89 6 6
## 849 1043 0.5 7 42 0.1 111 3 8
## 850 1703 0.5 5 23 0.2 92 6 7
## 851 1414 2.6 1 27 0.6 137 4 20
## 852 1659 2.5 2 41 0.1 88 7 18
## 853 1540 0.7 1 60 0.8 197 7 12
## 854 1342 1.0 0 31 0.6 86 7 1
## 855 1189 2.3 13 8 0.5 198 3 14
## 856 518 2.4 0 62 0.6 128 6 0
## 857 1449 0.8 3 10 0.2 144 1 12
## 858 516 1.1 0 39 0.4 91 5 7
## 859 1312 0.8 0 24 0.9 156 6 0
## 860 1893 0.5 1 23 0.1 179 8 3
## 861 1076 2.5 3 14 0.2 105 5 4
## 862 530 2.6 1 40 0.9 118 6 2
## 863 1044 2.8 7 33 0.6 129 4 13
## 864 1330 1.1 11 37 0.7 126 7 20
## 865 1836 1.1 0 14 0.7 104 5 1
## 866 1741 0.5 1 55 0.1 140 5 3
## 867 1869 2.6 0 30 0.4 152 1 13
## 868 605 1.5 9 18 0.9 162 2 14
## 869 1245 0.6 2 30 0.7 105 5 3
## 870 1709 0.8 8 58 0.8 136 2 10
## 871 688 1.5 0 2 1.0 89 3 2
## 872 775 2.5 1 10 0.1 185 7 14
## 873 1154 2.0 6 35 0.8 159 5 16
## 874 1375 0.5 2 35 0.1 194 2 3
## 875 1827 0.8 4 61 0.9 130 7 9
## 876 1697 0.5 0 60 0.1 90 4 0
## 877 741 1.5 1 14 0.3 178 5 13
## 878 1672 0.5 13 32 0.7 174 1 20
## 879 514 0.7 4 57 0.1 186 3 7
## 880 1375 1.3 2 12 0.1 105 1 13
## 881 989 1.8 4 55 0.7 124 1 12
## 882 1510 2.5 11 47 0.7 138 2 13
## 883 1396 2.4 0 46 0.1 138 7 1
## 884 808 2.3 3 45 0.8 161 1 9
## 885 1542 2.3 1 43 0.8 182 2 11
## 886 1735 1.9 0 59 0.7 148 6 0
## 887 586 0.5 1 57 0.3 188 2 5
## 888 1341 2.1 0 36 0.4 107 8 1
## 889 781 1.3 1 64 0.2 88 2 7
## 890 1224 1.8 2 44 0.2 139 2 9
## 891 1970 1.6 11 58 0.3 80 8 19
## 892 618 2.2 0 63 0.9 138 2 1
## 893 1537 2.9 11 46 0.8 158 4 17
## 894 1018 2.2 8 16 0.6 153 8 10
## 895 1545 1.3 15 35 0.1 173 5 20
## 896 1871 2.1 4 41 0.4 144 8 5
## 897 1729 2.1 1 12 0.9 186 1 6
## 898 680 2.1 9 9 0.1 131 3 11
## 899 1106 1.9 4 54 0.7 107 3 9
## 900 618 0.6 8 16 0.2 143 1 14
## 901 1671 0.5 13 5 0.9 179 8 18
## 902 1975 1.9 2 31 0.9 151 1 17
## 903 831 0.7 2 62 0.7 134 8 9
## 904 1524 1.8 0 10 0.6 174 4 1
## 905 1528 1.8 3 55 0.8 129 5 6
## 906 1447 2.5 2 9 0.2 155 8 16
## 907 564 3.0 1 8 0.7 130 2 3
## 908 921 0.6 1 60 0.1 83 2 3
## 909 802 0.5 3 43 0.6 153 5 6
## 910 1923 1.3 3 24 1.0 86 4 4
## 911 1193 3.0 10 56 0.4 196 3 17
## 912 904 1.2 10 31 0.8 112 3 11
## 913 1285 2.4 1 51 1.0 140 7 2
## 914 1635 2.6 0 2 0.6 182 4 0
## 915 1923 1.0 5 14 0.9 154 4 6
## 916 1851 1.9 3 12 0.9 198 6 11
## 917 727 1.5 0 32 1.0 131 7 1
## 918 1396 1.5 0 44 0.1 118 6 1
## 919 877 1.3 9 46 0.8 135 1 14
## 920 696 2.4 11 35 0.2 103 3 13
## 921 1009 1.0 2 2 0.6 102 8 14
## 922 1526 1.1 2 38 0.3 116 5 6
## 923 987 2.0 1 20 0.8 192 7 3
## 924 1414 2.2 0 49 0.5 101 4 1
## 925 1732 0.8 2 61 0.3 172 5 3
## 926 1587 0.5 0 50 0.9 148 8 15
## 927 1097 0.8 10 21 0.1 160 7 15
## 928 504 2.8 0 40 0.5 178 3 0
## 929 1159 2.7 13 28 0.2 80 8 18
## 930 1910 1.4 0 29 0.6 146 7 2
## 931 1930 2.0 11 16 0.8 186 8 12
## 932 860 1.3 8 40 0.7 170 3 13
## 933 1268 2.9 9 42 0.9 182 6 17
## 934 999 1.8 0 27 0.1 167 4 1
## 935 622 0.7 0 9 0.3 127 2 0
## 936 1250 1.4 8 44 0.2 182 7 20
## 937 763 0.5 9 51 0.3 104 5 12
## 938 1100 1.6 12 15 0.1 112 8 14
## 939 553 2.2 0 18 0.7 136 2 1
## 940 673 2.2 2 3 0.4 84 2 9
## 941 563 2.0 3 47 0.3 115 7 12
## 942 1031 2.1 2 11 0.1 121 2 9
## 943 1880 0.5 0 7 0.6 104 4 1
## 944 1479 1.8 0 24 0.1 146 6 0
## 945 1271 1.5 0 2 0.9 161 1 1
## 946 709 1.9 11 13 0.1 81 8 15
## 947 1744 2.8 0 62 0.7 158 1 0
## 948 1314 2.2 0 40 0.8 116 7 1
## 949 1136 0.5 0 9 0.9 150 6 2
## 950 1039 2.7 11 56 0.3 125 8 13
## 951 732 2.3 7 39 0.6 188 7 11
## 952 511 0.7 1 52 0.7 180 2 10
## 953 1092 0.5 10 11 0.5 167 3 14
## 954 1030 0.5 4 37 0.7 147 1 8
## 955 1130 1.8 0 27 0.9 101 1 8
## 956 1299 2.8 2 60 0.8 187 8 6
## 957 708 1.9 0 26 0.3 116 7 1
## 958 1481 2.0 0 35 0.5 105 3 0
## 959 1068 2.7 0 45 0.6 194 5 1
## 960 1487 0.5 6 57 0.7 84 3 12
## 961 1816 1.6 0 50 0.1 186 5 2
## 962 1895 0.5 2 5 0.7 134 1 11
## 963 805 1.7 0 58 0.9 168 3 10
## 964 713 1.6 7 2 0.4 181 8 9
## 965 864 2.5 10 24 0.7 148 4 15
## 966 582 0.5 8 32 0.7 107 3 11
## 967 1898 1.8 3 56 0.2 185 7 4
## 968 1158 0.7 1 29 0.7 123 2 20
## 969 1451 0.5 5 57 0.6 181 8 12
## 970 1820 1.4 5 51 0.9 163 7 10
## 971 984 0.5 3 9 0.8 140 3 20
## 972 580 2.1 0 24 0.4 135 4 5
## 973 514 1.6 7 37 0.1 172 1 9
## 974 1515 2.6 0 56 0.9 184 3 1
## 975 721 0.5 6 6 0.4 194 4 9
## 976 603 2.5 3 35 0.7 126 3 16
## 977 1820 1.7 0 64 0.6 161 1 3
## 978 1215 1.0 4 16 0.1 130 4 8
## 979 717 0.5 4 10 0.6 198 1 5
## 980 1348 2.0 5 36 0.3 145 6 6
## 981 1164 2.6 0 61 0.3 88 1 6
## 982 578 0.5 9 62 0.3 170 5 13
## 983 1971 0.5 0 40 0.3 186 7 19
## 984 1794 0.5 5 55 0.5 86 7 7
## 985 1597 0.9 4 4 0.5 85 1 19
## 986 1240 1.6 0 39 0.1 146 2 9
## 987 1221 2.3 7 35 0.4 152 7 19
## 988 1180 1.2 5 50 1.0 144 4 20
## 989 718 2.8 10 59 0.8 113 6 13
## 990 1670 1.6 0 47 0.8 139 8 15
## 991 534 2.5 1 21 0.3 164 7 9
## 992 1566 0.5 0 20 0.5 126 2 0
## 993 1935 0.5 0 29 0.6 124 5 9
## 994 504 2.8 2 54 0.4 163 2 10
## 995 913 1.7 1 41 0.3 103 1 14
## 996 1317 1.8 2 43 0.5 113 5 3
## 997 917 0.5 1 31 0.5 88 6 2
## 998 1083 1.0 6 52 0.9 126 2 20
## 999 1039 0.6 5 6 0.1 136 6 8
## 1000 1449 0.5 0 52 0.6 105 6 0
## 1001 1796 1.6 5 7 0.1 101 4 6
## 1002 1097 1.4 0 60 0.8 181 1 17
## 1003 1562 0.5 1 20 0.8 172 8 10
## 1004 1433 2.3 8 27 0.9 153 7 12
## 1005 740 1.8 1 12 0.2 123 1 11
## 1006 1020 1.5 13 7 0.7 96 3 18
## 1007 896 1.0 3 4 0.3 111 1 4
## 1008 1824 0.8 4 19 0.9 143 2 5
## 1009 1512 0.6 1 54 0.4 134 3 2
## 1010 1053 2.9 7 42 0.9 126 6 17
## 1011 1944 1.8 0 31 0.8 87 4 10
## 1012 1174 0.7 1 25 0.8 91 2 8
## 1013 627 1.6 3 12 0.2 131 7 17
## 1014 586 1.4 10 8 0.5 142 8 14
## 1015 1528 2.4 2 37 0.2 156 8 3
## 1016 875 0.7 2 23 0.1 110 7 7
## 1017 1602 0.6 12 58 0.4 170 1 13
## 1018 1370 1.4 5 6 0.2 158 6 8
## 1019 609 0.5 4 10 0.1 81 6 6
## 1020 840 1.2 5 15 0.3 178 6 8
## 1021 991 2.0 2 12 0.3 158 5 6
## 1022 1724 1.8 0 9 0.8 100 2 1
## 1023 584 1.8 0 19 0.3 168 3 0
## 1024 860 1.3 1 49 0.7 141 6 13
## 1025 1541 2.8 10 28 0.2 165 5 15
## 1026 615 0.5 7 58 0.5 130 5 8
## 1027 912 2.8 6 46 0.4 84 4 11
## 1028 1365 0.6 0 31 0.3 125 4 0
## 1029 1347 1.5 10 56 0.3 95 1 19
## 1030 1644 2.8 0 44 1.0 137 8 0
## 1031 956 1.9 1 42 0.1 133 7 11
## 1032 1089 1.4 10 2 0.1 153 7 20
## 1033 664 1.5 5 6 0.9 164 2 19
## 1034 1874 0.6 1 19 0.8 192 2 9
## 1035 1928 2.6 5 5 0.8 162 5 16
## 1036 888 2.6 2 33 0.4 198 2 17
## 1037 1077 1.7 1 58 0.8 86 7 13
## 1038 1023 2.3 5 30 0.2 80 8 7
## 1039 1426 0.8 2 42 0.6 84 3 10
## 1040 831 1.7 7 26 0.7 177 5 11
## 1041 1496 1.3 0 6 0.5 85 3 9
## 1042 643 0.7 3 23 0.8 156 1 9
## 1043 1142 1.1 0 6 0.5 198 4 0
## 1044 730 0.5 3 27 0.1 112 5 10
## 1045 1901 0.5 5 41 0.6 131 2 17
## 1046 1510 0.6 1 32 0.7 135 3 8
## 1047 1924 2.6 0 59 0.9 136 6 0
## 1048 1275 0.5 3 5 0.2 152 1 9
## 1049 987 2.3 9 17 0.8 124 7 17
## 1050 1538 2.3 6 13 0.2 121 7 7
## 1051 1965 0.5 4 29 0.5 149 5 13
## 1052 1507 0.9 0 42 0.4 123 5 0
## 1053 912 0.7 3 47 0.9 182 3 6
## 1054 769 1.2 2 17 0.1 95 8 4
## 1055 948 2.3 14 22 0.1 173 5 20
## 1056 507 1.9 0 39 0.7 142 1 0
## 1057 1766 1.3 1 29 0.5 194 8 20
## 1058 1407 1.8 5 16 0.2 170 4 6
## 1059 614 2.9 3 24 0.2 94 4 7
## 1060 1972 2.9 9 14 0.4 196 7 18
## 1061 1811 2.5 4 5 0.6 98 4 16
## 1062 1159 1.5 3 36 0.4 104 4 8
## 1063 1848 1.3 7 12 0.1 178 2 19
## 1064 1988 0.5 0 16 0.1 94 8 1
## 1065 1423 0.6 12 44 0.8 131 5 13
## 1066 1974 0.5 9 10 0.8 145 6 12
## 1067 835 0.8 2 42 0.8 128 5 18
## 1068 1429 0.6 8 42 0.9 151 5 9
## 1069 947 0.5 1 51 0.1 144 2 15
## 1070 1446 2.8 0 18 0.3 178 8 1
## 1071 1332 1.3 5 7 0.8 168 7 19
## 1072 1498 1.2 0 14 0.1 191 4 1
## 1073 1576 0.9 0 14 0.4 173 2 4
## 1074 1398 1.6 8 26 0.8 150 1 12
## 1075 798 0.5 7 2 0.8 148 2 15
## 1076 1436 2.5 0 42 0.9 124 8 1
## 1077 1998 1.2 8 31 0.6 196 8 19
## 1078 1321 1.3 0 63 0.4 109 8 9
## 1079 1021 1.3 7 9 0.7 192 2 12
## 1080 1339 2.3 0 40 0.1 123 2 1
## 1081 1210 1.6 9 57 0.5 111 4 20
## 1082 882 0.7 9 28 0.2 151 6 16
## 1083 1549 1.5 0 27 0.4 113 5 7
## 1084 1991 2.2 1 42 0.7 82 1 13
## 1085 796 0.5 2 12 0.5 114 1 8
## 1086 1012 0.5 4 39 0.1 161 4 6
## 1087 598 0.8 8 62 0.1 117 8 9
## 1088 541 2.3 4 51 0.4 200 8 17
## 1089 818 0.5 2 23 0.2 192 5 17
## 1090 1414 0.5 0 15 0.8 168 2 0
## 1091 601 1.7 3 6 0.8 169 4 11
## 1092 561 2.8 2 40 0.1 99 7 6
## 1093 1616 2.3 3 52 0.1 100 5 8
## 1094 1263 1.4 1 32 0.4 103 2 2
## 1095 1604 1.9 0 63 0.6 93 5 2
## 1096 539 2.0 1 24 0.5 86 4 2
## 1097 1071 2.6 0 49 0.3 199 4 5
## 1098 826 0.5 0 57 0.5 135 2 0
## 1099 1811 2.9 0 33 0.2 132 8 2
## 1100 1842 2.2 7 56 0.4 179 4 10
## 1101 1420 2.8 1 50 0.1 100 8 8
## 1102 1763 0.5 12 10 0.2 184 3 19
## 1103 1163 1.9 6 2 0.9 133 5 20
## 1104 1805 0.5 0 29 0.2 182 4 2
## 1105 610 1.6 9 52 0.4 90 4 19
## 1106 1924 0.5 3 63 0.3 111 2 10
## 1107 1801 0.5 6 52 0.9 120 6 10
## 1108 1726 1.9 1 42 0.6 106 5 3
## 1109 1686 0.5 4 28 0.8 182 5 8
## 1110 1444 2.1 0 6 0.9 103 1 1
## 1111 1004 1.6 0 30 1.0 160 8 1
## 1112 1242 2.4 8 28 0.2 166 4 9
## 1113 672 0.5 7 35 0.1 99 8 17
## 1114 1325 0.5 0 13 1.0 133 4 8
## 1115 950 0.5 6 55 0.1 124 8 7
## 1116 948 1.5 6 18 0.3 119 4 7
## 1117 1328 0.5 5 27 0.3 144 3 18
## 1118 1447 0.5 3 40 0.7 184 2 5
## 1119 1973 0.5 3 21 0.8 95 8 7
## 1120 1731 2.3 18 60 0.5 171 4 20
## 1121 1617 1.5 1 63 0.7 111 2 10
## 1122 1791 0.5 1 33 0.5 110 5 4
## 1123 851 2.3 7 54 1.0 101 2 9
## 1124 856 1.9 0 14 0.6 115 1 7
## 1125 951 0.5 6 15 0.6 81 2 17
## 1126 1303 2.1 6 10 0.4 109 8 15
## 1127 1550 2.7 2 32 0.1 126 7 5
## 1128 1759 1.4 10 15 0.4 101 4 13
## 1129 1448 0.5 18 2 0.2 100 5 19
## 1130 1987 1.9 0 50 0.7 119 8 18
## 1131 908 2.1 1 44 0.6 95 5 2
## 1132 820 0.5 2 58 0.3 160 4 4
## 1133 904 2.7 4 19 0.6 147 6 7
## 1134 667 1.3 4 6 0.1 149 6 10
## 1135 1333 1.4 3 59 0.7 183 7 5
## 1136 1352 2.4 5 6 0.9 92 6 9
## 1137 1600 2.5 1 19 0.6 88 6 9
## 1138 1454 1.4 8 37 0.8 80 8 20
## 1139 1823 2.8 11 10 0.6 81 8 19
## 1140 1581 1.0 9 41 0.4 172 4 19
## 1141 1672 1.7 1 44 0.5 172 8 4
## 1142 1283 0.7 0 27 0.2 80 4 6
## 1143 630 2.0 9 14 0.5 180 8 13
## 1144 659 2.2 0 24 0.7 151 8 2
## 1145 1811 0.5 0 32 1.0 115 6 0
## 1146 688 2.5 9 40 0.2 109 5 20
## 1147 514 2.9 5 18 0.7 141 2 8
## 1148 1933 2.8 1 16 0.5 154 3 9
## 1149 1006 2.2 4 22 0.8 135 4 8
## 1150 1134 0.9 2 13 0.4 92 4 19
## 1151 599 2.9 0 64 0.3 186 8 1
## 1152 973 0.5 5 53 0.5 196 5 7
## 1153 1180 2.5 0 27 0.1 90 6 1
## 1154 1237 2.5 7 13 0.8 98 4 12
## 1155 659 0.5 7 5 0.3 169 8 10
## 1156 1002 2.7 16 11 0.4 185 6 18
## 1157 602 0.9 9 50 0.9 141 8 18
## 1158 1249 0.5 3 55 0.8 140 3 8
## 1159 531 0.5 0 12 0.5 121 4 3
## 1160 1450 2.1 1 31 0.6 114 5 4
## 1161 1342 2.8 1 50 0.1 173 1 8
## 1162 832 0.5 5 22 0.6 198 1 8
## 1163 867 1.5 0 57 0.1 159 4 0
## 1164 1404 2.1 16 44 0.9 151 1 20
## 1165 840 0.9 6 33 0.7 150 6 7
## 1166 1368 0.5 10 42 0.2 91 1 20
## 1167 1927 0.9 3 11 0.4 190 8 12
## 1168 1714 0.5 7 31 0.4 120 2 10
## 1169 1201 0.5 1 10 0.4 198 6 5
## 1170 1796 2.3 4 44 0.9 184 2 11
## 1171 1147 0.8 11 28 0.7 158 6 13
## 1172 776 1.7 0 20 1.0 146 4 9
## 1173 510 2.6 0 33 0.1 110 6 10
## 1174 1045 1.2 3 40 0.1 175 6 7
## 1175 1497 0.9 4 10 0.1 153 8 20
## 1176 726 2.2 0 40 0.8 94 4 20
## 1177 1576 2.0 0 20 0.6 148 3 0
## 1178 714 2.1 5 11 0.1 197 3 6
## 1179 1595 1.9 1 63 0.9 88 1 20
## 1180 541 1.0 0 10 0.4 127 4 0
## 1181 1154 1.1 2 5 0.2 98 1 13
## 1182 1638 1.0 6 2 0.2 189 1 11
## 1183 1150 2.7 0 39 0.4 151 8 1
## 1184 1254 2.3 0 44 0.2 149 1 10
## 1185 603 1.5 8 12 0.7 114 6 20
## 1186 1834 2.1 7 40 0.1 99 4 11
## 1187 520 2.3 0 31 0.8 145 6 14
## 1188 565 0.6 1 50 0.1 158 2 8
## 1189 1689 0.5 11 9 0.5 150 5 15
## 1190 742 2.3 0 21 0.7 104 8 1
## 1191 761 0.5 9 3 0.8 196 7 15
## 1192 959 1.3 0 36 0.4 196 5 14
## 1193 772 1.2 7 8 0.6 134 7 8
## 1194 1824 1.9 9 2 0.7 103 1 14
## 1195 1130 0.5 1 29 0.7 130 1 14
## 1196 1183 0.5 3 48 0.1 120 8 12
## 1197 1472 3.0 4 20 0.3 169 2 6
## 1198 1868 0.5 2 40 0.9 179 8 17
## 1199 1375 2.9 2 20 0.4 147 6 7
## 1200 881 1.0 1 64 0.5 160 3 6
## 1201 1742 0.5 5 43 0.9 176 3 7
## 1202 1225 0.7 6 60 0.1 107 2 15
## 1203 1970 0.5 0 15 1.0 132 2 0
## 1204 1186 0.5 2 21 0.4 160 8 4
## 1205 1762 0.7 7 60 0.1 157 4 10
## 1206 852 1.8 5 8 0.8 160 5 9
## 1207 1575 2.5 0 11 0.2 185 7 20
## 1208 1972 1.6 0 48 0.5 92 8 2
## 1209 827 0.6 9 35 0.2 87 6 17
## 1210 1063 1.5 7 3 0.9 155 4 10
## 1211 1695 2.3 1 34 0.4 84 5 10
## 1212 1343 2.4 5 14 0.9 81 8 10
## 1213 834 0.5 2 27 0.2 123 1 16
## 1214 595 2.6 0 20 0.4 122 2 18
## 1215 844 2.2 1 22 0.9 197 7 2
## 1216 1335 3.0 3 60 0.8 180 2 4
## 1217 1883 1.6 9 24 0.1 87 1 16
## 1218 1128 0.5 6 33 0.6 147 8 7
## 1219 826 2.4 14 57 0.6 117 2 17
## 1220 1650 2.3 0 38 0.7 89 1 1
## 1221 1162 1.0 0 50 0.5 104 4 10
## 1222 1517 2.5 2 8 0.8 169 4 18
## 1223 963 0.5 1 60 0.8 156 3 3
## 1224 1837 1.0 5 12 0.9 103 8 6
## 1225 1028 2.8 1 30 0.1 193 1 10
## 1226 1831 2.2 4 55 1.0 189 8 9
## 1227 571 2.0 5 58 0.6 101 6 11
## 1228 1770 2.3 0 9 0.6 183 6 5
## 1229 970 1.3 5 59 0.2 84 7 19
## 1230 642 0.5 0 38 0.8 86 5 10
## 1231 622 1.9 12 11 0.8 193 8 14
## 1232 1412 0.6 9 57 0.2 86 3 10
## 1233 1070 3.0 9 19 0.8 160 7 16
## 1234 875 0.7 1 30 0.7 146 8 8
## 1235 1994 0.7 5 5 0.4 189 2 7
## 1236 823 1.0 9 18 0.2 182 5 16
## 1237 1908 0.5 9 30 1.0 111 2 19
## 1238 790 0.5 7 44 0.1 89 8 8
## 1239 1330 1.8 1 40 0.5 177 1 5
## 1240 1660 0.5 2 15 0.7 100 8 8
## 1241 1410 0.5 0 15 0.3 92 3 1
## 1242 1772 1.6 17 45 0.5 159 2 18
## 1243 1280 1.3 0 39 1.0 108 7 1
## 1244 1712 0.9 2 23 1.0 155 7 19
## 1245 1562 0.7 0 55 0.1 140 8 1
## 1246 891 1.9 1 18 0.1 153 6 3
## 1247 1110 0.6 1 42 0.9 192 5 2
## 1248 875 1.4 1 43 0.6 104 8 3
## 1249 1211 1.2 0 28 0.3 130 2 3
## 1250 769 0.5 12 54 0.2 112 4 19
## 1251 671 2.9 7 2 0.1 95 6 19
## 1252 1872 0.5 5 26 0.5 172 4 10
## 1253 911 2.2 1 28 0.5 182 2 2
## 1254 1273 0.7 1 23 0.6 178 6 14
## 1255 1062 0.5 3 39 0.9 124 4 4
## 1256 940 2.9 0 16 0.7 115 4 1
## 1257 729 2.0 0 14 0.6 135 6 2
## 1258 1494 2.4 6 3 0.9 93 7 17
## 1259 1546 1.6 2 27 0.7 124 2 6
## 1260 1253 0.7 0 34 0.9 113 4 10
## 1261 895 0.5 6 16 0.3 164 8 14
## 1262 793 1.9 0 43 0.3 124 8 2
## 1263 1628 1.8 11 56 0.1 136 2 13
## 1264 625 2.7 2 42 0.8 138 8 3
## 1265 1110 1.5 2 12 0.3 153 8 5
## 1266 999 1.7 6 49 0.6 132 1 17
## 1267 1856 0.5 0 24 0.1 155 5 1
## 1268 1715 2.6 9 15 0.6 120 6 20
## 1269 1897 1.9 2 54 0.3 154 2 7
## 1270 1171 1.7 2 19 0.3 167 7 19
## 1271 1973 2.4 6 46 0.1 193 4 10
## 1272 1992 0.6 11 2 0.1 141 1 14
## 1273 1093 0.5 1 31 0.6 171 2 12
## 1274 1880 2.0 11 35 0.1 200 4 14
## 1275 1765 1.1 3 11 0.7 87 2 7
## 1276 1640 0.5 1 36 0.9 87 8 11
## 1277 1830 0.5 0 7 0.9 191 6 12
## 1278 826 0.6 3 62 0.7 187 4 19
## 1279 1864 2.2 0 7 0.1 142 1 2
## 1280 586 0.6 16 42 0.3 121 7 17
## 1281 1206 3.0 10 30 0.6 127 3 13
## 1282 832 0.5 1 5 0.1 165 5 2
## 1283 848 2.8 1 8 0.1 103 7 3
## 1284 1851 2.9 0 53 0.7 112 6 2
## 1285 1166 0.5 7 59 0.5 80 1 11
## 1286 1776 2.1 10 35 0.1 172 4 16
## 1287 1254 1.2 2 27 0.2 134 2 10
## 1288 1494 1.6 4 24 0.2 101 1 17
## 1289 984 2.2 0 39 0.5 119 8 18
## 1290 513 1.9 8 27 0.4 174 8 13
## 1291 557 0.5 3 17 0.2 89 6 4
## 1292 744 1.7 0 33 0.5 105 4 2
## 1293 1129 0.8 9 21 0.6 116 2 10
## 1294 1793 2.7 12 44 0.7 175 5 17
## 1295 1310 2.3 3 15 0.2 150 5 11
## 1296 1604 1.2 1 6 0.5 191 6 15
## 1297 1676 1.6 2 37 0.2 145 1 3
## 1298 1001 2.8 1 40 0.2 117 3 7
## 1299 1086 2.7 0 24 0.1 135 8 19
## 1300 1986 0.5 7 39 0.1 89 7 19
## 1301 634 0.8 5 41 0.2 192 1 12
## 1302 502 0.8 7 52 1.0 82 6 8
## 1303 1641 1.1 6 48 0.7 97 8 7
## 1304 623 1.4 0 15 0.2 189 3 1
## 1305 962 1.1 3 17 0.1 161 8 20
## 1306 1057 2.7 3 41 0.1 97 4 10
## 1307 1862 1.5 1 62 1.0 182 1 2
## 1308 865 2.9 5 16 0.1 89 4 12
## 1309 999 2.6 11 56 1.0 165 4 13
## 1310 1232 1.2 12 30 0.9 131 6 14
## 1311 1194 2.3 6 34 0.2 165 2 12
## 1312 612 0.5 1 20 0.8 179 8 6
## 1313 1362 2.9 0 47 0.5 186 6 4
## 1314 1469 1.7 11 44 0.5 169 2 14
## 1315 986 0.8 13 28 0.8 182 4 20
## 1316 1843 2.8 1 62 0.8 104 3 7
## 1317 1561 0.5 10 4 0.5 134 2 13
## 1318 1695 0.5 2 47 0.5 189 5 13
## 1319 667 2.6 3 8 0.8 185 1 7
## 1320 1768 2.0 0 2 0.8 88 7 2
## 1321 1269 2.8 16 41 1.0 180 8 17
## 1322 1109 2.8 6 57 0.9 151 1 12
## 1323 936 0.5 14 14 0.1 164 3 17
## 1324 1175 1.7 1 45 0.6 167 2 7
## 1325 1940 1.0 0 5 0.2 194 4 1
## 1326 771 0.5 0 33 0.1 157 3 17
## 1327 645 2.5 10 63 1.0 174 4 14
## 1328 1307 1.8 2 7 0.6 88 7 3
## 1329 1948 2.9 4 5 0.2 136 1 13
## 1330 1509 0.6 5 50 0.3 81 4 8
## 1331 626 2.8 3 11 0.8 109 2 9
## 1332 1190 2.0 0 40 0.2 93 5 1
## 1333 1727 2.1 10 57 0.3 158 6 18
## 1334 1670 3.0 14 62 0.5 114 8 20
## 1335 1150 0.5 4 27 1.0 134 1 17
## 1336 1208 2.9 5 16 1.0 175 1 19
## 1337 1812 1.3 4 42 1.0 162 7 15
## 1338 1559 1.3 10 10 0.3 114 8 17
## 1339 1751 2.5 1 4 0.6 146 8 9
## 1340 1003 2.9 16 16 0.6 119 8 19
## 1341 1715 0.5 1 18 0.1 96 6 3
## 1342 1967 0.5 0 46 0.6 199 1 7
## 1343 962 1.0 14 2 0.9 125 1 20
## 1344 1569 1.6 4 54 0.9 190 4 6
## 1345 1146 3.0 5 57 0.4 111 5 10
## 1346 1379 1.5 6 9 0.8 118 8 13
## 1347 1504 1.9 7 25 0.3 152 4 9
## 1348 1630 2.8 0 32 0.9 80 6 1
## 1349 1125 2.7 10 23 0.4 117 1 14
## 1350 614 0.7 4 51 0.1 178 7 7
## 1351 854 1.0 5 17 0.9 101 4 9
## 1352 1826 0.5 5 49 0.6 172 8 6
## 1353 807 0.5 0 37 0.2 127 4 0
## 1354 1996 2.8 0 7 0.1 138 5 4
## 1355 1083 2.0 3 41 0.7 158 5 5
## 1356 1035 0.5 14 49 0.3 178 2 15
## 1357 1521 1.6 5 62 0.3 143 1 9
## 1358 1314 2.8 13 5 1.0 87 4 18
## 1359 535 1.9 5 24 0.2 190 8 19
## 1360 673 1.9 13 35 0.4 142 6 14
## 1361 1219 1.6 5 4 0.3 169 6 7
## 1362 1603 0.5 1 17 0.5 114 2 8
## 1363 695 0.5 18 12 0.6 196 2 19
## 1364 1441 0.6 0 53 0.5 106 1 12
## 1365 1470 2.2 4 33 0.4 138 7 10
## 1366 1152 2.1 0 26 0.3 82 2 0
## 1367 1619 0.9 3 20 0.9 117 6 9
## 1368 1002 1.8 1 45 0.7 180 7 7
## 1369 742 0.5 0 36 0.7 139 3 1
## 1370 1306 2.1 2 33 0.4 174 3 9
## 1371 1424 2.9 0 20 0.6 180 5 0
## 1372 942 1.4 8 27 0.5 91 4 9
## 1373 1934 2.7 8 2 0.1 118 4 16
## 1374 1849 0.5 1 29 0.5 181 7 12
## 1375 1922 0.5 15 42 0.6 153 8 16
## 1376 548 1.0 0 56 0.4 146 4 14
## 1377 916 0.5 0 47 0.2 82 4 1
## 1378 1783 0.5 8 14 0.6 183 8 11
## 1379 1083 1.4 2 49 0.8 156 5 9
## 1380 1698 2.8 10 7 0.8 131 8 12
## 1381 1969 0.8 5 7 0.2 85 8 20
## 1382 869 1.9 0 2 0.6 197 7 3
## 1383 1337 2.7 0 5 0.7 200 4 1
## 1384 637 0.8 2 28 0.6 194 4 6
## 1385 1248 2.4 6 9 0.4 89 3 8
## 1386 1174 0.9 7 30 1.0 147 4 20
## 1387 1831 2.3 3 44 0.2 148 7 14
## 1388 1010 2.0 2 8 0.9 139 5 3
## 1389 1117 0.5 2 21 0.1 177 2 19
## 1390 990 2.7 3 15 0.9 153 2 7
## 1391 1065 2.8 0 21 0.1 158 5 3
## 1392 1982 1.1 12 24 0.1 129 7 16
## 1393 1066 3.0 6 5 0.5 167 5 7
## 1394 681 0.5 12 17 0.9 194 5 19
## 1395 1163 2.1 0 19 0.9 132 2 1
## 1396 1285 0.8 4 11 0.8 199 5 7
## 1397 753 0.5 11 48 0.1 121 8 20
## 1398 1779 2.6 2 5 0.8 90 1 3
## 1399 987 1.9 4 52 0.5 83 3 8
## 1400 511 0.9 15 24 0.6 136 3 18
## 1401 1044 1.8 4 12 0.7 104 6 5
## 1402 1855 0.5 5 32 0.6 187 8 18
## 1403 946 1.4 9 26 0.1 186 6 10
## 1404 657 1.9 2 21 0.1 110 8 8
## 1405 1673 1.6 2 42 0.5 182 5 6
## 1406 731 0.5 0 28 0.5 130 8 0
## 1407 1261 0.5 3 52 0.3 86 2 9
## 1408 618 1.1 11 45 0.2 188 3 20
## 1409 936 2.3 5 57 0.2 129 2 6
## 1410 1485 0.9 0 28 0.8 158 5 1
## 1411 1339 0.5 10 21 0.5 80 4 11
## 1412 508 1.3 1 50 0.7 82 5 9
## 1413 1938 0.5 5 44 0.5 121 2 9
## 1414 1322 2.3 1 35 0.1 157 1 3
## 1415 1864 1.9 2 57 0.1 181 6 6
## 1416 1944 0.5 1 26 0.5 92 4 9
## 1417 581 0.6 5 56 0.2 180 2 10
## 1418 1936 1.3 2 44 0.1 139 3 3
## 1419 1329 1.7 5 23 0.6 198 8 6
## 1420 808 0.5 3 46 0.5 105 8 15
## 1421 994 1.2 4 40 0.9 164 2 17
## 1422 1811 2.0 3 22 0.2 82 2 8
## 1423 993 2.3 0 16 0.4 106 7 13
## 1424 1136 1.6 15 32 0.5 150 6 19
## 1425 719 0.5 0 23 0.4 113 6 9
## 1426 1946 2.7 8 8 0.3 123 8 19
## 1427 1230 1.6 0 48 0.7 111 7 2
## 1428 579 2.5 3 7 1.0 89 8 10
## 1429 574 3.0 1 30 0.9 126 2 3
## 1430 1072 2.2 6 19 0.4 114 8 16
## 1431 843 1.3 1 16 0.9 119 7 4
## 1432 1492 1.5 5 11 0.9 85 3 7
## 1433 1807 2.9 0 16 0.2 125 3 0
## 1434 909 0.7 7 47 0.1 146 3 9
## 1435 1876 1.3 9 64 1.0 98 3 19
## 1436 1251 0.5 10 29 0.7 133 6 16
## 1437 636 0.5 8 28 0.9 152 2 15
## 1438 1354 0.7 3 26 0.5 136 4 6
## 1439 930 0.5 3 34 0.5 190 4 16
## 1440 1830 1.0 7 47 0.3 117 1 11
## 1441 1424 0.5 16 23 0.6 80 4 18
## 1442 583 0.9 1 51 0.9 88 8 2
## 1443 648 0.5 9 54 0.4 109 5 20
## 1444 1299 2.8 0 64 0.5 83 6 6
## 1445 1494 1.9 2 47 0.4 129 2 9
## 1446 527 0.7 4 53 0.9 128 1 11
## 1447 873 0.5 6 8 0.6 152 7 9
## 1448 722 1.1 12 12 1.0 93 6 20
## 1449 1358 1.7 0 60 0.6 147 3 1
## 1450 1035 1.9 8 11 1.0 173 6 10
## 1451 1289 0.8 3 16 0.9 161 6 17
## 1452 1445 2.2 2 37 0.4 82 2 6
## 1453 1702 2.3 12 47 0.6 107 2 19
## 1454 713 1.4 4 53 0.8 98 1 17
## 1455 1207 0.5 0 58 0.3 112 8 0
## 1456 683 2.1 7 30 0.1 197 7 8
## 1457 594 2.6 1 27 0.1 196 5 2
## 1458 537 2.0 1 55 0.3 103 7 2
## 1459 1565 0.5 0 38 0.1 121 5 1
## 1460 1052 0.9 1 6 0.8 195 4 16
## 1461 1766 0.5 1 16 0.6 141 5 7
## 1462 584 1.7 1 19 0.4 199 4 4
## 1463 1541 1.5 6 5 0.5 109 4 10
## 1464 935 0.5 3 58 0.1 155 5 12
## 1465 680 2.4 13 34 0.4 157 2 17
## 1466 877 1.5 0 17 0.1 195 7 0
## 1467 1493 1.5 13 53 0.5 102 5 16
## 1468 1762 0.6 3 26 0.1 83 6 14
## 1469 842 1.8 4 57 1.0 124 3 10
## 1470 925 2.1 9 56 0.2 196 1 14
## 1471 1576 2.2 8 5 0.7 139 3 17
## 1472 1065 1.7 4 48 0.3 162 6 8
## 1473 958 1.6 9 60 0.4 200 1 12
## 1474 1680 1.2 6 37 0.7 168 4 7
## 1475 1715 1.3 0 12 0.3 94 8 6
## 1476 983 0.6 0 45 0.1 130 4 11
## 1477 720 0.9 12 14 0.8 165 4 14
## 1478 1564 1.5 1 42 0.7 108 2 2
## 1479 969 1.8 2 44 0.6 158 3 7
## 1480 1356 0.5 0 35 0.8 131 1 19
## 1481 504 1.0 8 14 0.5 189 7 9
## 1482 1546 0.5 3 6 0.2 101 1 4
## 1483 689 2.9 3 7 0.8 147 2 6
## 1484 1685 0.6 14 16 0.6 137 7 15
## 1485 1786 1.2 11 61 0.1 123 3 14
## 1486 1944 0.6 9 59 0.2 132 7 10
## 1487 1077 2.5 13 49 0.6 131 4 14
## 1488 648 1.8 1 60 0.1 195 4 4
## 1489 1702 0.5 8 2 0.6 185 6 19
## 1490 856 1.1 14 19 0.7 125 6 19
## 1491 1786 2.6 6 14 1.0 115 4 15
## 1492 914 0.7 1 60 0.9 198 5 4
## 1493 649 1.4 4 19 0.8 190 7 12
## 1494 1646 2.5 3 25 0.6 200 2 5
## 1495 1043 2.7 3 29 0.2 83 8 17
## 1496 1920 0.6 3 19 0.5 131 3 8
## 1497 1220 2.8 1 54 0.7 109 7 20
## 1498 1748 0.5 11 53 0.5 114 4 20
## 1499 1766 0.5 1 22 0.8 122 8 4
## 1500 1278 1.0 7 24 0.8 170 3 16
## 1501 1745 2.9 0 3 0.9 105 3 0
## 1502 868 0.9 5 5 0.4 137 4 7
## 1503 864 1.6 0 53 0.3 88 6 1
## 1504 1142 1.2 0 2 0.6 114 6 0
## 1505 608 0.5 0 64 0.9 94 4 0
## 1506 983 1.8 0 53 0.9 187 1 4
## 1507 1720 1.6 18 2 0.8 188 5 20
## 1508 591 2.1 18 16 0.5 196 7 20
## 1509 1205 2.7 5 10 0.1 163 6 10
## 1510 1369 1.7 3 28 0.6 108 1 18
## 1511 1082 2.3 11 2 1.0 101 4 20
## 1512 1719 2.1 0 52 0.1 187 3 1
## 1513 1090 2.3 1 8 0.6 128 8 8
## 1514 1991 2.8 3 15 0.1 181 1 13
## 1515 1179 0.5 7 32 0.3 182 2 12
## 1516 721 2.7 1 15 0.2 102 5 18
## 1517 1549 1.8 0 62 0.7 91 4 4
## 1518 1349 2.1 3 46 0.9 134 6 12
## 1519 1799 1.0 1 30 0.6 123 3 9
## 1520 1075 0.5 2 11 0.2 122 1 16
## 1521 684 0.9 3 63 1.0 157 5 9
## 1522 1837 0.5 9 36 0.9 162 4 20
## 1523 1619 1.9 2 21 0.4 138 1 9
## 1524 965 1.4 1 13 0.9 197 8 2
## 1525 730 1.5 9 9 0.7 122 4 15
## 1526 1494 1.6 3 45 0.8 158 5 4
## 1527 1807 0.8 0 38 0.2 97 8 0
## 1528 709 2.4 1 11 0.1 130 5 11
## 1529 689 1.4 2 45 0.5 103 5 11
## 1530 955 0.5 2 2 0.6 155 4 10
## 1531 1872 1.9 5 35 0.6 111 3 14
## 1532 1259 1.2 0 13 0.5 97 2 0
## 1533 695 1.2 1 41 0.4 169 8 8
## 1534 1969 1.2 9 63 0.2 132 4 11
## 1535 759 2.5 3 39 0.3 162 2 8
## 1536 936 1.0 1 18 0.2 153 3 18
## 1537 1802 2.7 0 10 0.6 101 7 9
## 1538 772 1.0 0 23 0.6 97 7 11
## 1539 591 0.5 1 16 0.9 90 6 17
## 1540 569 2.5 0 41 0.3 124 1 1
## 1541 1056 2.1 8 37 0.7 101 3 11
## 1542 1982 1.6 2 12 0.2 80 5 20
## 1543 1122 0.5 0 40 0.3 156 7 14
## 1544 1023 0.7 2 63 0.7 153 7 8
## 1545 1283 2.4 8 33 1.0 93 3 15
## 1546 1602 1.6 0 35 0.1 99 7 7
## 1547 732 1.3 5 32 0.4 163 8 7
## 1548 1854 0.9 0 33 0.4 195 1 0
## 1549 897 2.0 3 2 0.6 154 8 10
## 1550 1405 1.7 0 8 0.3 107 4 7
## 1551 1035 2.7 1 40 0.2 157 5 15
## 1552 1396 0.6 0 37 0.7 134 4 15
## 1553 1749 0.5 7 49 0.8 178 1 8
## 1554 1522 2.2 4 21 0.7 162 6 11
## 1555 1279 2.5 5 7 0.5 105 6 6
## 1556 719 2.9 7 6 0.7 102 6 8
## 1557 544 0.5 2 7 0.2 129 5 15
## 1558 1007 2.9 5 21 0.8 185 8 12
## 1559 590 1.2 0 32 0.4 141 6 1
## 1560 1788 0.5 0 6 0.2 141 6 16
## 1561 748 1.4 0 25 1.0 110 7 14
## 1562 757 0.9 4 64 0.5 157 7 5
## 1563 1561 1.6 1 27 0.8 174 3 10
## 1564 797 1.5 4 18 0.6 81 8 20
## 1565 727 2.5 0 64 0.2 193 1 9
## 1566 686 2.7 0 36 0.1 194 4 1
## 1567 1515 2.1 4 24 0.9 176 5 6
## 1568 555 0.5 2 17 0.2 163 5 12
## 1569 1976 2.6 6 45 1.0 136 4 19
## 1570 1063 2.0 0 27 0.2 86 6 7
## 1571 1753 0.7 4 40 0.6 191 4 11
## 1572 659 1.4 2 3 0.1 85 8 6
## 1573 1583 1.2 0 14 0.2 148 2 4
## 1574 1667 1.0 0 21 0.9 138 2 0
## 1575 640 1.3 5 6 0.6 143 8 20
## 1576 1913 1.8 0 29 0.6 111 5 4
## 1577 1191 0.8 6 46 0.8 89 6 9
## 1578 816 3.0 2 9 0.1 117 1 3
## 1579 915 0.5 9 33 0.3 199 2 16
## 1580 1157 0.8 7 27 0.1 88 8 8
## 1581 1201 0.5 2 10 1.0 99 7 7
## 1582 1483 2.2 3 53 0.7 169 5 11
## 1583 1614 1.2 1 9 0.1 161 3 2
## 1584 930 1.0 4 4 0.9 144 8 10
## 1585 1784 1.6 4 41 0.4 164 6 20
## 1586 1262 1.8 12 34 0.1 149 5 16
## 1587 797 2.2 0 37 0.9 144 7 2
## 1588 1829 2.1 8 59 0.1 91 5 15
## 1589 618 1.0 9 13 0.1 80 4 15
## 1590 1547 2.9 2 57 0.4 114 1 3
## 1591 586 2.8 2 15 0.2 83 3 11
## 1592 1617 2.4 8 36 0.8 85 1 9
## 1593 1882 2.0 11 44 0.8 113 8 19
## 1594 674 2.9 1 21 0.2 198 3 4
## 1595 1467 0.5 0 18 0.6 122 5 0
## 1596 794 0.5 0 2 0.8 106 6 14
## 1597 1965 2.6 0 39 0.2 187 4 3
## 1598 1911 0.9 1 36 0.7 108 8 3
## 1599 1512 0.9 4 46 0.1 145 5 5
## 1600 510 2.0 5 45 0.9 168 6 16
## px_height px_width ram sc_h sc_w talk_time price_range
## 1 20 756 2549 9 7 19 1
## 2 905 1988 2631 17 3 7 2
## 3 1263 1716 2603 11 2 9 2
## 4 1208 1212 1411 8 2 15 1
## 5 1004 1654 1067 17 1 10 1
## 6 381 1018 3220 13 8 18 3
## 7 386 836 1099 17 1 20 0
## 8 1137 1224 513 19 10 12 0
## 9 248 874 3946 5 2 7 3
## 10 151 1005 3826 14 9 13 3
## 11 344 1440 2680 7 1 4 2
## 12 356 563 373 14 9 3 0
## 13 862 1864 568 17 15 11 0
## 14 984 1850 3554 10 9 19 3
## 15 441 810 3752 10 2 18 3
## 16 658 878 1835 19 13 16 1
## 17 902 1064 2337 11 1 18 1
## 18 974 1385 3283 17 1 15 3
## 19 407 822 1433 11 5 20 1
## 20 466 788 1037 8 7 20 0
## 21 201 1245 2583 11 0 12 1
## 22 550 645 3763 16 1 4 3
## 23 511 1075 3286 17 8 12 3
## 24 187 1311 2373 10 1 10 2
## 25 85 1152 2227 18 5 3 1
## 26 178 1919 3845 7 0 12 3
## 27 1134 1249 1326 10 4 15 1
## 28 609 1307 3429 6 5 4 3
## 29 347 730 3169 6 1 2 2
## 30 1448 1613 2150 18 12 11 2
## 31 434 967 2484 18 2 11 2
## 32 685 714 1878 15 0 4 1
## 33 880 1456 1629 15 12 14 1
## 34 1580 1652 504 9 3 12 0
## 35 186 1810 1152 8 3 20 1
## 36 202 1791 3587 10 5 3 3
## 37 27 774 2296 16 12 12 1
## 38 885 1854 3238 16 13 10 3
## 39 1042 1832 2059 5 0 15 2
## 40 1382 1383 2053 19 12 16 2
## 41 546 629 3112 12 5 10 3
## 42 690 804 2908 6 0 18 2
## 43 822 1449 905 14 11 17 0
## 44 581 820 3963 9 4 20 3
## 45 265 713 2056 7 5 4 1
## 46 361 975 431 15 6 6 0
## 47 322 547 470 7 0 15 0
## 48 504 1570 2955 10 4 19 2
## 49 651 1618 3366 18 8 13 3
## 50 108 1781 3834 16 11 8 3
## 51 459 1225 1050 11 1 4 0
## 52 205 603 3993 7 1 7 3
## 53 570 1724 3378 13 11 2 3
## 54 1172 1217 2192 9 7 13 2
## 55 160 1026 392 15 6 4 0
## 56 4 638 3709 11 0 12 3
## 57 763 1413 590 9 2 6 0
## 58 339 1242 1814 5 4 13 1
## 59 1040 1071 907 10 7 17 0
## 60 319 1206 3464 19 10 6 3
## 61 708 1752 3484 9 6 11 3
## 62 1077 1122 2009 10 3 11 1
## 63 928 1049 2048 11 5 17 1
## 64 516 1894 837 15 9 4 1
## 65 96 741 854 16 0 7 0
## 66 557 1402 2334 10 1 11 2
## 67 1605 1924 2822 19 3 16 3
## 68 1226 1242 1971 18 7 14 2
## 69 769 802 1410 16 10 5 0
## 70 942 1179 3616 13 5 12 3
## 71 651 891 880 19 4 8 0
## 72 1572 1684 1601 18 17 14 2
## 73 836 1739 2600 16 8 20 3
## 74 824 881 1308 16 7 11 1
## 75 391 984 2413 17 14 15 1
## 76 320 520 1047 14 10 18 0
## 77 325 902 1204 11 4 8 0
## 78 125 1504 1799 5 2 11 1
## 79 846 1634 2676 12 6 11 3
## 80 293 1846 3029 19 17 9 3
## 81 768 874 2213 18 13 7 1
## 82 70 1974 790 13 6 3 0
## 83 117 513 3182 8 4 5 2
## 84 1158 1244 3472 9 1 20 3
## 85 1055 1661 2871 12 1 13 3
## 86 635 973 3187 12 9 4 2
## 87 322 771 1945 15 4 10 1
## 88 275 989 488 5 3 9 0
## 89 1445 1954 1214 5 0 14 1
## 90 550 1338 2598 12 9 6 2
## 91 1242 1712 3242 18 11 4 3
## 92 597 863 3534 10 4 6 3
## 93 59 1203 2488 13 5 15 2
## 94 819 902 3255 19 14 9 3
## 95 179 786 2863 9 3 18 2
## 96 396 1732 3801 12 0 7 3
## 97 713 1398 666 12 10 13 0
## 98 622 792 1366 7 0 20 1
## 99 522 1056 2399 10 5 7 1
## 100 313 1264 2338 12 8 20 2
## 101 226 1248 2700 12 3 9 2
## 102 605 669 2235 19 4 4 1
## 103 692 1686 3825 12 11 2 3
## 104 304 1042 1427 9 1 9 0
## 105 1590 1926 1324 17 0 17 2
## 106 229 616 398 17 0 5 0
## 107 839 1131 3685 18 17 17 3
## 108 436 536 278 19 0 10 0
## 109 387 1269 2196 12 6 19 1
## 110 1265 1298 3799 16 1 4 3
## 111 1852 1967 2268 13 5 4 2
## 112 875 1025 2965 13 10 15 2
## 113 577 1359 3557 16 11 12 3
## 114 975 1996 1354 16 7 19 1
## 115 510 1013 2147 19 1 11 2
## 116 674 1925 1394 16 10 6 1
## 117 1125 1989 374 18 4 8 0
## 118 747 826 506 10 0 11 0
## 119 1612 1983 3702 17 0 19 3
## 120 311 1545 1078 8 0 10 1
## 121 116 1533 2945 10 5 16 2
## 122 554 621 2981 15 6 7 2
## 123 730 1729 536 17 14 4 0
## 124 577 1071 2317 16 4 7 2
## 125 450 1554 2126 18 9 7 1
## 126 45 1942 1260 9 2 17 1
## 127 299 845 3770 5 3 13 3
## 128 506 627 1886 17 4 20 1
## 129 635 842 3323 9 1 7 2
## 130 650 1740 2177 7 5 13 2
## 131 433 648 728 14 8 4 0
## 132 839 1136 2478 14 8 20 3
## 133 650 879 1617 17 4 19 1
## 134 218 1065 1472 14 7 7 0
## 135 564 831 764 19 12 15 0
## 136 986 1191 2337 6 1 17 2
## 137 561 1631 3965 16 1 6 3
## 138 65 588 3693 17 14 16 3
## 139 420 500 797 13 2 15 0
## 140 1365 1698 1687 11 0 18 2
## 141 619 1299 2940 13 9 2 3
## 142 849 898 1545 9 5 10 1
## 143 611 1381 3834 17 7 13 3
## 144 953 984 2505 15 4 15 1
## 145 642 1533 2243 12 10 6 2
## 146 1417 1464 3600 17 9 7 3
## 147 247 539 3971 18 12 10 3
## 148 710 1179 2844 7 5 18 2
## 149 718 751 2227 18 10 3 2
## 150 1100 1497 1665 17 9 12 2
## 151 59 1215 3355 15 2 4 3
## 152 286 1235 1046 5 4 13 0
## 153 1273 1345 1441 7 4 13 1
## 154 295 589 690 14 6 6 0
## 155 852 1182 2504 6 4 15 2
## 156 710 1052 2677 19 15 8 2
## 157 1277 1429 2243 11 7 4 2
## 158 129 873 2984 13 4 17 2
## 159 690 836 1362 5 0 12 1
## 160 701 1478 3629 19 15 6 3
## 161 309 1460 3559 18 17 6 3
## 162 1112 1666 2107 15 9 10 2
## 163 55 1020 2844 14 9 10 2
## 164 1052 1516 3117 18 5 8 3
## 165 541 1499 978 5 3 7 0
## 166 809 1958 1179 16 7 20 1
## 167 1738 1995 3844 11 8 4 3
## 168 404 642 593 19 17 10 0
## 169 759 1404 3836 12 11 11 3
## 170 42 1040 2669 12 10 13 2
## 171 233 517 3388 6 4 16 3
## 172 1022 1560 2377 18 8 13 2
## 173 411 1226 2458 19 16 3 2
## 174 954 1200 2766 7 2 7 3
## 175 638 1615 625 17 6 13 0
## 176 148 1606 707 19 8 19 0
## 177 730 1543 1457 18 12 20 1
## 178 1290 1441 1733 9 0 11 1
## 179 632 1383 720 12 2 11 0
## 180 529 1009 3560 11 1 16 2
## 181 1273 1754 2311 7 6 7 2
## 182 667 860 3846 10 1 3 3
## 183 1170 1543 1717 7 1 16 2
## 184 169 1108 3704 8 6 11 3
## 185 105 673 3800 8 3 14 3
## 186 793 1758 278 16 3 11 0
## 187 490 886 1767 18 15 11 1
## 188 246 1038 829 9 0 10 0
## 189 417 946 2986 7 1 6 2
## 190 1177 1462 2394 17 9 7 2
## 191 78 654 2169 6 5 7 1
## 192 323 1604 1181 13 5 15 0
## 193 707 1882 575 15 9 12 0
## 194 457 1816 2977 6 4 2 2
## 195 356 765 532 12 8 10 0
## 196 936 1398 1702 17 0 5 1
## 197 912 1980 1074 14 8 15 0
## 198 179 1559 3352 9 1 3 3
## 199 156 952 461 7 3 8 0
## 200 865 1741 3271 14 0 15 3
## 201 1552 1596 606 9 3 4 1
## 202 546 1564 2513 10 8 16 3
## 203 1097 1222 1675 18 4 14 1
## 204 372 692 2698 9 6 17 2
## 205 1242 1571 468 19 13 6 0
## 206 594 1767 3086 17 6 5 3
## 207 303 714 1595 8 4 9 1
## 208 1858 1935 2332 17 13 14 2
## 209 403 1933 2589 19 11 15 2
## 210 590 954 1851 8 2 9 1
## 211 235 662 417 8 4 9 0
## 212 630 888 294 13 9 11 0
## 213 357 527 3576 17 16 9 3
## 214 1054 1929 2044 15 5 2 1
## 215 607 1092 1477 12 0 6 1
## 216 104 1664 2211 7 4 18 1
## 217 637 763 3822 14 3 18 3
## 218 410 572 3922 11 1 8 3
## 219 1830 1963 1203 14 12 18 2
## 220 802 1489 2822 15 5 11 3
## 221 311 1294 447 15 13 12 0
## 222 56 997 1652 5 0 20 1
## 223 1053 1458 1228 8 6 6 1
## 224 221 1243 666 19 10 5 0
## 225 506 519 1499 12 8 19 1
## 226 1728 1767 3321 5 4 14 3
## 227 63 566 1206 7 1 8 0
## 228 418 515 952 19 3 16 0
## 229 854 1168 3927 5 3 15 3
## 230 178 882 311 18 5 19 0
## 231 1869 1942 955 18 11 7 1
## 232 185 776 1109 13 1 19 1
## 233 170 564 2969 8 6 17 2
## 234 454 1975 725 11 10 19 0
## 235 866 1212 1655 7 6 15 2
## 236 44 1118 3490 16 12 13 3
## 237 1285 1462 3615 13 0 6 3
## 238 741 1130 714 18 2 13 0
## 239 520 754 2768 19 2 12 2
## 240 114 1726 3739 14 1 13 3
## 241 1153 1876 610 8 1 6 1
## 242 39 557 663 13 12 7 0
## 243 584 775 834 16 0 5 0
## 244 207 1370 651 9 6 7 0
## 245 363 994 796 13 3 14 0
## 246 209 1078 785 10 7 5 0
## 247 649 1104 1322 15 11 7 1
## 248 1001 1224 2125 17 15 15 1
## 249 712 1442 990 6 1 2 1
## 250 418 1023 1595 14 0 12 1
## 251 384 1361 1213 5 0 16 1
## 252 119 652 2500 17 1 11 2
## 253 195 1292 1201 14 12 4 0
## 254 14 1256 3139 14 7 14 2
## 255 745 1364 1938 15 11 16 2
## 256 253 1786 3185 8 4 15 2
## 257 333 1429 2111 18 7 2 1
## 258 629 682 2369 7 0 18 2
## 259 707 1199 3625 17 1 12 3
## 260 1314 1688 1051 13 10 15 0
## 261 13 1463 2855 5 0 19 2
## 262 1243 1584 2727 13 3 8 2
## 263 300 1059 2973 12 5 18 2
## 264 58 1253 2581 15 11 15 1
## 265 134 939 916 13 10 12 0
## 266 275 986 2563 19 17 19 2
## 267 1236 1280 941 8 4 9 1
## 268 336 898 2280 17 14 4 2
## 269 1116 1381 827 15 9 6 0
## 270 1010 1565 473 11 1 20 0
## 271 205 1299 3865 10 7 11 3
## 272 1453 1726 1274 18 7 3 1
## 273 788 1331 364 11 1 7 0
## 274 1301 1922 3699 17 11 13 3
## 275 463 1038 2927 14 13 11 2
## 276 188 992 1470 13 0 14 0
## 277 1028 1186 1050 6 4 8 0
## 278 1419 1920 2616 18 8 10 2
## 279 1471 1866 2915 8 2 10 3
## 280 168 981 2686 11 7 18 2
## 281 130 1706 3264 16 1 13 3
## 282 481 749 2261 7 6 16 1
## 283 1411 1711 2993 5 1 20 3
## 284 79 681 706 12 9 16 0
## 285 1187 1974 2542 10 1 8 3
## 286 561 1510 2523 17 16 9 2
## 287 96 814 336 13 11 4 0
## 288 77 1025 2457 11 7 17 1
## 289 964 1233 1246 8 1 2 1
## 290 634 642 2971 12 8 12 2
## 291 127 1670 3121 18 2 16 2
## 292 431 550 3801 10 6 2 2
## 293 71 699 3333 17 15 7 3
## 294 111 1138 3475 19 3 3 3
## 295 356 1903 2360 17 16 18 1
## 296 408 1139 3097 5 0 18 3
## 297 373 926 2101 10 7 2 1
## 298 1362 1506 3918 9 2 8 3
## 299 475 726 3869 19 18 10 3
## 300 743 1753 732 14 1 14 1
## 301 447 794 3684 9 7 18 3
## 302 42 1161 3969 12 8 6 3
## 303 30 1350 1414 12 1 7 0
## 304 162 619 1892 10 0 15 1
## 305 347 660 3131 8 3 7 3
## 306 346 1468 2532 10 5 4 2
## 307 727 1705 3215 18 0 20 3
## 308 180 1476 3323 13 12 6 3
## 309 1641 1666 1529 13 8 3 2
## 310 269 1026 2403 9 2 9 2
## 311 1186 1529 3648 9 0 16 3
## 312 77 559 1974 12 10 12 0
## 313 379 1223 2239 13 9 13 2
## 314 264 956 582 15 7 4 0
## 315 1486 1797 3890 17 10 10 3
## 316 694 882 1141 5 2 6 1
## 317 495 574 3838 9 2 7 3
## 318 329 1053 2330 19 5 20 1
## 319 531 758 1851 15 13 2 1
## 320 191 1495 808 11 7 5 0
## 321 96 1352 2492 16 14 10 2
## 322 841 1304 2122 15 1 2 2
## 323 195 1205 1122 12 6 16 0
## 324 21 562 3709 14 2 10 2
## 325 1417 1441 624 9 3 19 0
## 326 286 1075 3937 8 1 7 3
## 327 966 1975 1458 5 1 15 1
## 328 1172 1798 3348 18 16 16 3
## 329 270 545 1834 5 2 20 1
## 330 781 1657 2775 13 8 14 3
## 331 418 1341 1277 13 6 19 1
## 332 126 817 457 17 10 16 0
## 333 228 1058 438 17 0 2 0
## 334 1091 1203 3210 15 7 2 3
## 335 674 740 3701 15 8 8 3
## 336 398 1133 1052 8 1 3 0
## 337 449 889 1175 16 0 8 0
## 338 1792 1850 1175 12 9 6 1
## 339 177 1905 301 13 5 13 0
## 340 1013 1158 489 15 11 8 0
## 341 1058 1325 1733 5 3 8 2
## 342 592 772 2756 11 3 4 2
## 343 810 1743 2612 8 2 16 2
## 344 533 882 1724 5 1 14 1
## 345 558 1208 2190 16 12 4 1
## 346 1017 1366 3915 9 8 2 3
## 347 765 899 1043 6 2 11 0
## 348 1176 1224 3817 8 7 9 3
## 349 947 1131 2362 17 2 19 2
## 350 1314 1884 1107 7 3 5 1
## 351 609 1370 2982 14 9 12 3
## 352 754 1497 2042 17 15 19 1
## 353 619 867 1343 5 1 9 0
## 354 453 681 2610 12 3 10 2
## 355 205 884 2335 15 7 7 2
## 356 130 998 1138 9 8 10 0
## 357 494 1989 604 16 14 11 0
## 358 275 862 3652 14 10 9 3
## 359 447 1785 3955 19 4 18 3
## 360 6 793 1066 11 1 16 0
## 361 292 695 1731 9 5 6 1
## 362 164 728 3860 7 5 16 3
## 363 631 1940 3630 9 7 17 3
## 364 897 1304 1965 6 2 6 1
## 365 268 1078 1459 6 1 19 0
## 366 331 790 3291 11 2 13 2
## 367 509 548 918 9 1 12 0
## 368 622 1256 3488 16 2 3 3
## 369 957 1911 3961 16 14 14 3
## 370 494 1649 590 11 3 6 0
## 371 236 1264 3701 15 9 8 3
## 372 980 1262 463 14 6 11 0
## 373 1789 1972 3644 15 10 6 3
## 374 831 1484 1713 16 4 10 1
## 375 902 1090 429 9 4 14 0
## 376 636 1259 2765 13 12 10 2
## 377 831 1439 2084 7 1 18 2
## 378 233 1777 2048 6 0 18 2
## 379 725 870 1223 8 0 18 0
## 380 157 948 3100 17 5 13 2
## 381 1142 1393 343 11 8 18 0
## 382 840 982 1970 16 8 15 1
## 383 395 1579 3635 7 4 18 3
## 384 48 644 2180 7 4 16 1
## 385 982 1157 2488 6 1 14 2
## 386 478 539 3864 13 3 11 3
## 387 581 750 1155 7 1 20 0
## 388 594 1337 864 9 8 2 1
## 389 683 1663 869 19 13 9 0
## 390 536 861 591 9 2 9 0
## 391 83 773 1333 15 6 11 1
## 392 249 1849 2610 18 14 15 2
## 393 803 1613 2819 19 17 9 2
## 394 459 1259 1955 9 4 15 2
## 395 584 1151 3416 14 6 8 3
## 396 1596 1807 3803 7 3 16 3
## 397 418 1763 574 5 3 19 0
## 398 364 1029 2129 13 9 12 1
## 399 946 952 1948 13 1 18 1
## 400 186 654 2978 18 7 18 2
## 401 1428 1827 1619 7 1 10 1
## 402 1613 1916 3716 15 13 11 3
## 403 691 1580 3777 7 2 13 3
## 404 1802 1942 2712 7 2 6 3
## 405 859 1234 3968 16 12 3 3
## 406 187 517 3383 16 5 2 3
## 407 1673 1759 3970 16 8 18 3
## 408 710 939 2674 14 8 10 2
## 409 1331 1463 2776 19 17 14 3
## 410 263 848 2336 10 4 7 2
## 411 97 1803 2430 7 4 6 2
## 412 129 915 891 18 3 9 0
## 413 189 1143 3886 14 10 7 3
## 414 628 632 1711 7 3 9 1
## 415 320 899 2856 14 3 20 2
## 416 837 1642 2447 16 2 3 2
## 417 1087 1450 814 11 7 19 0
## 418 781 1463 2951 7 0 10 2
## 419 251 1013 3653 7 1 15 2
## 420 262 1587 1891 18 3 12 1
## 421 767 1665 701 17 15 15 0
## 422 1226 1815 3139 6 1 12 3
## 423 389 739 2811 7 6 7 1
## 424 920 963 1701 11 4 4 1
## 425 1027 1508 1853 17 14 12 1
## 426 271 1000 2445 6 1 6 1
## 427 369 544 2287 15 1 8 1
## 428 1258 1627 2003 8 7 14 2
## 429 417 759 3595 7 5 19 2
## 430 613 1088 3952 8 6 3 3
## 431 912 1596 850 5 1 4 1
## 432 666 760 1446 17 2 8 0
## 433 236 857 1300 8 7 18 1
## 434 186 1100 719 14 10 10 0
## 435 168 1239 2912 11 10 15 2
## 436 159 1578 3173 9 4 17 3
## 437 120 1878 1667 17 6 17 1
## 438 600 1109 1214 9 8 15 1
## 439 286 1171 3269 8 7 10 2
## 440 1350 1949 445 15 14 14 0
## 441 27 1722 1073 6 4 8 0
## 442 1597 1920 665 18 5 5 1
## 443 402 964 3501 13 11 3 3
## 444 577 1237 3461 6 3 10 3
## 445 52 1082 3206 12 6 16 3
## 446 364 828 3717 11 0 10 3
## 447 250 545 861 7 4 6 0
## 448 214 1710 2885 19 7 14 2
## 449 612 1793 2777 9 2 14 2
## 450 1111 1463 1017 12 7 9 0
## 451 202 1951 2775 14 4 17 3
## 452 729 1704 3309 15 3 3 3
## 453 361 809 1257 16 10 16 0
## 454 338 1306 759 9 3 14 0
## 455 1052 1536 475 7 6 6 0
## 456 951 1545 1336 8 5 15 1
## 457 1197 1987 1185 11 2 20 2
## 458 473 1022 705 11 3 6 0
## 459 1294 1337 3169 14 9 6 2
## 460 410 522 3483 10 1 15 3
## 461 178 1883 1183 14 3 17 1
## 462 434 910 2801 6 1 5 2
## 463 464 781 565 18 12 9 0
## 464 492 1176 3421 16 2 5 2
## 465 1250 1285 316 15 14 17 0
## 466 427 684 2366 16 8 2 1
## 467 1024 1838 3881 14 10 9 3
## 468 886 1552 1480 6 1 17 1
## 469 183 559 2711 8 2 3 2
## 470 88 1682 2144 13 12 12 2
## 471 1016 1383 1165 19 6 4 0
## 472 80 655 2998 5 4 13 2
## 473 611 1715 2249 14 3 4 2
## 474 304 637 1409 16 5 3 1
## 475 276 1230 3072 7 3 5 3
## 476 1131 1315 1305 12 8 3 0
## 477 551 599 3597 14 5 9 2
## 478 436 1302 3132 8 7 6 2
## 479 553 727 3856 18 4 18 3
## 480 1391 1787 1524 15 10 7 1
## 481 981 1096 3673 12 1 20 3
## 482 680 1173 3293 8 4 7 2
## 483 687 816 3035 12 7 19 2
## 484 1060 1970 584 17 14 13 1
## 485 217 1101 2675 12 11 15 2
## 486 1140 1654 2382 9 0 19 2
## 487 304 1191 1391 19 13 12 1
## 488 504 1089 2346 13 12 2 1
## 489 832 1033 606 6 0 18 0
## 490 446 901 1076 16 8 4 0
## 491 942 1701 643 17 1 8 1
## 492 897 1308 783 10 8 20 0
## 493 1208 1232 3809 17 16 5 3
## 494 159 630 2104 7 4 16 1
## 495 632 1184 1762 5 3 6 1
## 496 1308 1991 3137 12 1 11 3
## 497 1002 1256 1756 6 3 17 1
## 498 150 510 2437 14 0 9 2
## 499 1248 1511 1432 12 9 9 1
## 500 108 1813 1620 17 15 12 1
## 501 320 747 1655 18 0 9 0
## 502 437 1947 2832 5 0 8 2
## 503 1058 1421 2608 6 0 2 2
## 504 79 1652 514 7 4 13 0
## 505 593 672 2304 7 1 18 2
## 506 693 1031 933 14 0 5 0
## 507 300 527 1896 14 10 20 1
## 508 234 739 3655 7 3 20 3
## 509 1545 1596 1973 12 9 16 1
## 510 59 575 3278 6 3 13 2
## 511 1103 1364 2183 8 3 11 2
## 512 91 1478 824 15 5 13 0
## 513 177 1282 2598 13 5 8 1
## 514 58 1353 1254 15 9 9 0
## 515 447 1435 3622 7 0 20 3
## 516 942 1651 1704 17 13 2 2
## 517 88 516 1624 15 13 9 1
## 518 371 1000 3809 17 12 17 3
## 519 1446 1754 2574 10 1 5 2
## 520 181 1703 950 17 9 9 0
## 521 1791 1898 446 16 13 15 1
## 522 706 721 1122 12 9 2 0
## 523 871 1048 707 19 2 5 0
## 524 1074 1873 751 7 3 3 0
## 525 501 1465 3393 12 7 8 2
## 526 271 1477 3771 15 6 12 3
## 527 920 1748 1851 15 3 16 1
## 528 1171 1383 2735 7 2 9 2
## 529 833 1630 452 13 6 14 0
## 530 485 857 1419 12 11 18 0
## 531 182 1293 969 15 1 7 0
## 532 207 1162 3441 19 1 5 2
## 533 1698 1747 3786 17 12 12 3
## 534 165 736 2710 13 1 8 2
## 535 1086 1418 3533 16 14 15 3
## 536 192 757 1735 7 0 13 0
## 537 1156 1750 3587 14 11 20 3
## 538 377 549 2298 17 5 2 2
## 539 729 974 2406 13 10 20 2
## 540 543 938 2419 8 7 4 1
## 541 553 1505 995 7 6 18 0
## 542 526 1116 2078 5 3 11 2
## 543 637 991 1145 19 1 7 0
## 544 113 577 626 10 4 4 0
## 545 304 1674 3508 13 8 5 3
## 546 471 800 2385 16 9 19 1
## 547 806 1016 1301 13 12 19 1
## 548 80 1327 2167 11 10 7 1
## 549 204 501 3914 10 3 8 3
## 550 545 1621 2312 9 1 18 2
## 551 112 858 2297 14 6 8 2
## 552 1331 1532 411 8 2 2 0
## 553 1055 1294 665 10 0 4 0
## 554 290 811 740 11 10 18 0
## 555 1039 1318 3878 19 16 4 3
## 556 1485 1655 3406 5 1 11 3
## 557 1122 1702 2440 9 5 10 2
## 558 462 1013 3959 10 4 17 3
## 559 1502 1862 3714 19 3 10 3
## 560 589 1301 337 16 3 5 0
## 561 82 1605 3176 6 2 2 2
## 562 68 718 1205 18 7 14 1
## 563 689 699 2473 16 11 2 2
## 564 1339 1558 2896 12 11 16 3
## 565 722 766 2039 11 6 19 1
## 566 717 1799 520 10 2 18 0
## 567 433 1658 1378 18 15 4 1
## 568 186 1998 1803 5 4 7 1
## 569 1397 1491 2501 16 12 4 3
## 570 1214 1262 1796 16 6 13 1
## 571 613 650 990 14 11 6 0
## 572 649 907 2630 18 8 9 1
## 573 22 519 3487 15 6 11 2
## 574 1464 1595 1201 12 3 18 1
## 575 280 895 3015 6 1 15 3
## 576 39 1564 433 10 7 6 0
## 577 337 1384 1906 17 13 13 2
## 578 1036 1611 898 16 8 13 0
## 579 1347 1733 2799 5 3 15 3
## 580 22 1634 2020 6 0 16 2
## 581 327 922 770 12 1 20 0
## 582 778 1291 305 11 8 16 0
## 583 482 874 2953 9 5 13 2
## 584 229 1604 2574 12 4 14 2
## 585 901 1162 3772 17 8 18 3
## 586 715 1648 2870 16 0 17 3
## 587 681 723 2246 8 1 3 1
## 588 867 999 2405 15 0 16 2
## 589 286 1300 3377 5 1 6 2
## 590 451 819 3619 14 8 7 3
## 591 1119 1238 1184 6 2 2 1
## 592 526 643 2965 15 7 14 2
## 593 1299 1633 3713 8 3 8 3
## 594 217 735 3798 19 10 8 3
## 595 1109 1226 1026 13 5 20 1
## 596 434 1339 1086 18 14 13 1
## 597 671 1445 2236 8 7 12 2
## 598 1404 1424 1303 12 3 20 1
## 599 856 883 3048 10 3 2 2
## 600 381 1452 616 15 6 19 0
## 601 85 806 3764 10 7 4 2
## 602 562 849 419 7 4 3 0
## 603 1111 1812 2858 6 2 18 3
## 604 209 947 504 14 4 10 0
## 605 997 1605 392 10 5 8 0
## 606 91 1916 820 12 6 11 0
## 607 730 1895 3387 11 3 3 3
## 608 606 1123 3447 14 11 19 3
## 609 445 1409 1403 15 8 2 0
## 610 286 1484 2295 15 11 16 2
## 611 985 1005 1973 10 2 8 2
## 612 1617 1675 3755 17 14 8 3
## 613 375 1724 2073 7 1 10 1
## 614 1795 1958 3872 9 8 11 3
## 615 486 1930 302 18 5 8 0
## 616 757 1518 1519 18 6 8 1
## 617 273 1704 2549 10 8 4 2
## 618 491 589 404 18 5 19 0
## 619 762 859 595 18 4 15 0
## 620 448 821 1663 14 0 17 0
## 621 231 1671 2764 9 3 7 3
## 622 1713 1865 2614 7 0 14 2
## 623 438 822 1094 10 5 15 0
## 624 68 1018 2826 18 0 2 2
## 625 510 522 2103 19 8 7 1
## 626 965 1009 966 5 0 13 0
## 627 532 1448 905 19 10 12 0
## 628 860 1330 3914 17 13 13 3
## 629 461 670 363 9 5 17 0
## 630 109 1909 3233 13 3 4 3
## 631 193 1185 2532 16 14 15 2
## 632 961 1008 3836 15 3 14 3
## 633 243 1642 3076 7 4 18 2
## 634 1626 1668 1513 11 8 7 2
## 635 480 1482 1724 11 2 19 1
## 636 123 1229 542 11 1 14 0
## 637 400 823 3481 10 8 8 3
## 638 190 1326 3991 15 2 10 3
## 639 626 932 1790 19 12 15 1
## 640 1 926 509 19 1 3 0
## 641 548 1641 282 10 9 14 0
## 642 632 660 3006 6 4 3 2
## 643 349 1051 1913 12 7 6 1
## 644 1686 1889 1333 18 0 2 2
## 645 1455 1982 3746 10 0 5 3
## 646 580 1163 3970 13 1 11 3
## 647 297 589 3925 7 6 7 3
## 648 951 962 424 5 3 15 0
## 649 667 697 1284 17 13 2 0
## 650 1047 1312 277 11 2 12 0
## 651 408 1862 1713 10 3 4 1
## 652 1150 1469 485 12 7 7 0
## 653 333 952 3002 6 5 7 2
## 654 653 671 1652 17 4 14 1
## 655 103 1583 2893 8 5 9 2
## 656 517 809 1406 14 12 20 0
## 657 785 1151 629 5 3 11 0
## 658 531 791 3724 16 15 20 3
## 659 564 1391 3835 15 8 11 3
## 660 761 1336 1354 15 13 13 1
## 661 1179 1638 2376 5 1 19 3
## 662 673 690 3438 17 12 13 3
## 663 412 643 3361 16 10 13 3
## 664 977 1660 3535 17 0 16 3
## 665 359 1395 2150 9 5 7 1
## 666 714 1208 2736 9 1 8 3
## 667 195 799 676 15 0 4 0
## 668 843 1145 2941 13 12 17 3
## 669 308 1169 3336 6 4 14 3
## 670 364 1360 3654 18 8 15 3
## 671 518 1152 2889 5 2 11 2
## 672 519 1577 3315 17 7 14 3
## 673 792 1181 1993 9 0 5 2
## 674 382 1228 2509 17 2 11 1
## 675 214 1523 3534 18 17 11 3
## 676 859 867 2246 14 11 15 2
## 677 713 1513 286 14 4 17 0
## 678 172 812 776 7 6 8 0
## 679 18 662 424 10 5 4 0
## 680 987 1081 3486 12 8 18 3
## 681 535 697 3237 19 12 9 2
## 682 1092 1406 1944 16 6 12 1
## 683 327 1001 643 14 2 19 0
## 684 517 1995 2317 6 0 12 2
## 685 994 1500 1436 16 1 15 1
## 686 268 832 2338 16 1 15 2
## 687 210 1026 1334 18 7 2 1
## 688 144 672 3657 17 3 19 2
## 689 729 1267 2080 16 11 12 2
## 690 82 670 1609 16 11 3 0
## 691 188 928 3078 10 1 18 2
## 692 150 1897 590 13 2 6 0
## 693 331 1011 3355 12 5 13 2
## 694 1178 1929 1968 12 7 9 2
## 695 347 1076 3358 7 3 19 3
## 696 492 1403 942 16 12 14 0
## 697 65 734 3669 9 1 10 3
## 698 881 1234 3612 13 5 4 3
## 699 1842 1968 604 10 4 8 0
## 700 758 1165 1285 5 0 9 1
## 701 647 1876 3869 17 5 14 3
## 702 1102 1793 3412 13 8 16 3
## 703 319 1367 509 12 3 19 0
## 704 215 1125 2547 10 3 5 2
## 705 381 1203 1369 9 6 6 1
## 706 546 828 2942 11 5 16 2
## 707 575 618 3497 9 7 11 2
## 708 342 670 3483 17 4 16 3
## 709 291 563 1475 11 4 7 1
## 710 352 1084 3592 7 6 6 3
## 711 165 1723 3573 17 3 20 3
## 712 385 831 2020 6 0 18 1
## 713 1920 1933 1329 6 1 7 2
## 714 396 1980 3568 6 2 18 3
## 715 1134 1469 1947 5 4 6 2
## 716 721 1514 1998 12 7 15 2
## 717 687 937 725 11 3 20 0
## 718 777 1119 3302 11 0 20 3
## 719 176 1556 3884 17 0 20 3
## 720 458 1263 1185 15 4 6 0
## 721 485 1819 1656 5 3 17 2
## 722 641 1638 2870 19 16 10 2
## 723 798 1435 911 14 2 9 0
## 724 256 1394 3892 18 7 19 3
## 725 370 1197 3897 12 10 10 3
## 726 191 1108 656 14 10 18 0
## 727 1571 1956 606 7 2 2 1
## 728 276 1026 1653 11 7 17 1
## 729 573 614 860 14 1 14 0
## 730 160 516 2197 16 2 9 1
## 731 90 1334 3458 7 5 11 2
## 732 138 806 2066 12 9 17 1
## 733 275 687 1518 16 11 8 1
## 734 42 507 1360 15 12 11 0
## 735 793 1670 3449 5 2 14 3
## 736 625 765 2524 13 11 7 2
## 737 521 1190 703 10 8 7 0
## 738 1530 1744 1251 13 4 18 1
## 739 442 1248 582 6 4 15 0
## 740 152 714 1732 8 5 13 1
## 741 1242 1973 1252 6 4 19 2
## 742 29 1670 2146 11 10 2 1
## 743 501 506 1927 17 4 6 1
## 744 648 1019 2462 15 7 17 2
## 745 521 674 1172 12 11 17 0
## 746 985 1420 2437 19 2 2 2
## 747 341 774 3271 14 12 8 3
## 748 751 1377 1511 17 8 9 1
## 749 993 1517 2981 7 3 14 2
## 750 43 1663 3119 9 4 15 3
## 751 361 511 3148 18 7 6 2
## 752 618 940 2373 11 9 20 2
## 753 815 1666 3998 6 3 13 3
## 754 138 1330 999 16 12 9 0
## 755 584 983 1808 12 8 13 1
## 756 750 772 589 18 6 4 0
## 757 820 1366 3317 8 0 19 3
## 758 8 1219 670 16 3 8 0
## 759 171 507 584 11 9 6 0
## 760 960 1735 2173 12 3 8 2
## 761 160 1291 1561 10 5 17 1
## 762 612 719 2073 16 7 9 1
## 763 709 1915 1401 19 14 11 1
## 764 538 730 1275 13 7 12 0
## 765 1295 1808 1464 17 4 12 1
## 766 1164 1908 2885 13 11 5 3
## 767 829 1977 3458 9 4 9 3
## 768 199 1452 3019 11 6 19 3
## 769 519 930 3538 18 1 15 3
## 770 169 1190 2493 14 12 13 2
## 771 1734 1968 3726 8 5 15 3
## 772 76 1165 1531 12 0 7 1
## 773 819 1997 2991 8 7 4 2
## 774 387 671 3912 11 2 19 3
## 775 970 974 323 9 3 3 0
## 776 767 1759 1489 10 9 3 1
## 777 512 1465 1322 9 2 15 1
## 778 56 760 1846 7 0 3 1
## 779 590 661 417 8 0 6 0
## 780 462 1304 3269 19 6 8 3
## 781 126 698 3731 17 13 15 2
## 782 655 1782 1241 12 3 9 1
## 783 88 1261 1018 17 1 16 0
## 784 1692 1902 3142 19 3 17 3
## 785 358 1782 1444 7 4 20 1
## 786 64 1667 934 16 10 9 0
## 787 259 1040 3863 17 12 9 3
## 788 236 1960 3506 14 5 12 3
## 789 237 890 971 11 10 10 0
## 790 650 864 3984 17 14 8 3
## 791 541 823 2019 10 4 16 1
## 792 361 1552 1587 9 4 19 1
## 793 335 1463 1870 17 2 18 1
## 794 1638 1910 1352 5 1 11 1
## 795 1109 1392 570 7 6 19 0
## 796 1285 1427 3624 12 11 16 3
## 797 322 875 1209 19 12 12 0
## 798 1220 1348 2752 15 2 7 3
## 799 1081 1767 347 17 0 12 0
## 800 297 653 3227 12 6 8 2
## 801 1512 1716 3299 13 4 16 3
## 802 275 570 1028 8 4 17 0
## 803 1136 1813 714 18 2 2 1
## 804 1145 1356 2641 8 2 7 3
## 805 477 1553 1229 12 0 14 1
## 806 741 1471 929 13 6 18 0
## 807 547 705 1211 15 10 7 0
## 808 146 906 511 9 0 7 0
## 809 471 663 3034 13 6 8 2
## 810 1895 1976 2438 13 11 10 3
## 811 1537 1761 3744 11 1 7 3
## 812 236 1520 1377 5 3 15 1
## 813 466 1768 3796 17 10 12 3
## 814 400 631 336 14 8 13 0
## 815 284 519 325 9 1 6 0
## 816 465 1152 1223 19 12 7 0
## 817 705 1810 1649 6 1 14 1
## 818 38 1682 1898 12 4 3 1
## 819 51 739 2609 12 11 19 2
## 820 850 1005 2265 5 3 20 2
## 821 349 676 2775 9 4 3 2
## 822 387 1595 1445 17 16 12 1
## 823 706 1623 2614 5 4 15 2
## 824 426 1964 3745 17 1 4 3
## 825 653 1413 1229 6 0 3 0
## 826 1578 1963 1402 14 10 3 1
## 827 280 1795 2085 13 5 8 1
## 828 1432 1649 2030 7 0 6 2
## 829 477 571 816 16 2 3 0
## 830 1122 1746 1667 10 0 12 1
## 831 145 1903 2958 17 1 19 2
## 832 416 1055 3705 6 4 13 3
## 833 847 893 2800 11 6 16 2
## 834 868 877 3773 11 9 6 3
## 835 594 1088 2050 7 2 17 1
## 836 518 830 1754 17 11 9 1
## 837 663 1805 1904 12 8 13 1
## 838 371 541 2705 17 3 10 1
## 839 1749 1781 3537 16 13 3 3
## 840 409 646 1222 13 2 7 1
## 841 1008 1758 2268 7 5 4 2
## 842 442 1079 2648 7 5 18 1
## 843 983 1087 2173 7 3 19 2
## 844 875 982 3162 19 18 3 3
## 845 952 1191 1368 14 5 17 1
## 846 244 1361 2746 10 4 7 2
## 847 65 1055 619 7 4 8 0
## 848 1749 1767 724 17 3 9 1
## 849 716 1626 912 16 9 6 0
## 850 134 521 793 7 3 17 0
## 851 804 1564 312 5 2 8 0
## 852 787 1515 545 12 0 9 0
## 853 704 822 1591 19 0 2 1
## 854 991 1638 2746 15 5 10 3
## 855 379 932 893 9 6 13 0
## 856 489 710 2514 16 12 7 1
## 857 1518 1883 1032 10 0 8 1
## 858 855 1401 819 8 0 10 0
## 859 115 1791 3833 14 13 17 3
## 860 1203 1432 1482 15 7 17 2
## 861 545 1300 2043 7 5 14 1
## 862 818 1090 1229 16 11 2 0
## 863 42 1262 1816 17 16 15 1
## 864 1103 1637 2166 10 1 4 2
## 865 168 1068 2469 18 6 7 2
## 866 719 755 2575 13 10 7 2
## 867 163 874 1375 15 3 19 1
## 868 329 656 2301 15 9 8 1
## 869 330 1434 2676 17 8 7 2
## 870 956 1292 1781 18 3 17 2
## 871 271 869 391 5 2 8 0
## 872 444 1555 1719 19 3 18 1
## 873 1003 1827 3262 16 15 16 3
## 874 227 772 1947 15 13 15 1
## 875 253 1764 2693 8 3 7 3
## 876 88 1046 441 15 1 11 0
## 877 334 594 1886 19 11 9 0
## 878 1281 1896 1267 17 4 20 1
## 879 168 1320 687 6 0 20 0
## 880 898 1687 2890 17 1 12 3
## 881 46 1069 1737 8 5 10 1
## 882 118 1170 887 18 6 5 0
## 883 1191 1347 2389 7 3 15 2
## 884 526 1324 3431 15 6 13 3
## 885 346 966 3601 14 4 16 3
## 886 137 846 3230 6 1 9 3
## 887 1226 1389 3646 15 4 11 3
## 888 473 574 2200 18 14 15 1
## 889 368 607 3862 17 10 9 3
## 890 622 709 621 11 4 14 0
## 891 654 882 2372 14 12 2 2
## 892 853 1781 2574 14 3 5 2
## 893 686 1305 2857 17 14 2 3
## 894 992 1433 3143 17 12 20 3
## 895 115 1352 3197 14 9 6 3
## 896 275 1966 2727 15 8 11 3
## 897 461 1178 1519 9 4 5 1
## 898 1428 1500 2438 14 2 17 2
## 899 614 1212 2674 9 3 9 2
## 900 1105 1602 2262 16 15 4 2
## 901 398 1263 815 16 6 10 0
## 902 775 1607 3022 13 5 19 3
## 903 1482 1490 2573 16 12 18 2
## 904 154 550 2678 16 5 13 2
## 905 454 1762 1282 11 9 11 1
## 906 737 1455 568 12 11 8 0
## 907 853 1737 2227 9 2 14 1
## 908 1211 1229 1080 10 0 6 0
## 909 527 734 3063 17 13 4 2
## 910 874 1264 2479 9 2 15 2
## 911 674 864 2394 19 11 14 2
## 912 657 1014 2814 13 3 19 2
## 913 475 500 2094 8 0 10 1
## 914 1209 1411 2315 10 1 2 2
## 915 15 525 2519 13 0 16 2
## 916 293 1967 735 7 6 14 0
## 917 813 1377 1891 19 13 7 1
## 918 173 1843 1386 7 1 8 1
## 919 457 652 3260 14 12 6 2
## 920 533 797 2022 14 11 4 1
## 921 912 1043 1716 7 5 15 1
## 922 798 1418 3317 7 1 8 3
## 923 412 565 1454 11 3 8 0
## 924 130 887 2290 13 0 12 1
## 925 201 656 3940 17 11 20 3
## 926 299 874 461 17 4 4 0
## 927 1277 1352 2219 15 6 12 2
## 928 626 1195 470 6 0 16 0
## 929 378 751 2495 10 0 9 2
## 930 333 639 2944 8 7 2 2
## 931 1901 1920 1783 17 3 13 2
## 932 1573 1581 2832 13 7 20 2
## 933 360 912 931 11 5 20 0
## 934 20 745 1344 17 10 5 0
## 935 98 511 3916 13 1 5 2
## 936 703 1011 1882 14 1 14 1
## 937 273 1278 2865 11 6 9 2
## 938 919 1344 757 12 2 16 0
## 939 1287 1892 3300 12 10 20 3
## 940 1109 1247 2719 19 2 9 2
## 941 211 684 3012 14 4 15 2
## 942 532 1247 2385 11 7 16 2
## 943 890 1019 2968 9 0 3 3
## 944 327 645 3762 12 10 5 3
## 945 1260 1788 892 15 13 20 1
## 946 570 1422 467 17 11 5 0
## 947 621 1558 345 10 6 14 0
## 948 328 1858 3033 9 8 17 3
## 949 1307 1767 2110 9 5 16 2
## 950 497 618 3105 14 8 5 2
## 951 1163 1554 3681 19 3 15 3
## 952 24 759 3865 8 3 7 2
## 953 468 571 737 14 4 11 0
## 954 503 551 2800 8 6 12 2
## 955 1495 1579 315 14 5 11 0
## 956 1333 1838 1412 13 1 20 1
## 957 206 620 1419 10 6 16 0
## 958 249 522 2635 17 16 4 2
## 959 935 1499 1754 17 7 9 1
## 960 140 768 2563 18 4 14 2
## 961 800 1089 3637 11 7 16 3
## 962 725 1335 774 15 4 6 1
## 963 81 1544 3397 7 6 2 2
## 964 1211 1378 485 10 6 4 0
## 965 1362 1378 3964 13 8 12 3
## 966 1043 1702 2977 6 1 20 2
## 967 444 739 2114 10 5 19 2
## 968 311 1796 1542 17 9 15 1
## 969 812 1456 3038 15 7 18 3
## 970 202 1884 2481 11 9 11 2
## 971 531 707 841 18 15 16 0
## 972 404 898 3565 12 11 8 2
## 973 956 1723 3392 12 8 5 3
## 974 1438 1593 262 14 4 20 0
## 975 1446 1988 530 8 4 8 0
## 976 692 1057 3703 13 1 9 3
## 977 1157 1951 574 10 2 18 1
## 978 454 1473 2632 7 1 15 2
## 979 217 1106 688 10 7 15 0
## 980 468 741 2554 19 1 12 2
## 981 1107 1656 3859 11 10 17 3
## 982 626 1134 2977 15 12 5 2
## 983 485 922 571 8 7 17 0
## 984 43 627 402 14 7 14 0
## 985 880 1145 2130 8 3 15 2
## 986 1511 1756 3260 13 1 17 3
## 987 765 1899 2638 19 16 14 2
## 988 925 1252 1464 7 3 20 1
## 989 1185 1387 1824 11 9 3 1
## 990 1706 1886 876 16 0 5 1
## 991 347 1477 2706 7 5 18 2
## 992 1603 1930 659 14 9 13 1
## 993 806 860 455 9 2 6 0
## 994 1207 1539 2378 17 11 2 2
## 995 431 1144 2278 15 3 18 1
## 996 179 613 3436 16 13 5 3
## 997 423 909 3424 19 2 13 3
## 998 925 1469 681 19 18 19 0
## 999 62 544 3212 15 7 7 2
## 1000 211 947 2036 5 1 10 1
## 1001 667 798 1642 9 4 3 1
## 1002 405 519 509 15 12 12 0
## 1003 678 1676 3465 16 11 6 3
## 1004 1619 1651 3900 18 16 2 3
## 1005 935 1147 1105 7 6 19 0
## 1006 870 1613 1142 13 9 8 1
## 1007 1379 1469 1060 12 10 4 1
## 1008 1055 1738 2381 14 1 5 2
## 1009 220 1838 3366 7 3 11 3
## 1010 419 1261 1339 17 0 14 0
## 1011 1111 1362 2560 7 3 8 3
## 1012 1097 1461 285 19 7 3 0
## 1013 447 819 2476 17 1 10 1
## 1014 116 598 3178 9 7 10 2
## 1015 306 564 733 15 0 14 0
## 1016 300 1316 756 14 5 6 0
## 1017 1259 1746 3622 17 2 17 3
## 1018 1392 1783 3703 13 10 11 3
## 1019 113 1524 1905 16 3 18 1
## 1020 526 1728 2636 12 2 6 2
## 1021 1209 1678 2014 11 9 10 2
## 1022 655 828 3056 18 16 6 3
## 1023 1123 1622 465 12 8 4 0
## 1024 692 979 3869 16 14 20 3
## 1025 796 1052 1108 12 1 7 1
## 1026 1021 1958 1906 14 5 5 1
## 1027 995 1310 851 8 4 4 0
## 1028 293 970 1300 14 7 13 0
## 1029 398 1528 2456 13 0 13 2
## 1030 358 791 2360 7 4 19 2
## 1031 403 1614 3700 10 0 7 3
## 1032 211 1409 2620 7 5 13 2
## 1033 1076 1472 3774 11 5 18 3
## 1034 1238 1418 1218 15 10 17 1
## 1035 229 1004 696 14 12 15 0
## 1036 327 1683 3407 12 1 20 3
## 1037 135 1892 3902 9 3 3 3
## 1038 5 1744 2086 10 0 19 1
## 1039 715 1163 1356 15 9 16 1
## 1040 511 621 1704 6 5 20 0
## 1041 549 1383 3571 16 1 11 3
## 1042 314 1400 2096 13 6 2 1
## 1043 739 1766 1273 19 0 6 1
## 1044 62 846 3623 9 4 15 2
## 1045 3 1662 368 5 1 12 0
## 1046 963 1520 854 8 2 10 1
## 1047 1438 1923 3646 10 9 16 3
## 1048 19 501 3784 13 4 4 3
## 1049 887 1274 3885 11 0 14 3
## 1050 567 986 1494 18 10 12 1
## 1051 584 676 2973 11 1 20 3
## 1052 682 888 1486 15 3 19 1
## 1053 724 794 1321 7 2 6 0
## 1054 1384 1454 1877 18 2 11 1
## 1055 257 1654 3499 11 7 19 3
## 1056 17 1084 2124 6 0 12 1
## 1057 439 1306 2316 19 2 6 2
## 1058 925 1206 1068 6 0 8 1
## 1059 409 603 1930 13 12 4 1
## 1060 293 952 1316 8 1 8 1
## 1061 447 568 2700 13 7 8 2
## 1062 717 1206 2115 8 7 18 2
## 1063 195 533 3915 9 7 8 3
## 1064 153 696 3144 16 2 14 3
## 1065 501 640 2253 10 8 9 1
## 1066 530 1496 1433 6 1 17 1
## 1067 396 1378 2583 15 2 13 2
## 1068 670 1552 1817 6 1 16 1
## 1069 193 887 2915 17 1 6 2
## 1070 351 1769 3340 8 0 16 3
## 1071 846 855 2790 19 11 10 2
## 1072 111 1167 577 14 12 12 0
## 1073 1213 1386 3220 17 2 6 3
## 1074 755 1284 3488 14 3 11 3
## 1075 497 627 3593 14 9 3 2
## 1076 1826 1836 2736 12 5 19 3
## 1077 50 1356 2311 17 9 18 2
## 1078 817 941 1464 7 6 16 1
## 1079 901 1793 1355 6 1 14 1
## 1080 212 1269 2382 6 4 7 2
## 1081 329 1039 1886 16 0 17 1
## 1082 248 884 751 19 11 8 0
## 1083 632 1162 436 10 8 11 0
## 1084 366 613 722 6 5 10 0
## 1085 174 518 1075 7 3 2 0
## 1086 882 1195 2802 8 0 10 2
## 1087 118 764 3066 11 2 19 2
## 1088 1012 1226 403 11 2 12 0
## 1089 586 1552 2518 19 12 3 2
## 1090 294 1233 1069 18 6 14 0
## 1091 818 1827 2651 14 8 9 2
## 1092 426 865 2974 15 12 19 2
## 1093 151 1133 657 7 3 2 0
## 1094 409 644 3372 18 13 10 3
## 1095 641 1156 1464 15 9 20 1
## 1096 958 967 1326 7 4 3 0
## 1097 1328 1970 3393 6 5 7 3
## 1098 354 530 323 17 3 7 0
## 1099 1099 1553 2610 8 3 13 3
## 1100 783 1227 3721 16 11 4 3
## 1101 194 1393 763 17 1 17 0
## 1102 238 1893 792 17 12 13 0
## 1103 919 1076 1568 14 0 19 1
## 1104 758 891 1276 19 4 17 1
## 1105 1039 1273 3472 10 4 12 3
## 1106 122 686 3423 13 3 19 3
## 1107 100 1708 258 14 8 9 0
## 1108 1221 1804 1868 16 7 6 2
## 1109 599 1073 780 11 10 5 0
## 1110 1274 1340 712 17 5 10 0
## 1111 62 581 1012 7 4 13 0
## 1112 315 1796 318 11 10 19 0
## 1113 574 1267 1403 16 7 6 0
## 1114 1353 1915 3707 19 3 18 3
## 1115 86 1681 1305 6 5 4 0
## 1116 340 1728 2926 17 13 9 2
## 1117 602 1156 2674 7 2 5 2
## 1118 963 1062 471 8 4 20 0
## 1119 127 507 1724 12 10 8 1
## 1120 142 1039 1220 9 3 20 1
## 1121 274 1079 2754 19 10 8 2
## 1122 371 682 594 17 8 16 0
## 1123 788 1007 1005 12 2 14 0
## 1124 395 1027 2439 6 3 12 1
## 1125 854 935 1083 16 11 16 0
## 1126 1335 1873 2219 9 3 15 2
## 1127 1541 1619 1571 12 3 14 2
## 1128 382 1163 2107 18 5 16 2
## 1129 846 1144 593 9 4 18 0
## 1130 960 1925 857 12 4 6 1
## 1131 154 941 3518 16 12 7 3
## 1132 468 694 1284 17 7 17 0
## 1133 1094 1496 545 19 18 16 0
## 1134 272 870 3566 9 5 12 2
## 1135 394 1301 3442 18 16 5 3
## 1136 629 1299 1309 12 1 14 1
## 1137 831 1713 1179 10 3 18 1
## 1138 903 910 1713 16 0 19 1
## 1139 362 1250 1812 5 0 19 2
## 1140 693 850 1384 19 9 2 1
## 1141 1250 1435 1348 11 6 7 1
## 1142 1135 1923 3957 17 15 6 3
## 1143 66 1448 1212 12 8 12 0
## 1144 924 1877 3925 8 1 10 3
## 1145 204 1132 348 17 11 18 0
## 1146 376 1957 1382 12 7 8 1
## 1147 1083 1492 980 13 7 16 0
## 1148 229 1473 838 14 2 18 0
## 1149 35 991 909 18 4 7 0
## 1150 18 903 3632 17 8 19 3
## 1151 103 646 3396 5 3 7 2
## 1152 105 588 1861 13 10 16 1
## 1153 168 540 348 7 5 19 0
## 1154 18 1004 2528 11 8 16 2
## 1155 659 730 2190 16 10 16 1
## 1156 285 512 961 10 7 19 0
## 1157 1571 1717 2189 7 5 16 2
## 1158 605 1193 531 18 10 13 0
## 1159 158 1657 2331 18 12 20 1
## 1160 1573 1639 794 11 5 9 1
## 1161 127 620 2644 7 2 11 2
## 1162 343 1431 1666 15 4 20 1
## 1163 289 724 2157 8 6 18 1
## 1164 1703 1880 1196 10 6 5 1
## 1165 1482 1591 2072 10 8 19 2
## 1166 907 925 1590 12 0 14 1
## 1167 491 1506 2916 16 11 18 3
## 1168 287 627 3707 5 1 19 3
## 1169 1151 1723 726 11 4 3 0
## 1170 1024 1363 3577 17 7 4 3
## 1171 1085 1134 2627 18 3 15 2
## 1172 991 1803 685 15 11 7 0
## 1173 1052 1897 1693 6 2 5 1
## 1174 1078 1564 2137 11 8 12 2
## 1175 98 977 3696 9 0 3 3
## 1176 323 978 1342 16 11 4 0
## 1177 410 1551 1379 17 1 16 1
## 1178 1750 1857 1164 15 12 19 1
## 1179 1475 1988 908 18 13 6 1
## 1180 1836 1873 1019 19 9 15 1
## 1181 539 1240 1470 12 0 12 1
## 1182 440 563 2299 19 9 9 2
## 1183 397 800 999 6 2 11 0
## 1184 204 1021 3011 7 4 12 2
## 1185 1003 1284 1543 7 5 19 1
## 1186 0 1987 3692 13 0 16 3
## 1187 264 519 2753 13 7 17 1
## 1188 543 703 2156 13 6 6 1
## 1189 467 675 2637 8 4 3 2
## 1190 875 925 331 11 8 7 0
## 1191 1325 1945 2248 19 3 15 2
## 1192 1313 1331 3073 19 1 8 3
## 1193 552 1802 422 7 6 15 0
## 1194 50 1105 3958 12 2 19 3
## 1195 593 1327 432 6 1 10 0
## 1196 268 1010 1152 8 3 3 0
## 1197 443 892 797 6 1 11 0
## 1198 576 884 298 8 2 17 0
## 1199 596 679 3847 8 0 12 3
## 1200 115 636 2110 18 9 6 1
## 1201 356 1407 1921 9 2 3 2
## 1202 10 1567 2423 17 11 6 2
## 1203 1399 1684 1658 15 9 20 2
## 1204 68 584 2361 17 8 7 1
## 1205 643 790 1380 14 5 14 1
## 1206 683 1349 315 7 6 20 0
## 1207 492 818 2182 7 3 17 2
## 1208 290 804 2872 7 2 11 3
## 1209 23 826 2367 5 3 15 1
## 1210 394 849 1919 12 10 20 1
## 1211 713 1742 3296 17 6 14 3
## 1212 1063 1205 462 18 7 11 0
## 1213 1421 1463 1591 11 9 20 1
## 1214 623 816 1593 19 11 2 0
## 1215 777 1995 3154 14 11 7 3
## 1216 83 559 3615 5 2 9 3
## 1217 203 915 3563 17 10 3 3
## 1218 938 1226 1440 17 15 19 1
## 1219 1077 1183 1907 15 14 5 1
## 1220 773 881 324 14 10 2 0
## 1221 332 866 1404 10 3 15 1
## 1222 570 1645 2738 18 16 12 2
## 1223 742 821 2722 12 6 16 2
## 1224 1420 1539 2294 8 7 20 3
## 1225 1801 1923 1424 18 4 13 1
## 1226 1693 1790 1133 14 3 10 1
## 1227 31 1536 555 19 10 11 0
## 1228 1098 1373 3785 13 10 14 3
## 1229 1765 1786 3675 12 0 12 3
## 1230 887 1775 435 9 2 2 0
## 1231 760 1964 3183 5 0 4 2
## 1232 2 660 1853 15 10 18 1
## 1233 90 1614 2647 14 13 20 2
## 1234 469 853 702 19 15 15 0
## 1235 86 1546 1836 12 11 18 2
## 1236 1180 1350 3894 13 10 9 3
## 1237 140 1046 2944 19 17 17 3
## 1238 1100 1437 3424 13 11 9 3
## 1239 194 892 2678 9 4 14 2
## 1240 440 569 2424 12 5 9 2
## 1241 192 1311 2304 18 4 18 2
## 1242 837 1405 1146 6 1 17 1
## 1243 356 588 2335 6 4 2 1
## 1244 155 663 1422 5 0 15 1
## 1245 1032 1776 2096 15 3 3 2
## 1246 40 1435 1303 17 16 4 0
## 1247 1179 1262 456 12 8 18 0
## 1248 902 969 3396 14 2 18 3
## 1249 58 963 2750 6 1 14 2
## 1250 1385 1780 941 15 8 20 0
## 1251 614 1617 2278 13 4 2 2
## 1252 672 1088 2273 8 4 8 2
## 1253 157 763 2638 8 4 13 1
## 1254 485 1399 558 8 5 13 0
## 1255 571 817 1345 16 0 11 0
## 1256 499 1090 2192 15 13 9 1
## 1257 563 1055 3397 19 15 17 2
## 1258 944 1003 1208 14 13 17 1
## 1259 1138 1491 267 12 10 16 0
## 1260 161 647 3518 7 3 8 3
## 1261 126 1575 3122 7 0 4 2
## 1262 15 1196 1050 11 9 3 0
## 1263 500 1713 3358 17 11 12 3
## 1264 203 1771 2929 10 4 17 2
## 1265 961 1950 624 9 7 15 0
## 1266 608 1970 1598 7 0 8 1
## 1267 277 1155 2052 17 6 10 2
## 1268 114 1849 1249 16 2 4 1
## 1269 630 738 412 17 11 2 0
## 1270 248 755 263 6 5 8 0
## 1271 699 851 639 6 0 3 0
## 1272 649 895 3206 10 9 10 3
## 1273 1310 1420 1646 12 0 6 1
## 1274 930 1983 1930 17 7 15 2
## 1275 835 1175 3695 6 5 18 3
## 1276 664 1806 3235 6 1 10 3
## 1277 460 1583 2334 8 7 14 2
## 1278 778 1615 1998 17 4 17 1
## 1279 225 1545 2258 10 1 10 2
## 1280 785 1118 1869 12 2 7 1
## 1281 1581 1713 1825 7 1 4 2
## 1282 9 1944 3946 12 5 14 3
## 1283 7 1294 436 10 6 15 0
## 1284 174 1175 2678 6 0 8 2
## 1285 437 638 3887 8 6 7 3
## 1286 278 623 1813 16 13 20 1
## 1287 1288 1372 817 14 1 16 0
## 1288 979 1190 3614 15 12 9 3
## 1289 56 1931 2013 11 7 16 1
## 1290 282 710 3400 16 6 13 2
## 1291 322 710 2623 17 16 16 1
## 1292 1252 1963 2700 9 3 19 2
## 1293 1128 1702 1400 11 0 13 1
## 1294 655 1459 2803 19 9 6 3
## 1295 954 970 2518 18 17 5 2
## 1296 68 1042 3564 7 2 2 3
## 1297 134 1197 2515 14 8 4 2
## 1298 922 1836 3654 13 9 10 3
## 1299 1874 1976 3930 17 1 18 3
## 1300 251 599 3476 11 8 17 3
## 1301 1362 1853 3242 18 11 4 3
## 1302 281 1159 2666 5 4 20 2
## 1303 224 1994 1695 13 6 17 2
## 1304 778 844 3510 14 4 18 2
## 1305 546 1424 3438 14 3 20 3
## 1306 397 891 2033 16 9 2 1
## 1307 386 1046 1017 11 3 9 1
## 1308 335 1009 3494 13 3 14 3
## 1309 342 1641 3083 8 4 11 2
## 1310 625 761 1656 8 4 15 1
## 1311 494 509 313 15 1 19 0
## 1312 1145 1487 3282 16 2 18 3
## 1313 600 1042 3629 19 17 5 3
## 1314 484 711 1687 15 5 5 1
## 1315 199 679 1149 8 7 18 0
## 1316 21 832 3899 5 4 6 3
## 1317 885 1811 1783 13 12 10 2
## 1318 308 1208 1629 8 6 11 1
## 1319 1015 1947 2528 11 8 8 2
## 1320 1303 1814 3256 9 6 5 3
## 1321 406 1036 2341 10 9 16 2
## 1322 124 833 2785 15 10 16 2
## 1323 150 1677 3761 13 1 12 3
## 1324 90 557 2240 10 6 5 1
## 1325 588 1809 820 19 18 3 1
## 1326 776 1247 920 16 2 10 0
## 1327 278 584 3615 18 8 13 2
## 1328 897 1103 1412 16 9 5 1
## 1329 651 700 361 6 1 10 0
## 1330 1010 1684 1540 12 8 3 1
## 1331 737 1759 1077 6 1 4 0
## 1332 1399 1646 3610 13 7 9 3
## 1333 376 1288 512 5 2 6 0
## 1334 1117 1330 3024 6 0 20 3
## 1335 234 1947 1610 11 0 16 1
## 1336 691 1028 2201 17 16 12 1
## 1337 380 1550 3338 18 13 11 3
## 1338 117 681 2203 7 5 15 1
## 1339 1499 1952 1155 19 14 12 1
## 1340 190 1905 1798 16 7 10 1
## 1341 674 1375 2598 17 2 3 3
## 1342 518 1374 1567 15 2 7 1
## 1343 1618 1916 1491 16 2 14 1
## 1344 371 665 3905 6 2 4 3
## 1345 73 583 291 19 9 11 0
## 1346 98 755 1693 11 4 18 1
## 1347 395 1853 1788 13 11 13 1
## 1348 712 1726 1751 12 10 20 2
## 1349 774 939 1641 14 8 12 1
## 1350 1012 1040 2854 10 7 12 2
## 1351 730 1148 3115 7 0 2 2
## 1352 973 1633 1612 17 9 17 2
## 1353 245 829 2319 5 4 4 1
## 1354 937 1083 1258 17 14 16 1
## 1355 937 1164 1543 18 3 10 1
## 1356 574 1171 3991 12 4 19 3
## 1357 678 1630 3555 5 2 19 3
## 1358 82 512 3411 17 16 18 3
## 1359 398 1775 524 9 3 4 0
## 1360 1096 1570 3541 18 17 4 3
## 1361 1419 1698 1998 9 2 4 2
## 1362 430 1207 1637 17 3 11 1
## 1363 1649 1829 2855 16 13 7 2
## 1364 254 954 1393 9 4 4 1
## 1365 1790 1988 1633 18 4 4 2
## 1366 1304 1673 3284 15 2 8 3
## 1367 813 1180 464 7 5 16 0
## 1368 948 1007 1234 19 13 18 0
## 1369 1161 1288 292 8 0 6 0
## 1370 867 1258 2521 6 5 16 2
## 1371 181 1199 2658 15 0 19 2
## 1372 754 1532 587 6 3 13 0
## 1373 242 1127 2190 18 1 9 2
## 1374 978 1986 3330 14 4 15 3
## 1375 725 1882 3564 12 1 17 3
## 1376 657 1657 562 17 14 8 0
## 1377 1147 1393 1659 11 10 11 1
## 1378 910 1566 1816 12 7 7 2
## 1379 295 503 1338 16 8 5 0
## 1380 26 508 2992 15 3 17 2
## 1381 332 764 315 16 7 7 0
## 1382 519 533 3249 19 0 12 2
## 1383 161 1029 3242 10 1 20 2
## 1384 1080 1289 2725 15 10 7 2
## 1385 71 1686 2454 9 8 13 2
## 1386 744 1244 804 17 14 16 0
## 1387 437 1113 2460 10 5 18 2
## 1388 547 957 2625 15 11 12 2
## 1389 495 1035 1999 15 9 2 1
## 1390 1466 1717 2698 11 8 6 2
## 1391 760 1722 1193 16 13 20 1
## 1392 764 1873 3511 16 14 15 3
## 1393 53 1504 1044 8 7 11 0
## 1394 371 1702 2548 6 1 5 2
## 1395 728 818 2940 13 0 15 2
## 1396 1791 1896 3966 17 15 2 3
## 1397 606 1316 3484 11 10 16 3
## 1398 1225 1717 1246 12 2 20 1
## 1399 419 736 2757 17 12 15 2
## 1400 367 1264 2378 18 3 4 1
## 1401 1230 1263 1794 18 7 19 1
## 1402 267 1161 3933 7 0 7 3
## 1403 273 891 1637 7 3 6 0
## 1404 705 1905 783 6 1 11 0
## 1405 214 679 3094 12 4 20 2
## 1406 424 1032 1774 12 8 10 1
## 1407 605 1429 1365 9 3 16 1
## 1408 1175 1586 2339 19 2 10 2
## 1409 657 938 2049 12 8 5 1
## 1410 371 1023 3241 14 11 11 3
## 1411 833 1441 867 8 3 3 0
## 1412 102 1195 2175 14 4 14 1
## 1413 1299 1935 3497 18 10 18 3
## 1414 443 708 3684 15 8 6 3
## 1415 1279 1793 3586 19 9 8 3
## 1416 313 712 2322 19 8 7 2
## 1417 523 565 354 10 4 13 0
## 1418 570 1021 1400 17 7 3 1
## 1419 380 682 2308 11 10 9 1
## 1420 119 529 1082 15 5 10 0
## 1421 296 839 3104 17 7 11 2
## 1422 358 614 2847 9 2 4 2
## 1423 424 1639 1113 14 1 6 0
## 1424 961 1030 3210 10 3 8 3
## 1425 431 1727 3990 14 9 12 3
## 1426 647 1849 3117 6 0 2 3
## 1427 1960 1963 1622 18 17 16 2
## 1428 808 1517 2908 12 4 11 2
## 1429 94 1334 1167 5 3 19 0
## 1430 380 877 2715 15 9 16 2
## 1431 593 732 1734 17 8 14 1
## 1432 1684 1746 2259 14 12 19 3
## 1433 1221 1415 2921 8 5 17 3
## 1434 410 1643 1244 14 10 16 0
## 1435 600 1211 3132 17 0 2 3
## 1436 308 591 1052 11 9 11 0
## 1437 148 742 2927 15 6 3 2
## 1438 297 918 2090 5 1 13 1
## 1439 1050 1726 2131 7 2 6 2
## 1440 1191 1882 3362 13 10 11 3
## 1441 1399 1663 1053 14 11 16 1
## 1442 1658 1794 1799 16 9 7 1
## 1443 1153 1817 3124 5 0 13 3
## 1444 46 908 1938 19 8 5 1
## 1445 207 1177 3202 13 1 14 3
## 1446 67 1924 1302 13 7 13 0
## 1447 1257 1411 575 15 8 7 0
## 1448 138 1371 2359 13 6 3 1
## 1449 889 1635 440 14 10 13 0
## 1450 831 1709 2002 17 8 5 1
## 1451 713 1854 3167 14 8 20 3
## 1452 527 1393 3480 17 16 17 3
## 1453 1284 1643 595 10 0 19 1
## 1454 123 1477 1462 19 18 13 0
## 1455 406 681 503 7 3 5 0
## 1456 927 1135 2066 9 0 15 1
## 1457 407 528 708 17 5 17 0
## 1458 1041 1430 2029 10 5 12 1
## 1459 781 1364 308 19 17 7 0
## 1460 1191 1839 552 6 1 11 0
## 1461 125 1227 3314 9 7 17 3
## 1462 919 1343 3996 12 9 17 3
## 1463 536 1656 1795 7 3 4 1
## 1464 639 1087 2473 10 4 9 2
## 1465 686 1418 3845 15 1 4 3
## 1466 1064 1985 2004 11 8 9 1
## 1467 240 1414 2083 19 15 20 2
## 1468 525 704 2661 9 7 4 2
## 1469 348 730 1903 8 4 14 1
## 1470 1262 1520 2466 8 0 5 2
## 1471 258 789 3904 5 1 7 3
## 1472 1188 1948 265 8 0 12 0
## 1473 730 751 1417 10 4 19 0
## 1474 679 1231 1459 15 3 2 1
## 1475 1224 1676 1633 15 10 5 2
## 1476 948 1205 2052 5 3 6 1
## 1477 366 1234 1086 14 7 3 0
## 1478 1619 1962 3376 7 0 7 3
## 1479 167 909 641 9 1 8 0
## 1480 782 951 3451 5 2 17 3
## 1481 881 1129 1607 15 0 10 0
## 1482 91 1317 3521 15 11 9 3
## 1483 487 722 1241 9 0 16 0
## 1484 120 1429 881 6 3 17 0
## 1485 74 865 3033 14 11 17 3
## 1486 634 1079 769 19 13 3 0
## 1487 334 1079 3454 17 11 2 3
## 1488 931 1811 819 10 3 4 0
## 1489 921 1351 435 12 9 3 0
## 1490 1057 1931 650 17 0 10 0
## 1491 616 912 3038 18 0 14 3
## 1492 740 840 3736 14 8 5 3
## 1493 344 1551 739 18 1 2 0
## 1494 211 1608 686 8 6 11 0
## 1495 887 893 1183 5 3 8 0
## 1496 670 1152 3943 13 0 4 3
## 1497 383 1249 1037 6 4 15 0
## 1498 718 1109 2633 16 0 9 2
## 1499 291 1318 3297 7 5 16 3
## 1500 1352 1775 3141 6 2 11 3
## 1501 426 1629 1308 5 0 2 1
## 1502 40 1311 1519 13 0 2 0
## 1503 262 1436 493 18 9 10 0
## 1504 623 769 1747 7 0 14 1
## 1505 4 1351 2517 16 8 7 1
## 1506 1001 1176 1389 17 8 8 1
## 1507 334 896 2522 10 5 2 2
## 1508 952 1726 704 14 5 4 0
## 1509 321 1870 953 18 11 20 0
## 1510 1451 1469 3206 16 15 4 3
## 1511 398 724 2177 12 2 17 1
## 1512 417 520 2610 10 1 11 2
## 1513 1217 1670 3839 17 3 11 3
## 1514 298 1037 1861 8 5 17 1
## 1515 85 1451 340 16 5 16 0
## 1516 655 1255 3606 10 0 5 3
## 1517 167 990 799 7 3 9 0
## 1518 212 623 2367 11 7 10 1
## 1519 617 1386 445 10 8 10 0
## 1520 271 1769 3927 8 3 7 3
## 1521 159 1738 3756 17 5 12 3
## 1522 786 872 770 14 1 15 0
## 1523 46 562 1641 7 2 13 1
## 1524 645 1275 2343 17 12 15 2
## 1525 1371 1677 2787 10 8 7 2
## 1526 449 790 3208 11 9 4 3
## 1527 1332 1814 1069 7 6 7 1
## 1528 119 1531 3568 16 14 3 3
## 1529 256 823 3585 10 1 10 3
## 1530 428 1254 1974 14 1 18 1
## 1531 1546 1879 2419 15 5 19 3
## 1532 1210 1989 340 17 13 4 0
## 1533 789 1211 2282 5 2 17 1
## 1534 390 756 298 10 1 10 0
## 1535 110 1317 968 6 2 2 0
## 1536 1330 1686 2391 12 6 3 2
## 1537 253 590 696 14 7 3 0
## 1538 317 1805 2782 7 0 17 2
## 1539 892 1603 3746 5 0 5 3
## 1540 388 605 2651 17 7 4 1
## 1541 914 979 2651 15 5 3 2
## 1542 287 593 1824 13 3 14 1
## 1543 675 1163 1456 9 3 20 1
## 1544 367 1985 3155 11 10 7 3
## 1545 510 980 773 17 7 2 0
## 1546 1325 1800 1829 18 17 13 2
## 1547 1661 1836 3883 12 2 5 3
## 1548 665 718 1675 14 12 9 1
## 1549 0 994 1958 7 5 7 1
## 1550 284 1036 2376 19 6 11 2
## 1551 757 1912 3548 14 4 5 3
## 1552 560 1177 2694 18 3 19 2
## 1553 470 775 2195 5 4 4 2
## 1554 533 1696 3767 15 9 16 3
## 1555 560 1633 1150 7 2 7 1
## 1556 405 1141 841 9 1 2 0
## 1557 227 610 1675 13 4 17 0
## 1558 479 831 1301 8 6 12 0
## 1559 227 509 1817 10 0 6 0
## 1560 149 1022 2321 7 5 20 2
## 1561 88 709 1974 17 5 13 1
## 1562 114 819 3433 6 5 10 2
## 1563 662 874 1205 12 7 3 1
## 1564 888 1466 2052 9 3 14 1
## 1565 664 711 3654 9 1 16 3
## 1566 64 745 1503 10 0 13 0
## 1567 747 1247 3104 6 5 20 3
## 1568 956 1010 343 19 14 3 0
## 1569 652 1933 2470 11 1 3 3
## 1570 201 582 2668 12 5 19 2
## 1571 1652 1983 1112 8 2 15 1
## 1572 190 657 967 10 1 14 0
## 1573 1171 1673 2498 13 6 8 2
## 1574 1211 1396 635 17 7 15 0
## 1575 976 1353 2711 15 7 8 2
## 1576 675 742 2023 17 13 8 2
## 1577 42 807 824 19 18 7 0
## 1578 1196 1651 3851 10 3 14 3
## 1579 503 986 2156 7 3 13 1
## 1580 1694 1798 2885 8 4 2 3
## 1581 306 558 495 15 6 14 0
## 1582 291 651 1744 6 3 10 1
## 1583 173 1219 1832 15 8 11 1
## 1584 1017 1289 2016 13 10 16 1
## 1585 610 1437 2313 14 1 11 2
## 1586 223 737 3248 13 3 4 2
## 1587 206 1167 2216 9 5 6 1
## 1588 1457 1919 3142 16 6 5 3
## 1589 591 724 1424 15 12 7 0
## 1590 347 957 1620 9 2 19 1
## 1591 241 854 2592 12 8 3 1
## 1592 743 1426 296 5 3 7 0
## 1593 4 743 3579 19 8 20 3
## 1594 576 1809 1180 6 3 4 0
## 1595 888 1099 3962 15 11 5 3
## 1596 1222 1890 668 13 4 19 0
## 1597 915 1965 2032 11 10 16 2
## 1598 868 1632 3057 9 1 5 3
## 1599 336 670 869 18 10 19 0
## 1600 483 754 3919 19 4 2 3Preprocessing
train_x <- data.matrix(train_reduced[,-15])
train_y <- train_reduced[,15]
validation_x <- data.matrix(validation_reduced[,-15])
validation_y <- validation_reduced[,15]validation_y## [1] 1 0 0 1 3 2 2 1 1 1 2 3 2 3 3 3 1 3 1 3 3 0 0 1 3 2 1 2 0 3 2 2 1 0 1 0 1
## [38] 3 3 2 2 1 0 3 3 0 0 0 3 0 3 1 3 1 1 3 1 1 0 2 0 1 2 2 1 0 1 0 1 3 3 0 1 1
## [75] 1 0 1 0 2 0 1 1 3 0 0 3 0 1 2 0 0 2 3 0 0 3 3 1 2 2 0 1 2 1 1 3 3 0 1 3 2
## [112] 0 0 3 2 1 2 0 2 2 2 3 2 3 0 2 3 1 3 0 3 1 3 2 0 3 0 2 3 3 1 3 3 3 0 0 2 0
## [149] 3 3 3 3 2 0 1 0 1 1 1 3 3 0 0 1 1 2 1 1 1 1 0 1 2 2 3 0 0 1 2 3 1 2 2 1 0
## [186] 2 2 3 2 0 1 0 2 0 1 0 2 3 1 1 3 1 1 0 3 3 2 3 2 0 1 0 3 3 1 3 2 2 2 1 0 2
## [223] 0 3 0 0 2 2 1 1 2 1 1 0 1 1 1 1 3 0 0 0 1 2 1 0 3 1 0 2 2 3 2 3 2 1 0 2 0
## [260] 0 1 3 3 2 2 3 2 3 1 0 1 3 1 1 1 1 3 2 3 3 0 2 2 3 3 1 2 1 0 2 2 0 0 1 2 3
## [297] 3 3 0 1 2 0 0 1 2 2 3 1 1 2 1 3 1 1 1
## Levels: 0 1 2 3Next, we need to convert the train and test data into xgb matrix type.
xgb_train <- xgb.DMatrix(data=train_x, label=train_y)
xgb_test <- xgb.DMatrix(data=validation_x, label=validation_y)Hyperparamaters
eta: learning rate/shrinkage. Default = 0.3gamma: minimum loss reduction needed to make another partition in a given tree. The larger value, the more conservative the tree will be. Default = 0.max.depth: The maximum depth that you allow the tree to grow to. The deeper you allow, the more complex your model will become. For training error, it is easy to see what will happen. If you increase max_depth , training error will always go down (or at least not go up). Default = 6.subsample: Fraction of training samples to use in each “boosting iteration”. Default = 1 (no subsampling).colsample_bytree: Fraction of columns to be used when constructing each tree. This is an idea used in RandomForests. Default = 1 (no sampling).min_child_weight: This is the minimum number of instances that have to been in a node. It’s a regularization parameter So, if it’s set to 10, each leaf has to have at least 10 instances assigned to it. The higher the value, the more conservative the tree will be. Default = 1nrounds: the number of decision trees in the final model. So, how many weak learners get added to the ensemble.
# booster = 'gbtree': Possible to also have linear boosters as your weak learners.
params_booster <- list(booster = 'gbtree', eta = 1, gamma = 0, max.depth = 2, subsample = 1, colsample_bytree = 1, min_child_weight = 1, objective='multi:softmax')typeof(params_booster)## [1] "list"Let’s use xgb.cv() to determine how many rounds (nrounds) we should use for training.
train_reduced$price_range <- as.factor(train_reduced$price_range)model_xgb <- xgboost(data= xgb_train, nrounds=50)## [1] train-rmse:1.625547
## [2] train-rmse:1.159670
## [3] train-rmse:0.833087
## [4] train-rmse:0.603965
## [5] train-rmse:0.445092
## [6] train-rmse:0.336870
## [7] train-rmse:0.263754
## [8] train-rmse:0.214578
## [9] train-rmse:0.181862
## [10] train-rmse:0.160168
## [11] train-rmse:0.145543
## [12] train-rmse:0.135125
## [13] train-rmse:0.126012
## [14] train-rmse:0.119892
## [15] train-rmse:0.115471
## [16] train-rmse:0.111966
## [17] train-rmse:0.107397
## [18] train-rmse:0.103576
## [19] train-rmse:0.100815
## [20] train-rmse:0.096675
## [21] train-rmse:0.094538
## [22] train-rmse:0.092162
## [23] train-rmse:0.090066
## [24] train-rmse:0.085751
## [25] train-rmse:0.083730
## [26] train-rmse:0.080554
## [27] train-rmse:0.078098
## [28] train-rmse:0.076071
## [29] train-rmse:0.074886
## [30] train-rmse:0.072286
## [31] train-rmse:0.070580
## [32] train-rmse:0.068643
## [33] train-rmse:0.067727
## [34] train-rmse:0.067082
## [35] train-rmse:0.064621
## [36] train-rmse:0.062630
## [37] train-rmse:0.062104
## [38] train-rmse:0.061067
## [39] train-rmse:0.060876
## [40] train-rmse:0.059987
## [41] train-rmse:0.059366
## [42] train-rmse:0.058086
## [43] train-rmse:0.056035
## [44] train-rmse:0.055050
## [45] train-rmse:0.053148
## [46] train-rmse:0.051739
## [47] train-rmse:0.049753
## [48] train-rmse:0.048730
## [49] train-rmse:0.047848
## [50] train-rmse:0.047122nc <- length(unique(train_y))pred_xgb <- predict(model_xgb, xgb_test)
pred_y <- as.factor((levels(validation_y))[round(pred_xgb)])confusionMatrix(data = pred_y, reference = as.factor(validation$price_range))## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3
## 0 75 0 0 0
## 1 0 91 0 0
## 2 0 0 71 0
## 3 0 0 0 78
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9884, 1)
## No Information Rate : 0.2889
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3
## Sensitivity 1.0000 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000 1.0000
## Prevalence 0.2381 0.2889 0.2254 0.2476
## Detection Rate 0.2381 0.2889 0.2254 0.2476
## Detection Prevalence 0.2381 0.2889 0.2254 0.2476
## Balanced Accuracy 1.0000 1.0000 1.0000 1.0000next time do a k-folds cv to avoid overfitting?? is this even an overfitting wtf me confused
K-folds Cross-validation to Estimate Error
The number of fold will typically be 5 or 10, but it really depends on data size, signal:noise ratio, and training time. For this dataset, we are going to use 5 fold cross validation.
# NB: keep in mind xgb.cv() is used to select the correct hyperparams.
# Here I'm only looking for a decent value for nrounds; We won't do full hyperparameter tuning.
# Once you have them, train using xgb.train() or xgboost() to get the final model.
# train.cv <- xgb.cv(data = xgb_train,
# label = train_reduced$price_range,
# params = params_booster,
# num.class = length(levels(train_reduced$price_range)),
# nrounds = 300,
# nfold = 5,
# print_every_n = 20,
# verbose = 2)
# Note, we can also implement early-stopping: early_stopping_rounds = 3, so that if there has been no improvement in test accuracy for a specified number of rounds, the algorithm stops.XGBoost Model Tuning
There are many ways of controlling overfitting, but they can mostly be summed up in four categories:
- Regularization
- Pruning
- Sampling
- Early stopping