Classification Model Series III : Compare Classification Models Use Cross-Validation In R By Field Goals Data Set
2016-02-23
In before articles about classification model, I often only split the data set, then compare their performance, but if you split the data set again, the components of train set will differ, the test set will differ, at the same time, the performance of the classifier will change.
So, we should repeat split the train set and test set, compute the performance, then summary repeated measures, such as the mean of measure, the standard deviation of measures.
In the same way, in before articles about classification model, I often chose kinds of models which are designed by some default parameters, then compare their performance, and find out some classifier which suit data set better.
Suppose we have chosen some better models by simple split the data set and using some default models, how to compare these classifier in the depth of deeper?
Ok, we want to talk about this question in this article.
Firstly, we need to tune parameters in my model, not to use only default parameters, because maybe other parameters will lead to better prediction values.
Secondly, we tune parameters by using train set and test set.
However, different train-set or test-set will have different performance.
So, we must split our raw train set many times, each time we compute a measures, in the end, we could have series measures.
Ordinary, we choose 10-fold cross validation method to split data set and tune parameters.
- Lastly, we compare models in performance.
Here, I recommence to use caret package.
The Classification and Regression Training (caret) package by Max Kuhn includes functions to streamline the model training process.
This package also provides a large number of tools for preparing, training, evaluating, and visualizing machine learning models and data.
We still use field goals data set as an example.
In before articles (classification model series II), we get the better models are logistic regression, artificial neural networks, boosting, so we use repeated cross-validation to tune parameters and compare their performance.
- Firstly, let we look at the logistic regression model.
We get a series of measures by cross-validation, such as the mean of sensitivity is 0.06, the standard deviation of sensitivity is 0.06, ROC is auc (area under the ROC curve).
We could still tune the cut-off value to get better auc and tpr values.
# load the data set
library(nutshell)
data(field.goals)
# create a new binary variable for dataset
data <- transform(field.goals,
play.type = as.factor(ifelse(play.type == "FG good", "good", "bad")))
# head(data)
# summary(data)
###############################
### split the dataset
###############################
library(caret)
set.seed(1234)
train_index <- createDataPartition(data$play.type, p = .8, list = FALSE)
trainset <- data[train_index, ]
testset <- data[-train_index, ]
###############################
### compare models
###############################
ctrl <- trainControl(method = "repeatedcv", repeats = 5,
summaryFunction = twoClassSummary,
classProbs = TRUE,
savePredictions = TRUE)
### tune logistic regression
logistic_model <- train(play.type ~ yards, data = trainset,
method = "glm", family = binomial,
metric = "Sens",
trControl = ctrl)
logistic_model## Generalized Linear Model
##
## 786 samples
## 1 predictor
## 2 classes: 'bad', 'good'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 5 times)
## Summary of sample sizes: 707, 708, 708, 707, 707, 707, ...
## Resampling results:
##
## ROC Sens Spec
## 0.7503095 0.09083333 0.9809524logistic_pred <- predict(logistic_model, testset, type = "prob")
fpr_tpr <- function(p, dataset) {
TPR <- rep(0, length(p))
FPR <- rep(0, length(p))
for(i in 1 : length(p)){
p0 <- p[order(p)[i]]
label_true <- ifelse(dataset$play.type == "good", 1, 0)
label_pred <- 1 * (p > p0)
TPR[i] <- sum(label_pred * label_true) / sum(label_true)
FPR[i] <- sum(label_pred * (1 - label_true)) / sum(1 - label_true)
}
list(fpr = FPR, tpr = TPR)
}
# plot logistic regression roc
logic_measures <- fpr_tpr(p = logistic_pred$good, dataset = testset)
plot(logic_measures$fpr, logic_measures$tpr, type = "l", col = 2,
ylab = "TPR", xlab = "FPR")
title("ROC curve")
points(c(0, 1), c(0, 1), type = "l", lty = 2)
Then, we could see the artificial neural network model.
Here, the function will be tune parameters “size” and “decay”, and automatically chose the optimal model by the largest auc value.
The performance nearly the same as logistic regression model after tune cut-off values.
### tune ann
ann_model <- train(play.type ~ yards, data = trainset,
method = "nnet",
trControl = ctrl)## # weights: 4
## initial value 407.760405
## iter 10 value 309.916935
## iter 20 value 304.627226
## iter 30 value 304.272741
## iter 40 value 304.138587
## iter 50 value 304.090826
## iter 60 value 304.058485
## iter 70 value 304.049885
## iter 80 value 304.038649
## iter 90 value 304.032579
## iter 100 value 304.026519
## final value 304.026519
## stopped after 100 iterations
## # weights: 10
## initial value 657.307708
## iter 10 value 351.834960
## iter 20 value 351.248745
## iter 30 value 305.554581
## iter 40 value 304.368818
## iter 50 value 304.178925
## iter 60 value 304.065917
## iter 70 value 304.029034
## iter 80 value 304.017769
## iter 90 value 304.012093
## iter 100 value 304.009131
## final value 304.009131
## stopped after 100 iterations
## # weights: 16
## initial value 605.272447
## iter 10 value 350.005842
## iter 20 value 305.283257
## iter 30 value 304.057220
## iter 40 value 303.989329
## iter 50 value 303.911388
## iter 60 value 303.381991
## iter 70 value 303.260656
## iter 80 value 303.164386
## iter 90 value 303.122246
## iter 100 value 303.006836
## final value 303.006836
## stopped after 100 iterations
## # weights: 4
## initial value 393.978058
## final value 351.951519
## converged
## # weights: 10
## initial value 665.158336
## iter 10 value 350.331763
## iter 20 value 311.253800
## iter 30 value 308.254132
## iter 40 value 307.987057
## iter 50 value 307.669238
## iter 60 value 307.486852
## iter 70 value 307.411112
## final value 307.403968
## converged
## # weights: 16
## initial value 588.995599
## iter 10 value 323.973694
## iter 20 value 309.971436
## iter 30 value 308.205699
## iter 40 value 307.678290
## iter 50 value 307.314737
## iter 60 value 307.091271
## iter 70 value 306.940668
## iter 80 value 306.865568
## final value 306.862341
## converged
## # weights: 4
## initial value 384.970686
## final value 351.835498
## converged
## # weights: 10
## initial value 634.973735
## iter 10 value 332.524127
## iter 20 value 304.904268
## iter 30 value 304.213070
## iter 40 value 304.120898
## iter 50 value 304.094230
## iter 50 value 304.094228
## final value 304.094228
## converged
## # weights: 16
## initial value 495.413958
## iter 10 value 348.545573
## iter 20 value 305.381516
## iter 30 value 304.099103
## iter 40 value 304.062607
## iter 50 value 304.060972
## final value 304.059480
## converged
## # weights: 4
## initial value 735.333575
## iter 10 value 343.461183
## iter 20 value 305.482249
## iter 30 value 304.624084
## iter 40 value 304.364367
## iter 50 value 304.294710
## iter 60 value 304.263142
## iter 70 value 304.247873
## iter 80 value 304.232740
## iter 90 value 304.221205
## final value 304.219374
## converged
## # weights: 10
## initial value 748.723861
## iter 10 value 350.607543
## iter 20 value 304.708992
## iter 30 value 304.243302
## iter 40 value 304.227053
## iter 50 value 304.206768
## iter 60 value 304.189075
## iter 70 value 304.181657
## iter 80 value 304.165050
## iter 90 value 304.158140
## final value 304.157845
## converged
## # weights: 16
## initial value 474.671447
## iter 10 value 310.873279
## iter 20 value 305.293653
## iter 30 value 304.372958
## iter 40 value 304.275105
## iter 50 value 304.246007
## iter 60 value 304.187824
## final value 304.185087
## converged
## # weights: 4
## initial value 424.619975
## iter 10 value 353.762654
## iter 20 value 348.534276
## iter 30 value 309.435823
## final value 308.863570
## converged
## # weights: 10
## initial value 448.765249
## iter 10 value 330.662695
## iter 20 value 308.994013
## iter 30 value 308.262441
## iter 40 value 307.951395
## iter 50 value 307.659043
## iter 60 value 307.614102
## final value 307.612105
## converged
## # weights: 16
## initial value 545.390739
## iter 10 value 313.630609
## iter 20 value 310.124766
## iter 30 value 308.527706
## iter 40 value 307.960136
## iter 50 value 307.658489
## iter 60 value 307.590441
## final value 307.588848
## converged
## # weights: 4
## initial value 485.477478
## final value 353.451780
## converged
## # weights: 10
## initial value 453.982967
## iter 10 value 353.465528
## final value 353.452390
## converged
## # weights: 16
## initial value 723.550904
## iter 10 value 353.452406
## final value 353.452373
## converged
## # weights: 4
## initial value 635.277599
## final value 351.835038
## converged
## # weights: 10
## initial value 854.788889
## iter 10 value 351.835049
## final value 351.835016
## converged
## # weights: 16
## initial value 982.162325
## final value 351.834927
## converged
## # weights: 4
## initial value 397.983930
## iter 10 value 326.105050
## iter 20 value 308.618880
## final value 308.618572
## converged
## # weights: 10
## initial value 655.251362
## iter 10 value 325.270718
## iter 20 value 312.453188
## iter 30 value 309.295341
## final value 309.273249
## converged
## # weights: 16
## initial value 676.591126
## iter 10 value 348.412754
## iter 20 value 308.877902
## iter 30 value 307.806039
## iter 40 value 307.462387
## iter 50 value 307.304594
## iter 60 value 307.275925
## iter 70 value 307.256361
## final value 307.255506
## converged
## # weights: 4
## initial value 511.403378
## final value 351.835635
## converged
## # weights: 10
## initial value 444.961850
## iter 10 value 351.835810
## final value 351.835718
## converged
## # weights: 16
## initial value 355.650179
## iter 10 value 350.888546
## iter 20 value 308.128990
## iter 30 value 304.470041
## final value 304.421954
## converged
## # weights: 4
## initial value 399.390139
## final value 351.835023
## converged
## # weights: 10
## initial value 448.310668
## iter 10 value 351.835035
## final value 351.835018
## converged
## # weights: 16
## initial value 362.495685
## iter 10 value 305.060029
## iter 20 value 303.941845
## iter 30 value 303.619157
## iter 40 value 303.552377
## iter 50 value 303.492656
## iter 60 value 303.478651
## iter 70 value 303.448239
## iter 80 value 303.351646
## iter 90 value 303.163650
## iter 100 value 302.973360
## final value 302.973360
## stopped after 100 iterations
## # weights: 4
## initial value 489.011504
## iter 10 value 351.955177
## final value 351.951683
## converged
## # weights: 10
## initial value 421.968775
## iter 10 value 344.306516
## iter 20 value 308.126023
## iter 30 value 307.400545
## iter 40 value 306.948934
## iter 50 value 306.712399
## iter 60 value 306.669510
## final value 306.665023
## converged
## # weights: 16
## initial value 397.306744
## iter 10 value 324.997805
## iter 20 value 308.418138
## iter 30 value 307.891716
## iter 40 value 307.454173
## iter 50 value 306.969537
## iter 60 value 306.749786
## iter 70 value 306.648257
## iter 80 value 306.640162
## final value 306.640158
## converged
## # weights: 4
## initial value 568.555454
## iter 10 value 351.835205
## iter 10 value 351.835205
## final value 351.835205
## converged
## # weights: 10
## initial value 687.698014
## iter 10 value 318.384816
## iter 20 value 304.087462
## iter 30 value 303.558029
## iter 40 value 303.539750
## final value 303.533262
## converged
## # weights: 16
## initial value 358.878726
## iter 10 value 325.756020
## iter 20 value 304.445920
## iter 30 value 303.740076
## iter 40 value 303.641723
## iter 50 value 303.537025
## iter 60 value 303.507049
## iter 70 value 303.494213
## final value 303.492489
## converged
## # weights: 4
## initial value 424.628709
## final value 353.451594
## converged
## # weights: 10
## initial value 731.576748
## iter 10 value 353.448606
## iter 20 value 346.960264
## iter 30 value 311.499748
## iter 40 value 310.057754
## iter 50 value 309.842833
## final value 309.764691
## converged
## # weights: 16
## initial value 392.605460
## iter 10 value 342.742225
## iter 20 value 312.105873
## iter 30 value 310.189214
## iter 40 value 309.880199
## iter 50 value 309.825130
## iter 60 value 309.739882
## iter 70 value 309.717153
## iter 80 value 309.709170
## iter 90 value 309.701932
## iter 100 value 309.700481
## final value 309.700481
## stopped after 100 iterations
## # weights: 4
## initial value 573.163019
## final value 353.566930
## converged
## # weights: 10
## initial value 417.774052
## iter 10 value 323.451022
## iter 20 value 315.081659
## iter 30 value 314.721726
## final value 314.721460
## converged
## # weights: 16
## initial value 648.112037
## iter 10 value 353.895088
## iter 20 value 326.788085
## iter 30 value 315.318685
## iter 40 value 314.337454
## iter 50 value 313.559646
## iter 60 value 313.325105
## iter 70 value 313.246026
## iter 80 value 313.225620
## final value 313.225540
## converged
## # weights: 4
## initial value 455.923372
## final value 353.452138
## converged
## # weights: 10
## initial value 433.326645
## iter 10 value 353.451810
## final value 353.451781
## converged
## # weights: 16
## initial value 505.497060
## iter 10 value 353.449870
## iter 20 value 317.277229
## iter 30 value 310.943392
## iter 40 value 309.947337
## iter 50 value 309.824714
## iter 60 value 309.737506
## iter 70 value 308.526485
## iter 80 value 308.280437
## iter 90 value 307.911135
## iter 100 value 307.383707
## final value 307.383707
## stopped after 100 iterations
## # weights: 4
## initial value 596.843376
## final value 353.451552
## converged
## # weights: 10
## initial value 453.533761
## iter 10 value 353.451509
## final value 353.451470
## converged
## # weights: 16
## initial value 578.303080
## iter 10 value 347.147631
## iter 20 value 312.947762
## iter 30 value 311.963127
## iter 40 value 310.976582
## iter 50 value 310.691903
## iter 60 value 310.484031
## iter 70 value 310.377920
## iter 80 value 310.252245
## iter 90 value 310.124426
## iter 100 value 309.930836
## final value 309.930836
## stopped after 100 iterations
## # weights: 4
## initial value 472.293114
## final value 353.567161
## converged
## # weights: 10
## initial value 375.312743
## iter 10 value 353.561851
## iter 20 value 353.549095
## final value 353.549074
## converged
## # weights: 16
## initial value 721.843570
## iter 10 value 350.300331
## iter 20 value 317.006616
## iter 30 value 315.671006
## iter 40 value 314.967535
## iter 50 value 314.767347
## iter 60 value 314.715297
## iter 70 value 314.704398
## final value 314.704332
## converged
## # weights: 4
## initial value 479.367772
## final value 353.451775
## converged
## # weights: 10
## initial value 423.181956
## iter 10 value 321.563186
## iter 20 value 312.244470
## iter 30 value 311.890510
## iter 40 value 311.689741
## iter 50 value 310.724913
## iter 60 value 310.601201
## iter 70 value 310.206368
## iter 80 value 309.891611
## iter 90 value 309.644017
## final value 309.606785
## converged
## # weights: 16
## initial value 563.383177
## iter 10 value 353.452572
## final value 353.452355
## converged
## # weights: 4
## initial value 645.537132
## final value 351.835025
## converged
## # weights: 10
## initial value 529.559424
## iter 10 value 351.804858
## iter 20 value 302.525193
## iter 30 value 300.517979
## iter 40 value 300.339561
## iter 50 value 300.268268
## iter 60 value 300.200621
## iter 70 value 300.169649
## iter 80 value 300.145032
## iter 90 value 300.139728
## final value 300.117648
## converged
## # weights: 16
## initial value 592.436467
## iter 10 value 349.485245
## iter 20 value 301.545366
## iter 30 value 300.488930
## iter 40 value 300.428247
## iter 50 value 300.268575
## iter 60 value 300.200685
## iter 70 value 300.174242
## iter 80 value 300.142262
## iter 90 value 300.135116
## iter 100 value 300.110015
## final value 300.110015
## stopped after 100 iterations
## # weights: 4
## initial value 496.616331
## final value 351.951756
## converged
## # weights: 10
## initial value 491.032635
## iter 10 value 349.726980
## iter 20 value 305.264635
## iter 30 value 304.752910
## iter 40 value 304.296345
## iter 50 value 303.912582
## iter 60 value 303.802122
## iter 70 value 303.752248
## final value 303.751090
## converged
## # weights: 16
## initial value 515.464862
## iter 10 value 345.693467
## iter 20 value 305.157795
## iter 30 value 304.649322
## iter 40 value 304.326202
## iter 50 value 304.193281
## iter 60 value 304.088208
## iter 70 value 304.063651
## final value 304.062073
## converged
## # weights: 4
## initial value 484.893857
## iter 10 value 351.835270
## iter 10 value 351.835270
## final value 351.835270
## converged
## # weights: 10
## initial value 745.144513
## iter 10 value 350.767247
## iter 20 value 304.096463
## iter 30 value 300.640229
## iter 40 value 300.427651
## iter 50 value 300.333694
## iter 60 value 300.245862
## iter 70 value 300.237712
## iter 80 value 300.220831
## iter 90 value 300.219295
## final value 300.218767
## converged
## # weights: 16
## initial value 532.970488
## iter 10 value 348.963668
## iter 20 value 300.713452
## iter 30 value 300.382552
## iter 40 value 300.359623
## iter 50 value 300.240437
## iter 60 value 299.534703
## iter 70 value 298.817395
## iter 80 value 298.492711
## iter 90 value 298.117251
## iter 100 value 297.976743
## final value 297.976743
## stopped after 100 iterations
## # weights: 4
## initial value 743.347655
## iter 10 value 351.835058
## iter 10 value 351.835057
## final value 351.835057
## converged
## # weights: 10
## initial value 620.293125
## iter 10 value 351.832715
## iter 20 value 345.343886
## iter 30 value 303.038049
## iter 40 value 301.590701
## iter 50 value 301.385371
## iter 60 value 301.360857
## iter 70 value 301.285446
## iter 80 value 301.254463
## iter 90 value 301.243347
## iter 100 value 301.210705
## final value 301.210705
## stopped after 100 iterations
## # weights: 16
## initial value 436.184964
## iter 10 value 327.892214
## iter 20 value 301.889876
## iter 30 value 301.492178
## iter 40 value 301.292905
## iter 50 value 300.750108
## iter 60 value 300.189694
## iter 70 value 299.895621
## iter 80 value 299.413324
## iter 90 value 298.745042
## iter 100 value 297.420572
## final value 297.420572
## stopped after 100 iterations
## # weights: 4
## initial value 485.666283
## final value 351.951804
## converged
## # weights: 10
## initial value 463.923172
## iter 10 value 351.009038
## iter 20 value 306.698566
## iter 30 value 305.906576
## iter 40 value 305.550408
## iter 50 value 305.120603
## iter 60 value 304.873294
## iter 70 value 304.759550
## iter 80 value 304.748683
## final value 304.748465
## converged
## # weights: 16
## initial value 452.604598
## iter 10 value 349.972323
## iter 20 value 306.257850
## iter 30 value 305.751550
## iter 40 value 305.389776
## iter 50 value 305.220199
## iter 60 value 305.099865
## iter 70 value 304.985457
## iter 80 value 304.719250
## iter 90 value 304.640656
## iter 100 value 304.620859
## final value 304.620859
## stopped after 100 iterations
## # weights: 4
## initial value 743.382218
## final value 351.845835
## converged
## # weights: 10
## initial value 527.075900
## iter 10 value 351.835479
## final value 351.835455
## converged
## # weights: 16
## initial value 864.239359
## iter 10 value 341.915819
## iter 20 value 302.974497
## iter 30 value 301.178464
## iter 40 value 300.386436
## iter 50 value 300.105213
## iter 60 value 299.771881
## iter 70 value 299.581249
## iter 80 value 299.413650
## iter 90 value 298.781583
## iter 100 value 298.085206
## final value 298.085206
## stopped after 100 iterations
## # weights: 4
## initial value 498.596338
## iter 10 value 353.450644
## iter 20 value 350.351679
## iter 30 value 312.111105
## iter 40 value 306.079383
## iter 50 value 305.738162
## iter 60 value 305.618524
## iter 70 value 305.571508
## iter 80 value 305.558350
## iter 90 value 305.547557
## iter 100 value 305.537866
## final value 305.537866
## stopped after 100 iterations
## # weights: 10
## initial value 819.004248
## iter 10 value 333.286030
## iter 20 value 306.753880
## iter 30 value 305.743599
## iter 40 value 305.625298
## iter 50 value 305.544952
## iter 60 value 305.515176
## iter 70 value 305.508751
## final value 305.496532
## converged
## # weights: 16
## initial value 684.367124
## iter 10 value 337.194692
## iter 20 value 306.649541
## iter 30 value 305.659528
## iter 40 value 305.583489
## iter 50 value 305.559279
## iter 60 value 305.531101
## iter 70 value 305.517508
## iter 80 value 305.503779
## iter 90 value 305.408212
## iter 100 value 305.141876
## final value 305.141876
## stopped after 100 iterations
## # weights: 4
## initial value 515.340525
## iter 10 value 343.378940
## iter 20 value 310.224161
## final value 310.059896
## converged
## # weights: 10
## initial value 565.026388
## iter 10 value 353.342732
## iter 20 value 315.680438
## iter 30 value 310.034798
## iter 40 value 309.640541
## iter 50 value 309.446468
## iter 60 value 309.390369
## final value 309.390128
## converged
## # weights: 16
## initial value 1097.366680
## iter 10 value 401.856579
## iter 20 value 339.611867
## iter 30 value 310.571656
## iter 40 value 309.803153
## iter 50 value 309.646187
## iter 60 value 309.094336
## iter 70 value 308.889943
## iter 80 value 308.795393
## iter 90 value 308.775793
## iter 100 value 308.775383
## final value 308.775383
## stopped after 100 iterations
## # weights: 4
## initial value 499.878030
## final value 353.451781
## converged
## # weights: 10
## initial value 562.815320
## final value 353.451872
## converged
## # weights: 16
## initial value 374.681229
## iter 10 value 343.827506
## iter 20 value 308.494318
## iter 30 value 305.751691
## iter 40 value 305.004876
## iter 50 value 304.175431
## iter 60 value 303.869041
## iter 70 value 303.860084
## iter 80 value 303.844701
## iter 90 value 303.794273
## iter 100 value 303.744366
## final value 303.744366
## stopped after 100 iterations
## # weights: 4
## initial value 735.784515
## iter 10 value 310.229878
## iter 20 value 307.909477
## iter 30 value 307.696000
## iter 40 value 307.575957
## iter 50 value 307.553555
## iter 60 value 307.544410
## iter 70 value 307.531147
## iter 80 value 307.523727
## iter 90 value 307.517623
## iter 100 value 307.513289
## final value 307.513289
## stopped after 100 iterations
## # weights: 10
## initial value 586.874635
## iter 10 value 351.835084
## final value 351.835018
## converged
## # weights: 16
## initial value 905.343106
## iter 10 value 351.834815
## iter 20 value 348.641748
## iter 30 value 307.999166
## iter 40 value 307.535207
## iter 50 value 307.257000
## iter 60 value 306.693942
## iter 70 value 306.110903
## iter 80 value 306.088780
## iter 90 value 306.069082
## iter 100 value 306.048692
## final value 306.048692
## stopped after 100 iterations
## # weights: 4
## initial value 388.683499
## final value 351.951744
## converged
## # weights: 10
## initial value 498.460498
## iter 10 value 326.061571
## iter 20 value 311.986431
## iter 30 value 311.429948
## iter 40 value 311.095051
## iter 50 value 310.772414
## iter 60 value 310.621328
## iter 70 value 310.605408
## final value 310.605401
## converged
## # weights: 16
## initial value 440.766095
## iter 10 value 326.343941
## iter 20 value 311.734050
## iter 30 value 310.638558
## iter 40 value 310.453164
## iter 50 value 310.304772
## iter 60 value 310.246179
## iter 70 value 310.225748
## final value 310.225353
## converged
## # weights: 4
## initial value 543.458468
## iter 10 value 351.835269
## iter 10 value 351.835269
## final value 351.835269
## converged
## # weights: 10
## initial value 582.821496
## iter 10 value 343.477808
## iter 20 value 309.423628
## iter 30 value 308.009565
## iter 40 value 307.693516
## iter 50 value 307.629747
## iter 60 value 307.579135
## final value 307.562701
## converged
## # weights: 16
## initial value 437.422388
## iter 10 value 319.846620
## iter 20 value 308.058067
## iter 30 value 307.740744
## iter 40 value 307.657051
## iter 50 value 307.572628
## iter 60 value 307.549363
## iter 70 value 307.541145
## iter 80 value 307.522792
## iter 90 value 307.460553
## iter 100 value 306.832569
## final value 306.832569
## stopped after 100 iterations
## # weights: 4
## initial value 359.864559
## iter 10 value 351.835035
## iter 10 value 351.835035
## final value 351.835035
## converged
## # weights: 10
## initial value 565.700042
## final value 351.835073
## converged
## # weights: 16
## initial value 644.170970
## iter 10 value 351.798387
## iter 20 value 347.334596
## iter 30 value 308.238735
## iter 40 value 307.526548
## iter 50 value 307.449006
## iter 60 value 307.408890
## iter 70 value 307.352574
## iter 80 value 307.330224
## iter 90 value 307.318712
## iter 100 value 307.314110
## final value 307.314110
## stopped after 100 iterations
## # weights: 4
## initial value 642.281995
## iter 10 value 322.810015
## iter 20 value 311.757453
## final value 311.533246
## converged
## # weights: 10
## initial value 471.835377
## iter 10 value 341.122546
## iter 20 value 314.179021
## iter 30 value 311.589001
## iter 40 value 311.104496
## iter 50 value 310.749008
## iter 60 value 310.542008
## iter 70 value 310.449750
## iter 80 value 310.442652
## final value 310.442488
## converged
## # weights: 16
## initial value 711.625146
## iter 10 value 350.799767
## iter 20 value 314.780748
## iter 30 value 311.264610
## iter 40 value 310.776776
## iter 50 value 310.370149
## iter 60 value 310.189646
## iter 70 value 310.104948
## iter 80 value 310.087170
## iter 90 value 310.085316
## iter 90 value 310.085314
## final value 310.085314
## converged
## # weights: 4
## initial value 403.968568
## final value 351.835482
## converged
## # weights: 10
## initial value 647.806005
## iter 10 value 351.835556
## final value 351.835437
## converged
## # weights: 16
## initial value 523.389946
## iter 10 value 312.783410
## iter 20 value 309.099470
## iter 30 value 307.942834
## iter 40 value 307.600201
## iter 50 value 307.477733
## iter 60 value 307.429305
## iter 70 value 307.411479
## iter 80 value 307.324344
## iter 90 value 306.823942
## iter 100 value 306.543883
## final value 306.543883
## stopped after 100 iterations
## # weights: 4
## initial value 476.148948
## final value 351.835031
## converged
## # weights: 10
## initial value 760.326081
## final value 351.835000
## converged
## # weights: 16
## initial value 972.491404
## iter 10 value 351.518408
## iter 20 value 309.260903
## iter 30 value 307.516102
## iter 40 value 307.391313
## iter 50 value 307.258837
## iter 60 value 307.197217
## iter 70 value 307.158063
## iter 80 value 307.106404
## iter 90 value 306.819826
## iter 100 value 306.689009
## final value 306.689009
## stopped after 100 iterations
## # weights: 4
## initial value 492.091682
## iter 10 value 339.447244
## iter 20 value 311.313867
## final value 311.312983
## converged
## # weights: 10
## initial value 441.853606
## iter 10 value 339.849254
## iter 20 value 311.342553
## iter 30 value 310.907255
## iter 40 value 310.547598
## iter 50 value 310.330242
## iter 60 value 310.250000
## final value 310.248433
## converged
## # weights: 16
## initial value 422.361839
## iter 10 value 331.850911
## iter 20 value 311.679581
## iter 30 value 311.104008
## iter 40 value 310.836974
## iter 50 value 310.666528
## iter 60 value 310.613497
## iter 70 value 310.603449
## final value 310.603385
## converged
## # weights: 4
## initial value 401.270288
## final value 351.835323
## converged
## # weights: 10
## initial value 435.887244
## iter 10 value 351.143892
## iter 20 value 321.388205
## iter 30 value 309.505262
## iter 40 value 307.523279
## iter 50 value 307.322572
## iter 60 value 307.221755
## iter 70 value 307.208754
## iter 80 value 307.187881
## final value 307.187041
## converged
## # weights: 16
## initial value 729.414109
## iter 10 value 351.804011
## iter 20 value 308.702930
## iter 30 value 307.239753
## final value 307.186294
## converged
## # weights: 4
## initial value 433.680756
## final value 351.835003
## converged
## # weights: 10
## initial value 357.288673
## iter 10 value 351.835886
## final value 351.835595
## converged
## # weights: 16
## initial value 506.119312
## iter 10 value 351.094890
## iter 20 value 338.715820
## iter 30 value 312.893420
## iter 40 value 307.693730
## iter 50 value 307.443797
## iter 60 value 307.389368
## iter 70 value 307.370780
## final value 307.368351
## converged
## # weights: 4
## initial value 406.731726
## final value 351.951595
## converged
## # weights: 10
## initial value 628.782654
## iter 10 value 342.906006
## iter 20 value 313.195003
## iter 30 value 312.165073
## iter 40 value 311.472844
## iter 50 value 311.180049
## iter 60 value 311.132731
## final value 311.132574
## converged
## # weights: 16
## initial value 568.937962
## iter 10 value 348.364623
## iter 20 value 312.111592
## iter 30 value 311.681816
## iter 40 value 311.187735
## iter 50 value 310.763943
## iter 60 value 310.486491
## iter 70 value 310.279370
## iter 80 value 310.157193
## iter 90 value 310.096345
## iter 100 value 310.086846
## final value 310.086846
## stopped after 100 iterations
## # weights: 4
## initial value 506.248616
## iter 10 value 349.638173
## iter 20 value 309.131317
## iter 30 value 307.863222
## iter 40 value 307.603889
## iter 50 value 307.566253
## iter 60 value 307.543340
## iter 70 value 307.520158
## iter 80 value 307.508425
## iter 90 value 307.500828
## iter 100 value 307.496342
## final value 307.496342
## stopped after 100 iterations
## # weights: 10
## initial value 831.886744
## iter 10 value 345.975343
## iter 20 value 317.065914
## iter 30 value 309.591810
## iter 40 value 307.797659
## iter 50 value 307.590047
## iter 60 value 307.546055
## iter 70 value 307.505814
## iter 80 value 307.499999
## iter 90 value 307.488236
## iter 100 value 307.487159
## final value 307.487159
## stopped after 100 iterations
## # weights: 16
## initial value 842.662416
## iter 10 value 320.856043
## iter 20 value 308.078473
## iter 30 value 307.350971
## iter 40 value 307.052269
## iter 50 value 306.686399
## iter 60 value 306.457347
## iter 70 value 306.371226
## iter 80 value 306.234629
## iter 90 value 306.042818
## iter 100 value 305.887422
## final value 305.887422
## stopped after 100 iterations
## # weights: 4
## initial value 671.859211
## final value 353.451227
## converged
## # weights: 10
## initial value 452.369034
## iter 10 value 353.459621
## iter 20 value 353.451556
## iter 20 value 353.451553
## iter 20 value 353.451553
## final value 353.451553
## converged
## # weights: 16
## initial value 1253.812268
## final value 353.451562
## converged
## # weights: 4
## initial value 554.365004
## iter 10 value 354.327356
## iter 20 value 313.366030
## iter 30 value 312.635843
## iter 30 value 312.635840
## iter 30 value 312.635840
## final value 312.635840
## converged
## # weights: 10
## initial value 763.174244
## iter 10 value 314.304481
## iter 20 value 311.198409
## iter 30 value 310.396464
## iter 40 value 310.064017
## iter 50 value 309.884578
## iter 60 value 309.844476
## iter 70 value 309.841126
## final value 309.841097
## converged
## # weights: 16
## initial value 383.808341
## iter 10 value 332.871301
## iter 20 value 312.613829
## iter 30 value 311.054564
## iter 40 value 310.451729
## iter 50 value 310.290325
## iter 60 value 310.179346
## iter 70 value 310.130930
## iter 80 value 310.126250
## final value 310.126237
## converged
## # weights: 4
## initial value 579.064629
## final value 353.452475
## converged
## # weights: 10
## initial value 653.239490
## iter 10 value 311.845780
## iter 20 value 306.961054
## iter 30 value 306.744262
## iter 40 value 306.692148
## iter 50 value 306.652354
## iter 60 value 306.623285
## iter 70 value 306.551809
## iter 80 value 305.906679
## iter 90 value 305.846001
## iter 100 value 305.664543
## final value 305.664543
## stopped after 100 iterations
## # weights: 16
## initial value 662.936069
## iter 10 value 353.456105
## iter 20 value 352.945495
## iter 30 value 307.533276
## iter 40 value 306.869549
## iter 50 value 306.744729
## iter 60 value 306.557457
## iter 70 value 305.996759
## iter 80 value 305.675720
## iter 90 value 305.553950
## iter 100 value 305.440413
## final value 305.440413
## stopped after 100 iterations
## # weights: 4
## initial value 613.013037
## iter 10 value 351.835133
## iter 10 value 351.835133
## final value 351.835133
## converged
## # weights: 10
## initial value 657.729522
## final value 351.835033
## converged
## # weights: 16
## initial value 578.552934
## iter 10 value 351.834768
## iter 20 value 330.408526
## iter 30 value 300.554597
## iter 40 value 300.383927
## iter 50 value 300.367982
## iter 60 value 300.293239
## iter 70 value 300.269758
## iter 80 value 300.256777
## final value 300.239972
## converged
## # weights: 4
## initial value 618.843416
## iter 10 value 351.951567
## final value 351.951366
## converged
## # weights: 10
## initial value 415.954054
## iter 10 value 349.794004
## iter 20 value 311.810390
## iter 30 value 303.854481
## iter 40 value 303.728425
## iter 50 value 303.675134
## final value 303.674817
## converged
## # weights: 16
## initial value 461.826882
## iter 10 value 350.474267
## iter 20 value 308.828744
## iter 30 value 305.537201
## final value 305.385187
## converged
## # weights: 4
## initial value 544.818733
## iter 10 value 348.738558
## iter 20 value 300.992656
## iter 30 value 300.420966
## final value 300.372816
## converged
## # weights: 10
## initial value 420.218645
## iter 10 value 351.835302
## final value 351.835286
## converged
## # weights: 16
## initial value 359.237886
## iter 10 value 347.310635
## iter 20 value 301.469256
## iter 30 value 300.321671
## final value 300.299542
## converged
## # weights: 4
## initial value 441.736015
## iter 10 value 309.635399
## iter 20 value 306.014842
## iter 30 value 305.182942
## iter 40 value 304.899657
## iter 50 value 304.832965
## iter 60 value 304.797208
## iter 70 value 304.772547
## iter 80 value 304.752428
## final value 304.736540
## converged
## # weights: 10
## initial value 383.682591
## iter 10 value 346.670630
## iter 20 value 305.388924
## iter 30 value 304.813138
## iter 40 value 304.742349
## iter 50 value 304.702091
## iter 60 value 304.542333
## iter 70 value 304.002277
## iter 80 value 303.658985
## iter 90 value 303.184732
## iter 100 value 302.534715
## final value 302.534715
## stopped after 100 iterations
## # weights: 16
## initial value 371.141122
## iter 10 value 351.835059
## final value 351.835011
## converged
## # weights: 4
## initial value 354.969456
## final value 351.951773
## converged
## # weights: 10
## initial value 383.069848
## iter 10 value 313.495008
## iter 20 value 309.323455
## iter 30 value 308.875114
## iter 40 value 308.739691
## iter 50 value 308.715317
## iter 60 value 308.703491
## final value 308.701865
## converged
## # weights: 16
## initial value 378.969977
## iter 10 value 351.406070
## iter 20 value 310.159526
## iter 30 value 309.156815
## iter 40 value 308.588404
## iter 50 value 308.218021
## iter 60 value 308.089699
## iter 70 value 308.007450
## iter 80 value 307.963569
## iter 90 value 307.963100
## final value 307.963094
## converged
## # weights: 4
## initial value 570.075868
## iter 10 value 351.835274
## iter 10 value 351.835274
## final value 351.835274
## converged
## # weights: 10
## initial value 766.440901
## iter 10 value 310.544655
## iter 20 value 305.803274
## iter 30 value 305.193894
## iter 40 value 304.876421
## iter 50 value 304.829678
## iter 60 value 304.789605
## iter 70 value 304.779994
## iter 80 value 304.776710
## final value 304.776495
## converged
## # weights: 16
## initial value 365.968272
## iter 10 value 341.976501
## iter 20 value 305.578087
## iter 30 value 304.917064
## iter 40 value 304.800313
## iter 50 value 304.771494
## iter 60 value 304.642272
## iter 70 value 304.189122
## iter 80 value 303.787232
## iter 90 value 303.421542
## iter 100 value 303.073568
## final value 303.073568
## stopped after 100 iterations
## # weights: 4
## initial value 407.283871
## iter 10 value 345.838533
## iter 20 value 306.220900
## iter 30 value 306.014111
## iter 40 value 305.920773
## iter 50 value 305.870330
## iter 60 value 305.848128
## iter 70 value 305.838153
## iter 80 value 305.830856
## iter 90 value 305.825323
## iter 100 value 305.818925
## final value 305.818925
## stopped after 100 iterations
## # weights: 10
## initial value 695.216633
## iter 10 value 351.835075
## final value 351.835017
## converged
## # weights: 16
## initial value 354.719444
## iter 10 value 320.079876
## iter 20 value 306.361346
## iter 30 value 305.915072
## iter 40 value 305.851007
## iter 50 value 305.810654
## iter 60 value 305.065665
## iter 70 value 304.951423
## iter 80 value 304.847457
## iter 90 value 304.652188
## iter 100 value 304.455756
## final value 304.455756
## stopped after 100 iterations
## # weights: 4
## initial value 451.214831
## iter 10 value 352.187543
## final value 351.951566
## converged
## # weights: 10
## initial value 395.478972
## iter 10 value 351.617749
## iter 20 value 340.771132
## iter 30 value 310.128245
## iter 40 value 309.377369
## iter 50 value 309.291125
## iter 60 value 309.084111
## iter 70 value 309.040335
## iter 80 value 309.032735
## iter 80 value 309.032734
## iter 80 value 309.032734
## final value 309.032734
## converged
## # weights: 16
## initial value 542.998246
## iter 10 value 352.105898
## iter 20 value 310.814279
## iter 30 value 310.432008
## iter 40 value 309.818658
## iter 50 value 309.610022
## iter 60 value 309.471260
## final value 309.464525
## converged
## # weights: 4
## initial value 517.491206
## iter 10 value 348.355075
## iter 20 value 310.696504
## iter 30 value 307.869081
## iter 40 value 306.350952
## iter 50 value 306.031928
## iter 60 value 305.942775
## iter 70 value 305.890933
## iter 80 value 305.883074
## iter 90 value 305.874557
## iter 90 value 305.874557
## final value 305.874557
## converged
## # weights: 10
## initial value 390.586542
## iter 10 value 351.835322
## final value 351.835315
## converged
## # weights: 16
## initial value 352.101020
## iter 10 value 308.922228
## iter 20 value 306.392861
## iter 30 value 305.885269
## iter 40 value 305.713261
## iter 50 value 304.922807
## iter 60 value 304.636772
## iter 70 value 304.066069
## iter 80 value 303.842059
## iter 90 value 302.899708
## iter 100 value 302.836484
## final value 302.836484
## stopped after 100 iterations
## # weights: 4
## initial value 406.159803
## final value 353.451526
## converged
## # weights: 10
## initial value 538.802585
## iter 10 value 353.451596
## final value 353.451556
## converged
## # weights: 16
## initial value 362.774245
## iter 10 value 350.286676
## iter 20 value 308.247774
## iter 30 value 304.264165
## iter 40 value 303.483986
## iter 50 value 303.397140
## iter 60 value 303.358215
## iter 70 value 303.296075
## iter 80 value 303.282862
## iter 90 value 303.278178
## iter 100 value 303.269128
## final value 303.269128
## stopped after 100 iterations
## # weights: 4
## initial value 500.280177
## iter 10 value 354.900640
## iter 20 value 344.192117
## iter 30 value 308.408053
## final value 308.213092
## converged
## # weights: 10
## initial value 453.030834
## iter 10 value 352.078590
## iter 20 value 310.775513
## iter 30 value 308.112675
## iter 40 value 307.876338
## iter 50 value 307.528591
## iter 60 value 307.410530
## iter 70 value 307.389785
## final value 307.389367
## converged
## # weights: 16
## initial value 721.542360
## iter 10 value 337.549294
## iter 20 value 308.855276
## iter 30 value 308.399577
## iter 40 value 307.465872
## iter 50 value 306.792459
## iter 60 value 306.619021
## iter 70 value 306.531604
## iter 80 value 306.477626
## iter 90 value 306.471489
## iter 90 value 306.471487
## iter 90 value 306.471487
## final value 306.471487
## converged
## # weights: 4
## initial value 653.876840
## iter 10 value 353.451813
## iter 10 value 353.451813
## final value 353.451813
## converged
## # weights: 10
## initial value 490.649279
## iter 10 value 353.452170
## final value 353.452131
## converged
## # weights: 16
## initial value 567.633693
## iter 10 value 345.820607
## iter 20 value 305.601153
## iter 30 value 303.588581
## iter 40 value 303.419073
## iter 50 value 303.391905
## iter 60 value 303.367533
## final value 303.356398
## converged
## # weights: 4
## initial value 434.859412
## final value 353.451503
## converged
## # weights: 10
## initial value 370.728389
## iter 10 value 353.451593
## final value 353.451551
## converged
## # weights: 16
## initial value 417.676708
## iter 10 value 338.953808
## iter 20 value 308.340801
## iter 30 value 306.958597
## iter 40 value 306.778364
## iter 50 value 306.669845
## iter 60 value 306.547692
## iter 70 value 306.528241
## iter 80 value 306.514552
## iter 90 value 306.510171
## iter 100 value 306.486004
## final value 306.486004
## stopped after 100 iterations
## # weights: 4
## initial value 453.622427
## iter 10 value 353.580634
## final value 353.567255
## converged
## # weights: 10
## initial value 580.410680
## iter 10 value 353.696744
## iter 20 value 353.633242
## iter 30 value 353.549671
## final value 353.549383
## converged
## # weights: 16
## initial value 422.118437
## iter 10 value 339.096195
## iter 20 value 312.131360
## iter 30 value 310.503129
## iter 40 value 309.958330
## iter 50 value 309.840819
## iter 60 value 309.785798
## final value 309.784268
## converged
## # weights: 4
## initial value 578.452674
## final value 353.451795
## converged
## # weights: 10
## initial value 475.139157
## iter 10 value 337.282092
## iter 20 value 307.062585
## iter 30 value 306.729511
## iter 40 value 306.628106
## iter 50 value 306.548949
## iter 60 value 306.255612
## iter 70 value 305.929759
## iter 80 value 305.620370
## iter 90 value 305.434103
## iter 100 value 305.302875
## final value 305.302875
## stopped after 100 iterations
## # weights: 16
## initial value 470.387841
## iter 10 value 353.450869
## iter 20 value 309.898965
## iter 30 value 306.734907
## iter 40 value 306.699846
## iter 50 value 306.654024
## iter 60 value 306.638498
## iter 70 value 306.587131
## final value 306.584738
## converged
## # weights: 4
## initial value 644.614345
## iter 10 value 353.451681
## iter 10 value 353.451681
## final value 353.451681
## converged
## # weights: 10
## initial value 1068.660371
## iter 10 value 316.467817
## iter 20 value 303.591130
## iter 30 value 303.035244
## iter 40 value 302.973416
## iter 50 value 302.871969
## iter 60 value 302.860451
## iter 70 value 302.818161
## iter 80 value 302.813398
## iter 90 value 302.789989
## iter 100 value 302.785452
## final value 302.785452
## stopped after 100 iterations
## # weights: 16
## initial value 765.044742
## iter 10 value 353.451638
## final value 353.451526
## converged
## # weights: 4
## initial value 355.240204
## final value 353.567492
## converged
## # weights: 10
## initial value 621.191716
## iter 10 value 342.450344
## iter 20 value 307.748330
## iter 30 value 307.330981
## iter 40 value 306.741327
## iter 50 value 306.511449
## iter 60 value 306.438741
## iter 70 value 306.427718
## final value 306.427704
## converged
## # weights: 16
## initial value 513.709775
## iter 10 value 351.069662
## iter 20 value 311.195720
## iter 30 value 307.475415
## iter 40 value 307.019493
## iter 50 value 306.413446
## iter 60 value 306.208721
## iter 70 value 306.087428
## iter 80 value 306.029380
## iter 90 value 306.001268
## iter 100 value 305.995624
## final value 305.995624
## stopped after 100 iterations
## # weights: 4
## initial value 715.088825
## final value 353.451711
## converged
## # weights: 10
## initial value 630.036088
## iter 10 value 339.592779
## iter 20 value 303.183874
## iter 30 value 302.081966
## iter 40 value 300.779738
## iter 50 value 300.449084
## iter 60 value 300.236308
## iter 70 value 300.184846
## iter 80 value 300.161184
## iter 90 value 300.110076
## iter 100 value 299.804829
## final value 299.804829
## stopped after 100 iterations
## # weights: 16
## initial value 634.238876
## iter 10 value 353.361389
## iter 20 value 330.973127
## iter 30 value 302.985755
## iter 40 value 301.414288
## iter 50 value 300.890837
## iter 60 value 300.818109
## iter 70 value 300.695126
## iter 80 value 300.584610
## iter 90 value 300.486116
## iter 100 value 300.391581
## final value 300.391581
## stopped after 100 iterations
## # weights: 4
## initial value 556.051016
## final value 351.835016
## converged
## # weights: 10
## initial value 669.974785
## iter 10 value 351.835288
## final value 351.835024
## converged
## # weights: 16
## initial value 467.162877
## iter 10 value 346.426961
## iter 20 value 305.418602
## iter 30 value 304.996421
## iter 40 value 304.905308
## iter 50 value 304.870251
## iter 60 value 304.859616
## iter 70 value 304.832417
## iter 80 value 304.824734
## iter 90 value 304.822090
## iter 100 value 304.810931
## final value 304.810931
## stopped after 100 iterations
## # weights: 4
## initial value 448.851907
## iter 10 value 356.737346
## iter 20 value 349.196965
## iter 30 value 309.726826
## iter 40 value 309.271579
## iter 40 value 309.271579
## iter 40 value 309.271579
## final value 309.271579
## converged
## # weights: 10
## initial value 545.889729
## iter 10 value 351.493393
## iter 20 value 318.020556
## iter 30 value 308.610013
## iter 40 value 308.378660
## iter 50 value 308.176054
## iter 60 value 308.129756
## iter 70 value 308.126564
## iter 70 value 308.126562
## iter 70 value 308.126562
## final value 308.126562
## converged
## # weights: 16
## initial value 426.522466
## iter 10 value 337.782517
## iter 20 value 309.408050
## iter 30 value 308.734244
## iter 40 value 308.364161
## iter 50 value 308.114087
## iter 60 value 307.850713
## iter 70 value 307.749382
## iter 80 value 307.730461
## final value 307.727190
## converged
## # weights: 4
## initial value 425.395731
## iter 10 value 351.835195
## iter 10 value 351.835194
## final value 351.835194
## converged
## # weights: 10
## initial value 374.036082
## iter 10 value 351.769782
## iter 20 value 313.179759
## iter 30 value 305.225837
## iter 40 value 304.954453
## iter 50 value 304.921407
## iter 60 value 304.908131
## iter 70 value 304.834127
## iter 80 value 304.468721
## iter 90 value 303.894177
## iter 100 value 303.693736
## final value 303.693736
## stopped after 100 iterations
## # weights: 16
## initial value 488.115143
## iter 10 value 351.835495
## final value 351.835450
## converged
## # weights: 4
## initial value 500.379879
## final value 351.835074
## converged
## # weights: 10
## initial value 569.394565
## iter 10 value 343.630482
## iter 20 value 308.255270
## iter 30 value 307.328895
## iter 40 value 306.849588
## iter 50 value 306.697873
## iter 60 value 306.662450
## iter 70 value 306.652741
## iter 80 value 306.631714
## iter 90 value 306.625373
## iter 100 value 306.616159
## final value 306.616159
## stopped after 100 iterations
## # weights: 16
## initial value 706.893258
## iter 10 value 351.835130
## final value 351.835019
## converged
## # weights: 4
## initial value 372.039499
## final value 351.951541
## converged
## # weights: 10
## initial value 440.028579
## iter 10 value 351.111267
## iter 20 value 311.950256
## iter 30 value 310.762636
## iter 40 value 310.354142
## iter 50 value 310.068098
## iter 60 value 309.969685
## iter 70 value 309.935107
## final value 309.934888
## converged
## # weights: 16
## initial value 456.544714
## iter 10 value 351.988231
## iter 20 value 351.814601
## iter 30 value 340.395999
## iter 40 value 311.533301
## iter 50 value 310.724147
## iter 60 value 310.533319
## iter 70 value 310.282617
## iter 80 value 310.149304
## iter 90 value 310.028739
## iter 100 value 309.822601
## final value 309.822601
## stopped after 100 iterations
## # weights: 4
## initial value 357.412562
## final value 351.835214
## converged
## # weights: 10
## initial value 437.727094
## iter 10 value 351.837108
## final value 351.837051
## converged
## # weights: 16
## initial value 660.029678
## iter 10 value 351.714369
## iter 20 value 320.415723
## iter 30 value 307.718896
## iter 40 value 306.913139
## iter 50 value 306.749357
## iter 60 value 306.695229
## iter 70 value 306.663556
## iter 80 value 306.518679
## iter 90 value 305.807867
## iter 100 value 305.599204
## final value 305.599204
## stopped after 100 iterations
## # weights: 4
## initial value 433.746950
## final value 351.835016
## converged
## # weights: 10
## initial value 375.755712
## iter 10 value 349.636218
## iter 20 value 306.255832
## iter 30 value 305.760753
## iter 40 value 305.680580
## iter 50 value 305.660794
## iter 60 value 305.632502
## iter 70 value 305.613221
## iter 80 value 305.609636
## iter 90 value 305.590665
## iter 100 value 305.586797
## final value 305.586797
## stopped after 100 iterations
## # weights: 16
## initial value 632.551091
## iter 10 value 351.833400
## iter 20 value 307.108834
## iter 30 value 306.115833
## iter 40 value 305.859852
## iter 50 value 305.722029
## iter 60 value 305.667120
## iter 70 value 305.636352
## iter 80 value 305.626189
## iter 90 value 305.604228
## iter 100 value 305.593241
## final value 305.593241
## stopped after 100 iterations
## # weights: 4
## initial value 513.522011
## iter 10 value 355.189455
## iter 20 value 329.462160
## iter 30 value 310.127217
## final value 309.880033
## converged
## # weights: 10
## initial value 677.202173
## iter 10 value 346.506336
## iter 20 value 311.693268
## iter 30 value 309.850382
## iter 40 value 309.551403
## iter 50 value 309.077242
## iter 60 value 308.880460
## iter 70 value 308.821269
## iter 80 value 308.819794
## final value 308.819776
## converged
## # weights: 16
## initial value 361.925453
## iter 10 value 345.251700
## iter 20 value 310.201885
## iter 30 value 309.585946
## iter 40 value 308.948524
## iter 50 value 308.711482
## iter 60 value 308.531155
## iter 70 value 308.454365
## iter 80 value 308.436402
## final value 308.436155
## converged
## # weights: 4
## initial value 600.694253
## final value 351.835211
## converged
## # weights: 10
## initial value 400.779087
## iter 10 value 349.842025
## iter 20 value 306.893835
## iter 30 value 305.820048
## iter 40 value 305.754383
## iter 50 value 305.691566
## iter 60 value 305.674258
## iter 70 value 305.667588
## iter 80 value 305.663099
## iter 90 value 305.660620
## final value 305.659769
## converged
## # weights: 16
## initial value 444.049417
## iter 10 value 349.834325
## iter 20 value 306.065821
## iter 30 value 305.608443
## iter 40 value 304.991782
## iter 50 value 304.759212
## iter 60 value 304.523256
## iter 70 value 304.282218
## iter 80 value 303.747221
## iter 90 value 303.211221
## iter 100 value 303.130552
## final value 303.130552
## stopped after 100 iterations
## # weights: 4
## initial value 544.199775
## final value 353.451549
## converged
## # weights: 10
## initial value 589.795128
## final value 353.451488
## converged
## # weights: 16
## initial value 526.031517
## iter 10 value 299.038719
## iter 20 value 296.182858
## iter 30 value 296.095955
## iter 40 value 296.006233
## iter 50 value 295.963361
## iter 60 value 295.767620
## iter 70 value 294.887238
## iter 80 value 294.741500
## iter 90 value 294.584747
## iter 100 value 294.512474
## final value 294.512474
## stopped after 100 iterations
## # weights: 4
## initial value 561.243033
## final value 353.567360
## converged
## # weights: 10
## initial value 424.675619
## iter 10 value 313.735947
## iter 20 value 302.791683
## iter 30 value 302.024203
## iter 40 value 301.704574
## iter 50 value 301.557462
## final value 301.550764
## converged
## # weights: 16
## initial value 623.562437
## iter 10 value 340.343621
## iter 20 value 304.801451
## iter 30 value 302.069469
## iter 40 value 301.752725
## iter 50 value 301.490352
## iter 60 value 301.262390
## iter 70 value 301.111788
## iter 80 value 300.981069
## iter 90 value 300.941152
## final value 300.938213
## converged
## # weights: 4
## initial value 455.359405
## iter 10 value 353.451825
## iter 10 value 353.451825
## final value 353.451825
## converged
## # weights: 10
## initial value 417.962640
## final value 353.452009
## converged
## # weights: 16
## initial value 673.212405
## iter 10 value 353.452629
## final value 353.452539
## converged
## # weights: 4
## initial value 550.307036
## final value 351.835016
## converged
## # weights: 10
## initial value 411.845797
## iter 10 value 320.975863
## iter 20 value 309.587391
## iter 30 value 309.360971
## iter 40 value 309.284392
## iter 50 value 309.260147
## iter 60 value 309.247369
## final value 309.246471
## converged
## # weights: 16
## initial value 613.526235
## iter 10 value 351.023452
## iter 20 value 343.735996
## iter 30 value 310.823477
## iter 40 value 309.547852
## iter 50 value 309.431667
## iter 60 value 309.314347
## iter 70 value 309.271631
## iter 80 value 309.248750
## iter 90 value 309.234529
## iter 100 value 309.220113
## final value 309.220113
## stopped after 100 iterations
## # weights: 4
## initial value 467.045367
## iter 10 value 339.781426
## iter 20 value 313.465939
## final value 313.463634
## converged
## # weights: 10
## initial value 525.491254
## iter 10 value 348.539981
## iter 20 value 313.643560
## iter 30 value 313.123551
## iter 40 value 312.622822
## iter 50 value 312.416532
## iter 60 value 312.360376
## iter 70 value 312.359392
## iter 70 value 312.359389
## iter 70 value 312.359389
## final value 312.359389
## converged
## # weights: 16
## initial value 391.280538
## iter 10 value 316.695505
## iter 20 value 313.125316
## iter 30 value 312.448786
## iter 40 value 312.340877
## iter 50 value 312.324248
## final value 312.324243
## converged
## # weights: 4
## initial value 638.269289
## final value 351.835205
## converged
## # weights: 10
## initial value 544.771426
## iter 10 value 346.037286
## iter 20 value 310.343265
## iter 30 value 309.345606
## iter 40 value 309.027682
## iter 50 value 308.325922
## iter 60 value 308.085317
## iter 70 value 307.678439
## iter 80 value 307.302465
## iter 90 value 307.025546
## iter 100 value 306.998781
## final value 306.998781
## stopped after 100 iterations
## # weights: 16
## initial value 575.193288
## iter 10 value 349.570838
## iter 20 value 311.667557
## iter 30 value 308.665983
## iter 40 value 307.249160
## iter 50 value 307.131673
## iter 60 value 307.085677
## iter 70 value 307.061173
## iter 80 value 307.058238
## iter 80 value 307.058237
## iter 80 value 307.058236
## final value 307.058236
## converged
## # weights: 4
## initial value 537.091244
## iter 10 value 351.835034
## iter 10 value 351.835034
## final value 351.835034
## converged
## # weights: 10
## initial value 468.607075
## iter 10 value 351.835053
## final value 351.835016
## converged
## # weights: 16
## initial value 563.974410
## iter 10 value 320.045976
## iter 20 value 310.424026
## iter 30 value 309.359656
## iter 40 value 309.267505
## final value 309.258440
## converged
## # weights: 4
## initial value 421.995775
## iter 10 value 348.423165
## iter 20 value 312.852317
## iter 20 value 312.852317
## final value 312.852317
## converged
## # weights: 10
## initial value 499.633253
## iter 10 value 352.649705
## iter 20 value 320.112725
## iter 30 value 312.785304
## iter 40 value 312.436438
## iter 50 value 312.129458
## iter 60 value 311.902783
## iter 70 value 311.853995
## iter 80 value 311.847985
## final value 311.847942
## converged
## # weights: 16
## initial value 392.445851
## iter 10 value 351.942800
## final value 351.942535
## converged
## # weights: 4
## initial value 835.660226
## final value 351.841377
## converged
## # weights: 10
## initial value 519.694063
## iter 10 value 351.835498
## final value 351.835480
## converged
## # weights: 16
## initial value 353.738695
## iter 10 value 347.185911
## iter 20 value 310.707135
## iter 30 value 309.340732
## iter 40 value 309.237420
## iter 50 value 309.026744
## iter 60 value 308.962981
## iter 70 value 308.894309
## iter 80 value 308.713313
## iter 90 value 308.310260
## iter 100 value 308.141126
## final value 308.141126
## stopped after 100 iterations
## # weights: 4
## initial value 633.435514
## final value 351.835013
## converged
## # weights: 10
## initial value 417.602616
## iter 10 value 351.835065
## final value 351.835026
## converged
## # weights: 16
## initial value 548.705463
## iter 10 value 346.056867
## iter 20 value 303.719575
## iter 30 value 303.611876
## iter 40 value 303.600718
## iter 50 value 303.482270
## iter 60 value 303.433295
## iter 70 value 303.299933
## iter 80 value 303.068268
## iter 90 value 302.962541
## iter 100 value 302.905590
## final value 302.905590
## stopped after 100 iterations
## # weights: 4
## initial value 550.958898
## iter 10 value 351.208643
## iter 20 value 311.087343
## iter 30 value 307.745766
## final value 307.744485
## converged
## # weights: 10
## initial value 552.244000
## iter 10 value 350.512648
## iter 20 value 309.520673
## iter 30 value 307.648303
## iter 40 value 307.062672
## iter 50 value 306.790910
## iter 60 value 306.714229
## iter 70 value 306.711827
## final value 306.711823
## converged
## # weights: 16
## initial value 658.100357
## iter 10 value 346.948275
## iter 20 value 308.792495
## iter 30 value 307.410887
## iter 40 value 307.195050
## iter 50 value 306.802553
## iter 60 value 306.574415
## iter 70 value 306.420073
## iter 80 value 306.392145
## final value 306.390074
## converged
## # weights: 4
## initial value 429.103541
## iter 10 value 337.093617
## iter 20 value 303.938013
## iter 30 value 303.620255
## iter 40 value 303.553726
## iter 50 value 303.512862
## iter 60 value 303.504412
## iter 70 value 303.486987
## final value 303.485580
## converged
## # weights: 10
## initial value 352.635591
## iter 10 value 351.835442
## final value 351.835432
## converged
## # weights: 16
## initial value 460.870528
## iter 10 value 342.757246
## iter 20 value 304.415927
## iter 30 value 303.508837
## iter 40 value 303.502399
## iter 50 value 303.486789
## iter 60 value 303.468655
## iter 70 value 303.463575
## final value 303.462213
## converged
## # weights: 4
## initial value 549.857138
## iter 10 value 315.332767
## iter 20 value 307.394175
## iter 30 value 306.899686
## iter 40 value 306.726998
## iter 50 value 306.653980
## iter 60 value 306.621407
## iter 70 value 306.612141
## iter 80 value 306.603818
## iter 90 value 306.595906
## iter 100 value 306.589028
## final value 306.589028
## stopped after 100 iterations
## # weights: 10
## initial value 564.251024
## iter 10 value 310.585709
## iter 20 value 307.113000
## final value 306.937516
## converged
## # weights: 16
## initial value 562.611655
## iter 10 value 353.451583
## final value 353.451552
## converged
## # weights: 4
## initial value 668.168774
## iter 10 value 353.682282
## iter 20 value 316.265947
## iter 30 value 311.079502
## final value 311.043775
## converged
## # weights: 10
## initial value 580.512810
## iter 10 value 352.258690
## iter 20 value 313.551520
## iter 30 value 311.188193
## iter 40 value 310.928025
## iter 50 value 310.557609
## iter 60 value 310.425801
## iter 70 value 310.392261
## final value 310.391622
## converged
## # weights: 16
## initial value 485.637656
## iter 10 value 354.821143
## iter 20 value 311.274778
## iter 30 value 310.399108
## iter 40 value 309.939478
## iter 50 value 309.605788
## iter 60 value 309.416969
## iter 70 value 309.353279
## iter 80 value 309.341243
## final value 309.341210
## converged
## # weights: 4
## initial value 432.247766
## iter 10 value 313.357778
## iter 20 value 307.521632
## iter 30 value 306.853676
## iter 40 value 306.745454
## iter 40 value 306.745452
## final value 306.745452
## converged
## # weights: 10
## initial value 579.844669
## iter 10 value 353.451508
## iter 20 value 352.671304
## iter 30 value 327.222384
## iter 40 value 307.042410
## iter 50 value 306.709095
## iter 60 value 306.654437
## iter 70 value 306.634624
## iter 70 value 306.634622
## iter 70 value 306.634620
## final value 306.634620
## converged
## # weights: 16
## initial value 467.387533
## iter 10 value 353.451890
## iter 20 value 350.989956
## iter 30 value 308.535701
## iter 40 value 306.994525
## iter 50 value 306.774789
## iter 60 value 306.685264
## iter 70 value 306.660483
## iter 80 value 306.626117
## final value 306.625360
## converged
## # weights: 4
## initial value 618.987859
## iter 10 value 352.505238
## iter 20 value 304.572124
## iter 30 value 303.838271
## iter 40 value 303.608063
## iter 50 value 303.452502
## iter 60 value 303.417006
## iter 70 value 303.385765
## iter 80 value 303.365397
## iter 90 value 303.357412
## iter 100 value 303.349373
## final value 303.349373
## stopped after 100 iterations
## # weights: 10
## initial value 440.592621
## final value 353.451606
## converged
## # weights: 16
## initial value 452.792068
## iter 10 value 352.308332
## iter 20 value 304.275013
## iter 30 value 303.474195
## iter 40 value 303.343349
## iter 50 value 303.341128
## iter 60 value 303.328180
## iter 70 value 303.321093
## final value 303.320513
## converged
## # weights: 4
## initial value 531.040069
## final value 353.567177
## converged
## # weights: 10
## initial value 430.286634
## iter 10 value 314.008579
## iter 20 value 308.247181
## iter 30 value 307.367110
## iter 40 value 307.069147
## iter 50 value 306.881634
## iter 60 value 306.834166
## iter 60 value 306.834166
## final value 306.834166
## converged
## # weights: 16
## initial value 358.084909
## iter 10 value 311.401423
## iter 20 value 308.044475
## iter 30 value 307.234349
## iter 40 value 306.825957
## iter 50 value 306.661989
## iter 60 value 306.530346
## iter 70 value 306.502536
## final value 306.499125
## converged
## # weights: 4
## initial value 448.210226
## iter 10 value 353.451832
## iter 10 value 353.451832
## final value 353.451832
## converged
## # weights: 10
## initial value 451.709267
## iter 10 value 353.451899
## final value 353.451848
## converged
## # weights: 16
## initial value 1011.993685
## iter 10 value 353.454410
## final value 353.454361
## converged
## # weights: 4
## initial value 520.646908
## iter 10 value 353.451575
## iter 10 value 353.451575
## final value 353.451575
## converged
## # weights: 10
## initial value 601.708309
## final value 353.451617
## converged
## # weights: 16
## initial value 356.827438
## iter 10 value 350.318015
## iter 20 value 306.992304
## iter 30 value 305.712719
## iter 40 value 305.285878
## iter 50 value 304.254268
## iter 60 value 303.369960
## iter 70 value 301.017781
## iter 80 value 300.338733
## iter 90 value 300.253195
## iter 100 value 300.120170
## final value 300.120170
## stopped after 100 iterations
## # weights: 4
## initial value 631.848443
## iter 10 value 352.411376
## iter 20 value 312.906587
## iter 30 value 310.015491
## iter 30 value 310.015489
## iter 30 value 310.015489
## final value 310.015489
## converged
## # weights: 10
## initial value 404.202424
## iter 10 value 348.565715
## iter 20 value 309.993781
## iter 30 value 309.283085
## iter 40 value 308.951931
## iter 50 value 308.750093
## iter 60 value 308.689650
## final value 308.685771
## converged
## # weights: 16
## initial value 410.513863
## iter 10 value 353.622657
## iter 20 value 353.608774
## iter 30 value 323.146738
## iter 40 value 313.381974
## iter 50 value 310.754703
## iter 60 value 310.422593
## iter 70 value 310.372501
## iter 80 value 309.347546
## iter 90 value 309.228701
## iter 100 value 309.217659
## final value 309.217659
## stopped after 100 iterations
## # weights: 4
## initial value 680.857005
## final value 353.455555
## converged
## # weights: 10
## initial value 421.440960
## final value 353.452003
## converged
## # weights: 16
## initial value 370.327494
## iter 10 value 328.999279
## iter 20 value 305.500850
## iter 30 value 304.361446
## final value 304.169256
## converged
## # weights: 4
## initial value 519.485521
## iter 10 value 351.835041
## iter 10 value 351.835041
## final value 351.835041
## converged
## # weights: 10
## initial value 532.446564
## final value 351.835063
## converged
## # weights: 16
## initial value 407.292586
## final value 351.834962
## converged
## # weights: 4
## initial value 649.075072
## iter 10 value 350.807430
## iter 20 value 312.841971
## final value 312.377730
## converged
## # weights: 10
## initial value 356.653205
## iter 10 value 324.242643
## iter 20 value 313.954065
## iter 30 value 312.434630
## iter 40 value 312.199369
## iter 50 value 311.981693
## iter 60 value 311.919681
## iter 70 value 311.867995
## iter 80 value 311.582613
## iter 90 value 311.374533
## iter 100 value 311.324070
## final value 311.324070
## stopped after 100 iterations
## # weights: 16
## initial value 709.334830
## iter 10 value 317.689816
## iter 20 value 312.414233
## iter 30 value 312.113421
## iter 40 value 311.599477
## iter 50 value 311.396872
## iter 60 value 311.234208
## iter 70 value 311.207567
## final value 311.205858
## converged
## # weights: 4
## initial value 494.770352
## final value 351.836846
## converged
## # weights: 10
## initial value 575.948252
## iter 10 value 351.835355
## final value 351.835346
## converged
## # weights: 16
## initial value 520.003413
## iter 10 value 351.820906
## iter 20 value 310.070927
## iter 30 value 308.382993
## iter 40 value 308.270337
## iter 50 value 308.256153
## iter 60 value 308.255400
## final value 308.253449
## converged
## # weights: 4
## initial value 476.012060
## final value 351.835038
## converged
## # weights: 10
## initial value 630.185711
## iter 10 value 312.304057
## iter 20 value 308.704697
## iter 30 value 308.219334
## iter 40 value 308.074981
## iter 50 value 308.000254
## iter 60 value 307.980228
## iter 70 value 307.955927
## iter 80 value 307.941846
## iter 90 value 307.916202
## iter 100 value 307.911588
## final value 307.911588
## stopped after 100 iterations
## # weights: 16
## initial value 446.001743
## iter 10 value 311.689518
## iter 20 value 308.213244
## iter 30 value 307.513697
## iter 40 value 307.365175
## iter 50 value 307.181844
## iter 60 value 307.028144
## iter 70 value 306.891886
## iter 80 value 306.754300
## iter 90 value 306.576767
## iter 100 value 305.309646
## final value 305.309646
## stopped after 100 iterations
## # weights: 4
## initial value 555.459866
## iter 10 value 334.496442
## iter 20 value 312.091657
## final value 312.064622
## converged
## # weights: 10
## initial value 551.237080
## iter 10 value 351.442756
## iter 20 value 314.788764
## iter 30 value 312.705617
## final value 312.694604
## converged
## # weights: 16
## initial value 479.507094
## iter 10 value 343.618989
## iter 20 value 312.396802
## iter 30 value 311.598869
## iter 40 value 311.141415
## iter 50 value 310.913972
## iter 60 value 310.739839
## iter 70 value 310.690897
## iter 80 value 310.682764
## iter 90 value 310.680930
## final value 310.680914
## converged
## # weights: 4
## initial value 580.387495
## iter 10 value 351.835290
## iter 10 value 351.835290
## final value 351.835290
## converged
## # weights: 10
## initial value 408.911835
## final value 351.835419
## converged
## # weights: 16
## initial value 453.832021
## iter 10 value 330.010523
## iter 20 value 308.888178
## iter 30 value 308.216615
## iter 40 value 308.138071
## iter 50 value 308.025596
## iter 60 value 307.985820
## iter 70 value 307.405635
## iter 80 value 307.327306
## iter 90 value 307.135959
## iter 100 value 306.970206
## final value 306.970206
## stopped after 100 iterations
## # weights: 4
## initial value 472.306581
## iter 10 value 351.835035
## iter 10 value 351.835035
## final value 351.835035
## converged
## # weights: 10
## initial value 648.298634
## iter 10 value 348.583168
## iter 20 value 302.296313
## iter 30 value 301.898048
## iter 40 value 301.480933
## iter 50 value 301.283189
## final value 301.174317
## converged
## # weights: 16
## initial value 558.656167
## iter 10 value 330.311161
## iter 20 value 302.413294
## iter 30 value 301.771952
## iter 40 value 301.736681
## iter 50 value 301.643414
## iter 60 value 301.607807
## iter 70 value 301.582962
## iter 80 value 301.567115
## iter 90 value 301.559361
## iter 100 value 301.551170
## final value 301.551170
## stopped after 100 iterations
## # weights: 4
## initial value 393.564085
## iter 10 value 352.787146
## iter 20 value 351.955710
## final value 351.951707
## converged
## # weights: 10
## initial value 450.642598
## iter 10 value 351.942968
## iter 20 value 311.033209
## iter 30 value 306.018236
## iter 40 value 305.670055
## iter 50 value 305.561124
## iter 60 value 305.529130
## iter 70 value 305.526758
## iter 80 value 305.526112
## final value 305.525841
## converged
## # weights: 16
## initial value 459.676828
## iter 10 value 351.960192
## iter 20 value 309.779975
## iter 30 value 305.912470
## iter 40 value 305.208510
## iter 50 value 304.877641
## iter 60 value 304.587823
## iter 70 value 304.467297
## iter 80 value 304.416833
## iter 90 value 304.413344
## final value 304.412959
## converged
## # weights: 4
## initial value 438.765726
## final value 351.835472
## converged
## # weights: 10
## initial value 386.967198
## iter 10 value 351.737243
## iter 20 value 303.469182
## iter 30 value 301.664702
## iter 40 value 301.627335
## iter 50 value 301.623809
## final value 301.623753
## converged
## # weights: 16
## initial value 395.662966
## iter 10 value 335.915617
## iter 20 value 303.149516
## iter 30 value 301.385410
## iter 40 value 301.031300
## iter 50 value 300.829256
## iter 60 value 300.750533
## iter 70 value 300.730784
## iter 80 value 300.712353
## iter 90 value 300.676913
## iter 100 value 300.668316
## final value 300.668316
## stopped after 100 iterations
## # weights: 4
## initial value 671.694802
## final value 351.835016
## converged
## # weights: 10
## initial value 403.725190
## iter 10 value 351.835062
## final value 351.835023
## converged
## # weights: 16
## initial value 768.886325
## iter 10 value 341.917517
## iter 20 value 303.186450
## iter 30 value 302.018825
## iter 40 value 301.692485
## iter 50 value 301.621807
## iter 60 value 301.586480
## iter 70 value 301.557323
## final value 301.548803
## converged
## # weights: 4
## initial value 482.346870
## iter 10 value 353.705389
## iter 20 value 351.972626
## final value 351.951819
## converged
## # weights: 10
## initial value 512.847756
## iter 10 value 352.179986
## iter 20 value 344.533520
## iter 30 value 306.385500
## iter 40 value 306.132555
## iter 50 value 305.828696
## iter 60 value 305.353960
## iter 70 value 305.092836
## iter 80 value 304.983595
## iter 90 value 304.977419
## final value 304.977371
## converged
## # weights: 16
## initial value 370.499046
## iter 10 value 318.403193
## iter 20 value 306.447406
## iter 30 value 305.851084
## iter 40 value 305.594372
## iter 50 value 305.185715
## iter 60 value 304.733285
## iter 70 value 304.515761
## iter 80 value 304.455770
## iter 90 value 304.427641
## final value 304.426364
## converged
## # weights: 4
## initial value 669.033498
## final value 351.835219
## converged
## # weights: 10
## initial value 394.057012
## iter 10 value 323.475623
## iter 20 value 302.496364
## iter 30 value 301.005332
## iter 40 value 300.739362
## iter 50 value 300.494332
## iter 60 value 300.281027
## iter 70 value 300.244744
## iter 80 value 300.180354
## iter 90 value 299.994594
## iter 100 value 299.894824
## final value 299.894824
## stopped after 100 iterations
## # weights: 16
## initial value 463.834577
## iter 10 value 320.509389
## iter 20 value 303.201908
## iter 30 value 301.703886
## final value 301.681644
## converged
## # weights: 4
## initial value 416.191491
## final value 353.451549
## converged
## # weights: 10
## initial value 374.570303
## iter 10 value 353.451561
## final value 353.451546
## converged
## # weights: 16
## initial value 605.152310
## iter 10 value 353.447860
## iter 20 value 351.112845
## iter 30 value 311.335662
## iter 40 value 303.536134
## iter 50 value 303.342783
## final value 303.336459
## converged
## # weights: 4
## initial value 388.515365
## iter 10 value 354.022433
## iter 20 value 353.569956
## final value 353.567332
## converged
## # weights: 10
## initial value 395.809460
## iter 10 value 338.037871
## iter 20 value 309.353000
## iter 30 value 308.435472
## iter 40 value 307.573027
## iter 50 value 307.275092
## iter 60 value 307.240950
## final value 307.240532
## converged
## # weights: 16
## initial value 377.906624
## iter 10 value 320.222116
## iter 20 value 308.155414
## iter 30 value 307.519060
## iter 40 value 306.920305
## iter 50 value 306.674769
## iter 60 value 306.593458
## iter 70 value 306.567642
## final value 306.567512
## converged
## # weights: 4
## initial value 448.376682
## iter 10 value 353.429121
## iter 20 value 304.410989
## iter 30 value 303.592051
## iter 40 value 303.383493
## iter 50 value 303.329606
## iter 60 value 303.307795
## iter 70 value 303.298298
## iter 80 value 303.282814
## iter 90 value 303.276961
## iter 100 value 303.269288
## final value 303.269288
## stopped after 100 iterations
## # weights: 10
## initial value 624.047854
## iter 10 value 311.834440
## iter 20 value 303.690886
## iter 30 value 303.489120
## iter 40 value 302.629281
## iter 50 value 302.198654
## iter 60 value 301.899293
## iter 70 value 301.580223
## iter 80 value 299.892001
## iter 90 value 299.511282
## iter 100 value 299.443087
## final value 299.443087
## stopped after 100 iterations
## # weights: 16
## initial value 627.423989
## iter 10 value 327.692102
## iter 20 value 303.528641
## iter 30 value 303.265150
## final value 303.264137
## converged
## # weights: 4
## initial value 573.330632
## final value 351.835016
## converged
## # weights: 10
## initial value 560.847149
## final value 351.834770
## converged
## # weights: 16
## initial value 581.701327
## iter 10 value 332.465826
## iter 20 value 302.286343
## iter 30 value 300.362481
## iter 40 value 299.817071
## iter 50 value 299.746700
## iter 60 value 299.704304
## iter 70 value 299.648022
## iter 80 value 299.122448
## iter 90 value 297.570215
## iter 100 value 297.431442
## final value 297.431442
## stopped after 100 iterations
## # weights: 4
## initial value 495.517536
## iter 10 value 352.031924
## iter 20 value 326.470363
## iter 30 value 306.448365
## final value 306.392351
## converged
## # weights: 10
## initial value 603.008454
## iter 10 value 353.709892
## iter 20 value 307.304721
## iter 30 value 304.713533
## iter 40 value 304.290211
## iter 50 value 303.888443
## iter 60 value 303.669925
## iter 70 value 303.532000
## iter 80 value 303.520802
## final value 303.520222
## converged
## # weights: 16
## initial value 508.503354
## iter 10 value 347.231203
## iter 20 value 311.684065
## iter 30 value 304.804946
## iter 40 value 304.571109
## iter 50 value 304.141829
## iter 60 value 303.946821
## iter 70 value 303.833173
## iter 80 value 303.811223
## iter 90 value 303.805398
## final value 303.805371
## converged
## # weights: 4
## initial value 440.176371
## final value 351.835339
## converged
## # weights: 10
## initial value 476.547937
## iter 10 value 338.058900
## iter 20 value 300.160449
## iter 30 value 299.798263
## iter 40 value 299.125851
## iter 50 value 297.309556
## iter 60 value 297.172580
## iter 70 value 296.825997
## iter 80 value 296.613941
## iter 90 value 296.603067
## iter 100 value 296.599667
## final value 296.599667
## stopped after 100 iterations
## # weights: 16
## initial value 416.859071
## iter 10 value 348.933118
## iter 20 value 300.609620
## iter 30 value 299.846127
## iter 40 value 299.774473
## iter 50 value 299.756892
## iter 60 value 299.746303
## final value 299.746298
## converged
## # weights: 4
## initial value 396.240889
## iter 10 value 353.451562
## iter 10 value 353.451562
## final value 353.451562
## converged
## # weights: 10
## initial value 612.196447
## iter 10 value 315.171119
## iter 20 value 309.191216
## iter 30 value 308.898680
## iter 40 value 308.840321
## iter 50 value 308.777608
## iter 60 value 308.749764
## iter 70 value 308.354595
## iter 80 value 307.829772
## iter 90 value 307.697481
## iter 100 value 307.568352
## final value 307.568352
## stopped after 100 iterations
## # weights: 16
## initial value 913.184757
## iter 10 value 353.451594
## final value 353.451557
## converged
## # weights: 4
## initial value 613.582754
## iter 10 value 351.936267
## iter 20 value 313.649806
## final value 313.078090
## converged
## # weights: 10
## initial value 378.230031
## iter 10 value 319.884803
## iter 20 value 313.048963
## iter 30 value 312.840677
## iter 40 value 312.527633
## iter 50 value 312.468491
## final value 312.465422
## converged
## # weights: 16
## initial value 845.110029
## iter 10 value 353.623753
## iter 20 value 313.184635
## iter 30 value 312.673115
## iter 40 value 312.362052
## iter 50 value 312.243205
## iter 60 value 312.238250
## final value 312.238135
## converged
## # weights: 4
## initial value 487.101862
## iter 10 value 353.451841
## iter 10 value 353.451841
## final value 353.451841
## converged
## # weights: 10
## initial value 373.839703
## iter 10 value 353.451172
## iter 20 value 340.277907
## iter 30 value 310.118631
## iter 40 value 309.158773
## iter 50 value 308.943865
## iter 60 value 308.890487
## iter 70 value 308.835206
## iter 80 value 308.810690
## iter 90 value 308.808728
## iter 100 value 308.802856
## final value 308.802856
## stopped after 100 iterations
## # weights: 16
## initial value 535.868261
## iter 10 value 321.495383
## iter 20 value 309.274494
## iter 30 value 308.805026
## iter 40 value 308.803262
## final value 308.802802
## converged
## # weights: 4
## initial value 626.020490
## final value 353.451549
## converged
## # weights: 10
## initial value 459.242091
## iter 10 value 353.451923
## final value 353.451795
## converged
## # weights: 16
## initial value 403.167247
## iter 10 value 316.562878
## iter 20 value 307.561091
## iter 30 value 307.281114
## iter 40 value 307.178959
## iter 50 value 306.838306
## iter 60 value 306.706661
## iter 70 value 306.651914
## iter 80 value 306.599404
## iter 90 value 306.523524
## iter 100 value 306.089616
## final value 306.089616
## stopped after 100 iterations
## # weights: 4
## initial value 504.364736
## iter 10 value 355.455366
## iter 20 value 353.596694
## final value 353.567151
## converged
## # weights: 10
## initial value 491.230961
## iter 10 value 353.366706
## iter 20 value 313.067911
## iter 30 value 312.103047
## iter 30 value 312.103046
## iter 30 value 312.103046
## final value 312.103046
## converged
## # weights: 16
## initial value 474.470631
## iter 10 value 353.498422
## iter 20 value 315.853080
## iter 30 value 311.316269
## iter 40 value 310.749749
## iter 50 value 310.529925
## iter 60 value 310.249146
## iter 70 value 310.147747
## iter 80 value 310.116087
## iter 90 value 310.113989
## final value 310.113766
## converged
## # weights: 4
## initial value 386.180207
## final value 353.451828
## converged
## # weights: 10
## initial value 367.259817
## final value 353.452686
## converged
## # weights: 16
## initial value 571.352386
## iter 10 value 351.439171
## iter 20 value 308.767073
## iter 30 value 307.466687
## iter 40 value 307.435919
## iter 50 value 307.386052
## iter 60 value 307.352360
## iter 70 value 307.339650
## final value 307.338680
## converged
## # weights: 4
## initial value 404.808318
## iter 10 value 307.961896
## iter 20 value 305.628562
## iter 30 value 305.302861
## iter 40 value 305.191740
## iter 50 value 305.132134
## iter 60 value 305.112107
## iter 70 value 305.081716
## iter 80 value 305.073163
## iter 90 value 305.065689
## iter 100 value 305.055819
## final value 305.055819
## stopped after 100 iterations
## # weights: 10
## initial value 651.633573
## final value 351.835219
## converged
## # weights: 16
## initial value 391.121484
## iter 10 value 351.835243
## final value 351.834753
## converged
## # weights: 4
## initial value 402.948486
## iter 10 value 352.170952
## iter 20 value 347.952902
## iter 30 value 311.100190
## iter 40 value 309.814457
## final value 309.814379
## converged
## # weights: 10
## initial value 679.660660
## iter 10 value 349.357104
## iter 20 value 310.427110
## iter 30 value 309.462453
## iter 40 value 309.173065
## iter 50 value 309.041268
## iter 60 value 309.030738
## final value 309.030654
## converged
## # weights: 16
## initial value 647.210431
## iter 10 value 347.779469
## iter 20 value 311.639633
## iter 30 value 309.763480
## iter 40 value 309.304896
## iter 50 value 308.852813
## iter 60 value 308.558536
## iter 70 value 308.302581
## iter 80 value 308.138655
## iter 90 value 308.068819
## iter 100 value 308.052695
## final value 308.052695
## stopped after 100 iterations
## # weights: 4
## initial value 363.786612
## final value 351.835250
## converged
## # weights: 10
## initial value 708.485811
## iter 10 value 347.374149
## iter 20 value 306.446785
## iter 30 value 305.485237
## iter 40 value 305.194571
## iter 50 value 305.170825
## iter 60 value 305.145244
## final value 305.124009
## converged
## # weights: 16
## initial value 365.009554
## iter 10 value 331.214019
## iter 20 value 306.632960
## iter 30 value 305.499704
## iter 40 value 305.327791
## iter 50 value 305.223021
## iter 60 value 305.162707
## iter 70 value 305.142651
## iter 80 value 305.140518
## iter 90 value 305.136359
## iter 100 value 305.135672
## final value 305.135672
## stopped after 100 iterations
## # weights: 4
## initial value 652.462846
## final value 353.451547
## converged
## # weights: 10
## initial value 577.038998
## iter 10 value 324.914704
## iter 20 value 308.429622
## iter 30 value 308.172239
## iter 40 value 308.098519
## iter 50 value 308.033999
## iter 60 value 307.430799
## iter 70 value 307.108434
## iter 80 value 306.965177
## iter 90 value 306.809171
## iter 100 value 306.601012
## final value 306.601012
## stopped after 100 iterations
## # weights: 16
## initial value 434.725070
## iter 10 value 351.792625
## iter 20 value 309.613174
## iter 30 value 308.167327
## iter 40 value 308.040521
## iter 50 value 308.018876
## iter 60 value 307.987695
## iter 70 value 307.970316
## iter 80 value 307.952138
## iter 90 value 307.940201
## iter 100 value 307.923592
## final value 307.923592
## stopped after 100 iterations
## # weights: 4
## initial value 431.856003
## iter 10 value 353.567184
## iter 10 value 353.567184
## final value 353.567184
## converged
## # weights: 10
## initial value 959.251881
## iter 10 value 363.363733
## iter 20 value 351.719427
## iter 30 value 342.773067
## iter 40 value 312.290120
## iter 50 value 311.342474
## iter 60 value 311.234258
## iter 70 value 311.200904
## iter 80 value 311.199799
## final value 311.199770
## converged
## # weights: 16
## initial value 465.810597
## iter 10 value 352.588272
## iter 20 value 316.674759
## iter 30 value 311.897142
## iter 40 value 311.402782
## iter 50 value 311.164835
## iter 60 value 311.134104
## iter 70 value 311.130655
## iter 80 value 311.123385
## final value 311.122905
## converged
## # weights: 4
## initial value 429.156837
## final value 353.451805
## converged
## # weights: 10
## initial value 884.555446
## iter 10 value 353.453059
## final value 353.453041
## converged
## # weights: 16
## initial value 553.946686
## final value 353.452386
## converged
## # weights: 4
## initial value 543.149109
## iter 10 value 351.835042
## iter 10 value 351.835042
## final value 351.835042
## converged
## # weights: 10
## initial value 395.599066
## iter 10 value 338.212429
## iter 20 value 303.466566
## iter 30 value 302.332138
## final value 302.331259
## converged
## # weights: 16
## initial value 598.027632
## final value 351.834834
## converged
## # weights: 4
## initial value 384.142741
## final value 351.951616
## converged
## # weights: 10
## initial value 567.966878
## iter 10 value 351.675552
## iter 20 value 327.691495
## iter 30 value 307.025010
## iter 40 value 306.493408
## iter 50 value 305.906100
## iter 60 value 305.688013
## iter 70 value 305.582960
## final value 305.581500
## converged
## # weights: 16
## initial value 733.010491
## iter 10 value 338.909865
## iter 20 value 306.303709
## iter 30 value 305.807163
## iter 40 value 305.540554
## iter 50 value 305.367143
## iter 60 value 305.270432
## iter 70 value 305.198490
## iter 80 value 305.182665
## iter 90 value 305.177677
## final value 305.177132
## converged
## # weights: 4
## initial value 427.297036
## final value 351.835175
## converged
## # weights: 10
## initial value 506.408756
## iter 10 value 315.981541
## iter 20 value 302.879008
## iter 30 value 302.363326
## iter 40 value 302.297243
## final value 302.271879
## converged
## # weights: 16
## initial value 362.253967
## iter 10 value 304.272912
## iter 20 value 302.455467
## iter 30 value 302.241799
## final value 302.236274
## converged
## # weights: 4
## initial value 486.257206
## final value 353.451549
## converged
## # weights: 10
## initial value 817.439310
## iter 10 value 353.460279
## iter 20 value 353.451566
## final value 353.451555
## converged
## # weights: 16
## initial value 494.884781
## iter 10 value 350.497804
## iter 20 value 309.585122
## iter 30 value 305.959056
## iter 40 value 305.846042
## iter 50 value 305.761013
## iter 60 value 305.690232
## iter 70 value 305.653512
## iter 80 value 305.631575
## iter 90 value 305.630430
## iter 100 value 305.614157
## final value 305.614157
## stopped after 100 iterations
## # weights: 4
## initial value 603.078573
## iter 10 value 344.760940
## iter 20 value 312.394328
## iter 30 value 312.219175
## final value 312.219166
## converged
## # weights: 10
## initial value 535.587792
## iter 10 value 356.327035
## iter 20 value 313.131333
## iter 30 value 312.083330
## iter 40 value 311.510967
## iter 50 value 311.345291
## iter 60 value 311.089515
## iter 70 value 311.050667
## iter 80 value 311.042831
## final value 311.042824
## converged
## # weights: 16
## initial value 647.537700
## iter 10 value 334.247102
## iter 20 value 313.835491
## iter 30 value 312.046790
## iter 40 value 311.710220
## iter 50 value 311.205686
## iter 60 value 310.877055
## iter 70 value 310.699970
## iter 80 value 310.664451
## iter 90 value 310.635931
## final value 310.635391
## converged
## # weights: 4
## initial value 524.927048
## final value 353.451757
## converged
## # weights: 10
## initial value 674.633853
## iter 10 value 353.451923
## final value 353.451847
## converged
## # weights: 16
## initial value 362.615358
## iter 10 value 316.012532
## iter 20 value 308.169912
## iter 30 value 307.941049
## iter 40 value 307.860771
## iter 50 value 307.564923
## iter 60 value 306.708072
## iter 70 value 306.570777
## iter 80 value 306.503356
## iter 90 value 306.138855
## iter 100 value 305.891287
## final value 305.891287
## stopped after 100 iterations
## # weights: 4
## initial value 448.766588
## final value 351.834839
## converged
## # weights: 10
## initial value 372.876190
## iter 10 value 351.835028
## final value 351.835010
## converged
## # weights: 16
## initial value 476.150396
## iter 10 value 351.823650
## iter 20 value 315.373518
## iter 30 value 304.529460
## iter 40 value 304.427054
## iter 50 value 304.414179
## iter 60 value 304.409569
## final value 304.409038
## converged
## # weights: 4
## initial value 547.160904
## final value 351.951661
## converged
## # weights: 10
## initial value 546.108760
## iter 10 value 322.371661
## iter 20 value 310.238536
## iter 30 value 308.574345
## iter 40 value 308.147361
## iter 50 value 307.906383
## iter 60 value 307.739418
## iter 70 value 307.721852
## iter 70 value 307.721850
## final value 307.721850
## converged
## # weights: 16
## initial value 472.724239
## iter 10 value 323.204076
## iter 20 value 309.096163
## iter 30 value 308.216122
## iter 40 value 307.850656
## iter 50 value 307.619054
## iter 60 value 307.601546
## iter 70 value 307.599351
## final value 307.599344
## converged
## # weights: 4
## initial value 648.334141
## final value 351.835304
## converged
## # weights: 10
## initial value 370.834571
## iter 10 value 351.835352
## iter 20 value 351.835092
## iter 30 value 341.129385
## iter 40 value 304.990032
## iter 50 value 304.557996
## iter 60 value 304.517626
## iter 70 value 304.474416
## iter 80 value 304.057282
## iter 90 value 303.625069
## iter 100 value 303.416798
## final value 303.416798
## stopped after 100 iterations
## # weights: 16
## initial value 1021.932319
## iter 10 value 351.835380
## iter 20 value 344.596214
## iter 30 value 306.021582
## iter 40 value 304.693620
## iter 50 value 304.545036
## iter 60 value 304.507705
## iter 70 value 304.492708
## iter 80 value 304.489020
## iter 90 value 304.478071
## iter 100 value 304.352216
## final value 304.352216
## stopped after 100 iterations
## # weights: 4
## initial value 476.948937
## final value 351.835009
## converged
## # weights: 10
## initial value 449.525943
## final value 351.835113
## converged
## # weights: 16
## initial value 600.406244
## iter 10 value 351.835106
## final value 351.835020
## converged
## # weights: 4
## initial value 399.068161
## iter 10 value 352.665564
## final value 351.951579
## converged
## # weights: 10
## initial value 584.039959
## iter 10 value 345.405962
## iter 20 value 310.431001
## iter 30 value 309.073075
## iter 40 value 308.960671
## final value 308.955602
## converged
## # weights: 16
## initial value 552.513427
## iter 10 value 349.023074
## iter 20 value 311.917003
## iter 30 value 309.272764
## iter 40 value 308.683686
## iter 50 value 308.337326
## iter 60 value 308.086091
## iter 70 value 307.986431
## iter 80 value 307.963601
## final value 307.962522
## converged
## # weights: 4
## initial value 400.754350
## iter 10 value 351.835193
## iter 10 value 351.835193
## final value 351.835193
## converged
## # weights: 10
## initial value 427.690329
## iter 10 value 350.236320
## iter 20 value 306.211624
## iter 30 value 305.598795
## iter 40 value 305.391642
## iter 50 value 305.361180
## iter 60 value 305.340790
## iter 70 value 305.328904
## iter 80 value 305.328046
## iter 90 value 305.325947
## final value 305.325940
## converged
## # weights: 16
## initial value 610.609447
## iter 10 value 306.176423
## iter 20 value 305.406686
## iter 30 value 305.365114
## final value 305.365096
## converged
## # weights: 4
## initial value 692.084689
## iter 10 value 353.451698
## final value 353.451552
## converged
## # weights: 10
## initial value 768.189327
## iter 10 value 353.451587
## final value 353.451566
## converged
## # weights: 16
## initial value 551.935555
## iter 10 value 353.451743
## final value 353.451564
## converged
## # weights: 4
## initial value 404.724190
## iter 10 value 353.605695
## final value 353.567195
## converged
## # weights: 10
## initial value 444.233260
## iter 10 value 353.099615
## iter 20 value 312.783555
## iter 30 value 311.511282
## iter 40 value 311.055229
## iter 50 value 310.650328
## iter 60 value 310.601601
## final value 310.592980
## converged
## # weights: 16
## initial value 444.666082
## iter 10 value 351.189412
## iter 20 value 315.887164
## iter 30 value 311.922031
## iter 40 value 311.614740
## iter 50 value 311.283104
## iter 60 value 310.895695
## iter 70 value 310.731302
## iter 80 value 310.635749
## iter 90 value 310.625171
## iter 90 value 310.625169
## iter 90 value 310.625169
## final value 310.625169
## converged
## # weights: 4
## initial value 500.287005
## final value 353.452349
## converged
## # weights: 10
## initial value 387.526929
## iter 10 value 315.629280
## iter 20 value 309.177297
## iter 30 value 307.904301
## iter 40 value 307.630977
## iter 50 value 307.573272
## iter 60 value 307.527739
## iter 70 value 307.506204
## iter 80 value 307.497722
## iter 90 value 307.486175
## final value 307.485788
## converged
## # weights: 16
## initial value 432.948062
## iter 10 value 335.858787
## iter 20 value 308.219354
## iter 30 value 307.538941
## final value 307.537044
## converged
## # weights: 4
## initial value 404.346873
## final value 351.835017
## converged
## # weights: 10
## initial value 505.130648
## iter 10 value 310.949878
## iter 20 value 306.854458
## iter 30 value 306.478051
## iter 40 value 306.381316
## iter 50 value 306.323722
## iter 60 value 306.304022
## iter 70 value 306.275378
## iter 80 value 306.268697
## iter 90 value 306.248822
## iter 100 value 306.246554
## final value 306.246554
## stopped after 100 iterations
## # weights: 16
## initial value 648.058184
## iter 10 value 351.835050
## iter 20 value 329.834736
## iter 30 value 306.855374
## iter 40 value 306.448550
## iter 50 value 306.383016
## iter 60 value 306.335405
## iter 70 value 306.283611
## iter 80 value 306.267803
## iter 90 value 306.253268
## iter 100 value 306.250901
## final value 306.250901
## stopped after 100 iterations
## # weights: 4
## initial value 492.480081
## iter 10 value 350.379595
## iter 20 value 311.628218
## final value 310.590311
## converged
## # weights: 10
## initial value 713.530303
## iter 10 value 357.062646
## iter 20 value 312.280336
## iter 30 value 310.673671
## iter 40 value 310.497470
## iter 50 value 310.256171
## iter 60 value 310.089747
## iter 70 value 310.052024
## iter 80 value 310.044412
## iter 90 value 310.041659
## final value 310.041606
## converged
## # weights: 16
## initial value 559.222458
## iter 10 value 350.056271
## iter 20 value 312.678624
## iter 30 value 310.571934
## iter 40 value 309.883878
## iter 50 value 309.521260
## iter 60 value 309.257301
## iter 70 value 309.138230
## iter 80 value 309.095737
## iter 90 value 309.091759
## iter 90 value 309.091757
## iter 90 value 309.091757
## final value 309.091757
## converged
## # weights: 4
## initial value 659.474527
## final value 351.835294
## converged
## # weights: 10
## initial value 356.651676
## iter 10 value 351.835326
## final value 351.835319
## converged
## # weights: 16
## initial value 382.806100
## iter 10 value 341.211297
## iter 20 value 307.293001
## iter 30 value 306.467236
## iter 40 value 306.423052
## iter 50 value 306.369125
## iter 60 value 306.352263
## iter 70 value 306.339645
## iter 80 value 306.334730
## iter 90 value 306.327405
## final value 306.327038
## converged
## # weights: 4
## initial value 537.037647
## iter 10 value 351.835028
## iter 10 value 351.835028
## final value 351.835028
## converged
## # weights: 10
## initial value 588.821220
## iter 10 value 351.826045
## iter 20 value 309.716363
## iter 30 value 308.801860
## iter 40 value 308.723635
## iter 50 value 308.652961
## iter 60 value 308.640252
## iter 70 value 308.632722
## iter 80 value 308.612826
## iter 90 value 308.607194
## iter 100 value 308.597076
## final value 308.597076
## stopped after 100 iterations
## # weights: 16
## initial value 597.675900
## final value 351.835140
## converged
## # weights: 4
## initial value 485.890830
## iter 10 value 351.171921
## iter 20 value 336.760363
## iter 30 value 312.839397
## final value 312.838604
## converged
## # weights: 10
## initial value 518.903459
## iter 10 value 325.168024
## iter 20 value 313.394117
## iter 30 value 312.340546
## iter 40 value 311.934341
## iter 50 value 311.803644
## iter 60 value 311.731155
## final value 311.728129
## converged
## # weights: 16
## initial value 672.743267
## iter 10 value 351.778523
## iter 20 value 337.313341
## iter 30 value 316.330468
## iter 40 value 312.416600
## iter 50 value 312.080969
## iter 60 value 311.818662
## iter 70 value 311.757074
## iter 80 value 311.736341
## final value 311.735727
## converged
## # weights: 4
## initial value 550.279529
## iter 10 value 346.186691
## iter 20 value 310.313884
## iter 30 value 309.099870
## iter 40 value 308.841848
## iter 50 value 308.746192
## iter 60 value 308.715480
## final value 308.710867
## converged
## # weights: 10
## initial value 369.157759
## iter 10 value 351.845362
## iter 20 value 351.842538
## final value 351.836606
## converged
## # weights: 16
## initial value 462.185376
## iter 10 value 351.830235
## iter 20 value 331.837876
## iter 30 value 308.963216
## iter 40 value 308.775891
## iter 50 value 308.769072
## iter 60 value 308.701168
## iter 70 value 308.692978
## iter 80 value 308.679761
## final value 308.676837
## converged
## # weights: 4
## initial value 594.522033
## final value 353.451549
## converged
## # weights: 10
## initial value 364.090495
## iter 10 value 342.131061
## iter 20 value 305.195598
## iter 30 value 302.961090
## iter 40 value 302.751748
## iter 50 value 302.741358
## iter 60 value 302.702580
## iter 70 value 302.670671
## iter 80 value 302.666741
## iter 90 value 302.642439
## iter 100 value 302.640228
## final value 302.640228
## stopped after 100 iterations
## # weights: 16
## initial value 419.666058
## iter 10 value 336.291049
## iter 20 value 320.255655
## iter 30 value 311.994237
## iter 40 value 300.159901
## iter 50 value 299.059316
## iter 60 value 298.693664
## iter 70 value 297.870330
## iter 80 value 297.322704
## iter 90 value 297.214323
## iter 100 value 297.167230
## final value 297.167230
## stopped after 100 iterations
## # weights: 4
## initial value 618.279700
## iter 10 value 353.225091
## iter 20 value 308.093918
## final value 307.729294
## converged
## # weights: 10
## initial value 411.092806
## iter 10 value 353.465461
## iter 20 value 314.821011
## iter 30 value 308.150220
## iter 40 value 307.496994
## iter 50 value 306.954197
## iter 60 value 306.625905
## iter 70 value 306.401383
## iter 80 value 306.349193
## iter 90 value 306.342957
## final value 306.342945
## converged
## # weights: 16
## initial value 410.455146
## iter 10 value 349.670815
## iter 20 value 311.741084
## iter 30 value 307.037995
## iter 40 value 306.355595
## iter 50 value 306.180291
## iter 60 value 305.927778
## iter 70 value 305.889547
## final value 305.880030
## converged
## # weights: 4
## initial value 458.423802
## final value 353.452533
## converged
## # weights: 10
## initial value 585.050283
## final value 353.451540
## converged
## # weights: 16
## initial value 796.579735
## iter 10 value 353.451159
## iter 10 value 353.451157
## final value 353.451157
## converged
## # weights: 4
## initial value 611.643907
## final value 351.835016
## converged
## # weights: 10
## initial value 1105.033238
## final value 351.835032
## converged
## # weights: 16
## initial value 711.732673
## iter 10 value 351.860552
## iter 20 value 345.514677
## iter 30 value 305.143478
## iter 40 value 304.699778
## iter 50 value 304.492949
## iter 60 value 303.693698
## iter 70 value 303.452083
## iter 80 value 303.302036
## iter 90 value 303.252382
## iter 100 value 303.224976
## final value 303.224976
## stopped after 100 iterations
## # weights: 4
## initial value 576.476370
## final value 351.951606
## converged
## # weights: 10
## initial value 428.176573
## iter 10 value 349.449243
## iter 20 value 308.896038
## iter 30 value 308.602189
## iter 40 value 308.392349
## iter 50 value 308.327493
## final value 308.327124
## converged
## # weights: 16
## initial value 535.197788
## iter 10 value 350.664329
## iter 20 value 315.428380
## iter 30 value 308.562271
## iter 40 value 308.284195
## iter 50 value 308.198528
## iter 60 value 307.964047
## iter 70 value 307.587416
## iter 80 value 307.394501
## iter 90 value 307.334929
## iter 100 value 307.284273
## final value 307.284273
## stopped after 100 iterations
## # weights: 4
## initial value 473.622012
## iter 10 value 351.837733
## final value 351.836488
## converged
## # weights: 10
## initial value 487.929980
## iter 10 value 351.837124
## final value 351.835859
## converged
## # weights: 16
## initial value 361.334666
## iter 10 value 318.655461
## iter 20 value 305.430308
## iter 30 value 304.791548
## iter 40 value 304.728725
## iter 50 value 304.659343
## iter 60 value 304.633704
## iter 70 value 304.621107
## final value 304.555423
## converged
## # weights: 4
## initial value 457.531460
## final value 353.451549
## converged
## # weights: 10
## initial value 580.355861
## iter 10 value 353.451575
## final value 353.451447
## converged
## # weights: 16
## initial value 450.271029
## iter 10 value 350.949705
## iter 20 value 304.146542
## iter 30 value 303.271100
## iter 40 value 303.211435
## iter 50 value 303.132279
## iter 60 value 303.082733
## iter 70 value 303.055567
## iter 80 value 303.014554
## iter 90 value 302.950375
## iter 100 value 302.215787
## final value 302.215787
## stopped after 100 iterations
## # weights: 4
## initial value 453.819945
## final value 353.567359
## converged
## # weights: 10
## initial value 675.599902
## iter 10 value 323.002546
## iter 20 value 308.149401
## iter 30 value 307.728453
## iter 40 value 307.257303
## iter 50 value 306.842859
## iter 60 value 306.619149
## iter 70 value 306.556011
## final value 306.555577
## converged
## # weights: 16
## initial value 804.293159
## iter 10 value 332.781032
## iter 20 value 308.376082
## iter 30 value 307.219883
## iter 40 value 306.872199
## iter 50 value 306.575541
## iter 60 value 306.525905
## iter 70 value 306.522870
## iter 70 value 306.522869
## iter 70 value 306.522869
## final value 306.522869
## converged
## # weights: 4
## initial value 434.459070
## final value 353.452910
## converged
## # weights: 10
## initial value 355.504112
## iter 10 value 352.422991
## iter 20 value 303.879299
## iter 30 value 303.291412
## iter 40 value 303.164034
## iter 50 value 303.125372
## iter 60 value 303.115555
## iter 70 value 303.114164
## final value 303.113378
## converged
## # weights: 16
## initial value 522.862790
## iter 10 value 340.863142
## iter 20 value 305.617442
## iter 30 value 303.565152
## iter 40 value 303.275164
## iter 50 value 303.135944
## iter 60 value 303.028580
## iter 70 value 302.293949
## iter 80 value 301.913706
## iter 90 value 301.337658
## iter 100 value 300.654780
## final value 300.654780
## stopped after 100 iterations
## # weights: 16
## initial value 400.296220
## iter 10 value 361.030075
## iter 20 value 345.231166
## iter 30 value 343.797286
## iter 40 value 343.375311
## iter 50 value 342.983294
## iter 60 value 342.789364
## iter 70 value 342.649053
## iter 80 value 342.462723
## iter 90 value 342.448107
## iter 100 value 342.431308
## final value 342.431308
## stopped after 100 iterationsann_model## Neural Network
##
## 786 samples
## 1 predictor
## 2 classes: 'bad', 'good'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 5 times)
## Summary of sample sizes: 707, 708, 707, 707, 708, 708, ...
## Resampling results across tuning parameters:
##
## size decay ROC Sens Spec
## 1 0e+00 0.5267718 0.007666667 0.9965079
## 1 1e-04 0.5065370 0.008916667 0.9980952
## 1 1e-01 0.5167989 0.007666667 0.9984127
## 3 0e+00 0.5896376 0.016666667 0.9974603
## 3 1e-04 0.6031706 0.033250000 0.9923810
## 3 1e-01 0.7255509 0.039583333 0.9946032
## 5 0e+00 0.6854934 0.048333333 0.9901587
## 5 1e-04 0.7225311 0.058833333 0.9898413
## 5 1e-01 0.7386409 0.039583333 0.9930159
##
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were size = 5 and decay = 0.1.ann_pred <- predict(ann_model, testset, type = "prob")
ann_measures <- fpr_tpr(p = ann_pred$good, dataset = testset)
plot(ann_measures$fpr, ann_measures$tpr, type = "l", col = 2,
ylab = "TPR", xlab = "FPR")
title("ROC curve")
points(c(0, 1), c(0, 1), type = "l", lty = 2)
In the end, we look at the boost model.
It also tune parameters “interaction.depth” and “n.trees”, then chose the optimal models by the largest auc value.
After tune the cut-off point, the performance of the boost model is slight better.
### tune boosting
boost_model <- train(play.type ~ yards, data = trainset,
method = "gbm",
trControl = ctrl)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9734 nan 0.1000 0.0089
## 2 0.9606 nan 0.1000 0.0076
## 3 0.9433 nan 0.1000 0.0057
## 4 0.9331 nan 0.1000 0.0043
## 5 0.9244 nan 0.1000 0.0040
## 6 0.9165 nan 0.1000 0.0027
## 7 0.9100 nan 0.1000 0.0018
## 8 0.9051 nan 0.1000 0.0020
## 9 0.8982 nan 0.1000 0.0012
## 10 0.8929 nan 0.1000 0.0024
## 20 0.8620 nan 0.1000 0.0015
## 40 0.8442 nan 0.1000 -0.0002
## 60 0.8381 nan 0.1000 -0.0004
## 80 0.8366 nan 0.1000 -0.0002
## 100 0.8342 nan 0.1000 -0.0006
## 120 0.8307 nan 0.1000 -0.0004
## 140 0.8285 nan 0.1000 -0.0001
## 150 0.8275 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9657 nan 0.1000 0.0095
## 2 0.9441 nan 0.1000 0.0072
## 3 0.9282 nan 0.1000 0.0075
## 4 0.9162 nan 0.1000 0.0044
## 5 0.9089 nan 0.1000 0.0032
## 6 0.8996 nan 0.1000 0.0034
## 7 0.8914 nan 0.1000 0.0033
## 8 0.8853 nan 0.1000 0.0030
## 9 0.8812 nan 0.1000 0.0004
## 10 0.8766 nan 0.1000 0.0019
## 20 0.8480 nan 0.1000 -0.0001
## 40 0.8317 nan 0.1000 -0.0010
## 60 0.8233 nan 0.1000 -0.0007
## 80 0.8186 nan 0.1000 -0.0022
## 100 0.8167 nan 0.1000 -0.0009
## 120 0.8150 nan 0.1000 -0.0006
## 140 0.8142 nan 0.1000 -0.0006
## 150 0.8130 nan 0.1000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9662 nan 0.1000 0.0119
## 2 0.9420 nan 0.1000 0.0103
## 3 0.9262 nan 0.1000 0.0067
## 4 0.9138 nan 0.1000 0.0055
## 5 0.9021 nan 0.1000 0.0046
## 6 0.8933 nan 0.1000 0.0024
## 7 0.8863 nan 0.1000 0.0023
## 8 0.8803 nan 0.1000 0.0015
## 9 0.8748 nan 0.1000 0.0015
## 10 0.8669 nan 0.1000 0.0018
## 20 0.8401 nan 0.1000 -0.0005
## 40 0.8243 nan 0.1000 -0.0018
## 60 0.8161 nan 0.1000 -0.0018
## 80 0.8137 nan 0.1000 -0.0009
## 100 0.8124 nan 0.1000 -0.0017
## 120 0.8112 nan 0.1000 -0.0013
## 140 0.8115 nan 0.1000 -0.0016
## 150 0.8101 nan 0.1000 -0.0019
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9812 nan 0.1000 0.0079
## 2 0.9705 nan 0.1000 0.0032
## 3 0.9580 nan 0.1000 0.0067
## 4 0.9482 nan 0.1000 0.0051
## 5 0.9382 nan 0.1000 0.0038
## 6 0.9303 nan 0.1000 0.0025
## 7 0.9234 nan 0.1000 0.0022
## 8 0.9156 nan 0.1000 0.0024
## 9 0.9089 nan 0.1000 0.0021
## 10 0.9036 nan 0.1000 0.0018
## 20 0.8756 nan 0.1000 -0.0003
## 40 0.8660 nan 0.1000 -0.0011
## 60 0.8605 nan 0.1000 0.0004
## 80 0.8561 nan 0.1000 -0.0000
## 100 0.8549 nan 0.1000 -0.0006
## 120 0.8523 nan 0.1000 -0.0004
## 140 0.8522 nan 0.1000 -0.0003
## 150 0.8518 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9774 nan 0.1000 0.0106
## 2 0.9608 nan 0.1000 0.0060
## 3 0.9456 nan 0.1000 0.0078
## 4 0.9312 nan 0.1000 0.0049
## 5 0.9207 nan 0.1000 0.0047
## 6 0.9123 nan 0.1000 0.0034
## 7 0.9073 nan 0.1000 0.0017
## 8 0.9002 nan 0.1000 0.0023
## 9 0.8981 nan 0.1000 -0.0006
## 10 0.8921 nan 0.1000 0.0012
## 20 0.8697 nan 0.1000 0.0003
## 40 0.8576 nan 0.1000 -0.0001
## 60 0.8497 nan 0.1000 0.0003
## 80 0.8454 nan 0.1000 -0.0006
## 100 0.8442 nan 0.1000 -0.0007
## 120 0.8423 nan 0.1000 -0.0004
## 140 0.8419 nan 0.1000 -0.0010
## 150 0.8420 nan 0.1000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9750 nan 0.1000 0.0099
## 2 0.9561 nan 0.1000 0.0068
## 3 0.9418 nan 0.1000 0.0061
## 4 0.9297 nan 0.1000 0.0041
## 5 0.9186 nan 0.1000 0.0022
## 6 0.9089 nan 0.1000 0.0040
## 7 0.9024 nan 0.1000 0.0012
## 8 0.8971 nan 0.1000 0.0012
## 9 0.8930 nan 0.1000 0.0011
## 10 0.8890 nan 0.1000 -0.0008
## 20 0.8635 nan 0.1000 0.0003
## 40 0.8487 nan 0.1000 -0.0008
## 60 0.8424 nan 0.1000 -0.0011
## 80 0.8395 nan 0.1000 -0.0017
## 100 0.8376 nan 0.1000 -0.0020
## 120 0.8363 nan 0.1000 -0.0028
## 140 0.8359 nan 0.1000 -0.0009
## 150 0.8356 nan 0.1000 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9807 nan 0.1000 0.0071
## 2 0.9635 nan 0.1000 0.0073
## 3 0.9484 nan 0.1000 0.0064
## 4 0.9385 nan 0.1000 0.0052
## 5 0.9326 nan 0.1000 0.0027
## 6 0.9233 nan 0.1000 0.0026
## 7 0.9131 nan 0.1000 0.0024
## 8 0.9062 nan 0.1000 0.0028
## 9 0.8989 nan 0.1000 0.0027
## 10 0.8940 nan 0.1000 0.0013
## 20 0.8694 nan 0.1000 -0.0000
## 40 0.8524 nan 0.1000 -0.0001
## 60 0.8447 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## 140 0.8288 nan 0.1000 -0.0006
## 150 0.8281 nan 0.1000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9725 nan 0.1000 0.0096
## 2 0.9591 nan 0.1000 0.0057
## 3 0.9471 nan 0.1000 0.0064
## 4 0.9357 nan 0.1000 0.0053
## 5 0.9258 nan 0.1000 0.0054
## 6 0.9150 nan 0.1000 0.0027
## 7 0.9084 nan 0.1000 0.0017
## 8 0.8999 nan 0.1000 0.0033
## 9 0.8970 nan 0.1000 0.0006
## 10 0.8929 nan 0.1000 0.0010
## 20 0.8600 nan 0.1000 -0.0003
## 40 0.8442 nan 0.1000 -0.0003
## 60 0.8418 nan 0.1000 -0.0005
## 80 0.8384 nan 0.1000 -0.0002
## 100 0.8375 nan 0.1000 -0.0005
## 120 0.8351 nan 0.1000 -0.0004
## 140 0.8342 nan 0.1000 -0.0011
## 150 0.8334 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9752 nan 0.1000 0.0105
## 2 0.9516 nan 0.1000 0.0090
## 3 0.9351 nan 0.1000 0.0090
## 4 0.9181 nan 0.1000 0.0047
## 5 0.9066 nan 0.1000 0.0039
## 6 0.8978 nan 0.1000 0.0045
## 7 0.8882 nan 0.1000 0.0029
## 8 0.8821 nan 0.1000 0.0015
## 9 0.8776 nan 0.1000 0.0010
## 10 0.8716 nan 0.1000 0.0019
## 20 0.8464 nan 0.1000 -0.0005
## 40 0.8387 nan 0.1000 -0.0028
## 60 0.8343 nan 0.1000 -0.0005
## 80 0.8295 nan 0.1000 -0.0011
## 100 0.8266 nan 0.1000 -0.0012
## 120 0.8249 nan 0.1000 -0.0014
## 140 0.8221 nan 0.1000 -0.0005
## 150 0.8211 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9635 nan 0.1000 0.0137
## 2 0.9420 nan 0.1000 0.0071
## 3 0.9239 nan 0.1000 0.0057
## 4 0.9081 nan 0.1000 0.0038
## 5 0.8963 nan 0.1000 0.0031
## 6 0.8893 nan 0.1000 0.0027
## 7 0.8827 nan 0.1000 0.0021
## 8 0.8766 nan 0.1000 0.0016
## 9 0.8711 nan 0.1000 0.0014
## 10 0.8660 nan 0.1000 0.0015
## 20 0.8444 nan 0.1000 -0.0011
## 40 0.8298 nan 0.1000 -0.0015
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## 80 0.8198 nan 0.1000 -0.0002
## 100 0.8192 nan 0.1000 -0.0023
## 120 0.8181 nan 0.1000 -0.0018
## 140 0.8166 nan 0.1000 -0.0021
## 150 0.8157 nan 0.1000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9796 nan 0.1000 0.0088
## 2 0.9618 nan 0.1000 0.0074
## 3 0.9498 nan 0.1000 0.0058
## 4 0.9350 nan 0.1000 0.0067
## 5 0.9255 nan 0.1000 0.0033
## 6 0.9146 nan 0.1000 0.0035
## 7 0.9092 nan 0.1000 0.0017
## 8 0.9018 nan 0.1000 0.0031
## 9 0.8954 nan 0.1000 0.0026
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## 20 0.8600 nan 0.1000 0.0005
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## 60 0.8410 nan 0.1000 -0.0004
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## 100 0.8370 nan 0.1000 -0.0003
## 120 0.8352 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9680 nan 0.1000 0.0123
## 2 0.9495 nan 0.1000 0.0090
## 3 0.9335 nan 0.1000 0.0069
## 4 0.9182 nan 0.1000 0.0050
## 5 0.9079 nan 0.1000 0.0051
## 6 0.8984 nan 0.1000 0.0042
## 7 0.8903 nan 0.1000 0.0031
## 8 0.8829 nan 0.1000 0.0027
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## 20 0.8474 nan 0.1000 -0.0009
## 40 0.8383 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9648 nan 0.1000 0.0109
## 2 0.9438 nan 0.1000 0.0080
## 3 0.9266 nan 0.1000 0.0079
## 4 0.9112 nan 0.1000 0.0063
## 5 0.9035 nan 0.1000 0.0026
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## 7 0.8856 nan 0.1000 0.0003
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## 20 0.8444 nan 0.1000 -0.0010
## 40 0.8294 nan 0.1000 -0.0022
## 60 0.8244 nan 0.1000 -0.0004
## 80 0.8210 nan 0.1000 -0.0009
## 100 0.8200 nan 0.1000 -0.0016
## 120 0.8187 nan 0.1000 -0.0008
## 140 0.8185 nan 0.1000 -0.0006
## 150 0.8186 nan 0.1000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9782 nan 0.1000 0.0092
## 2 0.9616 nan 0.1000 0.0078
## 3 0.9471 nan 0.1000 0.0053
## 4 0.9343 nan 0.1000 0.0049
## 5 0.9269 nan 0.1000 0.0034
## 6 0.9213 nan 0.1000 0.0030
## 7 0.9152 nan 0.1000 0.0029
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## 10 0.8990 nan 0.1000 0.0024
## 20 0.8701 nan 0.1000 0.0004
## 40 0.8543 nan 0.1000 -0.0004
## 50 0.8523 nan 0.1000 -0.0002boost_model## Stochastic Gradient Boosting
##
## 786 samples
## 1 predictor
## 2 classes: 'bad', 'good'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 5 times)
## Summary of sample sizes: 707, 708, 708, 707, 707, 708, ...
## Resampling results across tuning parameters:
##
## interaction.depth n.trees ROC Sens Spec
## 1 50 0.7473307 0.11050000 0.9666667
## 1 100 0.7462831 0.10783333 0.9685714
## 1 150 0.7436766 0.09966667 0.9666667
## 2 50 0.7421177 0.10125000 0.9679365
## 2 100 0.7376832 0.09100000 0.9647619
## 2 150 0.7311687 0.10650000 0.9622222
## 3 50 0.7372745 0.10100000 0.9660317
## 3 100 0.7316958 0.10658333 0.9584127
## 3 150 0.7279180 0.10391667 0.9555556
##
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
## Tuning parameter
## 'n.minobsinnode' was held constant at a value of 10
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 50, interaction.depth = 1, shrinkage = 0.1
## and n.minobsinnode = 10.boost_pred <- predict(boost_model, testset, type = "prob")
boost_measures <- fpr_tpr(p = boost_pred$good, dataset = testset)
plot(boost_measures$fpr, boost_measures$tpr, type = "l", col = 2,
ylab = "TPR", xlab = "FPR")
title("ROC curve")
points(c(0, 1), c(0, 1), type = "l", lty = 2)
Here, we also could use some numbers and some visualizations in the caret package to compare three models.
resamp <- resamples(list(logistic = logistic_model, ann = ann_model, boost = boost_model))
summary(resamp)##
## Call:
## summary.resamples(object = resamp)
##
## Models: logistic, ann, boost
## Number of resamples: 50
##
## ROC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## logistic 0.6011905 0.6866319 0.7726025 0.7503095 0.8085979 0.8539683 0
## ann 0.1944444 0.6921627 0.7526455 0.7386409 0.8031746 0.8894180 0
## boost 0.6488095 0.6996528 0.7457672 0.7473307 0.7745536 0.8710317 0
##
## Sens
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## logistic 0 0.0000 0.06666667 0.09083333 0.1333333 0.2500000 0
## ann 0 0.0000 0.06250000 0.03958333 0.0625000 0.1333333 0
## boost 0 0.0625 0.06666667 0.11050000 0.1875000 0.3333333 0
##
## Spec
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## logistic 0.8571429 0.9722222 0.984127 0.9809524 1.000000 1 0
## ann 0.9682540 0.9841270 1.000000 0.9930159 1.000000 1 0
## boost 0.8730159 0.9523810 0.984127 0.9666667 0.984127 1 0summary(diff(resamp))##
## Call:
## summary.diff.resamples(object = diff(resamp))
##
## p-value adjustment: bonferroni
## Upper diagonal: estimates of the difference
## Lower diagonal: p-value for H0: difference = 0
##
## ROC
## logistic ann boost
## logistic 0.011669 0.002979
## ann 1 -0.008690
## boost 1 1
##
## Sens
## logistic ann boost
## logistic 0.05125 -0.01967
## ann 0.0004689 -0.07092
## boost 0.7704813 3.76e-05
##
## Spec
## logistic ann boost
## logistic -0.01206 0.01429
## ann 0.01405 0.02635
## boost 0.02712 3.761e-06# plot the difference between models
dotplot(resamp)
bwplot(resamp)
densityplot(resamp, auto.key = list(columns = 2))
Referenced:
Applied Predictive Modeling
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