Mburu 3/23/2020
library(tidyverse)
library(data.table)
library(tictoc)
library(caret)
library(knitr)In order to perform hyperparameter tuning, it is important to really understand what hyperparameters are (and what they are not). So let’s look at model parameters versus hyperparameters in detail. Note: The Breast Cancer Wisconsin (Diagnostic) Data Set has been loaded as breast_cancer_data for you.
breast_cancer_data <- fread("breast_cancer_data.csv")
# Fit a linear model on the breast_cancer_data.
linear_model <- lm(concavity_mean ~ symmetry_mean,data = breast_cancer_data)
# Look at the summary of the linear_model.
summary(linear_model)##
## Call:
## lm(formula = concavity_mean ~ symmetry_mean, data = breast_cancer_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.201877 -0.039201 -0.008432 0.030655 0.226150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.15311 0.04086 -3.747 0.000303 ***
## symmetry_mean 1.33366 0.21257 6.274 9.57e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06412 on 98 degrees of freedom
## Multiple R-squared: 0.2866, Adjusted R-squared: 0.2793
## F-statistic: 39.36 on 1 and 98 DF, p-value: 9.575e-09
# Extract the coefficients.
coef(linear_model)## (Intercept) symmetry_mean
## -0.1531055 1.3336568
To get a good feel for the difference between fitted model parameters and hyperparameters, we are going to take a closer look at those fitted parameters: in our simple linear model, the coefficients. The dataset breast_cancer_data has already been loaded for you and the linear model call was run as in the previous lesson, so you can directly access the object linear_model. In our linear model, we can extract the coefficients in the following way: linear_model$coefficients. And we can visualize the relationship we modeled with a plot. Remember, that a linear model has the basic formula: y = x * slope + intercept
# Plot linear relationship.
ggplot(data = breast_cancer_data,
aes(x = symmetry_mean, y = concavity_mean)) +
geom_point(color = "grey") +
geom_abline(slope = linear_model$coefficients[2],
intercept = linear_model$coefficients[1])
Before we can train machine learning models and tune hyperparameters, we need to prepare the data. The data has again been loaded into your workspace as breast_cancer_data. The library caret has already been loaded
# Create partition index
index <- createDataPartition(breast_cancer_data$diagnosis, p = 0.7, list = FALSE)
# Subset `breast_cancer_data` with index
bc_train_data <- breast_cancer_data[index, ]
bc_test_data <- breast_cancer_data[-index, ]
# Define 3x5 folds repeated cross-validation
fitControl <- trainControl(method = "repeatedcv", number = 5, repeats = 3)
# Run the train() function
gbm_model <- train(diagnosis ~ .,
data = bc_train_data,
method = "gbm",
trControl = fitControl,
verbose = FALSE)
# Look at the model
gbm_model## Stochastic Gradient Boosting
##
## 70 samples
## 10 predictors
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 3 times)
## Summary of sample sizes: 56, 56, 56, 56, 56, 56, ...
## Resampling results across tuning parameters:
##
## interaction.depth n.trees Accuracy Kappa
## 1 50 0.9857143 0.9714286
## 1 100 0.9857143 0.9714286
## 1 150 0.9904762 0.9809524
## 2 50 0.9809524 0.9619048
## 2 100 0.9857143 0.9714286
## 2 150 0.9904762 0.9809524
## 3 50 0.9761905 0.9523810
## 3 100 0.9857143 0.9714286
## 3 150 0.9904762 0.9809524
##
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
##
## Tuning parameter 'n.minobsinnode' was held constant at a value of 10
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 150,
## interaction.depth = 1, shrinkage = 0.1 and n.minobsinnode = 10.
When we examine the model object closely, we can see that caret already did some automatic hyperparameter tuning for us: train automatically creates a grid of tuning parameters. By default, if p is the number of tuning parameters, the grid size is 3^p. But we can also specify the number of different values to try for each hyperparameter. The data has again been preloaded as bc_train_data. The libraries caret and tictoc have also been preloaded.
set.seed(42)
# Start timer.
tic()
# Train model.
gbm_model <- train(diagnosis ~ .,
data = bc_train_data,
method = "gbm",
trControl = trainControl(method = "repeatedcv",
number = 5, repeats = 3),
verbose = FALSE,
tuneLength = 4)
# Stop timer.
toc()## 1.2 sec elapsed
gbm_model## Stochastic Gradient Boosting
##
## 70 samples
## 10 predictors
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 3 times)
## Summary of sample sizes: 56, 56, 56, 56, 56, 56, ...
## Resampling results across tuning parameters:
##
## interaction.depth n.trees Accuracy Kappa
## 1 50 0.9380952 0.8761905
## 1 100 0.9380952 0.8761905
## 1 150 0.9380952 0.8761905
## 1 200 0.9380952 0.8761905
## 2 50 0.9476190 0.8952381
## 2 100 0.9476190 0.8952381
## 2 150 0.9476190 0.8952381
## 2 200 0.9476190 0.8952381
## 3 50 0.9380952 0.8761905
## 3 100 0.9428571 0.8857143
## 3 150 0.9571429 0.9142857
## 3 200 0.9571429 0.9142857
## 4 50 0.9380952 0.8761905
## 4 100 0.9380952 0.8761905
## 4 150 0.9476190 0.8952381
## 4 200 0.9476190 0.8952381
##
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
##
## Tuning parameter 'n.minobsinnode' was held constant at a value of 10
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 150,
## interaction.depth = 3, shrinkage = 0.1 and n.minobsinnode = 10.
In chapter 1, you learned how to use the expand.grid() function to manually define hyperparameters. The same function can also be used to define a grid of hyperparameters. The voters_train_data dataset has already been preprocessed to make it a bit smaller so training will run faster; it has now 80 observations and balanced classes and has been loaded for you. The caret and tictoc packages have also been loaded and the trainControl object has been defined with repeated cross-validation:
fitControl <- trainControl(method = "repeatedcv",
number = 3,
repeats = 5)
voters_data <- fread("voters_data.csv")
index <- createDataPartition(voters_data$turnout16_2016, p = 0.7, list = FALSE)
# Subset `breast_cancer_data` with index
voters_train_data <-voters_data[index, ]
voters_test_data <- voters_data[-index, ]
# Define Cartesian grid
man_grid <- expand.grid(degree = c(1, 2, 3),
scale = c(0.1, 0.01, 0.001),
C = 0.5)
# Start timer, set seed & train model
tic()
set.seed(42)
svm_model_voters_grid <- train(turnout16_2016 ~ .,
data = voters_train_data,
method = "svmPoly",
trControl = fitControl,
verbose= FALSE,
tuneGrid = man_grid)
toc()## 2.43 sec elapsed
svm_model_voters_grid## Support Vector Machines with Polynomial Kernel
##
## 70 samples
## 39 predictors
## 2 classes: 'Did not vote', 'Voted'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 5 times)
## Summary of sample sizes: 47, 47, 46, 47, 46, 47, ...
## Resampling results across tuning parameters:
##
## degree scale Accuracy Kappa
## 1 0.001 0.4910628 0.01111111
## 1 0.010 0.5170290 0.05681199
## 1 0.100 0.5747585 0.15355334
## 2 0.001 0.4910628 0.01111111
## 2 0.010 0.5374396 0.08091976
## 2 0.100 0.6114734 0.22581901
## 3 0.001 0.4938406 0.01666667
## 3 0.010 0.5518116 0.10684649
## 3 0.100 0.5996377 0.19949308
##
## Tuning parameter 'C' was held constant at a value of 0.5
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were degree = 2, scale = 0.1 and C
## = 0.5.
# Plot default
plot(svm_model_voters_grid)
# Plot Kappa level-plot
plot(svm_model_voters_grid, metric = "Kappa", plottype = "level")
If you already know which hyperparameter values you want to set, you can also manually define hyperparameters as a grid. Go to modelLookup(‘gbm’) or search for gbm in the list of available models in caret and check under Tuning Parameters.
Note: Just as before,bc_train_data and the libraries caret and tictoc have been preloaded.
# Define hyperparameter grid.
hyperparams <- expand.grid(n.trees = 200,
interaction.depth = 1,
shrinkage = 0.1,
n.minobsinnode = 10)
# Apply hyperparameter grid to train().
set.seed(42)
gbm_model <- train(diagnosis ~ .,
data = bc_train_data,
method = "gbm",
trControl = trainControl(method = "repeatedcv",
number = 5,repeats = 3),
verbose = FALSE,
tuneGrid = hyperparams)
gbm_model## Stochastic Gradient Boosting
##
## 70 samples
## 10 predictors
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 3 times)
## Summary of sample sizes: 56, 56, 56, 56, 56, 56, ...
## Resampling results:
##
## Accuracy Kappa
## 0.947619 0.8952381
##
## Tuning parameter 'n.trees' was held constant at a value of 200
## 1
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
##
## Tuning parameter 'n.minobsinnode' was held constant at a value of 10
In chapter 1, you learned how to use the expand.grid() function to manually define a set of hyperparameters. The same function can also be used to define a grid with ranges of hyperparameters. The voters_train_data dataset has been loaded for you, as have the caret and tictoc packages.
# Define the grid with hyperparameter ranges
big_grid <- expand.grid(size = seq(from = 1, to = 5, by = 1), decay = c(0, 1))
# Train control with grid search
fitControl <- trainControl(method = "repeatedcv", number = 3, repeats = 5, search = "grid")
# Train neural net
tic()
set.seed(42)
nn_model_voters_big_grid <- train(turnout16_2016 ~ .,
data = voters_train_data,
method = "nnet",
trControl = fitControl,
verbose = FALSE)## # weights: 42
## initial value 37.138322
## iter 10 value 32.567037
## iter 20 value 32.566351
## iter 30 value 30.899224
## iter 40 value 18.006739
## iter 50 value 17.994803
## iter 60 value 17.994725
## final value 17.994725
## converged
## # weights: 124
## initial value 32.491600
## iter 10 value 20.787421
## iter 20 value 8.303061
## iter 30 value 8.275658
## iter 40 value 8.271932
## iter 50 value 8.271377
## iter 60 value 8.270560
## iter 70 value 8.270488
## iter 80 value 8.270456
## iter 90 value 8.270360
## final value 8.270358
## converged
## # weights: 206
## initial value 34.710434
## iter 10 value 14.932746
## iter 20 value 10.027934
## iter 30 value 8.079911
## iter 40 value 7.095446
## iter 50 value 5.648801
## iter 60 value 5.173779
## iter 70 value 4.900751
## iter 80 value 4.782411
## iter 90 value 4.780912
## iter 100 value 4.780760
## final value 4.780760
## stopped after 100 iterations
## # weights: 42
## initial value 38.344128
## iter 10 value 30.496596
## iter 20 value 13.206270
## iter 30 value 10.168056
## iter 40 value 10.093036
## iter 50 value 10.092239
## final value 10.092234
## converged
## # weights: 124
## initial value 36.334568
## iter 10 value 20.975290
## iter 20 value 12.279457
## iter 30 value 10.152080
## iter 40 value 7.967916
## iter 50 value 7.678386
## iter 60 value 7.627699
## iter 70 value 7.626699
## final value 7.626690
## converged
## # weights: 206
## initial value 35.260076
## iter 10 value 13.922542
## iter 20 value 8.431612
## iter 30 value 7.742616
## iter 40 value 7.399642
## iter 50 value 7.210452
## iter 60 value 7.192967
## iter 70 value 7.192614
## iter 80 value 7.192600
## iter 80 value 7.192600
## iter 80 value 7.192600
## final value 7.192600
## converged
## # weights: 42
## initial value 32.966387
## iter 10 value 32.567890
## final value 32.567822
## converged
## # weights: 124
## initial value 32.584642
## iter 10 value 13.845332
## iter 20 value 13.383216
## iter 30 value 13.375452
## iter 40 value 13.369854
## iter 50 value 13.364270
## iter 60 value 13.358571
## iter 70 value 13.354610
## iter 80 value 13.352717
## iter 90 value 13.351490
## iter 100 value 13.349026
## final value 13.349026
## stopped after 100 iterations
## # weights: 206
## initial value 37.367703
## iter 10 value 19.447628
## iter 20 value 13.274946
## iter 30 value 13.233949
## iter 40 value 13.205138
## iter 50 value 13.190925
## iter 60 value 13.183849
## iter 70 value 13.181510
## iter 80 value 12.549513
## iter 90 value 8.172932
## iter 100 value 4.300194
## final value 4.300194
## stopped after 100 iterations
## # weights: 42
## initial value 32.550204
## iter 10 value 30.373148
## iter 20 value 30.316479
## final value 30.316406
## converged
## # weights: 124
## initial value 34.759241
## iter 10 value 16.317194
## iter 20 value 7.701699
## iter 30 value 6.401910
## iter 40 value 6.368882
## iter 50 value 6.363627
## iter 60 value 6.362852
## final value 6.362784
## converged
## # weights: 206
## initial value 31.462407
## iter 10 value 11.562016
## iter 20 value 3.002230
## iter 30 value 2.773046
## iter 40 value 2.772591
## final value 2.772589
## converged
## # weights: 42
## initial value 33.729380
## iter 10 value 24.501446
## iter 20 value 16.162788
## iter 30 value 13.052881
## iter 40 value 11.023865
## iter 50 value 10.850147
## iter 60 value 10.849939
## iter 60 value 10.849939
## iter 60 value 10.849939
## final value 10.849939
## converged
## # weights: 124
## initial value 36.631374
## iter 10 value 23.069214
## iter 20 value 13.528209
## iter 30 value 10.889040
## iter 40 value 9.071781
## iter 50 value 8.790094
## iter 60 value 8.579499
## iter 70 value 8.520462
## iter 80 value 8.519375
## final value 8.519375
## converged
## # weights: 206
## initial value 35.892929
## iter 10 value 19.418160
## iter 20 value 14.042050
## iter 30 value 9.812626
## iter 40 value 8.967699
## iter 50 value 8.666277
## iter 60 value 8.522306
## iter 70 value 8.278827
## iter 80 value 8.214929
## iter 90 value 8.169273
## iter 100 value 8.134234
## final value 8.134234
## stopped after 100 iterations
## # weights: 42
## initial value 34.540476
## iter 10 value 31.343632
## iter 20 value 20.672582
## iter 30 value 20.632905
## iter 40 value 20.628823
## iter 50 value 20.627356
## iter 60 value 20.625384
## iter 70 value 20.623735
## iter 80 value 20.623023
## iter 90 value 17.711970
## iter 100 value 16.616884
## final value 16.616884
## stopped after 100 iterations
## # weights: 124
## initial value 32.260381
## iter 10 value 15.554442
## iter 20 value 6.737224
## iter 30 value 5.847377
## iter 40 value 5.641201
## iter 50 value 5.520411
## iter 60 value 4.513731
## iter 70 value 4.025861
## iter 80 value 3.416975
## iter 90 value 3.407775
## iter 100 value 2.287484
## final value 2.287484
## stopped after 100 iterations
## # weights: 206
## initial value 32.065780
## iter 10 value 8.858859
## iter 20 value 1.099506
## iter 30 value 0.239510
## iter 40 value 0.206114
## iter 50 value 0.177138
## iter 60 value 0.141209
## iter 70 value 0.127889
## iter 80 value 0.119888
## iter 90 value 0.113615
## iter 100 value 0.110907
## final value 0.110907
## stopped after 100 iterations
## # weights: 42
## initial value 32.418118
## iter 10 value 31.828391
## iter 20 value 11.594870
## iter 30 value 4.206525
## iter 40 value 4.158247
## iter 50 value 4.156928
## final value 4.156925
## converged
## # weights: 124
## initial value 40.659179
## iter 10 value 13.036133
## iter 20 value 4.514555
## iter 30 value 4.158193
## iter 40 value 4.156933
## final value 4.156927
## converged
## # weights: 206
## initial value 35.839518
## iter 10 value 24.107551
## iter 20 value 23.546847
## iter 30 value 23.545965
## iter 30 value 23.545964
## final value 23.545964
## converged
## # weights: 42
## initial value 34.055374
## iter 10 value 20.722532
## iter 20 value 10.867173
## iter 30 value 9.889161
## final value 9.888419
## converged
## # weights: 124
## initial value 36.212847
## iter 10 value 25.160018
## iter 20 value 14.737874
## iter 30 value 8.242734
## iter 40 value 7.473331
## iter 50 value 7.433053
## iter 60 value 7.432941
## final value 7.432941
## converged
## # weights: 206
## initial value 36.763593
## iter 10 value 18.992488
## iter 20 value 9.554745
## iter 30 value 8.740429
## iter 40 value 7.485818
## iter 50 value 7.129350
## iter 60 value 7.101764
## iter 70 value 7.037369
## iter 80 value 7.017195
## iter 90 value 7.013800
## iter 100 value 7.013664
## final value 7.013664
## stopped after 100 iterations
## # weights: 42
## initial value 34.049213
## iter 10 value 23.843743
## iter 20 value 23.591345
## iter 30 value 23.582750
## iter 40 value 23.578091
## iter 50 value 23.574798
## iter 60 value 23.566726
## iter 70 value 22.106178
## iter 80 value 21.426956
## iter 90 value 21.424894
## iter 100 value 21.409346
## final value 21.409346
## stopped after 100 iterations
## # weights: 124
## initial value 34.204443
## iter 10 value 4.222549
## iter 20 value 0.115536
## iter 30 value 0.058841
## iter 40 value 0.057024
## iter 50 value 0.055873
## iter 60 value 0.053731
## iter 70 value 0.052186
## iter 80 value 0.051543
## iter 90 value 0.051031
## iter 100 value 0.050688
## final value 0.050688
## stopped after 100 iterations
## # weights: 206
## initial value 37.955534
## iter 10 value 7.172408
## iter 20 value 6.037557
## iter 30 value 0.151792
## iter 40 value 0.143672
## iter 50 value 0.116684
## iter 60 value 0.110067
## iter 70 value 0.103737
## iter 80 value 0.090443
## iter 90 value 0.081569
## iter 100 value 0.075201
## final value 0.075201
## stopped after 100 iterations
## # weights: 42
## initial value 34.207941
## iter 10 value 23.992142
## iter 20 value 20.108503
## iter 30 value 19.067701
## iter 40 value 18.649545
## iter 50 value 18.647176
## iter 60 value 18.646917
## final value 18.646772
## converged
## # weights: 124
## initial value 37.437591
## iter 10 value 22.236591
## iter 20 value 1.818832
## iter 30 value 0.035498
## iter 40 value 0.013061
## iter 50 value 0.001055
## final value 0.000057
## converged
## # weights: 206
## initial value 39.135610
## iter 10 value 32.369807
## iter 20 value 28.008066
## iter 30 value 18.083351
## iter 40 value 17.997835
## iter 50 value 15.080790
## iter 60 value 15.014965
## iter 70 value 15.013418
## iter 80 value 11.672973
## iter 90 value 9.493927
## iter 100 value 9.389416
## final value 9.389416
## stopped after 100 iterations
## # weights: 42
## initial value 33.499306
## iter 10 value 28.306589
## iter 20 value 16.599767
## iter 30 value 13.550631
## iter 40 value 12.656175
## iter 50 value 10.905539
## iter 60 value 10.823095
## final value 10.823059
## converged
## # weights: 124
## initial value 35.163076
## iter 10 value 23.804076
## iter 20 value 14.949483
## iter 30 value 11.771198
## iter 40 value 9.948936
## iter 50 value 9.381415
## iter 60 value 9.358876
## iter 70 value 9.351510
## iter 80 value 9.203462
## iter 90 value 8.999876
## iter 100 value 8.995961
## final value 8.995961
## stopped after 100 iterations
## # weights: 206
## initial value 42.276985
## iter 10 value 22.814280
## iter 20 value 11.303930
## iter 30 value 9.027344
## iter 40 value 8.491127
## iter 50 value 8.381834
## iter 60 value 8.307150
## iter 70 value 8.291083
## iter 80 value 8.288351
## final value 8.288341
## converged
## # weights: 42
## initial value 34.516865
## iter 10 value 18.995965
## iter 20 value 11.586190
## iter 30 value 11.575648
## iter 40 value 11.563385
## iter 50 value 11.557125
## iter 60 value 11.551539
## iter 70 value 11.548624
## iter 80 value 11.542615
## iter 90 value 11.539088
## iter 100 value 9.565523
## final value 9.565523
## stopped after 100 iterations
## # weights: 124
## initial value 33.414591
## iter 10 value 23.930840
## iter 20 value 5.649803
## iter 30 value 4.266323
## iter 40 value 1.442154
## iter 50 value 0.145910
## iter 60 value 0.106844
## iter 70 value 0.094847
## iter 80 value 0.090703
## iter 90 value 0.083594
## iter 100 value 0.079320
## final value 0.079320
## stopped after 100 iterations
## # weights: 206
## initial value 32.857007
## iter 10 value 19.480310
## iter 20 value 5.306910
## iter 30 value 4.398381
## iter 40 value 3.990084
## iter 50 value 3.060991
## iter 60 value 2.112226
## iter 70 value 0.229094
## iter 80 value 0.185529
## iter 90 value 0.163159
## iter 100 value 0.156153
## final value 0.156153
## stopped after 100 iterations
## # weights: 42
## initial value 31.896491
## iter 10 value 26.636392
## iter 20 value 0.595448
## iter 30 value 0.000853
## final value 0.000055
## converged
## # weights: 124
## initial value 32.338753
## iter 10 value 22.374763
## iter 20 value 7.695260
## iter 30 value 4.026041
## iter 40 value 0.000274
## final value 0.000069
## converged
## # weights: 206
## initial value 32.036036
## iter 10 value 16.396274
## iter 20 value 7.002894
## iter 30 value 6.969741
## iter 40 value 6.527133
## iter 50 value 0.002980
## iter 60 value 0.000631
## final value 0.000066
## converged
## # weights: 42
## initial value 36.170963
## iter 10 value 24.171865
## iter 20 value 19.123100
## iter 30 value 14.947626
## iter 40 value 10.783141
## iter 50 value 9.680607
## iter 60 value 9.671415
## final value 9.671414
## converged
## # weights: 124
## initial value 35.008053
## iter 10 value 26.100772
## iter 20 value 14.995687
## iter 30 value 8.606395
## iter 40 value 7.634167
## iter 50 value 7.555421
## iter 60 value 7.524744
## iter 70 value 7.315395
## iter 80 value 7.218899
## iter 90 value 7.170277
## iter 100 value 7.166622
## final value 7.166622
## stopped after 100 iterations
## # weights: 206
## initial value 41.656922
## iter 10 value 28.186555
## iter 20 value 13.764544
## iter 30 value 7.871910
## iter 40 value 7.366928
## iter 50 value 7.084649
## iter 60 value 6.945875
## iter 70 value 6.787148
## iter 80 value 6.752342
## iter 90 value 6.733848
## iter 100 value 6.726397
## final value 6.726397
## stopped after 100 iterations
## # weights: 42
## initial value 32.416820
## iter 10 value 28.881714
## iter 20 value 11.784418
## iter 30 value 11.406996
## iter 40 value 11.386743
## iter 50 value 11.374973
## iter 60 value 11.370474
## iter 70 value 11.366366
## iter 80 value 11.363236
## iter 90 value 11.360690
## iter 100 value 11.359587
## final value 11.359587
## stopped after 100 iterations
## # weights: 124
## initial value 37.810586
## iter 10 value 15.277433
## iter 20 value 7.815065
## iter 30 value 3.790363
## iter 40 value 3.571352
## iter 50 value 2.855349
## iter 60 value 0.180214
## iter 70 value 0.114622
## iter 80 value 0.106663
## iter 90 value 0.104385
## iter 100 value 0.100720
## final value 0.100720
## stopped after 100 iterations
## # weights: 206
## initial value 36.828782
## iter 10 value 11.557567
## iter 20 value 2.351067
## iter 30 value 0.508083
## iter 40 value 0.106624
## iter 50 value 0.092913
## iter 60 value 0.089128
## iter 70 value 0.086920
## iter 80 value 0.081525
## iter 90 value 0.076124
## iter 100 value 0.070527
## final value 0.070527
## stopped after 100 iterations
## # weights: 42
## initial value 34.668728
## iter 10 value 24.280561
## iter 20 value 13.270382
## iter 30 value 13.139015
## iter 40 value 13.138169
## iter 50 value 11.366047
## iter 60 value 11.326171
## iter 70 value 11.326065
## iter 70 value 11.326065
## iter 70 value 11.326065
## final value 11.326065
## converged
## # weights: 124
## initial value 33.995467
## iter 10 value 25.102165
## iter 20 value 18.119954
## iter 30 value 17.997397
## iter 40 value 11.981328
## iter 50 value 11.487756
## iter 60 value 11.483318
## iter 70 value 11.482947
## iter 80 value 9.425540
## iter 90 value 9.418502
## final value 9.418474
## converged
## # weights: 206
## initial value 33.208888
## iter 10 value 16.878214
## iter 20 value 7.312249
## iter 30 value 1.146936
## iter 40 value 0.102939
## iter 50 value 0.030881
## iter 60 value 0.018041
## iter 70 value 0.005238
## iter 80 value 0.001219
## iter 90 value 0.000286
## final value 0.000073
## converged
## # weights: 42
## initial value 33.887641
## iter 10 value 24.592992
## iter 20 value 17.962387
## iter 30 value 14.465504
## iter 40 value 13.079709
## iter 50 value 13.074488
## final value 13.074488
## converged
## # weights: 124
## initial value 37.236638
## iter 10 value 23.400039
## iter 20 value 15.250442
## iter 30 value 14.246787
## iter 40 value 13.010269
## iter 50 value 10.220780
## iter 60 value 9.844318
## iter 70 value 9.822198
## iter 80 value 9.821406
## iter 90 value 9.821372
## final value 9.821371
## converged
## # weights: 206
## initial value 41.717800
## iter 10 value 18.719481
## iter 20 value 14.365162
## iter 30 value 10.475919
## iter 40 value 9.210428
## iter 50 value 9.043366
## iter 60 value 9.001454
## iter 70 value 8.922982
## iter 80 value 8.890284
## iter 90 value 8.889740
## final value 8.889738
## converged
## # weights: 42
## initial value 32.975429
## iter 10 value 27.738656
## iter 20 value 20.180926
## iter 30 value 20.114523
## iter 40 value 17.394645
## iter 50 value 15.353182
## iter 60 value 15.324842
## iter 70 value 15.323361
## iter 80 value 15.319990
## iter 90 value 15.318632
## iter 100 value 15.317575
## final value 15.317575
## stopped after 100 iterations
## # weights: 124
## initial value 34.828621
## iter 10 value 14.395361
## iter 20 value 9.239453
## iter 30 value 4.425942
## iter 40 value 4.221287
## iter 50 value 4.215486
## iter 60 value 4.213879
## iter 70 value 4.209636
## iter 80 value 4.206112
## iter 90 value 4.203923
## iter 100 value 4.201885
## final value 4.201885
## stopped after 100 iterations
## # weights: 206
## initial value 40.227681
## iter 10 value 20.333336
## iter 20 value 5.749095
## iter 30 value 4.345795
## iter 40 value 4.272345
## iter 50 value 4.267624
## iter 60 value 1.568857
## iter 70 value 0.258102
## iter 80 value 0.250244
## iter 90 value 0.237601
## iter 100 value 0.227726
## final value 0.227726
## stopped after 100 iterations
## # weights: 42
## initial value 32.314100
## iter 10 value 14.333381
## iter 20 value 10.968844
## iter 30 value 10.952431
## final value 10.952034
## converged
## # weights: 124
## initial value 33.317817
## iter 10 value 13.645506
## iter 20 value 3.519343
## iter 30 value 0.317590
## iter 40 value 0.019566
## final value 0.000081
## converged
## # weights: 206
## initial value 31.949605
## iter 10 value 21.113290
## iter 20 value 4.180959
## iter 30 value 0.289823
## iter 40 value 0.034464
## iter 50 value 0.007798
## iter 60 value 0.001623
## iter 70 value 0.000510
## iter 80 value 0.000195
## final value 0.000069
## converged
## # weights: 42
## initial value 32.815233
## iter 10 value 23.481895
## iter 20 value 12.800074
## iter 30 value 9.660430
## iter 40 value 9.594575
## final value 9.594568
## converged
## # weights: 124
## initial value 34.582692
## iter 10 value 22.678655
## iter 20 value 12.563966
## iter 30 value 7.980912
## iter 40 value 7.131103
## iter 50 value 7.092960
## iter 60 value 7.089468
## final value 7.089465
## converged
## # weights: 206
## initial value 35.059394
## iter 10 value 18.099957
## iter 20 value 8.569211
## iter 30 value 7.311399
## iter 40 value 6.864339
## iter 50 value 6.705996
## iter 60 value 6.660671
## iter 70 value 6.655006
## iter 80 value 6.653507
## iter 90 value 6.653417
## final value 6.653417
## converged
## # weights: 42
## initial value 31.931339
## iter 10 value 30.035261
## iter 20 value 11.536855
## iter 30 value 11.466038
## iter 40 value 11.399883
## iter 50 value 11.384625
## iter 60 value 11.377477
## iter 70 value 11.368204
## iter 80 value 7.276830
## iter 90 value 7.013031
## iter 100 value 7.009165
## final value 7.009165
## stopped after 100 iterations
## # weights: 124
## initial value 35.809378
## iter 10 value 28.630512
## iter 20 value 19.800423
## iter 30 value 11.549914
## iter 40 value 7.803147
## iter 50 value 3.638195
## iter 60 value 0.220125
## iter 70 value 0.158702
## iter 80 value 0.127301
## iter 90 value 0.080317
## iter 100 value 0.071102
## final value 0.071102
## stopped after 100 iterations
## # weights: 206
## initial value 33.887263
## iter 10 value 11.183135
## iter 20 value 0.173725
## iter 30 value 0.147919
## iter 40 value 0.119107
## iter 50 value 0.095334
## iter 60 value 0.086659
## iter 70 value 0.079446
## iter 80 value 0.075066
## iter 90 value 0.071991
## iter 100 value 0.065775
## final value 0.065775
## stopped after 100 iterations
## # weights: 42
## initial value 34.327102
## iter 10 value 14.941656
## iter 20 value 10.822172
## iter 30 value 9.445182
## iter 40 value 9.426061
## iter 50 value 9.418858
## iter 60 value 9.418671
## iter 70 value 9.418503
## final value 9.418493
## converged
## # weights: 124
## initial value 42.783083
## iter 10 value 22.405303
## iter 20 value 21.789124
## iter 30 value 21.787046
## final value 21.787042
## converged
## # weights: 206
## initial value 43.817719
## iter 10 value 9.884278
## iter 20 value 9.041229
## iter 30 value 4.447337
## iter 40 value 0.022051
## iter 50 value 0.003581
## iter 60 value 0.001016
## iter 70 value 0.000126
## final value 0.000094
## converged
## # weights: 42
## initial value 34.058388
## iter 10 value 20.085106
## iter 20 value 15.105858
## iter 30 value 14.954282
## iter 40 value 13.130561
## iter 50 value 11.136873
## iter 60 value 11.095066
## final value 11.095027
## converged
## # weights: 124
## initial value 35.160987
## iter 10 value 22.637535
## iter 20 value 13.178000
## iter 30 value 10.209372
## iter 40 value 9.224724
## iter 50 value 9.006025
## iter 60 value 8.794866
## iter 70 value 8.762496
## iter 80 value 8.761868
## iter 80 value 8.761868
## iter 80 value 8.761868
## final value 8.761868
## converged
## # weights: 206
## initial value 37.061857
## iter 10 value 19.841395
## iter 20 value 11.869660
## iter 30 value 9.282388
## iter 40 value 8.776425
## iter 50 value 8.497690
## iter 60 value 8.354765
## iter 70 value 8.336566
## iter 80 value 8.335912
## iter 90 value 8.335572
## iter 100 value 8.335563
## final value 8.335563
## stopped after 100 iterations
## # weights: 42
## initial value 35.587454
## iter 10 value 16.927975
## iter 20 value 11.086690
## iter 30 value 11.068681
## iter 40 value 11.059485
## iter 50 value 9.860872
## iter 60 value 8.334825
## iter 70 value 8.328491
## iter 80 value 8.326662
## iter 90 value 8.323935
## iter 100 value 8.318884
## final value 8.318884
## stopped after 100 iterations
## # weights: 124
## initial value 33.682952
## iter 10 value 18.480538
## iter 20 value 14.200562
## iter 30 value 9.702720
## iter 40 value 4.057261
## iter 50 value 3.285884
## iter 60 value 3.235367
## iter 70 value 3.220374
## iter 80 value 3.202360
## iter 90 value 3.187683
## iter 100 value 3.173399
## final value 3.173399
## stopped after 100 iterations
## # weights: 206
## initial value 34.863480
## iter 10 value 13.448310
## iter 20 value 3.884597
## iter 30 value 0.229869
## iter 40 value 0.133667
## iter 50 value 0.118463
## iter 60 value 0.109837
## iter 70 value 0.092487
## iter 80 value 0.081566
## iter 90 value 0.078268
## iter 100 value 0.076307
## final value 0.076307
## stopped after 100 iterations
## # weights: 42
## initial value 34.251949
## iter 10 value 17.455592
## iter 20 value 11.897929
## iter 30 value 11.022840
## iter 40 value 10.998167
## iter 50 value 10.997498
## final value 10.997497
## converged
## # weights: 124
## initial value 33.500616
## iter 10 value 20.979017
## iter 20 value 9.271695
## iter 30 value 6.719202
## iter 40 value 2.677725
## iter 50 value 1.951646
## iter 60 value 1.925282
## iter 70 value 1.910617
## iter 80 value 1.909773
## iter 90 value 1.909628
## iter 100 value 1.909584
## final value 1.909584
## stopped after 100 iterations
## # weights: 206
## initial value 38.706622
## iter 10 value 28.928694
## iter 20 value 6.573425
## iter 30 value 5.562319
## iter 40 value 3.875372
## iter 50 value 3.086160
## iter 60 value 1.935572
## iter 70 value 1.398383
## iter 80 value 1.388289
## iter 90 value 1.386837
## iter 100 value 1.386625
## final value 1.386625
## stopped after 100 iterations
## # weights: 42
## initial value 33.555902
## iter 10 value 27.833718
## iter 20 value 18.051216
## iter 30 value 14.029004
## iter 40 value 13.400562
## iter 50 value 13.397003
## final value 13.397002
## converged
## # weights: 124
## initial value 33.859072
## iter 10 value 20.496606
## iter 20 value 12.181875
## iter 30 value 10.588987
## iter 40 value 9.645164
## iter 50 value 9.279715
## iter 60 value 9.209551
## iter 70 value 9.209387
## final value 9.209387
## converged
## # weights: 206
## initial value 42.282658
## iter 10 value 21.059284
## iter 20 value 14.114715
## iter 30 value 11.220551
## iter 40 value 9.771410
## iter 50 value 9.398855
## iter 60 value 9.074158
## iter 70 value 8.873861
## iter 80 value 8.675614
## iter 90 value 8.612258
## iter 100 value 8.603274
## final value 8.603274
## stopped after 100 iterations
## # weights: 42
## initial value 33.522205
## iter 10 value 23.239578
## iter 20 value 21.838042
## iter 30 value 21.369100
## iter 40 value 21.365678
## iter 50 value 21.362999
## iter 60 value 21.362302
## iter 70 value 17.987690
## iter 80 value 17.972823
## iter 90 value 17.969927
## iter 100 value 16.294143
## final value 16.294143
## stopped after 100 iterations
## # weights: 124
## initial value 34.933522
## iter 10 value 22.556073
## iter 20 value 20.337143
## iter 30 value 15.637522
## iter 40 value 9.602916
## iter 50 value 9.351237
## iter 60 value 8.727652
## iter 70 value 0.811514
## iter 80 value 0.108802
## iter 90 value 0.098821
## iter 100 value 0.094501
## final value 0.094501
## stopped after 100 iterations
## # weights: 206
## initial value 31.950676
## iter 10 value 16.355411
## iter 20 value 11.155211
## iter 30 value 8.792638
## iter 40 value 7.159510
## iter 50 value 6.822208
## iter 60 value 6.805497
## iter 70 value 5.397866
## iter 80 value 5.188265
## iter 90 value 4.791876
## iter 100 value 4.783245
## final value 4.783245
## stopped after 100 iterations
## # weights: 42
## initial value 32.513000
## iter 10 value 12.327775
## iter 20 value 5.101813
## iter 30 value 4.167694
## iter 40 value 4.157910
## iter 50 value 4.157142
## iter 60 value 4.157063
## iter 70 value 4.157016
## final value 4.156930
## converged
## # weights: 124
## initial value 42.235441
## iter 10 value 24.051010
## iter 20 value 16.360985
## iter 30 value 13.240707
## iter 40 value 12.830964
## iter 50 value 10.959355
## iter 60 value 10.954485
## iter 70 value 10.952442
## iter 80 value 10.952218
## iter 90 value 10.952180
## iter 100 value 10.946128
## final value 10.946128
## stopped after 100 iterations
## # weights: 206
## initial value 35.580550
## iter 10 value 16.221274
## iter 20 value 5.556577
## iter 30 value 1.010043
## iter 40 value 0.010178
## iter 50 value 0.000266
## final value 0.000050
## converged
## # weights: 42
## initial value 34.535952
## iter 10 value 20.614179
## iter 20 value 10.308900
## iter 30 value 10.065026
## iter 40 value 10.059317
## final value 10.059317
## converged
## # weights: 124
## initial value 33.489178
## iter 10 value 17.843909
## iter 20 value 12.998284
## iter 30 value 9.648037
## iter 40 value 8.093310
## iter 50 value 7.744714
## iter 60 value 7.637568
## iter 70 value 7.635431
## final value 7.635428
## converged
## # weights: 206
## initial value 40.434325
## iter 10 value 26.123892
## iter 20 value 14.587982
## iter 30 value 8.951903
## iter 40 value 7.903801
## iter 50 value 7.644059
## iter 60 value 7.601414
## iter 70 value 7.572624
## iter 80 value 7.571383
## iter 90 value 7.571247
## final value 7.571202
## converged
## # weights: 42
## initial value 33.624974
## iter 10 value 26.973826
## iter 20 value 17.819156
## iter 30 value 16.081844
## iter 40 value 13.746975
## iter 50 value 13.625511
## iter 60 value 13.622424
## iter 70 value 13.620367
## iter 80 value 10.999074
## iter 90 value 8.530315
## iter 100 value 8.269332
## final value 8.269332
## stopped after 100 iterations
## # weights: 124
## initial value 37.731005
## iter 10 value 22.618018
## iter 20 value 13.672748
## iter 30 value 11.052060
## iter 40 value 8.589956
## iter 50 value 8.310173
## iter 60 value 8.279163
## iter 70 value 8.269638
## iter 80 value 8.263703
## iter 90 value 8.259943
## iter 100 value 8.257985
## final value 8.257985
## stopped after 100 iterations
## # weights: 206
## initial value 34.856428
## iter 10 value 14.361930
## iter 20 value 11.032644
## iter 30 value 11.009472
## iter 40 value 9.283090
## iter 50 value 7.040079
## iter 60 value 7.033272
## iter 70 value 7.024796
## iter 80 value 7.016989
## iter 90 value 7.012632
## iter 100 value 6.843384
## final value 6.843384
## stopped after 100 iterations
## # weights: 42
## initial value 32.957228
## final value 32.567275
## converged
## # weights: 124
## initial value 34.171962
## iter 10 value 21.188247
## iter 20 value 16.681943
## iter 30 value 15.438209
## iter 40 value 14.221782
## iter 50 value 13.512314
## iter 60 value 12.844720
## iter 70 value 12.503934
## iter 80 value 11.962649
## iter 90 value 11.950859
## iter 100 value 11.884623
## final value 11.884623
## stopped after 100 iterations
## # weights: 206
## initial value 32.687646
## iter 10 value 16.293247
## iter 20 value 10.552474
## iter 30 value 8.260850
## iter 40 value 7.649968
## iter 50 value 7.442415
## iter 60 value 7.434761
## iter 70 value 7.433735
## iter 80 value 7.433677
## iter 90 value 7.433312
## iter 100 value 7.433234
## final value 7.433234
## stopped after 100 iterations
## # weights: 42
## initial value 33.563280
## iter 10 value 23.409715
## iter 20 value 14.981054
## iter 30 value 11.216146
## iter 40 value 10.938498
## final value 10.938408
## converged
## # weights: 124
## initial value 36.025093
## iter 10 value 20.871219
## iter 20 value 11.357124
## iter 30 value 9.427975
## iter 40 value 8.879998
## iter 50 value 8.664729
## iter 60 value 8.648632
## final value 8.648620
## converged
## # weights: 206
## initial value 35.950475
## iter 10 value 19.175995
## iter 20 value 10.078911
## iter 30 value 8.944305
## iter 40 value 8.575674
## iter 50 value 8.363331
## iter 60 value 8.265961
## iter 70 value 8.212787
## iter 80 value 8.177086
## iter 90 value 8.175315
## iter 100 value 8.174465
## final value 8.174465
## stopped after 100 iterations
## # weights: 42
## initial value 36.741525
## iter 10 value 15.684120
## iter 20 value 9.492899
## iter 30 value 9.476251
## iter 40 value 9.471651
## iter 50 value 9.467354
## iter 60 value 9.463649
## iter 70 value 9.462228
## iter 80 value 9.460571
## iter 90 value 9.459975
## iter 100 value 9.459631
## final value 9.459631
## stopped after 100 iterations
## # weights: 124
## initial value 35.305471
## iter 10 value 20.001685
## iter 20 value 10.245078
## iter 30 value 9.618667
## iter 40 value 9.572013
## iter 50 value 9.551098
## iter 60 value 9.542580
## iter 70 value 9.525407
## iter 80 value 9.516998
## iter 90 value 9.502555
## iter 100 value 9.489706
## final value 9.489706
## stopped after 100 iterations
## # weights: 206
## initial value 33.797323
## iter 10 value 17.551393
## iter 20 value 8.690345
## iter 30 value 6.957725
## iter 40 value 6.896909
## iter 50 value 3.643814
## iter 60 value 2.479497
## iter 70 value 2.442547
## iter 80 value 2.419999
## iter 90 value 2.411185
## iter 100 value 1.854438
## final value 1.854438
## stopped after 100 iterations
## # weights: 42
## initial value 35.351629
## iter 10 value 27.758323
## iter 20 value 15.911109
## iter 30 value 15.842539
## iter 40 value 15.837601
## iter 50 value 15.837585
## iter 50 value 15.837585
## iter 50 value 15.837585
## final value 15.837585
## converged
## # weights: 124
## initial value 33.444018
## iter 10 value 13.993663
## iter 20 value 4.220874
## iter 30 value 4.198683
## iter 40 value 4.198609
## final value 4.198604
## converged
## # weights: 206
## initial value 34.408382
## iter 10 value 18.060986
## iter 20 value 11.346652
## iter 30 value 6.966001
## iter 40 value 3.963045
## iter 50 value 0.110897
## iter 60 value 0.001178
## iter 70 value 0.000451
## final value 0.000073
## converged
## # weights: 42
## initial value 36.487854
## iter 10 value 24.944613
## iter 20 value 16.503361
## iter 30 value 13.148934
## iter 40 value 10.530144
## iter 50 value 10.438442
## final value 10.438247
## converged
## # weights: 124
## initial value 40.340831
## iter 10 value 29.709755
## iter 20 value 15.398942
## iter 30 value 10.243055
## iter 40 value 9.491151
## iter 50 value 8.990665
## iter 60 value 8.460140
## iter 70 value 8.258288
## iter 80 value 8.255606
## iter 90 value 8.254359
## final value 8.254359
## converged
## # weights: 206
## initial value 37.565384
## iter 10 value 22.030745
## iter 20 value 11.199818
## iter 30 value 9.172823
## iter 40 value 8.656180
## iter 50 value 8.394591
## iter 60 value 8.186872
## iter 70 value 7.894255
## iter 80 value 7.768052
## iter 90 value 7.669603
## iter 100 value 7.638152
## final value 7.638152
## stopped after 100 iterations
## # weights: 42
## initial value 34.268060
## iter 10 value 31.972876
## iter 20 value 28.188006
## iter 30 value 28.138782
## iter 40 value 28.135453
## iter 50 value 28.134198
## iter 60 value 28.133313
## iter 70 value 26.799601
## iter 80 value 25.523887
## iter 90 value 23.029252
## iter 100 value 14.444278
## final value 14.444278
## stopped after 100 iterations
## # weights: 124
## initial value 34.720077
## iter 10 value 14.307801
## iter 20 value 9.857983
## iter 30 value 9.356248
## iter 40 value 9.352243
## iter 50 value 9.348236
## iter 60 value 9.340163
## iter 70 value 7.030596
## iter 80 value 6.892046
## iter 90 value 1.874769
## iter 100 value 0.107768
## final value 0.107768
## stopped after 100 iterations
## # weights: 206
## initial value 35.341096
## iter 10 value 22.575130
## iter 20 value 10.174572
## iter 30 value 8.339356
## iter 40 value 7.405686
## iter 50 value 6.489527
## iter 60 value 4.450316
## iter 70 value 4.110640
## iter 80 value 3.591204
## iter 90 value 3.575417
## iter 100 value 3.559629
## final value 3.559629
## stopped after 100 iterations
## # weights: 42
## initial value 33.327368
## final value 31.884801
## converged
## # weights: 124
## initial value 32.247088
## iter 10 value 26.619057
## iter 20 value 7.875741
## iter 30 value 2.822539
## iter 40 value 2.466757
## iter 50 value 2.252806
## iter 60 value 1.920273
## iter 70 value 1.915276
## iter 80 value 1.914180
## iter 90 value 1.910848
## iter 100 value 1.910301
## final value 1.910301
## stopped after 100 iterations
## # weights: 206
## initial value 34.794543
## iter 10 value 16.455005
## iter 20 value 11.632736
## iter 30 value 4.929345
## iter 40 value 3.365237
## iter 50 value 3.365059
## iter 50 value 3.365059
## iter 50 value 3.365059
## final value 3.365059
## converged
## # weights: 42
## initial value 32.824795
## iter 10 value 22.248825
## iter 20 value 17.848700
## iter 30 value 14.900646
## iter 40 value 14.736524
## iter 50 value 14.718529
## final value 14.718527
## converged
## # weights: 124
## initial value 34.206400
## iter 10 value 22.470680
## iter 20 value 13.745216
## iter 30 value 10.590372
## iter 40 value 9.360993
## iter 50 value 9.280872
## iter 60 value 9.263191
## iter 70 value 9.257922
## iter 80 value 9.257872
## final value 9.257872
## converged
## # weights: 206
## initial value 36.251712
## iter 10 value 25.897961
## iter 20 value 13.413571
## iter 30 value 10.211225
## iter 40 value 9.801051
## iter 50 value 9.537912
## iter 60 value 8.914417
## iter 70 value 8.646593
## iter 80 value 8.569091
## iter 90 value 8.524988
## iter 100 value 8.513736
## final value 8.513736
## stopped after 100 iterations
## # weights: 42
## initial value 32.411506
## final value 31.885497
## converged
## # weights: 124
## initial value 32.066855
## iter 10 value 15.612594
## iter 20 value 6.793610
## iter 30 value 6.595887
## iter 40 value 6.488563
## iter 50 value 6.458026
## iter 60 value 6.241846
## iter 70 value 6.059341
## iter 80 value 5.965838
## iter 90 value 5.809351
## iter 100 value 3.938508
## final value 3.938508
## stopped after 100 iterations
## # weights: 206
## initial value 31.421987
## iter 10 value 18.602995
## iter 20 value 6.428810
## iter 30 value 5.862202
## iter 40 value 3.713926
## iter 50 value 3.678841
## iter 60 value 3.641371
## iter 70 value 3.551542
## iter 80 value 3.451816
## iter 90 value 3.403556
## iter 100 value 0.167412
## final value 0.167412
## stopped after 100 iterations
## # weights: 42
## initial value 33.924305
## final value 32.567284
## converged
## # weights: 124
## initial value 36.125145
## iter 10 value 26.770480
## iter 20 value 5.568440
## iter 30 value 4.209674
## iter 40 value 4.201451
## iter 50 value 4.200381
## iter 60 value 2.128295
## iter 70 value 0.003724
## iter 80 value 0.001247
## iter 90 value 0.000541
## iter 100 value 0.000277
## final value 0.000277
## stopped after 100 iterations
## # weights: 206
## initial value 35.811102
## iter 10 value 18.996944
## iter 20 value 3.645030
## iter 30 value 2.875945
## iter 40 value 0.082092
## iter 50 value 0.008331
## iter 60 value 0.001231
## iter 70 value 0.000640
## iter 80 value 0.000268
## iter 90 value 0.000120
## final value 0.000099
## converged
## # weights: 42
## initial value 33.898631
## iter 10 value 20.783339
## iter 20 value 13.447242
## iter 30 value 10.410003
## iter 40 value 10.359746
## final value 10.359741
## converged
## # weights: 124
## initial value 34.440394
## iter 10 value 18.571375
## iter 20 value 10.927860
## iter 30 value 8.780327
## iter 40 value 8.275873
## iter 50 value 8.078079
## iter 60 value 7.936484
## iter 70 value 7.924291
## iter 80 value 7.924199
## final value 7.924183
## converged
## # weights: 206
## initial value 33.594430
## iter 10 value 17.396727
## iter 20 value 9.466398
## iter 30 value 7.994148
## iter 40 value 7.693935
## iter 50 value 7.531681
## iter 60 value 7.506946
## iter 70 value 7.506647
## final value 7.506644
## converged
## # weights: 42
## initial value 33.326844
## iter 10 value 31.278649
## iter 20 value 16.659023
## iter 30 value 16.358539
## iter 40 value 16.326153
## iter 50 value 13.187736
## iter 60 value 9.509434
## iter 70 value 7.706502
## iter 80 value 7.012708
## iter 90 value 7.005228
## iter 100 value 7.004013
## final value 7.004013
## stopped after 100 iterations
## # weights: 124
## initial value 33.795774
## iter 10 value 17.614397
## iter 20 value 15.052528
## iter 30 value 13.384717
## iter 40 value 13.377296
## iter 50 value 13.374119
## iter 60 value 13.369632
## iter 70 value 13.366089
## iter 80 value 13.363311
## iter 90 value 11.520112
## iter 100 value 11.514699
## final value 11.514699
## stopped after 100 iterations
## # weights: 206
## initial value 34.753222
## iter 10 value 22.647841
## iter 20 value 8.268115
## iter 30 value 7.923908
## iter 40 value 7.287789
## iter 50 value 6.519742
## iter 60 value 6.489593
## iter 70 value 6.475835
## iter 80 value 6.442479
## iter 90 value 6.021919
## iter 100 value 5.987774
## final value 5.987774
## stopped after 100 iterations
## # weights: 42
## initial value 33.512782
## iter 10 value 17.888612
## iter 20 value 17.701801
## iter 30 value 16.356173
## iter 40 value 16.299236
## iter 50 value 16.298331
## iter 60 value 16.298245
## final value 16.298239
## converged
## # weights: 124
## initial value 34.038822
## iter 10 value 11.930020
## iter 20 value 0.736303
## iter 30 value 0.016443
## iter 40 value 0.003357
## iter 50 value 0.001515
## final value 0.000081
## converged
## # weights: 206
## initial value 32.374855
## iter 10 value 16.048316
## iter 20 value 14.435712
## iter 30 value 13.955115
## final value 13.955092
## converged
## # weights: 42
## initial value 35.109807
## iter 10 value 21.185090
## iter 20 value 10.770049
## iter 30 value 10.030215
## final value 10.026776
## converged
## # weights: 124
## initial value 41.029542
## iter 10 value 14.892723
## iter 20 value 9.751116
## iter 30 value 8.158357
## iter 40 value 7.818611
## iter 50 value 7.573049
## iter 60 value 7.504664
## iter 70 value 7.501895
## final value 7.501895
## converged
## # weights: 206
## initial value 38.530227
## iter 10 value 19.026507
## iter 20 value 9.165747
## iter 30 value 7.561432
## iter 40 value 7.214158
## iter 50 value 7.098974
## iter 60 value 7.081080
## iter 70 value 7.080816
## iter 80 value 7.080547
## final value 7.080508
## converged
## # weights: 42
## initial value 32.517342
## iter 10 value 18.579282
## iter 20 value 0.387378
## iter 30 value 0.062503
## iter 40 value 0.059589
## iter 50 value 0.057889
## iter 60 value 0.057130
## iter 70 value 0.056680
## iter 80 value 0.055995
## iter 90 value 0.055797
## iter 100 value 0.055407
## final value 0.055407
## stopped after 100 iterations
## # weights: 124
## initial value 32.477528
## iter 10 value 13.746684
## iter 20 value 4.313725
## iter 30 value 4.247616
## iter 40 value 4.234370
## iter 50 value 4.222597
## iter 60 value 4.218076
## iter 70 value 4.208026
## iter 80 value 4.205210
## iter 90 value 4.202792
## iter 100 value 2.778399
## final value 2.778399
## stopped after 100 iterations
## # weights: 206
## initial value 32.655062
## iter 10 value 10.423306
## iter 20 value 4.400800
## iter 30 value 1.430047
## iter 40 value 0.092373
## iter 50 value 0.077895
## iter 60 value 0.073458
## iter 70 value 0.067051
## iter 80 value 0.061274
## iter 90 value 0.055826
## iter 100 value 0.051682
## final value 0.051682
## stopped after 100 iterations
## # weights: 124
## initial value 48.808566
## iter 10 value 29.996808
## iter 20 value 18.819948
## iter 30 value 4.405600
## iter 40 value 1.499995
## iter 50 value 0.285703
## iter 60 value 0.228562
## iter 70 value 0.211013
## iter 80 value 0.191504
## iter 90 value 0.163736
## iter 100 value 0.139805
## final value 0.139805
## stopped after 100 iterations
toc()## 2.53 sec elapsed
nn_model_voters_big_grid ## Neural Network
##
## 70 samples
## 39 predictors
## 2 classes: 'Did not vote', 'Voted'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 5 times)
## Summary of sample sizes: 47, 47, 46, 47, 46, 47, ...
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 1 0e+00 0.5774155 0.1645543
## 1 1e-04 0.5750000 0.1543303
## 1 1e-01 0.6200483 0.2436431
## 3 0e+00 0.5856280 0.1768993
## 3 1e-04 0.6340580 0.2707645
## 3 1e-01 0.6228261 0.2495455
## 5 0e+00 0.5887681 0.1822207
## 5 1e-04 0.5910628 0.1852026
## 5 1e-01 0.6287440 0.2618261
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 1e-04.
Now you are going to perform a random search instead of grid search! As before, the small voters_train_data dataset has been loaded for you, as have the caret and tictoc packages.
# Train control with random search
fitControl <- trainControl(method = "repeatedcv",
number = 3,
repeats = 5,
search = "random")
# Test 6 random hyperparameter combinations
tic()
nn_model_voters_big_grid <- train(turnout16_2016 ~ .,
data = voters_train_data,
method = "nnet",
trControl = fitControl,
verbose = FALSE,
tuneLength = 6)## # weights: 329
## initial value 32.675515
## iter 10 value 13.987050
## iter 20 value 7.635616
## iter 30 value 1.937148
## iter 40 value 0.224553
## iter 50 value 0.098937
## iter 60 value 0.088344
## iter 70 value 0.082774
## iter 80 value 0.076333
## iter 90 value 0.069810
## iter 100 value 0.063987
## final value 0.063987
## stopped after 100 iterations
## # weights: 411
## initial value 350.644668
## iter 10 value 39.271074
## iter 20 value 32.680223
## iter 30 value 32.579027
## iter 40 value 32.568995
## iter 50 value 32.568275
## final value 32.568260
## converged
## # weights: 657
## initial value 35.445389
## iter 10 value 9.298532
## iter 20 value 1.367902
## iter 30 value 0.801844
## iter 40 value 0.576025
## iter 50 value 0.444989
## iter 60 value 0.389531
## iter 70 value 0.347513
## iter 80 value 0.315354
## iter 90 value 0.288248
## iter 100 value 0.269558
## final value 0.269558
## stopped after 100 iterations
## # weights: 83
## initial value 33.514657
## iter 10 value 22.370855
## iter 20 value 6.337464
## iter 30 value 5.097149
## iter 40 value 4.416629
## iter 50 value 4.099183
## iter 60 value 3.959420
## iter 70 value 2.626041
## iter 80 value 1.054501
## iter 90 value 0.952068
## iter 100 value 0.898846
## final value 0.898846
## stopped after 100 iterations
## # weights: 206
## initial value 32.690049
## iter 10 value 14.933909
## iter 20 value 5.614965
## iter 30 value 3.451476
## iter 40 value 2.206271
## iter 50 value 1.732045
## iter 60 value 1.453204
## iter 70 value 1.376205
## iter 80 value 1.362385
## iter 90 value 1.318571
## iter 100 value 1.260248
## final value 1.260248
## stopped after 100 iterations
## # weights: 288
## initial value 76.446025
## iter 10 value 30.485944
## iter 20 value 26.357031
## iter 30 value 25.723329
## iter 40 value 25.399334
## iter 50 value 25.294186
## iter 60 value 25.280489
## iter 70 value 25.275692
## iter 80 value 25.275018
## iter 90 value 25.274932
## iter 90 value 25.274932
## iter 90 value 25.274932
## final value 25.274932
## converged
## # weights: 329
## initial value 34.162998
## iter 10 value 18.479381
## iter 20 value 7.670455
## iter 30 value 7.641219
## iter 40 value 7.519283
## iter 50 value 3.109617
## iter 60 value 0.331429
## iter 70 value 0.200798
## iter 80 value 0.192581
## iter 90 value 0.175956
## iter 100 value 0.157406
## final value 0.157406
## stopped after 100 iterations
## # weights: 411
## initial value 372.766558
## iter 10 value 40.283539
## iter 20 value 32.346962
## iter 30 value 31.926654
## iter 40 value 31.887726
## iter 50 value 31.884988
## final value 31.884770
## converged
## # weights: 657
## initial value 32.747148
## iter 10 value 8.955935
## iter 20 value 1.011743
## iter 30 value 0.617634
## iter 40 value 0.460086
## iter 50 value 0.386071
## iter 60 value 0.340342
## iter 70 value 0.323030
## iter 80 value 0.310411
## iter 90 value 0.301325
## iter 100 value 0.292652
## final value 0.292652
## stopped after 100 iterations
## # weights: 83
## initial value 32.393195
## iter 10 value 15.302722
## iter 20 value 7.986226
## iter 30 value 7.069048
## iter 40 value 5.709091
## iter 50 value 3.623753
## iter 60 value 2.636638
## iter 70 value 2.482063
## iter 80 value 2.000905
## iter 90 value 1.959985
## iter 100 value 1.900278
## final value 1.900278
## stopped after 100 iterations
## # weights: 206
## initial value 33.432143
## iter 10 value 14.313410
## iter 20 value 7.021195
## iter 30 value 2.215765
## iter 40 value 1.829364
## iter 50 value 1.693243
## iter 60 value 1.510429
## iter 70 value 1.380017
## iter 80 value 1.349705
## iter 90 value 1.330868
## iter 100 value 1.325559
## final value 1.325559
## stopped after 100 iterations
## # weights: 288
## initial value 92.706337
## iter 10 value 32.260138
## iter 20 value 26.787124
## iter 30 value 26.234355
## iter 40 value 26.068277
## iter 50 value 26.032865
## iter 60 value 26.009299
## iter 70 value 26.005666
## iter 80 value 26.005130
## final value 26.005086
## converged
## # weights: 329
## initial value 33.154171
## iter 10 value 11.337403
## iter 20 value 1.833701
## iter 30 value 0.106673
## iter 40 value 0.082885
## iter 50 value 0.078218
## iter 60 value 0.074769
## iter 70 value 0.069677
## iter 80 value 0.063345
## iter 90 value 0.061220
## iter 100 value 0.057691
## final value 0.057691
## stopped after 100 iterations
## # weights: 411
## initial value 360.790015
## iter 10 value 37.427599
## iter 20 value 32.687440
## iter 30 value 32.575836
## iter 40 value 32.569211
## iter 50 value 32.568353
## final value 32.568352
## converged
## # weights: 657
## initial value 38.748910
## iter 10 value 10.280751
## iter 20 value 1.587622
## iter 30 value 0.833267
## iter 40 value 0.514100
## iter 50 value 0.405129
## iter 60 value 0.346728
## iter 70 value 0.319366
## iter 80 value 0.298312
## iter 90 value 0.287180
## iter 100 value 0.280217
## final value 0.280217
## stopped after 100 iterations
## # weights: 83
## initial value 33.685017
## iter 10 value 29.584957
## iter 20 value 24.608350
## iter 30 value 22.452040
## iter 40 value 21.008882
## iter 50 value 18.833652
## iter 60 value 12.243374
## iter 70 value 7.854233
## iter 80 value 6.710467
## iter 90 value 2.928064
## iter 100 value 2.418586
## final value 2.418586
## stopped after 100 iterations
## # weights: 206
## initial value 34.627094
## iter 10 value 21.239834
## iter 20 value 5.631112
## iter 30 value 2.703384
## iter 40 value 1.708807
## iter 50 value 1.421396
## iter 60 value 1.346064
## iter 70 value 1.290902
## iter 80 value 1.241090
## iter 90 value 1.227550
## iter 100 value 1.226634
## final value 1.226634
## stopped after 100 iterations
## # weights: 288
## initial value 76.835733
## iter 10 value 30.794525
## iter 20 value 26.848438
## iter 30 value 25.967402
## iter 40 value 25.851443
## iter 50 value 25.783849
## iter 60 value 25.778988
## iter 70 value 25.778882
## final value 25.778856
## converged
## # weights: 329
## initial value 43.141126
## iter 10 value 19.889833
## iter 20 value 8.188146
## iter 30 value 6.925220
## iter 40 value 6.905954
## iter 50 value 6.185901
## iter 60 value 6.084464
## iter 70 value 6.078110
## iter 80 value 5.580544
## iter 90 value 3.600499
## iter 100 value 1.237607
## final value 1.237607
## stopped after 100 iterations
## # weights: 411
## initial value 375.944030
## iter 10 value 38.507571
## iter 20 value 32.451810
## iter 30 value 31.956455
## iter 40 value 31.888358
## iter 50 value 31.885213
## iter 60 value 31.884775
## final value 31.884771
## converged
## # weights: 657
## initial value 37.091133
## iter 10 value 14.129292
## iter 20 value 2.785152
## iter 30 value 1.204610
## iter 40 value 0.832396
## iter 50 value 0.599156
## iter 60 value 0.479057
## iter 70 value 0.397265
## iter 80 value 0.353783
## iter 90 value 0.327681
## iter 100 value 0.312080
## final value 0.312080
## stopped after 100 iterations
## # weights: 83
## initial value 32.062942
## iter 10 value 11.884987
## iter 20 value 11.362426
## iter 30 value 11.307879
## iter 40 value 11.288203
## iter 50 value 11.276318
## iter 60 value 8.817686
## iter 70 value 8.715255
## iter 80 value 8.633816
## iter 90 value 8.393076
## iter 100 value 8.226049
## final value 8.226049
## stopped after 100 iterations
## # weights: 206
## initial value 44.626881
## iter 10 value 24.794410
## iter 20 value 12.736799
## iter 30 value 11.746279
## iter 40 value 6.549356
## iter 50 value 5.338654
## iter 60 value 3.969196
## iter 70 value 2.602279
## iter 80 value 1.935893
## iter 90 value 1.670707
## iter 100 value 1.415768
## final value 1.415768
## stopped after 100 iterations
## # weights: 288
## initial value 76.938405
## iter 10 value 28.359628
## iter 20 value 25.546210
## iter 30 value 25.045356
## iter 40 value 24.551909
## iter 50 value 24.480435
## iter 60 value 24.476390
## iter 70 value 24.476095
## iter 80 value 24.476052
## final value 24.476049
## converged
## # weights: 329
## initial value 33.041666
## iter 10 value 11.540318
## iter 20 value 2.113141
## iter 30 value 2.022395
## iter 40 value 0.723788
## iter 50 value 0.202766
## iter 60 value 0.153989
## iter 70 value 0.135481
## iter 80 value 0.122169
## iter 90 value 0.110937
## iter 100 value 0.101187
## final value 0.101187
## stopped after 100 iterations
## # weights: 411
## initial value 346.829512
## iter 10 value 43.429783
## iter 20 value 32.853505
## iter 30 value 32.595987
## iter 40 value 32.572080
## iter 50 value 32.568551
## final value 32.568536
## converged
## # weights: 657
## initial value 40.422124
## iter 10 value 14.826693
## iter 20 value 4.103064
## iter 30 value 1.263133
## iter 40 value 0.828539
## iter 50 value 0.581646
## iter 60 value 0.450133
## iter 70 value 0.374273
## iter 80 value 0.316842
## iter 90 value 0.281734
## iter 100 value 0.266404
## final value 0.266404
## stopped after 100 iterations
## # weights: 83
## initial value 39.943738
## iter 10 value 32.236623
## iter 20 value 21.320727
## iter 30 value 7.014491
## iter 40 value 4.836751
## iter 50 value 1.613749
## iter 60 value 1.185766
## iter 70 value 0.971772
## iter 80 value 0.732172
## iter 90 value 0.593300
## iter 100 value 0.529810
## final value 0.529810
## stopped after 100 iterations
## # weights: 206
## initial value 33.568358
## iter 10 value 12.983502
## iter 20 value 4.999144
## iter 30 value 2.837215
## iter 40 value 2.003917
## iter 50 value 1.543751
## iter 60 value 1.359112
## iter 70 value 1.223559
## iter 80 value 1.178195
## iter 90 value 1.173145
## iter 100 value 1.170173
## final value 1.170173
## stopped after 100 iterations
## # weights: 288
## initial value 76.993774
## iter 10 value 29.902443
## iter 20 value 26.562705
## iter 30 value 26.103464
## iter 40 value 25.936125
## iter 50 value 25.786467
## iter 60 value 25.721763
## iter 70 value 25.705330
## iter 80 value 25.701034
## iter 90 value 25.700774
## final value 25.700718
## converged
## # weights: 329
## initial value 35.812247
## iter 10 value 10.127846
## iter 20 value 0.790310
## iter 30 value 0.226181
## iter 40 value 0.158657
## iter 50 value 0.139670
## iter 60 value 0.129921
## iter 70 value 0.117040
## iter 80 value 0.110599
## iter 90 value 0.099276
## iter 100 value 0.092720
## final value 0.092720
## stopped after 100 iterations
## # weights: 411
## initial value 370.466426
## iter 10 value 38.136577
## iter 20 value 32.983391
## iter 30 value 32.589309
## iter 40 value 32.568606
## iter 50 value 32.568253
## final value 32.568251
## converged
## # weights: 657
## initial value 46.353706
## iter 10 value 18.866758
## iter 20 value 7.007968
## iter 30 value 4.059093
## iter 40 value 2.265968
## iter 50 value 1.528274
## iter 60 value 1.008034
## iter 70 value 0.700267
## iter 80 value 0.511343
## iter 90 value 0.424481
## iter 100 value 0.383321
## final value 0.383321
## stopped after 100 iterations
## # weights: 83
## initial value 32.751620
## iter 10 value 21.330955
## iter 20 value 16.110761
## iter 30 value 15.653696
## iter 40 value 15.148053
## iter 50 value 14.391828
## iter 60 value 7.236255
## iter 70 value 1.163001
## iter 80 value 0.856431
## iter 90 value 0.795064
## iter 100 value 0.788100
## final value 0.788100
## stopped after 100 iterations
## # weights: 206
## initial value 33.594214
## iter 10 value 20.039407
## iter 20 value 6.573972
## iter 30 value 3.604325
## iter 40 value 2.558839
## iter 50 value 2.199322
## iter 60 value 1.852094
## iter 70 value 1.708348
## iter 80 value 1.594748
## iter 90 value 1.454911
## iter 100 value 1.433047
## final value 1.433047
## stopped after 100 iterations
## # weights: 288
## initial value 79.443696
## iter 10 value 31.926000
## iter 20 value 27.095717
## iter 30 value 26.079371
## iter 40 value 25.848448
## iter 50 value 25.769810
## iter 60 value 25.759185
## final value 25.757880
## converged
## # weights: 329
## initial value 34.028771
## iter 10 value 13.979040
## iter 20 value 1.006909
## iter 30 value 0.202456
## iter 40 value 0.155841
## iter 50 value 0.144177
## iter 60 value 0.127186
## iter 70 value 0.105539
## iter 80 value 0.087919
## iter 90 value 0.076341
## iter 100 value 0.068670
## final value 0.068670
## stopped after 100 iterations
## # weights: 411
## initial value 360.892437
## iter 10 value 47.881779
## iter 20 value 33.984873
## iter 30 value 32.663861
## iter 40 value 32.577672
## iter 50 value 32.563425
## iter 60 value 32.562050
## iter 70 value 32.561614
## iter 80 value 32.561591
## final value 32.561589
## converged
## # weights: 657
## initial value 62.250908
## iter 10 value 5.109265
## iter 20 value 0.876834
## iter 30 value 0.476953
## iter 40 value 0.365149
## iter 50 value 0.303974
## iter 60 value 0.272503
## iter 70 value 0.254601
## iter 80 value 0.246085
## iter 90 value 0.240248
## iter 100 value 0.236031
## final value 0.236031
## stopped after 100 iterations
## # weights: 83
## initial value 31.695316
## iter 10 value 14.510239
## iter 20 value 13.677027
## iter 30 value 11.667895
## iter 40 value 9.736942
## iter 50 value 9.670935
## iter 60 value 7.340651
## iter 70 value 1.357803
## iter 80 value 0.759548
## iter 90 value 0.735405
## iter 100 value 0.731350
## final value 0.731350
## stopped after 100 iterations
## # weights: 206
## initial value 41.393972
## iter 10 value 17.475979
## iter 20 value 4.174734
## iter 30 value 2.127451
## iter 40 value 1.608405
## iter 50 value 1.356761
## iter 60 value 1.202687
## iter 70 value 1.143349
## iter 80 value 1.116239
## iter 90 value 1.102401
## iter 100 value 1.082534
## final value 1.082534
## stopped after 100 iterations
## # weights: 288
## initial value 88.999896
## iter 10 value 31.615313
## iter 20 value 24.340617
## iter 30 value 23.606979
## iter 40 value 23.472051
## iter 50 value 23.360070
## iter 60 value 23.318986
## iter 70 value 23.312057
## iter 80 value 23.310721
## final value 23.310717
## converged
## # weights: 329
## initial value 39.955674
## iter 10 value 17.671557
## iter 20 value 0.200433
## iter 30 value 0.160724
## iter 40 value 0.133883
## iter 50 value 0.116505
## iter 60 value 0.099551
## iter 70 value 0.085064
## iter 80 value 0.075821
## iter 90 value 0.073945
## iter 100 value 0.071607
## final value 0.071607
## stopped after 100 iterations
## # weights: 411
## initial value 332.515755
## iter 10 value 48.558280
## iter 20 value 32.661366
## iter 30 value 31.924642
## iter 40 value 31.887685
## iter 50 value 31.884818
## final value 31.884774
## converged
## # weights: 657
## initial value 46.332549
## iter 10 value 5.868034
## iter 20 value 0.987982
## iter 30 value 0.618098
## iter 40 value 0.501207
## iter 50 value 0.430387
## iter 60 value 0.383873
## iter 70 value 0.345904
## iter 80 value 0.309807
## iter 90 value 0.294421
## iter 100 value 0.286895
## final value 0.286895
## stopped after 100 iterations
## # weights: 83
## initial value 33.015834
## iter 10 value 18.872592
## iter 20 value 11.267749
## iter 30 value 8.682708
## iter 40 value 7.505012
## iter 50 value 7.023813
## iter 60 value 2.097833
## iter 70 value 0.785377
## iter 80 value 0.742635
## iter 90 value 0.737226
## iter 100 value 0.736198
## final value 0.736198
## stopped after 100 iterations
## # weights: 206
## initial value 48.073276
## iter 10 value 21.846289
## iter 20 value 5.467973
## iter 30 value 3.012068
## iter 40 value 2.352293
## iter 50 value 2.115779
## iter 60 value 2.028746
## iter 70 value 1.844771
## iter 80 value 1.541723
## iter 90 value 1.411625
## iter 100 value 1.382780
## final value 1.382780
## stopped after 100 iterations
## # weights: 288
## initial value 75.324447
## iter 10 value 30.233226
## iter 20 value 26.545637
## iter 30 value 26.173715
## iter 40 value 26.120044
## iter 50 value 26.109609
## iter 60 value 26.108373
## iter 70 value 26.108195
## final value 26.108189
## converged
## # weights: 329
## initial value 35.505024
## iter 10 value 22.893861
## iter 20 value 8.059592
## iter 30 value 4.149436
## iter 40 value 3.006733
## iter 50 value 2.977791
## iter 60 value 2.944945
## iter 70 value 2.450399
## iter 80 value 2.352976
## iter 90 value 2.247362
## iter 100 value 2.008297
## final value 2.008297
## stopped after 100 iterations
## # weights: 411
## initial value 360.794800
## iter 10 value 35.545259
## iter 20 value 32.669393
## iter 30 value 32.575425
## iter 40 value 32.569512
## iter 50 value 32.568524
## final value 32.568520
## converged
## # weights: 657
## initial value 53.428798
## iter 10 value 14.514870
## iter 20 value 2.732535
## iter 30 value 1.531605
## iter 40 value 0.830332
## iter 50 value 0.610345
## iter 60 value 0.521328
## iter 70 value 0.460053
## iter 80 value 0.402179
## iter 90 value 0.363549
## iter 100 value 0.342162
## final value 0.342162
## stopped after 100 iterations
## # weights: 83
## initial value 34.409324
## iter 10 value 18.275642
## iter 20 value 14.471851
## iter 30 value 14.127297
## iter 40 value 11.524212
## iter 50 value 11.440724
## iter 60 value 8.805884
## iter 70 value 8.699175
## iter 80 value 5.872011
## iter 90 value 5.054813
## iter 100 value 4.599551
## final value 4.599551
## stopped after 100 iterations
## # weights: 206
## initial value 46.770089
## iter 10 value 28.850670
## iter 20 value 16.184851
## iter 30 value 9.019757
## iter 40 value 5.043416
## iter 50 value 2.997183
## iter 60 value 2.069234
## iter 70 value 1.910040
## iter 80 value 1.748883
## iter 90 value 1.628790
## iter 100 value 1.573615
## final value 1.573615
## stopped after 100 iterations
## # weights: 288
## initial value 102.096287
## iter 10 value 30.265233
## iter 20 value 27.208429
## iter 30 value 26.559832
## iter 40 value 26.378388
## iter 50 value 26.267167
## iter 60 value 26.257361
## iter 70 value 26.257144
## iter 80 value 26.257108
## final value 26.257103
## converged
## # weights: 329
## initial value 41.166567
## iter 10 value 7.120326
## iter 20 value 1.587634
## iter 30 value 0.194363
## iter 40 value 0.163441
## iter 50 value 0.134012
## iter 60 value 0.120645
## iter 70 value 0.112548
## iter 80 value 0.100983
## iter 90 value 0.085276
## iter 100 value 0.074891
## final value 0.074891
## stopped after 100 iterations
## # weights: 411
## initial value 364.273173
## iter 10 value 38.499685
## iter 20 value 33.163204
## iter 30 value 32.602339
## iter 40 value 32.573874
## iter 50 value 32.568967
## iter 60 value 32.568414
## iter 60 value 32.568413
## iter 60 value 32.568413
## final value 32.568413
## converged
## # weights: 657
## initial value 50.144200
## iter 10 value 14.680967
## iter 20 value 1.883924
## iter 30 value 0.983848
## iter 40 value 0.604675
## iter 50 value 0.463529
## iter 60 value 0.400853
## iter 70 value 0.362723
## iter 80 value 0.340124
## iter 90 value 0.323838
## iter 100 value 0.313282
## final value 0.313282
## stopped after 100 iterations
## # weights: 83
## initial value 40.322043
## iter 10 value 26.493630
## iter 20 value 18.370457
## iter 30 value 16.282087
## iter 40 value 15.073677
## iter 50 value 13.748902
## iter 60 value 13.728939
## iter 70 value 11.999472
## iter 80 value 5.475794
## iter 90 value 5.235510
## iter 100 value 4.968691
## final value 4.968691
## stopped after 100 iterations
## # weights: 206
## initial value 33.628792
## iter 10 value 13.071365
## iter 20 value 7.651444
## iter 30 value 4.168145
## iter 40 value 1.978097
## iter 50 value 1.575193
## iter 60 value 1.489761
## iter 70 value 1.448024
## iter 80 value 1.412494
## iter 90 value 1.381975
## iter 100 value 1.357666
## final value 1.357666
## stopped after 100 iterations
## # weights: 288
## initial value 77.244038
## iter 10 value 32.915880
## iter 20 value 27.500241
## iter 30 value 26.638059
## iter 40 value 26.247065
## iter 50 value 26.170817
## iter 60 value 26.124149
## iter 70 value 26.112461
## iter 80 value 26.107949
## iter 90 value 26.107192
## final value 26.107187
## converged
## # weights: 329
## initial value 48.573596
## iter 10 value 26.961807
## iter 20 value 13.310322
## iter 30 value 7.310547
## iter 40 value 5.147164
## iter 50 value 2.636320
## iter 60 value 2.592437
## iter 70 value 2.587178
## iter 80 value 2.576265
## iter 90 value 2.479870
## iter 100 value 0.100183
## final value 0.100183
## stopped after 100 iterations
## # weights: 411
## initial value 356.091862
## iter 10 value 36.507725
## iter 20 value 32.101946
## iter 30 value 31.921664
## iter 40 value 31.891114
## iter 50 value 31.885015
## iter 60 value 31.884771
## iter 60 value 31.884770
## iter 60 value 31.884770
## final value 31.884770
## converged
## # weights: 657
## initial value 33.076944
## iter 10 value 15.409288
## iter 20 value 6.719170
## iter 30 value 1.855443
## iter 40 value 1.153057
## iter 50 value 0.766677
## iter 60 value 0.578626
## iter 70 value 0.430908
## iter 80 value 0.336263
## iter 90 value 0.295969
## iter 100 value 0.268834
## final value 0.268834
## stopped after 100 iterations
## # weights: 83
## initial value 33.660139
## iter 10 value 18.464234
## iter 20 value 9.953928
## iter 30 value 6.242355
## iter 40 value 4.407846
## iter 50 value 2.826190
## iter 60 value 0.879289
## iter 70 value 0.675268
## iter 80 value 0.635976
## iter 90 value 0.588140
## iter 100 value 0.494146
## final value 0.494146
## stopped after 100 iterations
## # weights: 206
## initial value 33.776186
## iter 10 value 24.623715
## iter 20 value 12.327484
## iter 30 value 3.401360
## iter 40 value 2.046438
## iter 50 value 1.432001
## iter 60 value 1.177999
## iter 70 value 1.136524
## iter 80 value 1.127599
## iter 90 value 1.116194
## iter 100 value 1.098520
## final value 1.098520
## stopped after 100 iterations
## # weights: 288
## initial value 81.514879
## iter 10 value 31.331689
## iter 20 value 25.805831
## iter 30 value 24.787628
## iter 40 value 24.620761
## iter 50 value 24.499729
## iter 60 value 24.462262
## iter 70 value 24.452831
## iter 80 value 24.450692
## iter 90 value 24.450415
## iter 90 value 24.450414
## iter 90 value 24.450414
## final value 24.450414
## converged
## # weights: 329
## initial value 42.785193
## iter 10 value 14.451091
## iter 20 value 7.835862
## iter 30 value 3.540476
## iter 40 value 0.218108
## iter 50 value 0.121993
## iter 60 value 0.100080
## iter 70 value 0.095276
## iter 80 value 0.089044
## iter 90 value 0.076904
## iter 100 value 0.068466
## final value 0.068466
## stopped after 100 iterations
## # weights: 411
## initial value 341.161268
## iter 10 value 56.280537
## iter 20 value 34.183236
## iter 30 value 32.794246
## iter 40 value 32.618415
## iter 50 value 32.572522
## iter 60 value 32.568651
## final value 32.568519
## converged
## # weights: 657
## initial value 33.480991
## iter 10 value 12.389435
## iter 20 value 2.675241
## iter 30 value 1.087154
## iter 40 value 0.723254
## iter 50 value 0.499277
## iter 60 value 0.396775
## iter 70 value 0.342337
## iter 80 value 0.312064
## iter 90 value 0.296467
## iter 100 value 0.285051
## final value 0.285051
## stopped after 100 iterations
## # weights: 83
## initial value 34.992899
## iter 10 value 32.587615
## iter 20 value 30.824072
## iter 30 value 11.732761
## iter 40 value 6.607812
## iter 50 value 5.464892
## iter 60 value 5.107382
## iter 70 value 4.936792
## iter 80 value 1.692914
## iter 90 value 0.898178
## iter 100 value 0.803642
## final value 0.803642
## stopped after 100 iterations
## # weights: 206
## initial value 33.016872
## iter 10 value 10.189536
## iter 20 value 6.308367
## iter 30 value 5.588396
## iter 40 value 2.777615
## iter 50 value 2.004243
## iter 60 value 1.778478
## iter 70 value 1.656723
## iter 80 value 1.512513
## iter 90 value 1.394245
## iter 100 value 1.353265
## final value 1.353265
## stopped after 100 iterations
## # weights: 288
## initial value 75.932074
## iter 10 value 32.254333
## iter 20 value 26.836991
## iter 30 value 26.197272
## iter 40 value 26.064682
## iter 50 value 26.030385
## iter 60 value 26.010725
## iter 70 value 25.997710
## iter 80 value 25.994403
## iter 90 value 25.992540
## final value 25.992533
## converged
## # weights: 329
## initial value 35.257349
## iter 10 value 14.473147
## iter 20 value 1.854222
## iter 30 value 0.222590
## iter 40 value 0.129916
## iter 50 value 0.123699
## iter 60 value 0.116228
## iter 70 value 0.103376
## iter 80 value 0.095020
## iter 90 value 0.089025
## iter 100 value 0.079781
## final value 0.079781
## stopped after 100 iterations
## # weights: 411
## initial value 351.934877
## iter 10 value 46.512128
## iter 20 value 33.883928
## iter 30 value 33.291221
## iter 40 value 33.273029
## iter 50 value 33.271194
## iter 60 value 33.271069
## final value 33.271065
## converged
## # weights: 657
## initial value 46.637722
## iter 10 value 9.341348
## iter 20 value 2.041157
## iter 30 value 0.980060
## iter 40 value 0.607098
## iter 50 value 0.398528
## iter 60 value 0.326914
## iter 70 value 0.292982
## iter 80 value 0.277475
## iter 90 value 0.267668
## iter 100 value 0.260299
## final value 0.260299
## stopped after 100 iterations
## # weights: 83
## initial value 35.617963
## iter 10 value 20.344211
## iter 20 value 17.123596
## iter 30 value 12.975069
## iter 40 value 10.000187
## iter 50 value 9.878524
## iter 60 value 9.574845
## iter 70 value 1.958420
## iter 80 value 1.088782
## iter 90 value 1.011673
## iter 100 value 1.005174
## final value 1.005174
## stopped after 100 iterations
## # weights: 206
## initial value 32.191119
## iter 10 value 10.805387
## iter 20 value 2.924692
## iter 30 value 2.145144
## iter 40 value 1.967193
## iter 50 value 1.560119
## iter 60 value 1.390881
## iter 70 value 1.294847
## iter 80 value 1.255259
## iter 90 value 1.237982
## iter 100 value 1.207355
## final value 1.207355
## stopped after 100 iterations
## # weights: 288
## initial value 74.126491
## iter 10 value 31.424892
## iter 20 value 26.878042
## iter 30 value 25.856652
## iter 40 value 25.679040
## iter 50 value 25.650037
## iter 60 value 25.638385
## iter 70 value 25.636514
## iter 80 value 25.636258
## final value 25.636252
## converged
## # weights: 329
## initial value 40.608031
## iter 10 value 22.040979
## iter 20 value 11.452862
## iter 30 value 5.171567
## iter 40 value 1.818397
## iter 50 value 1.488882
## iter 60 value 1.482254
## iter 70 value 1.473856
## iter 80 value 1.462978
## iter 90 value 0.700427
## iter 100 value 0.075450
## final value 0.075450
## stopped after 100 iterations
## # weights: 411
## initial value 376.817591
## iter 10 value 34.455002
## iter 20 value 32.354267
## iter 30 value 31.927312
## iter 40 value 31.886758
## iter 50 value 31.884791
## final value 31.884770
## converged
## # weights: 657
## initial value 36.875210
## iter 10 value 12.075763
## iter 20 value 4.296993
## iter 30 value 1.462894
## iter 40 value 0.708565
## iter 50 value 0.496564
## iter 60 value 0.384634
## iter 70 value 0.326517
## iter 80 value 0.303479
## iter 90 value 0.286596
## iter 100 value 0.271817
## final value 0.271817
## stopped after 100 iterations
## # weights: 83
## initial value 32.262554
## iter 10 value 20.964218
## iter 20 value 10.475101
## iter 30 value 9.404907
## iter 40 value 8.061291
## iter 50 value 6.528430
## iter 60 value 1.111010
## iter 70 value 0.798447
## iter 80 value 0.691340
## iter 90 value 0.644216
## iter 100 value 0.613323
## final value 0.613323
## stopped after 100 iterations
## # weights: 206
## initial value 36.111931
## iter 10 value 17.706233
## iter 20 value 14.670164
## iter 30 value 6.301782
## iter 40 value 2.822954
## iter 50 value 1.860584
## iter 60 value 1.463816
## iter 70 value 1.358627
## iter 80 value 1.310395
## iter 90 value 1.229910
## iter 100 value 1.193383
## final value 1.193383
## stopped after 100 iterations
## # weights: 288
## initial value 76.274789
## iter 10 value 30.731507
## iter 20 value 25.798544
## iter 30 value 25.037268
## iter 40 value 24.950902
## iter 50 value 24.942358
## iter 60 value 24.940122
## iter 70 value 24.939389
## iter 80 value 24.939265
## final value 24.939241
## converged
## # weights: 329
## initial value 30.843546
## iter 10 value 8.632269
## iter 20 value 0.222268
## iter 30 value 0.136185
## iter 40 value 0.122448
## iter 50 value 0.092993
## iter 60 value 0.085952
## iter 70 value 0.079785
## iter 80 value 0.076663
## iter 90 value 0.074022
## iter 100 value 0.071084
## final value 0.071084
## stopped after 100 iterations
## # weights: 411
## initial value 351.017848
## iter 10 value 35.036753
## iter 20 value 31.959288
## iter 30 value 31.888562
## iter 40 value 31.884889
## final value 31.884771
## converged
## # weights: 657
## initial value 37.088668
## iter 10 value 12.294331
## iter 20 value 5.384745
## iter 30 value 1.634934
## iter 40 value 1.101456
## iter 50 value 0.704420
## iter 60 value 0.546317
## iter 70 value 0.456647
## iter 80 value 0.396644
## iter 90 value 0.369361
## iter 100 value 0.348148
## final value 0.348148
## stopped after 100 iterations
## # weights: 83
## initial value 33.858668
## iter 10 value 25.736993
## iter 20 value 12.393593
## iter 30 value 11.150417
## iter 40 value 5.210452
## iter 50 value 4.896460
## iter 60 value 3.502509
## iter 70 value 0.863496
## iter 80 value 0.826334
## iter 90 value 0.798665
## iter 100 value 0.769387
## final value 0.769387
## stopped after 100 iterations
## # weights: 206
## initial value 33.876189
## iter 10 value 11.153044
## iter 20 value 4.240495
## iter 30 value 2.812084
## iter 40 value 2.193992
## iter 50 value 1.799968
## iter 60 value 1.571094
## iter 70 value 1.459916
## iter 80 value 1.405163
## iter 90 value 1.372011
## iter 100 value 1.354022
## final value 1.354022
## stopped after 100 iterations
## # weights: 288
## initial value 74.488990
## iter 10 value 32.499520
## iter 20 value 26.452235
## iter 30 value 25.372023
## iter 40 value 25.044145
## iter 50 value 24.887405
## iter 60 value 24.771126
## iter 70 value 24.746056
## iter 80 value 24.737624
## iter 90 value 24.734432
## iter 100 value 24.732990
## final value 24.732990
## stopped after 100 iterations
## # weights: 329
## initial value 53.152716
## iter 10 value 20.181766
## iter 20 value 6.347726
## iter 30 value 3.584332
## iter 40 value 0.542400
## iter 50 value 0.419741
## iter 60 value 0.340006
## iter 70 value 0.271329
## iter 80 value 0.233944
## iter 90 value 0.215841
## iter 100 value 0.196990
## final value 0.196990
## stopped after 100 iterations
toc()## 5.16 sec elapsed
nn_model_voters_big_grid## Neural Network
##
## 70 samples
## 39 predictors
## 2 classes: 'Did not vote', 'Voted'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 5 times)
## Summary of sample sizes: 47, 46, 47, 46, 47, 47, ...
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 2.590967e-03 0.6223540 0.244509313
## 5 8.603504e-03 0.6136473 0.226452451
## 7 9.657082e-01 0.5940931 0.188142299
## 8 7.878699e-05 0.6513614 0.302359279
## 10 4.780570e+00 0.4855072 -0.005818182
## 16 1.378154e-03 0.5966184 0.192423282
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 8 and decay = 7.878699e-05.
Now you are going to train a model on the voter’s dataset using Adaptive Resampling! As before, the small voters_train_data dataset has been loaded for you, as have the caret and tictoc packages.
# Define trainControl function
fitControl <- trainControl(method = "adaptive_cv",
number = 3, repeats = 3,
adaptive = list(min = 3, alpha = 0.05, method = "BT", complete = FALSE),
search = "random")
# Start timer & train model
tic()
svm_model_voters_ar <- train(turnout16_2016 ~ .,
data = voters_train_data,
method = "nnet",
trControl = fitControl,
verbose = FALSE,
tuneLength = 6 )## # weights: 247
## initial value 40.246564
## iter 10 value 22.235678
## iter 20 value 8.897414
## iter 30 value 4.380020
## iter 40 value 2.145145
## iter 50 value 1.389862
## iter 60 value 1.238584
## iter 70 value 1.177423
## iter 80 value 1.147241
## iter 90 value 1.134806
## iter 100 value 1.130625
## final value 1.130625
## stopped after 100 iterations
## # weights: 247
## initial value 36.410218
## iter 10 value 13.241152
## iter 20 value 3.775255
## iter 30 value 2.208754
## iter 40 value 2.168239
## iter 50 value 2.122988
## iter 60 value 2.096884
## iter 70 value 2.065231
## iter 80 value 1.503023
## iter 90 value 0.292174
## iter 100 value 0.183549
## final value 0.183549
## stopped after 100 iterations
## # weights: 657
## initial value 82.390864
## iter 10 value 30.195979
## iter 20 value 18.844149
## iter 30 value 17.910407
## iter 40 value 17.486155
## iter 50 value 17.300568
## iter 60 value 17.182054
## iter 70 value 17.139128
## iter 80 value 17.122360
## iter 90 value 17.119254
## iter 100 value 17.117250
## final value 17.117250
## stopped after 100 iterations
## # weights: 83
## initial value 31.810217
## iter 10 value 20.427109
## iter 20 value 15.946745
## iter 30 value 11.365488
## iter 40 value 10.006128
## iter 50 value 7.423291
## iter 60 value 7.368086
## iter 70 value 6.433830
## iter 80 value 0.821642
## iter 90 value 0.538918
## iter 100 value 0.503962
## final value 0.503962
## stopped after 100 iterations
## # weights: 780
## initial value 52.056843
## iter 10 value 27.249328
## iter 20 value 11.864723
## iter 30 value 8.763751
## iter 40 value 8.211302
## iter 50 value 8.026264
## iter 60 value 7.947306
## iter 70 value 7.894564
## iter 80 value 7.855700
## iter 90 value 7.829849
## iter 100 value 7.809287
## final value 7.809287
## stopped after 100 iterations
## # weights: 370
## initial value 37.916479
## iter 10 value 22.418544
## iter 20 value 9.888899
## iter 30 value 5.653503
## iter 40 value 4.971229
## iter 50 value 4.645418
## iter 60 value 4.360371
## iter 70 value 4.249989
## iter 80 value 4.209987
## iter 90 value 4.201146
## iter 100 value 4.198539
## final value 4.198539
## stopped after 100 iterations
## # weights: 247
## initial value 33.849199
## iter 10 value 14.351321
## iter 20 value 6.934847
## iter 30 value 3.202319
## iter 40 value 2.380860
## iter 50 value 1.931440
## iter 60 value 1.680427
## iter 70 value 1.484056
## iter 80 value 1.346351
## iter 90 value 1.286053
## iter 100 value 1.268571
## final value 1.268571
## stopped after 100 iterations
## # weights: 247
## initial value 31.420293
## iter 10 value 13.501257
## iter 20 value 6.587240
## iter 30 value 1.932040
## iter 40 value 0.626463
## iter 50 value 0.525817
## iter 60 value 0.461897
## iter 70 value 0.350812
## iter 80 value 0.285200
## iter 90 value 0.243569
## iter 100 value 0.218179
## final value 0.218179
## stopped after 100 iterations
## # weights: 657
## initial value 80.396404
## iter 10 value 35.210763
## iter 20 value 20.397168
## iter 30 value 18.519274
## iter 40 value 18.061793
## iter 50 value 17.924498
## iter 60 value 17.882610
## iter 70 value 17.868678
## iter 80 value 17.861443
## iter 90 value 17.858637
## iter 100 value 17.856575
## final value 17.856575
## stopped after 100 iterations
## # weights: 83
## initial value 33.555659
## iter 10 value 24.407511
## iter 20 value 18.699561
## iter 30 value 16.883957
## iter 40 value 16.716183
## iter 50 value 13.574723
## iter 60 value 13.437250
## iter 70 value 8.335203
## iter 80 value 7.387518
## iter 90 value 7.351789
## iter 100 value 4.387029
## final value 4.387029
## stopped after 100 iterations
## # weights: 780
## initial value 46.268561
## iter 10 value 21.884472
## iter 20 value 11.626265
## iter 30 value 9.349530
## iter 40 value 8.881833
## iter 50 value 8.663778
## iter 60 value 8.577218
## iter 70 value 8.541853
## iter 80 value 8.516066
## iter 90 value 8.510665
## iter 100 value 8.507007
## final value 8.507007
## stopped after 100 iterations
## # weights: 370
## initial value 39.090625
## iter 10 value 21.597486
## iter 20 value 10.197827
## iter 30 value 5.691580
## iter 40 value 5.107607
## iter 50 value 4.854613
## iter 60 value 4.763603
## iter 70 value 4.727196
## iter 80 value 4.683412
## iter 90 value 4.658496
## iter 100 value 4.654292
## final value 4.654292
## stopped after 100 iterations
## # weights: 247
## initial value 38.667111
## iter 10 value 17.648185
## iter 20 value 12.100527
## iter 30 value 8.457569
## iter 40 value 3.804244
## iter 50 value 2.565050
## iter 60 value 1.892910
## iter 70 value 1.679125
## iter 80 value 1.545569
## iter 90 value 1.462947
## iter 100 value 1.354939
## final value 1.354939
## stopped after 100 iterations
## # weights: 247
## initial value 36.275376
## iter 10 value 29.748126
## iter 20 value 10.537256
## iter 30 value 3.826834
## iter 40 value 0.309648
## iter 50 value 0.196743
## iter 60 value 0.178417
## iter 70 value 0.156863
## iter 80 value 0.145004
## iter 90 value 0.141984
## iter 100 value 0.137315
## final value 0.137315
## stopped after 100 iterations
## # weights: 657
## initial value 82.230295
## iter 10 value 28.291897
## iter 20 value 19.891307
## iter 30 value 18.699125
## iter 40 value 18.330222
## iter 50 value 18.181527
## iter 60 value 18.118344
## iter 70 value 18.087810
## iter 80 value 18.076008
## iter 90 value 18.071216
## iter 100 value 18.069777
## final value 18.069777
## stopped after 100 iterations
## # weights: 83
## initial value 33.834197
## iter 10 value 33.277321
## iter 20 value 30.730500
## iter 30 value 13.028783
## iter 40 value 10.921711
## iter 50 value 10.414572
## iter 60 value 7.085499
## iter 70 value 6.618472
## iter 80 value 3.825561
## iter 90 value 3.451494
## iter 100 value 3.234339
## final value 3.234339
## stopped after 100 iterations
## # weights: 780
## initial value 62.038807
## iter 10 value 23.137687
## iter 20 value 12.132483
## iter 30 value 9.230903
## iter 40 value 8.822995
## iter 50 value 8.700413
## iter 60 value 8.625767
## iter 70 value 8.580448
## iter 80 value 8.562864
## iter 90 value 8.550036
## iter 100 value 8.534353
## final value 8.534353
## stopped after 100 iterations
## # weights: 370
## initial value 35.679718
## iter 10 value 13.166578
## iter 20 value 7.170397
## iter 30 value 5.680167
## iter 40 value 5.132693
## iter 50 value 4.895349
## iter 60 value 4.759320
## iter 70 value 4.706288
## iter 80 value 4.670332
## iter 90 value 4.664340
## iter 100 value 4.660899
## final value 4.660899
## stopped after 100 iterations
## # weights: 780
## initial value 64.281841
## iter 10 value 38.324484
## iter 20 value 21.626736
## iter 30 value 13.809633
## iter 40 value 12.788730
## iter 50 value 12.544744
## iter 60 value 12.425171
## iter 70 value 12.376974
## iter 80 value 12.319417
## iter 90 value 12.271320
## iter 100 value 12.234650
## final value 12.234650
## stopped after 100 iterations
toc()## 2.36 sec elapsed
As you have seen in the video just now, mlr is yet another popular machine learning package in R that comes with many functions to do hyperparameter tuning. Here, you are going to go over the basic workflow for training models with mlr. The knowledge_train_data dataset has already been loaded for you, as have the packages mlr, tidyverse and tictoc. Remember that starting to type in the console will suggest autocompleting options for functions and packages.
library(mlr)
knowledge_test_data <- read_csv("knowledge_test_data.csv")
knowledge_train_data <- read_csv("knowledge_train_data.csv")
# Create classification taks
task <- makeClassifTask(data = knowledge_train_data,
target = "UNS")# Call the list of learners
listLearners() %>%
as.data.frame() %>%
select(class, short.name, package) %>%
filter(grepl("classif.", class)) %>%
kable| class | short.name | package |
|---|---|---|
| classif.ada | ada | ada,rpart |
| classif.adaboostm1 | adaboostm1 | RWeka |
| classif.bartMachine | bartmachine | bartMachine |
| classif.binomial | binomial | stats |
| classif.boosting | adabag | adabag,rpart |
| classif.bst | bst | bst,rpart |
| classif.C50 | C50 | C50 |
| classif.cforest | cforest | party |
| classif.clusterSVM | clusterSVM | SwarmSVM,LiblineaR |
| classif.ctree | ctree | party |
| classif.cvglmnet | cvglmnet | glmnet |
| classif.dbnDNN | dbn.dnn | deepnet |
| classif.dcSVM | dcSVM | SwarmSVM,e1071 |
| classif.earth | fda | earth,stats |
| classif.evtree | evtree | evtree |
| classif.extraTrees | extraTrees | extraTrees |
| classif.fdausc.glm | fdausc.glm | fda.usc |
| classif.fdausc.kernel | fdausc.kernel | fda.usc |
| classif.fdausc.knn | fdausc.knn | fda.usc |
| classif.fdausc.np | fdausc.np | fda.usc |
| classif.featureless | featureless | mlr |
| classif.fnn | fnn | FNN |
| classif.gamboost | gamboost | mboost |
| classif.gaterSVM | gaterSVM | SwarmSVM |
| classif.gausspr | gausspr | kernlab |
| classif.gbm | gbm | gbm |
| classif.geoDA | geoda | DiscriMiner |
| classif.glmboost | glmboost | mboost |
| classif.glmnet | glmnet | glmnet |
| classif.h2o.deeplearning | h2o.dl | h2o |
| classif.h2o.gbm | h2o.gbm | h2o |
| classif.h2o.glm | h2o.glm | h2o |
| classif.h2o.randomForest | h2o.rf | h2o |
| classif.IBk | ibk | RWeka |
| classif.J48 | j48 | RWeka |
| classif.JRip | jrip | RWeka |
| classif.kknn | kknn | kknn |
| classif.knn | knn | class |
| classif.ksvm | ksvm | kernlab |
| classif.lda | lda | MASS |
| classif.LiblineaRL1L2SVC | liblinl1l2svc | LiblineaR |
| classif.LiblineaRL1LogReg | liblinl1logreg | LiblineaR |
| classif.LiblineaRL2L1SVC | liblinl2l1svc | LiblineaR |
| classif.LiblineaRL2LogReg | liblinl2logreg | LiblineaR |
| classif.LiblineaRL2SVC | liblinl2svc | LiblineaR |
| classif.LiblineaRMultiClassSVC | liblinmulticlasssvc | LiblineaR |
| classif.linDA | linda | DiscriMiner |
| classif.logreg | logreg | stats |
| classif.lssvm | lssvm | kernlab |
| classif.lvq1 | lvq1 | class |
| classif.mda | mda | mda |
| classif.mlp | mlp | RSNNS |
| classif.multinom | multinom | nnet |
| classif.naiveBayes | nbayes | e1071 |
| classif.neuralnet | neuralnet | neuralnet |
| classif.nnet | nnet | nnet |
| classif.nnTrain | nn.train | deepnet |
| classif.nodeHarvest | nodeHarvest | nodeHarvest |
| classif.OneR | oner | RWeka |
| classif.pamr | pamr | pamr |
| classif.PART | part | RWeka |
| classif.penalized | penalized | penalized |
| classif.plr | plr | stepPlr |
| classif.plsdaCaret | plsdacaret | caret,pls |
| classif.probit | probit | stats |
| classif.qda | qda | MASS |
| classif.quaDA | quada | DiscriMiner |
| classif.randomForest | rf | randomForest |
| classif.randomForestSRC | rfsrc | randomForestSRC |
| classif.ranger | ranger | ranger |
| classif.rda | rda | klaR |
| classif.rFerns | rFerns | rFerns |
| classif.rknn | rknn | rknn |
| classif.rotationForest | rotationForest | rotationForest |
| classif.rpart | rpart | rpart |
| classif.RRF | RRF | RRF |
| classif.rrlda | rrlda | rrlda |
| classif.saeDNN | sae.dnn | deepnet |
| classif.sda | sda | sda |
| classif.sparseLDA | sparseLDA | sparseLDA,MASS,elasticnet |
| classif.svm | svm | e1071 |
| classif.xgboost | xgboost | xgboost |
# Create learner
lrn <- makeLearner("classif.randomForest",
fix.factors.prediction = TRUE,
predict.type = "prob")Now, you are going to perform hyperparameter tuning with random search. You will prepare the different functions and objects you need to tune your model in the next exercise. The knowledge_train_data dataset has already been loaded for you, as have the packages mlr, tidyverse and tictoc. Remember to look into the function that lists all learners if you are unsure about the name of a learner.
getParamSet("classif.nnet")## Type len Def Constr Req Tunable Trafo
## size integer - 3 0 to Inf - TRUE -
## maxit integer - 100 1 to Inf - TRUE -
## skip logical - FALSE - - TRUE -
## rang numeric - 0.7 -Inf to Inf - TRUE -
## decay numeric - 0 -Inf to Inf - TRUE -
## Hess logical - FALSE - - TRUE -
## trace logical - TRUE - - FALSE -
## MaxNWts integer - 1000 1 to Inf - FALSE -
## abstol numeric - 0.0001 -Inf to Inf - TRUE -
## reltol numeric - 1e-08 -Inf to Inf - TRUE -
# Define task
task <- makeClassifTask(data = knowledge_train_data,
target = "UNS")
# Define learner
lrn <- makeLearner("classif.nnet", predict.type = "prob", fix.factors.prediction = TRUE)param_set <- makeParamSet(
makeDiscreteParam("size", values = c(2,3,5)),
makeNumericParam("decay", lower = 0.0001, upper = 0.1)
)
# Print parameter set
print(param_set)## Type len Def Constr Req Tunable Trafo
## size discrete - - 2,3,5 - TRUE -
## decay numeric - - 0.0001 to 0.1 - TRUE -
# Define a random search tuning method.
ctrl_random <- makeTuneControlRandom()Now, you can combine the prepared functions and objects from the previous exercise to actually perform hyperparameter tuning with random search. The knowledge_train_data dataset has already been loaded for you, as have the packages mlr, tidyverse and tictoc. And the following code has also been run already
# Define learner
lrn <- makeLearner("classif.nnet", predict.type = "prob", fix.factors.prediction = TRUE)
# Define set of parameters
param_set <- makeParamSet(
makeDiscreteParam("size", values = c(2,3,5)),
makeNumericParam("decay", lower = 0.0001, upper = 0.1)
)# Define a random search tuning method.
ctrl_random <- makeTuneControlRandom(maxit = 6)
# Define a 3 x 3 repeated cross-validation scheme
cross_val <- makeResampleDesc("RepCV", folds = 3 * 3)
# Tune hyperparameters
tic()
lrn_tune <- tuneParams(lrn,
task,
resampling = cross_val,
control = ctrl_random,
par.set = param_set)## # weights: 48
## initial value 123.511952
## iter 10 value 69.617123
## iter 20 value 53.217746
## iter 30 value 47.072924
## iter 40 value 44.202188
## iter 50 value 43.367249
## iter 60 value 43.165414
## iter 70 value 43.086275
## iter 80 value 42.892874
## iter 90 value 42.730210
## iter 100 value 42.706480
## final value 42.706480
## stopped after 100 iterations
## # weights: 48
## initial value 118.026374
## iter 10 value 56.894385
## iter 20 value 45.372323
## iter 30 value 43.670569
## iter 40 value 43.200898
## iter 50 value 42.881068
## iter 60 value 42.755373
## iter 70 value 42.684648
## iter 80 value 42.639711
## iter 90 value 42.625107
## iter 100 value 42.623235
## final value 42.623235
## stopped after 100 iterations
## # weights: 48
## initial value 119.487415
## iter 10 value 82.413775
## iter 20 value 50.895328
## iter 30 value 44.937877
## iter 40 value 43.731384
## iter 50 value 43.346331
## iter 60 value 42.632880
## iter 70 value 42.529744
## iter 80 value 42.481184
## iter 90 value 42.434529
## iter 100 value 42.423566
## final value 42.423566
## stopped after 100 iterations
## # weights: 48
## initial value 138.336740
## iter 10 value 58.864156
## iter 20 value 44.537776
## iter 30 value 43.241228
## iter 40 value 42.861955
## iter 50 value 42.669699
## iter 60 value 42.291840
## iter 70 value 42.234935
## iter 80 value 42.217498
## iter 90 value 42.203982
## iter 100 value 42.194081
## final value 42.194081
## stopped after 100 iterations
## # weights: 48
## initial value 127.029855
## iter 10 value 61.394491
## iter 20 value 42.898182
## iter 30 value 41.397307
## iter 40 value 40.768164
## iter 50 value 40.643728
## iter 60 value 40.558038
## iter 70 value 40.539824
## iter 80 value 40.536805
## iter 90 value 40.533428
## iter 100 value 40.532712
## final value 40.532712
## stopped after 100 iterations
## # weights: 48
## initial value 127.916466
## iter 10 value 64.443993
## iter 20 value 43.179555
## iter 30 value 41.304899
## iter 40 value 40.629494
## iter 50 value 40.479622
## iter 60 value 40.383148
## iter 70 value 40.328210
## iter 80 value 40.298616
## iter 90 value 40.251913
## iter 100 value 40.241102
## final value 40.241102
## stopped after 100 iterations
## # weights: 48
## initial value 119.665153
## iter 10 value 57.275558
## iter 20 value 42.417352
## iter 30 value 41.528806
## iter 40 value 41.161319
## iter 50 value 40.850447
## iter 60 value 40.803416
## iter 70 value 40.772918
## iter 80 value 40.761379
## iter 90 value 40.754418
## iter 100 value 40.748008
## final value 40.748008
## stopped after 100 iterations
## # weights: 48
## initial value 136.060828
## iter 10 value 63.588270
## iter 20 value 44.053425
## iter 30 value 42.813757
## iter 40 value 42.665791
## iter 50 value 42.571037
## iter 60 value 42.519346
## iter 70 value 42.500826
## iter 80 value 42.494576
## iter 90 value 42.494356
## final value 42.494319
## converged
## # weights: 48
## initial value 137.233173
## iter 10 value 57.327335
## iter 20 value 45.308724
## iter 30 value 43.470246
## iter 40 value 42.380925
## iter 50 value 42.132897
## iter 60 value 41.940068
## iter 70 value 41.768277
## iter 80 value 41.701602
## iter 90 value 41.663038
## iter 100 value 41.642821
## final value 41.642821
## stopped after 100 iterations
## # weights: 48
## initial value 119.758328
## iter 10 value 62.094827
## iter 20 value 42.721347
## iter 30 value 41.355413
## iter 40 value 41.131860
## iter 50 value 40.956921
## iter 60 value 40.897729
## iter 70 value 40.882563
## iter 80 value 40.879055
## iter 90 value 40.877808
## iter 100 value 40.874560
## final value 40.874560
## stopped after 100 iterations
## # weights: 48
## initial value 124.190132
## iter 10 value 54.477735
## iter 20 value 42.711441
## iter 30 value 42.183755
## iter 40 value 41.886148
## iter 50 value 41.861923
## iter 60 value 41.859795
## iter 70 value 41.859012
## iter 80 value 41.858740
## iter 90 value 41.858688
## iter 100 value 41.858262
## final value 41.858262
## stopped after 100 iterations
## # weights: 48
## initial value 133.482438
## iter 10 value 63.491213
## iter 20 value 48.663474
## iter 30 value 45.263119
## iter 40 value 43.826239
## iter 50 value 43.537113
## iter 60 value 43.077447
## iter 70 value 42.539279
## iter 80 value 42.397571
## iter 90 value 42.354876
## iter 100 value 42.335448
## final value 42.335448
## stopped after 100 iterations
## # weights: 48
## initial value 124.606490
## iter 10 value 57.503224
## iter 20 value 44.393329
## iter 30 value 42.935378
## iter 40 value 42.638722
## iter 50 value 42.545321
## iter 60 value 42.531721
## iter 70 value 42.529353
## iter 80 value 42.528718
## iter 90 value 42.527597
## iter 100 value 42.527294
## final value 42.527294
## stopped after 100 iterations
## # weights: 48
## initial value 145.726099
## iter 10 value 58.946482
## iter 20 value 45.294693
## iter 30 value 43.598405
## iter 40 value 42.911419
## iter 50 value 42.476942
## iter 60 value 42.384925
## iter 70 value 42.364707
## iter 80 value 42.353706
## iter 90 value 42.326868
## iter 100 value 42.319589
## final value 42.319589
## stopped after 100 iterations
## # weights: 48
## initial value 146.511960
## iter 10 value 59.839755
## iter 20 value 44.015472
## iter 30 value 42.305907
## iter 40 value 41.756941
## iter 50 value 41.467206
## iter 60 value 41.443792
## iter 70 value 41.411275
## iter 80 value 41.388944
## iter 90 value 41.382802
## iter 100 value 41.382230
## final value 41.382230
## stopped after 100 iterations
## # weights: 48
## initial value 135.103970
## iter 10 value 73.789823
## iter 20 value 47.994812
## iter 30 value 44.109338
## iter 40 value 42.595362
## iter 50 value 42.443762
## iter 60 value 42.304328
## iter 70 value 42.237846
## iter 80 value 42.228827
## iter 90 value 42.225342
## iter 100 value 42.222995
## final value 42.222995
## stopped after 100 iterations
## # weights: 48
## initial value 126.925911
## iter 10 value 68.235522
## iter 20 value 45.766868
## iter 30 value 42.400683
## iter 40 value 41.668472
## iter 50 value 41.500506
## iter 60 value 41.336942
## iter 70 value 41.279068
## iter 80 value 41.234781
## iter 90 value 41.221438
## iter 100 value 41.209560
## final value 41.209560
## stopped after 100 iterations
## # weights: 48
## initial value 147.979525
## iter 10 value 62.565503
## iter 20 value 43.317816
## iter 30 value 41.247010
## iter 40 value 40.826789
## iter 50 value 40.661946
## iter 60 value 40.631223
## iter 70 value 40.624178
## iter 80 value 40.622144
## iter 90 value 40.620742
## iter 100 value 40.620095
## final value 40.620095
## stopped after 100 iterations
## # weights: 48
## initial value 143.192558
## iter 10 value 84.134448
## iter 20 value 48.698413
## iter 30 value 44.831768
## iter 40 value 43.670236
## iter 50 value 43.384807
## iter 60 value 43.287367
## iter 70 value 43.110256
## iter 80 value 43.033287
## iter 90 value 43.025779
## iter 100 value 43.021584
## final value 43.021584
## stopped after 100 iterations
## # weights: 48
## initial value 147.776969
## iter 10 value 78.742248
## iter 20 value 45.621738
## iter 30 value 44.658232
## iter 40 value 43.934920
## iter 50 value 43.544800
## iter 60 value 43.417819
## iter 70 value 43.331616
## iter 80 value 43.251444
## iter 90 value 43.238343
## iter 100 value 43.235041
## final value 43.235041
## stopped after 100 iterations
## # weights: 48
## initial value 132.846836
## iter 10 value 51.551308
## iter 20 value 42.678992
## iter 30 value 42.233041
## iter 40 value 41.860368
## iter 50 value 41.801395
## iter 60 value 41.780419
## iter 70 value 41.775737
## iter 80 value 41.774109
## iter 90 value 41.772636
## final value 41.772222
## converged
## # weights: 48
## initial value 134.799669
## iter 10 value 69.901697
## iter 20 value 40.866997
## iter 30 value 38.694505
## iter 40 value 38.040285
## iter 50 value 37.725304
## iter 60 value 37.472737
## iter 70 value 37.347739
## iter 80 value 37.319633
## iter 90 value 37.313243
## iter 100 value 37.311003
## final value 37.311003
## stopped after 100 iterations
## # weights: 48
## initial value 141.438746
## iter 10 value 66.663100
## iter 20 value 42.192567
## iter 30 value 40.928894
## iter 40 value 40.622328
## iter 50 value 40.548870
## iter 60 value 40.499241
## iter 70 value 40.490171
## iter 80 value 40.488541
## iter 90 value 40.487643
## iter 100 value 40.487381
## final value 40.487381
## stopped after 100 iterations
## # weights: 48
## initial value 118.857791
## iter 10 value 58.105523
## iter 20 value 43.612705
## iter 30 value 42.717372
## iter 40 value 42.592018
## iter 50 value 42.551163
## iter 60 value 42.536392
## iter 70 value 42.533580
## iter 80 value 42.532324
## iter 90 value 42.532276
## iter 100 value 42.532261
## final value 42.532261
## stopped after 100 iterations
## # weights: 48
## initial value 124.020814
## iter 10 value 54.839797
## iter 20 value 43.420955
## iter 30 value 42.882055
## iter 40 value 42.620285
## iter 50 value 42.497880
## iter 60 value 42.457163
## iter 70 value 42.414873
## iter 80 value 42.398805
## iter 90 value 42.390990
## final value 42.386016
## converged
## # weights: 48
## initial value 128.457185
## iter 10 value 74.708259
## iter 20 value 43.196481
## iter 30 value 41.658503
## iter 40 value 41.466785
## iter 50 value 41.404491
## iter 60 value 41.351521
## iter 70 value 41.304210
## iter 80 value 41.291948
## iter 90 value 41.285332
## iter 100 value 41.282325
## final value 41.282325
## stopped after 100 iterations
## # weights: 48
## initial value 148.039092
## iter 10 value 60.585175
## iter 20 value 46.330137
## iter 30 value 44.238742
## iter 40 value 43.349766
## iter 50 value 43.003006
## iter 60 value 42.937454
## iter 70 value 42.932885
## iter 80 value 42.926456
## iter 90 value 42.903914
## iter 100 value 42.894756
## final value 42.894756
## stopped after 100 iterations
## # weights: 48
## initial value 129.782157
## iter 10 value 66.073125
## iter 20 value 44.401014
## iter 30 value 43.135085
## iter 40 value 42.248320
## iter 50 value 41.912264
## iter 60 value 41.704104
## iter 70 value 41.628801
## iter 80 value 41.588797
## iter 90 value 41.581788
## final value 41.581010
## converged
## # weights: 48
## initial value 123.849004
## iter 10 value 65.849570
## iter 20 value 43.557059
## iter 30 value 42.506728
## iter 40 value 42.338278
## iter 50 value 42.309019
## iter 60 value 42.305559
## iter 70 value 42.303600
## iter 80 value 42.303310
## iter 90 value 42.303246
## iter 100 value 42.303230
## final value 42.303230
## stopped after 100 iterations
## # weights: 48
## initial value 120.942160
## iter 10 value 67.267057
## iter 20 value 47.311905
## iter 30 value 43.875779
## iter 40 value 43.455273
## iter 50 value 43.081660
## iter 60 value 43.028710
## iter 70 value 43.022026
## iter 80 value 43.016312
## iter 90 value 43.013266
## iter 100 value 43.012683
## final value 43.012683
## stopped after 100 iterations
## # weights: 48
## initial value 118.607473
## iter 10 value 68.553853
## iter 20 value 43.622244
## iter 30 value 41.581298
## iter 40 value 40.599762
## iter 50 value 40.209505
## iter 60 value 40.112317
## iter 70 value 40.091271
## iter 80 value 40.073981
## iter 90 value 40.060672
## iter 100 value 40.050047
## final value 40.050047
## stopped after 100 iterations
## # weights: 48
## initial value 129.429633
## iter 10 value 60.948107
## iter 20 value 44.823707
## iter 30 value 43.575094
## iter 40 value 43.148385
## iter 50 value 42.936315
## iter 60 value 42.866732
## iter 70 value 42.813645
## iter 80 value 42.804326
## iter 90 value 42.796444
## iter 100 value 42.796066
## final value 42.796066
## stopped after 100 iterations
## # weights: 48
## initial value 123.043938
## iter 10 value 59.592119
## iter 20 value 48.236740
## iter 30 value 45.966129
## iter 40 value 44.051988
## iter 50 value 42.378063
## iter 60 value 42.072436
## iter 70 value 42.009037
## iter 80 value 42.001210
## iter 90 value 41.998473
## iter 100 value 41.994817
## final value 41.994817
## stopped after 100 iterations
## # weights: 48
## initial value 118.327811
## iter 10 value 55.200545
## iter 20 value 44.568856
## iter 30 value 43.966256
## iter 40 value 43.816088
## iter 50 value 43.608878
## iter 60 value 42.968243
## iter 70 value 42.798851
## iter 80 value 42.710238
## iter 90 value 42.689705
## iter 100 value 42.677618
## final value 42.677618
## stopped after 100 iterations
## # weights: 48
## initial value 117.633407
## iter 10 value 58.488958
## iter 20 value 41.269728
## iter 30 value 40.099583
## iter 40 value 39.999468
## iter 50 value 39.962320
## iter 60 value 39.941771
## iter 70 value 39.927593
## iter 80 value 39.926428
## iter 90 value 39.925925
## final value 39.925884
## converged
## # weights: 48
## initial value 146.551330
## iter 10 value 72.822575
## iter 20 value 45.752288
## iter 30 value 43.269113
## iter 40 value 41.992437
## iter 50 value 41.730142
## iter 60 value 41.676695
## iter 70 value 41.361338
## iter 80 value 41.275664
## iter 90 value 41.267997
## iter 100 value 41.260360
## final value 41.260360
## stopped after 100 iterations
## # weights: 48
## initial value 126.202366
## iter 10 value 75.754551
## iter 20 value 49.083713
## iter 30 value 45.248253
## iter 40 value 44.218955
## iter 50 value 43.649291
## iter 60 value 43.538063
## iter 70 value 43.384883
## iter 80 value 43.311815
## iter 90 value 43.292692
## iter 100 value 43.283933
## final value 43.283933
## stopped after 100 iterations
## # weights: 48
## initial value 128.458703
## iter 10 value 59.303939
## iter 20 value 44.320857
## iter 30 value 43.186521
## iter 40 value 42.912788
## iter 50 value 42.800537
## iter 60 value 42.648596
## iter 70 value 42.554467
## iter 80 value 42.543896
## iter 90 value 42.530526
## iter 100 value 42.525006
## final value 42.525006
## stopped after 100 iterations
## # weights: 48
## initial value 116.558244
## iter 10 value 60.493005
## iter 20 value 44.697587
## iter 30 value 43.748806
## iter 40 value 43.197620
## iter 50 value 42.688915
## iter 60 value 42.610250
## iter 70 value 42.557272
## iter 80 value 42.527296
## iter 90 value 42.520124
## iter 100 value 42.505870
## final value 42.505870
## stopped after 100 iterations
## # weights: 48
## initial value 124.405741
## iter 10 value 59.578608
## iter 20 value 44.645224
## iter 30 value 43.137289
## iter 40 value 42.813076
## iter 50 value 42.582693
## iter 60 value 42.525243
## iter 70 value 42.506488
## iter 80 value 42.504103
## iter 90 value 42.502929
## iter 100 value 42.502601
## final value 42.502601
## stopped after 100 iterations
## # weights: 48
## initial value 119.187626
## iter 10 value 59.482342
## iter 20 value 44.293628
## iter 30 value 42.800941
## iter 40 value 42.513924
## iter 50 value 42.475103
## iter 60 value 42.431974
## iter 70 value 42.424763
## iter 80 value 42.415809
## iter 90 value 42.383803
## iter 100 value 42.377685
## final value 42.377685
## stopped after 100 iterations
## # weights: 48
## initial value 121.308259
## iter 10 value 65.815619
## iter 20 value 45.846660
## iter 30 value 44.088429
## iter 40 value 43.327515
## iter 50 value 43.011616
## iter 60 value 42.717363
## iter 70 value 42.598816
## iter 80 value 42.437287
## iter 90 value 42.373521
## iter 100 value 42.365503
## final value 42.365503
## stopped after 100 iterations
## # weights: 48
## initial value 148.155885
## iter 10 value 79.756837
## iter 20 value 44.139612
## iter 30 value 42.803347
## iter 40 value 41.988473
## iter 50 value 41.543568
## iter 60 value 41.407607
## iter 70 value 41.316718
## iter 80 value 41.254803
## iter 90 value 41.225905
## iter 100 value 41.202216
## final value 41.202216
## stopped after 100 iterations
## # weights: 48
## initial value 122.285591
## iter 10 value 54.161564
## iter 20 value 42.721017
## iter 30 value 42.027945
## iter 40 value 41.770460
## iter 50 value 41.689600
## iter 60 value 41.612471
## iter 70 value 41.603810
## iter 80 value 41.594687
## iter 90 value 41.590804
## iter 100 value 41.587760
## final value 41.587760
## stopped after 100 iterations
## # weights: 48
## initial value 135.511254
## iter 10 value 56.108151
## iter 20 value 38.962450
## iter 30 value 37.975565
## iter 40 value 37.582952
## iter 50 value 37.398466
## iter 60 value 37.349581
## iter 70 value 37.332519
## iter 80 value 37.327366
## iter 90 value 37.325299
## iter 100 value 37.325214
## final value 37.325214
## stopped after 100 iterations
## # weights: 48
## initial value 121.861781
## iter 10 value 65.825590
## iter 20 value 47.596765
## iter 30 value 44.233293
## iter 40 value 43.677583
## iter 50 value 43.383655
## iter 60 value 42.538025
## iter 70 value 42.413520
## iter 80 value 42.377903
## iter 90 value 42.363536
## iter 100 value 42.337098
## final value 42.337098
## stopped after 100 iterations
## # weights: 48
## initial value 130.862033
## iter 10 value 63.491342
## iter 20 value 44.272361
## iter 30 value 43.418684
## iter 40 value 42.941934
## iter 50 value 42.737086
## iter 60 value 42.506398
## iter 70 value 42.208797
## iter 80 value 42.183369
## iter 90 value 42.177518
## iter 100 value 42.174300
## final value 42.174300
## stopped after 100 iterations
## # weights: 48
## initial value 135.550370
## iter 10 value 61.315158
## iter 20 value 46.628197
## iter 30 value 44.496269
## iter 40 value 43.712114
## iter 50 value 42.787639
## iter 60 value 42.580298
## iter 70 value 42.458910
## iter 80 value 42.426662
## iter 90 value 42.422221
## iter 100 value 42.419376
## final value 42.419376
## stopped after 100 iterations
## # weights: 48
## initial value 116.196279
## iter 10 value 49.060446
## iter 20 value 41.343930
## iter 30 value 40.641028
## iter 40 value 40.179336
## iter 50 value 40.084552
## iter 60 value 40.056353
## iter 70 value 40.006992
## iter 80 value 39.997140
## iter 90 value 39.996537
## iter 100 value 39.996224
## final value 39.996224
## stopped after 100 iterations
## # weights: 48
## initial value 124.112971
## iter 10 value 74.863398
## iter 20 value 48.027411
## iter 30 value 44.828862
## iter 40 value 43.765842
## iter 50 value 43.054937
## iter 60 value 42.918052
## iter 70 value 42.857315
## iter 80 value 42.833406
## iter 90 value 42.821402
## iter 100 value 42.813672
## final value 42.813672
## stopped after 100 iterations
## # weights: 48
## initial value 119.653460
## iter 10 value 54.966802
## iter 20 value 43.036693
## iter 30 value 42.157927
## iter 40 value 41.965474
## iter 50 value 41.921282
## iter 60 value 41.907933
## iter 70 value 41.891312
## iter 80 value 41.883562
## iter 90 value 41.880427
## iter 100 value 41.879834
## final value 41.879834
## stopped after 100 iterations
## # weights: 48
## initial value 153.466909
## iter 10 value 54.019028
## iter 20 value 40.951315
## iter 30 value 40.008623
## iter 40 value 39.725465
## iter 50 value 39.617905
## iter 60 value 39.537135
## iter 70 value 39.517658
## iter 80 value 39.492285
## iter 90 value 39.478163
## iter 100 value 39.473257
## final value 39.473257
## stopped after 100 iterations
## # weights: 48
## initial value 121.869299
## iter 10 value 55.867864
## iter 20 value 43.922407
## iter 30 value 42.714072
## iter 40 value 42.421522
## iter 50 value 42.386278
## iter 60 value 42.383014
## iter 70 value 42.382707
## iter 80 value 42.382639
## iter 90 value 42.382601
## iter 100 value 42.382335
## final value 42.382335
## stopped after 100 iterations
## # weights: 48
## initial value 126.437156
## iter 10 value 82.750974
## iter 20 value 46.315351
## iter 30 value 44.572902
## iter 40 value 43.787277
## iter 50 value 42.901128
## iter 60 value 42.327642
## iter 70 value 42.005867
## iter 80 value 41.860141
## iter 90 value 41.809907
## iter 100 value 41.747833
## final value 41.747833
## stopped after 100 iterations
## # weights: 48
## initial value 123.413749
## iter 10 value 54.452450
## iter 20 value 44.156285
## iter 30 value 43.138012
## iter 40 value 41.886333
## iter 50 value 41.504931
## iter 60 value 41.390047
## iter 70 value 41.334956
## iter 80 value 41.301158
## iter 90 value 41.297246
## iter 100 value 41.291877
## final value 41.291877
## stopped after 100 iterations
## # weights: 48
## initial value 119.026934
## iter 10 value 69.598086
## iter 20 value 46.828900
## iter 30 value 42.002630
## iter 40 value 41.135218
## iter 50 value 40.652764
## iter 60 value 40.510218
## iter 70 value 40.486839
## iter 80 value 40.479585
## iter 90 value 40.475746
## iter 100 value 40.474508
## final value 40.474508
## stopped after 100 iterations
## # weights: 48
## initial value 140.472049
## iter 10 value 68.569615
## iter 20 value 42.876951
## iter 30 value 41.792690
## iter 40 value 41.077440
## iter 50 value 40.667195
## iter 60 value 40.541442
## iter 70 value 40.515643
## iter 80 value 40.500000
## iter 90 value 40.496265
## iter 100 value 40.493842
## final value 40.493842
## stopped after 100 iterations
## # weights: 48
## initial value 119.715860
## iter 10 value 58.225150
## iter 20 value 43.021531
## iter 30 value 42.462310
## iter 40 value 42.316119
## iter 50 value 42.236851
## iter 60 value 42.186905
## iter 70 value 42.162216
## iter 80 value 42.121833
## iter 90 value 42.113604
## iter 100 value 42.107660
## final value 42.107660
## stopped after 100 iterations
## # weights: 48
## initial value 117.858824
## iter 10 value 57.858486
## iter 20 value 44.963504
## iter 30 value 42.902235
## iter 40 value 42.578275
## iter 50 value 42.305167
## iter 60 value 42.171360
## iter 70 value 42.082295
## iter 80 value 42.056567
## iter 90 value 42.052742
## iter 100 value 42.049030
## final value 42.049030
## stopped after 100 iterations
## # weights: 48
## initial value 120.226155
## iter 10 value 67.464938
## iter 20 value 47.481464
## iter 30 value 42.914017
## iter 40 value 42.481893
## iter 50 value 42.020290
## iter 60 value 41.896976
## iter 70 value 41.855785
## iter 80 value 41.850086
## iter 90 value 41.834276
## iter 100 value 41.825192
## final value 41.825192
## stopped after 100 iterations
## # weights: 48
## initial value 141.748922
## iter 10 value 66.358275
## iter 20 value 47.988795
## iter 30 value 44.178831
## iter 40 value 43.097848
## iter 50 value 42.977324
## iter 60 value 42.920666
## iter 70 value 42.880932
## iter 80 value 42.866010
## iter 90 value 42.861040
## iter 100 value 42.845884
## final value 42.845884
## stopped after 100 iterations
## # weights: 48
## initial value 126.120275
## iter 10 value 65.446055
## iter 20 value 44.595183
## iter 30 value 43.083668
## iter 40 value 42.477947
## iter 50 value 42.267216
## iter 60 value 42.225169
## iter 70 value 42.188108
## iter 80 value 42.148737
## iter 90 value 42.138045
## iter 100 value 42.124744
## final value 42.124744
## stopped after 100 iterations
## # weights: 48
## initial value 155.865905
## iter 10 value 49.552396
## iter 20 value 43.672922
## iter 30 value 42.845528
## iter 40 value 42.746446
## iter 50 value 42.672041
## iter 60 value 42.355950
## iter 70 value 42.269136
## iter 80 value 42.267281
## iter 90 value 42.265645
## iter 100 value 42.265452
## final value 42.265452
## stopped after 100 iterations
## # weights: 48
## initial value 126.607181
## iter 10 value 68.619870
## iter 20 value 44.795775
## iter 30 value 41.995838
## iter 40 value 41.543941
## iter 50 value 41.387632
## iter 60 value 41.174230
## iter 70 value 41.145289
## iter 80 value 41.129262
## iter 90 value 41.124713
## iter 100 value 41.124122
## final value 41.124122
## stopped after 100 iterations
## # weights: 48
## initial value 117.769637
## iter 10 value 72.513572
## iter 20 value 44.827458
## iter 30 value 42.053487
## iter 40 value 41.795932
## iter 50 value 41.719300
## iter 60 value 41.627402
## iter 70 value 41.594035
## iter 80 value 41.586997
## iter 90 value 41.582776
## iter 100 value 41.581669
## final value 41.581669
## stopped after 100 iterations
## # weights: 48
## initial value 138.818061
## iter 10 value 68.005430
## iter 20 value 46.091312
## iter 30 value 45.067220
## iter 40 value 43.986603
## iter 50 value 43.054462
## iter 60 value 42.894009
## iter 70 value 42.845982
## iter 80 value 42.837078
## iter 90 value 42.833839
## iter 100 value 42.833293
## final value 42.833293
## stopped after 100 iterations
## # weights: 48
## initial value 119.412487
## iter 10 value 60.007700
## iter 20 value 43.908225
## iter 30 value 41.936693
## iter 40 value 41.273656
## iter 50 value 41.233202
## iter 60 value 41.227158
## iter 70 value 41.223206
## iter 80 value 41.222787
## iter 90 value 41.222722
## final value 41.222711
## converged
## # weights: 48
## initial value 121.285876
## iter 10 value 72.212247
## iter 20 value 44.182416
## iter 30 value 42.898087
## iter 40 value 42.797062
## iter 50 value 42.777017
## iter 60 value 42.763046
## iter 70 value 42.753629
## iter 80 value 42.752185
## iter 90 value 42.746547
## iter 100 value 42.744227
## final value 42.744227
## stopped after 100 iterations
## # weights: 48
## initial value 123.400159
## iter 10 value 61.049308
## iter 20 value 44.975974
## iter 30 value 44.066374
## iter 40 value 43.551503
## iter 50 value 43.512296
## iter 60 value 43.505851
## iter 70 value 43.495198
## iter 80 value 43.490968
## iter 90 value 43.483654
## iter 100 value 43.482112
## final value 43.482112
## stopped after 100 iterations
## # weights: 48
## initial value 125.879516
## iter 10 value 62.249743
## iter 20 value 46.800887
## iter 30 value 44.226374
## iter 40 value 43.741041
## iter 50 value 43.437041
## iter 60 value 43.354235
## iter 70 value 43.333629
## iter 80 value 43.328436
## iter 90 value 43.327433
## iter 100 value 43.327268
## final value 43.327268
## stopped after 100 iterations
## # weights: 48
## initial value 144.848737
## iter 10 value 61.447865
## iter 20 value 42.989093
## iter 30 value 41.746595
## iter 40 value 41.440839
## iter 50 value 41.421446
## iter 60 value 41.407805
## iter 70 value 41.402191
## iter 80 value 41.397152
## iter 90 value 41.396138
## iter 100 value 41.395601
## final value 41.395601
## stopped after 100 iterations
## # weights: 48
## initial value 118.700076
## iter 10 value 60.082245
## iter 20 value 39.580092
## iter 30 value 38.822662
## iter 40 value 38.636693
## iter 50 value 38.554946
## iter 60 value 38.546116
## iter 70 value 38.544113
## iter 80 value 38.541286
## iter 90 value 38.539073
## final value 38.539061
## converged
## # weights: 48
## initial value 136.793730
## iter 10 value 65.346686
## iter 20 value 45.251490
## iter 30 value 44.316363
## iter 40 value 43.865789
## iter 50 value 43.286482
## iter 60 value 43.095840
## iter 70 value 43.017266
## iter 80 value 42.984307
## iter 90 value 42.964187
## iter 100 value 42.959535
## final value 42.959535
## stopped after 100 iterations
## # weights: 48
## initial value 121.821895
## iter 10 value 53.268488
## iter 20 value 43.792271
## iter 30 value 42.892919
## iter 40 value 42.561968
## iter 50 value 42.473972
## iter 60 value 42.463871
## iter 70 value 42.455564
## iter 80 value 42.453274
## iter 90 value 42.453085
## iter 100 value 42.453014
## final value 42.453014
## stopped after 100 iterations
## # weights: 48
## initial value 120.546588
## iter 10 value 50.191592
## iter 20 value 42.387479
## iter 30 value 41.915531
## iter 40 value 41.684621
## iter 50 value 41.595848
## iter 60 value 41.551613
## iter 70 value 41.538374
## iter 80 value 41.536088
## iter 90 value 41.534061
## final value 41.533939
## converged
## # weights: 48
## initial value 125.415386
## iter 10 value 60.442741
## iter 20 value 45.332391
## iter 30 value 43.547393
## iter 40 value 42.633252
## iter 50 value 42.490764
## iter 60 value 42.482999
## iter 70 value 42.476959
## iter 80 value 42.472994
## iter 90 value 42.471996
## final value 42.471910
## converged
## # weights: 48
## initial value 131.521483
## iter 10 value 56.432855
## iter 20 value 42.551031
## iter 30 value 40.983047
## iter 40 value 40.580099
## iter 50 value 40.465842
## iter 60 value 40.412109
## iter 70 value 40.402968
## iter 80 value 40.396976
## iter 90 value 40.387146
## iter 100 value 40.385562
## final value 40.385562
## stopped after 100 iterations
## # weights: 48
## initial value 121.539553
## iter 10 value 59.589897
## iter 20 value 45.737618
## iter 30 value 42.626811
## iter 40 value 41.314186
## iter 50 value 41.233104
## iter 60 value 41.218044
## iter 70 value 41.180530
## iter 80 value 41.168282
## iter 90 value 41.161992
## iter 100 value 41.158672
## final value 41.158672
## stopped after 100 iterations
## # weights: 48
## initial value 120.661246
## iter 10 value 58.121206
## iter 20 value 42.762424
## iter 30 value 42.431623
## iter 40 value 42.196952
## iter 50 value 42.079332
## iter 60 value 42.060338
## iter 70 value 42.015920
## iter 80 value 41.987003
## iter 90 value 41.981323
## iter 100 value 41.974839
## final value 41.974839
## stopped after 100 iterations
## # weights: 48
## initial value 127.927371
## iter 10 value 70.971555
## iter 20 value 44.594578
## iter 30 value 43.221739
## iter 40 value 42.924023
## iter 50 value 42.717981
## iter 60 value 42.662246
## iter 70 value 42.632997
## iter 80 value 42.619921
## iter 90 value 42.615893
## iter 100 value 42.612486
## final value 42.612486
## stopped after 100 iterations
## # weights: 48
## initial value 124.684932
## iter 10 value 59.363145
## iter 20 value 43.062168
## iter 30 value 41.553651
## iter 40 value 40.645461
## iter 50 value 39.960726
## iter 60 value 39.717441
## iter 70 value 39.690141
## iter 80 value 39.677519
## iter 90 value 39.648562
## iter 100 value 39.637068
## final value 39.637068
## stopped after 100 iterations
## # weights: 48
## initial value 124.516234
## iter 10 value 66.392976
## iter 20 value 42.961486
## iter 30 value 41.616045
## iter 40 value 40.588501
## iter 50 value 40.317339
## iter 60 value 40.204374
## iter 70 value 40.167046
## iter 80 value 40.156157
## iter 90 value 40.152813
## iter 100 value 40.152690
## final value 40.152690
## stopped after 100 iterations
## # weights: 48
## initial value 119.708174
## iter 10 value 54.420995
## iter 20 value 44.531487
## iter 30 value 42.468015
## iter 40 value 42.187988
## iter 50 value 42.156155
## iter 60 value 42.127147
## iter 70 value 42.098313
## iter 80 value 42.090392
## iter 90 value 42.088791
## iter 100 value 42.084892
## final value 42.084892
## stopped after 100 iterations
## # weights: 48
## initial value 117.235942
## iter 10 value 58.050124
## iter 20 value 44.850724
## iter 30 value 42.035129
## iter 40 value 41.671600
## iter 50 value 41.348924
## iter 60 value 41.327596
## iter 70 value 41.324009
## iter 80 value 41.323571
## iter 90 value 41.323249
## iter 100 value 41.323088
## final value 41.323088
## stopped after 100 iterations
## # weights: 48
## initial value 118.800607
## iter 10 value 53.639597
## iter 20 value 43.472000
## iter 30 value 43.168384
## iter 40 value 42.894345
## iter 50 value 42.749314
## iter 60 value 42.723829
## iter 70 value 42.714043
## iter 80 value 42.708636
## iter 90 value 42.708025
## iter 100 value 42.707812
## final value 42.707812
## stopped after 100 iterations
## # weights: 48
## initial value 120.404999
## iter 10 value 55.541546
## iter 20 value 44.244179
## iter 30 value 43.076289
## iter 40 value 42.738891
## iter 50 value 42.641214
## iter 60 value 42.580329
## iter 70 value 42.545178
## iter 80 value 42.534529
## iter 90 value 42.532125
## iter 100 value 42.530341
## final value 42.530341
## stopped after 100 iterations
## # weights: 48
## initial value 118.426006
## iter 10 value 53.489131
## iter 20 value 42.598793
## iter 30 value 41.546710
## iter 40 value 41.484764
## iter 50 value 41.448774
## iter 60 value 41.431337
## iter 70 value 41.428536
## iter 80 value 41.428265
## iter 90 value 41.428172
## final value 41.428100
## converged
## # weights: 48
## initial value 140.619026
## iter 10 value 54.549671
## iter 20 value 41.769832
## iter 30 value 40.755199
## iter 40 value 40.552072
## iter 50 value 40.489678
## iter 60 value 40.480583
## iter 70 value 40.476961
## iter 80 value 40.468722
## iter 90 value 40.467663
## iter 100 value 40.467288
## final value 40.467288
## stopped after 100 iterations
## # weights: 48
## initial value 121.756392
## iter 10 value 50.974682
## iter 20 value 43.118415
## iter 30 value 42.271885
## iter 40 value 41.747940
## iter 50 value 41.627907
## iter 60 value 41.602887
## iter 70 value 41.600278
## iter 80 value 41.600119
## iter 90 value 41.599969
## final value 41.599963
## converged
## # weights: 48
## initial value 150.737035
## iter 10 value 58.306711
## iter 20 value 44.118587
## iter 30 value 43.242632
## iter 40 value 42.973112
## iter 50 value 42.873126
## iter 60 value 42.840869
## iter 70 value 42.827329
## iter 80 value 42.824930
## iter 90 value 42.824545
## iter 100 value 42.823978
## final value 42.823978
## stopped after 100 iterations
## # weights: 48
## initial value 127.195631
## iter 10 value 54.315972
## iter 20 value 43.228599
## iter 30 value 42.167965
## iter 40 value 41.835309
## iter 50 value 41.471349
## iter 60 value 41.365292
## iter 70 value 41.275614
## iter 80 value 41.237201
## iter 90 value 41.224402
## iter 100 value 41.211823
## final value 41.211823
## stopped after 100 iterations
## # weights: 48
## initial value 131.675405
## iter 10 value 55.191716
## iter 20 value 43.089530
## iter 30 value 41.951252
## iter 40 value 41.591440
## iter 50 value 41.212672
## iter 60 value 41.162462
## iter 70 value 41.119814
## iter 80 value 41.108372
## iter 90 value 41.103191
## iter 100 value 41.102231
## final value 41.102231
## stopped after 100 iterations
## # weights: 48
## initial value 118.068428
## iter 10 value 62.126636
## iter 20 value 45.564558
## iter 30 value 43.411374
## iter 40 value 42.320577
## iter 50 value 41.547492
## iter 60 value 40.995260
## iter 70 value 40.802807
## iter 80 value 40.787990
## iter 90 value 40.775754
## iter 100 value 40.768383
## final value 40.768383
## stopped after 100 iterations
## # weights: 48
## initial value 125.234073
## iter 10 value 66.814092
## iter 20 value 43.941966
## iter 30 value 41.779844
## iter 40 value 41.296251
## iter 50 value 40.887694
## iter 60 value 40.758448
## iter 70 value 40.709412
## iter 80 value 40.692174
## iter 90 value 40.687087
## iter 100 value 40.684106
## final value 40.684106
## stopped after 100 iterations
## # weights: 48
## initial value 132.842861
## iter 10 value 60.063688
## iter 20 value 42.252755
## iter 30 value 40.555401
## iter 40 value 39.569128
## iter 50 value 39.167445
## iter 60 value 39.139739
## iter 70 value 39.124083
## iter 80 value 39.118300
## iter 90 value 39.117651
## final value 39.117590
## converged
## # weights: 48
## initial value 124.962910
## iter 10 value 80.414197
## iter 20 value 42.810106
## iter 30 value 39.765054
## iter 40 value 38.844819
## iter 50 value 38.671095
## iter 60 value 38.643456
## iter 70 value 38.621965
## iter 80 value 38.616899
## iter 90 value 38.614237
## iter 100 value 38.608786
## final value 38.608786
## stopped after 100 iterations
## # weights: 48
## initial value 133.594904
## iter 10 value 72.767669
## iter 20 value 40.599769
## iter 30 value 39.854063
## iter 40 value 39.253208
## iter 50 value 39.111558
## iter 60 value 39.061577
## iter 70 value 39.043061
## iter 80 value 39.041115
## iter 90 value 39.037064
## iter 100 value 39.036905
## final value 39.036905
## stopped after 100 iterations
## # weights: 48
## initial value 123.738292
## iter 10 value 51.833912
## iter 20 value 42.493951
## iter 30 value 41.512123
## iter 40 value 40.936146
## iter 50 value 40.873314
## iter 60 value 40.819922
## iter 70 value 40.802774
## iter 80 value 40.799387
## iter 90 value 40.798406
## iter 100 value 40.798097
## final value 40.798097
## stopped after 100 iterations
## # weights: 48
## initial value 133.332723
## iter 10 value 57.745773
## iter 20 value 40.870390
## iter 30 value 40.484816
## iter 40 value 40.375914
## iter 50 value 40.344370
## iter 60 value 40.300499
## iter 70 value 40.272670
## iter 80 value 40.262471
## iter 90 value 40.258617
## iter 100 value 40.258585
## final value 40.258585
## stopped after 100 iterations
## # weights: 48
## initial value 116.393464
## iter 10 value 52.103941
## iter 20 value 40.051264
## iter 30 value 39.737550
## iter 40 value 39.594506
## iter 50 value 39.375500
## iter 60 value 39.352692
## iter 70 value 39.352005
## iter 80 value 39.351325
## iter 90 value 39.351167
## final value 39.351111
## converged
## # weights: 48
## initial value 157.357177
## iter 10 value 66.374683
## iter 20 value 42.576796
## iter 30 value 41.506307
## iter 40 value 40.935422
## iter 50 value 40.737908
## iter 60 value 40.618766
## iter 70 value 40.536466
## iter 80 value 40.502302
## iter 90 value 40.468479
## iter 100 value 40.458785
## final value 40.458785
## stopped after 100 iterations
## # weights: 48
## initial value 167.420969
## iter 10 value 88.399646
## iter 20 value 49.820522
## iter 30 value 43.499634
## iter 40 value 42.469859
## iter 50 value 41.819977
## iter 60 value 41.169243
## iter 70 value 40.965337
## iter 80 value 40.890877
## iter 90 value 40.876533
## iter 100 value 40.859992
## final value 40.859992
## stopped after 100 iterations
## # weights: 48
## initial value 131.860150
## iter 10 value 58.083392
## iter 20 value 44.984029
## iter 30 value 42.233397
## iter 40 value 41.575695
## iter 50 value 41.125668
## iter 60 value 41.024263
## iter 70 value 40.930923
## iter 80 value 40.904528
## iter 90 value 40.882586
## iter 100 value 40.866030
## final value 40.866030
## stopped after 100 iterations
## # weights: 48
## initial value 125.349029
## iter 10 value 60.330024
## iter 20 value 45.706259
## iter 30 value 42.788054
## iter 40 value 41.576732
## iter 50 value 41.154280
## iter 60 value 40.797240
## iter 70 value 40.634053
## iter 80 value 40.614962
## iter 90 value 40.604050
## iter 100 value 40.594113
## final value 40.594113
## stopped after 100 iterations
## # weights: 48
## initial value 160.209865
## iter 10 value 65.971897
## iter 20 value 42.904888
## iter 30 value 41.404600
## iter 40 value 40.653342
## iter 50 value 40.381675
## iter 60 value 40.203945
## iter 70 value 40.157527
## iter 80 value 40.083886
## iter 90 value 40.067190
## iter 100 value 40.028287
## final value 40.028287
## stopped after 100 iterations
## # weights: 48
## initial value 145.603178
## iter 10 value 63.346705
## iter 20 value 43.055894
## iter 30 value 41.158054
## iter 40 value 40.960230
## iter 50 value 40.788206
## iter 60 value 40.659828
## iter 70 value 40.562068
## iter 80 value 40.557351
## iter 90 value 40.556674
## iter 100 value 40.556623
## final value 40.556623
## stopped after 100 iterations
## # weights: 48
## initial value 124.687412
## iter 10 value 50.536710
## iter 20 value 41.753262
## iter 30 value 40.925320
## iter 40 value 39.925542
## iter 50 value 39.549708
## iter 60 value 39.475061
## iter 70 value 39.471074
## iter 80 value 39.470343
## iter 90 value 39.470035
## iter 100 value 39.469985
## final value 39.469985
## stopped after 100 iterations
## # weights: 48
## initial value 120.147460
## iter 10 value 61.975605
## iter 20 value 42.025101
## iter 30 value 39.935132
## iter 40 value 39.345338
## iter 50 value 39.266828
## iter 60 value 39.177873
## iter 70 value 39.145509
## iter 80 value 39.131812
## iter 90 value 39.120054
## iter 100 value 39.115509
## final value 39.115509
## stopped after 100 iterations
## # weights: 48
## initial value 122.049715
## iter 10 value 55.255869
## iter 20 value 44.840909
## iter 30 value 42.467365
## iter 40 value 41.818979
## iter 50 value 41.578130
## iter 60 value 41.521888
## iter 70 value 41.494865
## iter 80 value 41.475293
## iter 90 value 41.465428
## iter 100 value 41.462259
## final value 41.462259
## stopped after 100 iterations
## # weights: 48
## initial value 134.045031
## iter 10 value 64.096740
## iter 20 value 47.191865
## iter 30 value 42.404784
## iter 40 value 41.755095
## iter 50 value 41.707935
## iter 60 value 41.680982
## iter 70 value 41.660855
## iter 80 value 41.655568
## iter 90 value 41.654554
## iter 100 value 41.654262
## final value 41.654262
## stopped after 100 iterations
## # weights: 48
## initial value 127.684235
## iter 10 value 64.123260
## iter 20 value 42.526677
## iter 30 value 41.788965
## iter 40 value 41.461044
## iter 50 value 40.635696
## iter 60 value 40.525072
## iter 70 value 40.474436
## iter 80 value 40.453729
## iter 90 value 40.448081
## iter 100 value 40.447650
## final value 40.447650
## stopped after 100 iterations
## # weights: 48
## initial value 137.789419
## iter 10 value 56.980481
## iter 20 value 38.414805
## iter 30 value 37.262857
## iter 40 value 36.686998
## iter 50 value 36.240719
## iter 60 value 36.060559
## iter 70 value 35.977907
## iter 80 value 35.924497
## iter 90 value 35.867562
## iter 100 value 35.847497
## final value 35.847497
## stopped after 100 iterations
## # weights: 48
## initial value 118.765214
## iter 10 value 65.107872
## iter 20 value 41.487264
## iter 30 value 39.676930
## iter 40 value 39.450036
## iter 50 value 39.299724
## iter 60 value 39.228564
## iter 70 value 39.130907
## iter 80 value 39.113319
## iter 90 value 39.111630
## iter 100 value 39.110810
## final value 39.110810
## stopped after 100 iterations
## # weights: 48
## initial value 142.935301
## iter 10 value 59.676359
## iter 20 value 43.606516
## iter 30 value 42.219779
## iter 40 value 41.334416
## iter 50 value 41.267333
## iter 60 value 41.229871
## iter 70 value 41.208718
## iter 80 value 41.195855
## iter 90 value 41.191975
## final value 41.189987
## converged
## # weights: 48
## initial value 163.441257
## iter 10 value 63.720405
## iter 20 value 45.639556
## iter 30 value 41.887110
## iter 40 value 41.317399
## iter 50 value 41.149952
## iter 60 value 40.910655
## iter 70 value 40.859687
## iter 80 value 40.846548
## iter 90 value 40.827927
## iter 100 value 40.818456
## final value 40.818456
## stopped after 100 iterations
## # weights: 48
## initial value 117.170242
## iter 10 value 50.013044
## iter 20 value 40.522651
## iter 30 value 39.936963
## iter 40 value 39.843441
## iter 50 value 39.830268
## iter 60 value 39.829804
## iter 70 value 39.829716
## iter 80 value 39.829488
## iter 90 value 39.822349
## iter 100 value 39.805102
## final value 39.805102
## stopped after 100 iterations
## # weights: 48
## initial value 128.844384
## iter 10 value 56.317664
## iter 20 value 43.876272
## iter 30 value 42.450546
## iter 40 value 42.075168
## iter 50 value 41.693709
## iter 60 value 41.538722
## iter 70 value 41.507743
## iter 80 value 41.496668
## iter 90 value 41.495392
## iter 100 value 41.495118
## final value 41.495118
## stopped after 100 iterations
## # weights: 48
## initial value 142.881074
## iter 10 value 55.448329
## iter 20 value 42.598422
## iter 30 value 40.785320
## iter 40 value 40.529895
## iter 50 value 40.343539
## iter 60 value 40.275197
## iter 70 value 40.240160
## iter 80 value 40.214691
## iter 90 value 40.199189
## iter 100 value 40.194592
## final value 40.194592
## stopped after 100 iterations
## # weights: 48
## initial value 129.886518
## iter 10 value 64.013724
## iter 20 value 42.648972
## iter 30 value 41.016070
## iter 40 value 40.712840
## iter 50 value 40.682964
## iter 60 value 40.665076
## iter 70 value 40.646873
## iter 80 value 40.640485
## iter 90 value 40.639078
## iter 100 value 40.638757
## final value 40.638757
## stopped after 100 iterations
## # weights: 48
## initial value 157.975226
## iter 10 value 61.704597
## iter 20 value 43.500492
## iter 30 value 42.276111
## iter 40 value 41.668013
## iter 50 value 41.572587
## iter 60 value 41.550190
## iter 70 value 41.526538
## iter 80 value 41.495170
## iter 90 value 41.444380
## iter 100 value 41.442907
## final value 41.442907
## stopped after 100 iterations
## # weights: 48
## initial value 131.898429
## iter 10 value 67.525896
## iter 20 value 43.575823
## iter 30 value 40.492116
## iter 40 value 39.568327
## iter 50 value 39.330280
## iter 60 value 39.238824
## iter 70 value 39.204215
## iter 80 value 39.190051
## iter 90 value 39.162974
## iter 100 value 39.043059
## final value 39.043059
## stopped after 100 iterations
## # weights: 48
## initial value 118.561711
## iter 10 value 64.612472
## iter 20 value 43.207350
## iter 30 value 41.872051
## iter 40 value 41.589600
## iter 50 value 41.509722
## iter 60 value 41.467462
## iter 70 value 41.410087
## iter 80 value 41.402733
## iter 90 value 41.401444
## iter 100 value 41.401381
## final value 41.401381
## stopped after 100 iterations
## # weights: 48
## initial value 128.601123
## iter 10 value 69.241589
## iter 20 value 46.341201
## iter 30 value 41.883313
## iter 40 value 40.947572
## iter 50 value 40.632373
## iter 60 value 40.561231
## iter 70 value 40.534851
## iter 80 value 40.509808
## iter 90 value 40.432754
## iter 100 value 40.428823
## final value 40.428823
## stopped after 100 iterations
## # weights: 48
## initial value 122.371261
## iter 10 value 51.528692
## iter 20 value 43.668754
## iter 30 value 42.438174
## iter 40 value 42.188851
## iter 50 value 42.161847
## iter 60 value 42.153611
## iter 70 value 42.150139
## iter 80 value 42.149320
## iter 90 value 42.148643
## final value 42.148532
## converged
## # weights: 48
## initial value 141.698132
## iter 10 value 54.188737
## iter 20 value 39.891461
## iter 30 value 38.851289
## iter 40 value 38.640919
## iter 50 value 38.473853
## iter 60 value 38.385412
## iter 70 value 38.347373
## iter 80 value 38.344160
## iter 90 value 38.343756
## final value 38.343520
## converged
## # weights: 48
## initial value 150.922919
## iter 10 value 58.081817
## iter 20 value 43.192986
## iter 30 value 40.207185
## iter 40 value 39.667027
## iter 50 value 39.566516
## iter 60 value 39.532586
## iter 70 value 39.519545
## iter 80 value 39.517043
## iter 90 value 39.516621
## iter 100 value 39.516557
## final value 39.516557
## stopped after 100 iterations
## # weights: 48
## initial value 123.702845
## iter 10 value 80.307415
## iter 20 value 50.911560
## iter 30 value 45.682562
## iter 40 value 44.863504
## iter 50 value 44.245931
## iter 60 value 43.461743
## iter 70 value 42.308352
## iter 80 value 42.062797
## iter 90 value 42.001159
## iter 100 value 41.907454
## final value 41.907454
## stopped after 100 iterations
## # weights: 48
## initial value 121.834853
## iter 10 value 57.210816
## iter 20 value 42.907859
## iter 30 value 41.700664
## iter 40 value 41.225713
## iter 50 value 41.019967
## iter 60 value 40.961517
## iter 70 value 40.958742
## iter 80 value 40.958124
## iter 90 value 40.958035
## final value 40.958021
## converged
## # weights: 48
## initial value 125.463930
## iter 10 value 68.807722
## iter 20 value 44.544785
## iter 30 value 42.216742
## iter 40 value 41.400914
## iter 50 value 41.166167
## iter 60 value 41.150946
## iter 70 value 41.095825
## iter 80 value 41.028908
## iter 90 value 41.014816
## iter 100 value 41.003595
## final value 41.003595
## stopped after 100 iterations
## # weights: 48
## initial value 126.223547
## iter 10 value 57.086078
## iter 20 value 45.632872
## iter 30 value 42.726389
## iter 40 value 41.254895
## iter 50 value 41.050130
## iter 60 value 41.024565
## iter 70 value 41.019654
## iter 80 value 41.012356
## iter 90 value 41.008166
## iter 100 value 41.002097
## final value 41.002097
## stopped after 100 iterations
## # weights: 48
## initial value 146.606186
## iter 10 value 60.463868
## iter 20 value 44.407307
## iter 30 value 41.603144
## iter 40 value 41.132908
## iter 50 value 40.982521
## iter 60 value 40.779275
## iter 70 value 40.754668
## iter 80 value 40.735423
## iter 90 value 40.702333
## iter 100 value 40.689820
## final value 40.689820
## stopped after 100 iterations
## # weights: 48
## initial value 127.061583
## iter 10 value 50.902372
## iter 20 value 42.206435
## iter 30 value 40.971682
## iter 40 value 40.871133
## iter 50 value 40.856212
## iter 60 value 40.846972
## iter 70 value 40.834505
## iter 80 value 40.830276
## iter 90 value 40.825787
## iter 100 value 40.824344
## final value 40.824344
## stopped after 100 iterations
## # weights: 48
## initial value 117.470625
## iter 10 value 56.277246
## iter 20 value 42.168118
## iter 30 value 40.356393
## iter 40 value 39.887507
## iter 50 value 39.800145
## iter 60 value 39.786053
## iter 70 value 39.780939
## iter 80 value 39.777898
## iter 90 value 39.777319
## final value 39.777265
## converged
## # weights: 48
## initial value 143.982665
## iter 10 value 60.423932
## iter 20 value 43.204573
## iter 30 value 41.802693
## iter 40 value 40.650869
## iter 50 value 40.454736
## iter 60 value 40.304137
## iter 70 value 40.245965
## iter 80 value 40.210643
## iter 90 value 40.193824
## iter 100 value 40.189111
## final value 40.189111
## stopped after 100 iterations
## # weights: 48
## initial value 121.921815
## iter 10 value 57.316964
## iter 20 value 39.664289
## iter 30 value 37.676507
## iter 40 value 36.515200
## iter 50 value 35.880850
## iter 60 value 35.677042
## iter 70 value 35.612972
## iter 80 value 35.559536
## iter 90 value 35.552927
## iter 100 value 35.550598
## final value 35.550598
## stopped after 100 iterations
## # weights: 48
## initial value 143.996490
## iter 10 value 52.419927
## iter 20 value 42.317677
## iter 30 value 41.068853
## iter 40 value 40.860094
## iter 50 value 40.737954
## iter 60 value 40.670144
## iter 70 value 40.653612
## iter 80 value 40.652270
## iter 90 value 40.651954
## iter 100 value 40.651873
## final value 40.651873
## stopped after 100 iterations
## # weights: 48
## initial value 163.337418
## iter 10 value 51.251465
## iter 20 value 41.899266
## iter 30 value 41.219410
## iter 40 value 41.081345
## iter 50 value 40.939979
## iter 60 value 40.865439
## iter 70 value 40.835982
## iter 80 value 40.830575
## iter 90 value 40.824815
## iter 100 value 40.824433
## final value 40.824433
## stopped after 100 iterations
## # weights: 48
## initial value 136.647191
## iter 10 value 60.783016
## iter 20 value 42.119332
## iter 30 value 41.210674
## iter 40 value 41.115609
## iter 50 value 41.041207
## iter 60 value 40.922720
## iter 70 value 40.807691
## iter 80 value 40.759429
## iter 90 value 40.756699
## iter 100 value 40.752778
## final value 40.752778
## stopped after 100 iterations
## # weights: 48
## initial value 127.834386
## iter 10 value 57.024302
## iter 20 value 40.718764
## iter 30 value 38.734668
## iter 40 value 38.437064
## iter 50 value 38.345037
## iter 60 value 38.286447
## iter 70 value 38.268085
## iter 80 value 38.234947
## iter 90 value 38.213819
## iter 100 value 38.211772
## final value 38.211772
## stopped after 100 iterations
## # weights: 48
## initial value 123.601979
## iter 10 value 72.221745
## iter 20 value 45.083052
## iter 30 value 42.650624
## iter 40 value 41.896829
## iter 50 value 41.323522
## iter 60 value 41.232206
## iter 70 value 41.200106
## iter 80 value 41.180566
## iter 90 value 41.161553
## iter 100 value 41.138777
## final value 41.138777
## stopped after 100 iterations
## # weights: 48
## initial value 147.595147
## iter 10 value 60.823013
## iter 20 value 43.518283
## iter 30 value 41.638717
## iter 40 value 41.045136
## iter 50 value 40.834080
## iter 60 value 40.681722
## iter 70 value 40.553655
## iter 80 value 40.476075
## iter 90 value 40.445838
## iter 100 value 40.426156
## final value 40.426156
## stopped after 100 iterations
## # weights: 48
## initial value 142.537353
## iter 10 value 55.004459
## iter 20 value 39.592680
## iter 30 value 38.838265
## iter 40 value 38.459790
## iter 50 value 38.258440
## iter 60 value 38.031045
## iter 70 value 37.999201
## iter 80 value 37.991346
## iter 90 value 37.983883
## iter 100 value 37.962206
## final value 37.962206
## stopped after 100 iterations
## # weights: 48
## initial value 120.385340
## iter 10 value 53.942107
## iter 20 value 41.789749
## iter 30 value 41.207655
## iter 40 value 41.114885
## iter 50 value 41.049226
## iter 60 value 41.025646
## iter 70 value 41.022236
## iter 80 value 41.022003
## iter 90 value 41.021832
## final value 41.021800
## converged
## # weights: 48
## initial value 132.347224
## iter 10 value 51.114178
## iter 20 value 41.384586
## iter 30 value 40.692436
## iter 40 value 40.409777
## iter 50 value 40.187491
## iter 60 value 40.051379
## iter 70 value 40.034957
## iter 80 value 40.020416
## iter 90 value 40.018460
## iter 100 value 40.016560
## final value 40.016560
## stopped after 100 iterations
## # weights: 48
## initial value 150.731027
## iter 10 value 70.959070
## iter 20 value 40.630195
## iter 30 value 39.939189
## iter 40 value 39.837479
## iter 50 value 39.740148
## iter 60 value 39.718822
## iter 70 value 39.711135
## iter 80 value 39.709322
## iter 90 value 39.708208
## iter 100 value 39.707924
## final value 39.707924
## stopped after 100 iterations
## # weights: 48
## initial value 133.550331
## iter 10 value 56.912754
## iter 20 value 39.735505
## iter 30 value 39.188111
## iter 40 value 38.949403
## iter 50 value 38.884299
## iter 60 value 38.774083
## iter 70 value 38.726439
## iter 80 value 38.718197
## iter 90 value 38.717347
## iter 100 value 38.717169
## final value 38.717169
## stopped after 100 iterations
## # weights: 48
## initial value 117.848915
## iter 10 value 55.583317
## iter 20 value 41.184803
## iter 30 value 39.543199
## iter 40 value 39.057127
## iter 50 value 38.909851
## iter 60 value 38.844460
## iter 70 value 38.808414
## iter 80 value 38.794534
## iter 90 value 38.790616
## iter 100 value 38.786523
## final value 38.786523
## stopped after 100 iterations
## # weights: 48
## initial value 123.887298
## iter 10 value 59.455565
## iter 20 value 44.023860
## iter 30 value 42.219714
## iter 40 value 41.364766
## iter 50 value 40.915041
## iter 60 value 40.537608
## iter 70 value 40.473857
## iter 80 value 40.449782
## iter 90 value 40.441790
## iter 100 value 40.439681
## final value 40.439681
## stopped after 100 iterations
## # weights: 48
## initial value 118.224722
## iter 10 value 55.894313
## iter 20 value 42.000600
## iter 30 value 41.221000
## iter 40 value 40.677775
## iter 50 value 40.505118
## iter 60 value 40.478478
## iter 70 value 40.474912
## iter 80 value 40.472646
## iter 90 value 40.472186
## iter 100 value 40.472038
## final value 40.472038
## stopped after 100 iterations
## # weights: 48
## initial value 141.011583
## iter 10 value 60.743455
## iter 20 value 44.272054
## iter 30 value 41.706032
## iter 40 value 41.012459
## iter 50 value 40.670875
## iter 60 value 40.527617
## iter 70 value 40.444663
## iter 80 value 40.430455
## iter 90 value 40.423024
## iter 100 value 40.417451
## final value 40.417451
## stopped after 100 iterations
## # weights: 48
## initial value 135.956333
## iter 10 value 76.124272
## iter 20 value 42.959203
## iter 30 value 41.552741
## iter 40 value 41.361137
## iter 50 value 41.328100
## iter 60 value 41.309219
## iter 70 value 41.302175
## iter 80 value 41.294986
## iter 90 value 41.294614
## iter 100 value 41.294365
## final value 41.294365
## stopped after 100 iterations
## # weights: 48
## initial value 127.214893
## iter 10 value 52.908628
## iter 20 value 42.967859
## iter 30 value 41.231873
## iter 40 value 40.791797
## iter 50 value 40.756059
## iter 60 value 40.747909
## iter 70 value 40.741099
## iter 80 value 40.736610
## iter 90 value 40.735052
## final value 40.734929
## converged
## # weights: 48
## initial value 130.800931
## iter 10 value 59.070540
## iter 20 value 43.336400
## iter 30 value 42.022753
## iter 40 value 41.528747
## iter 50 value 41.189968
## iter 60 value 40.973611
## iter 70 value 40.858988
## iter 80 value 40.806880
## iter 90 value 40.770635
## iter 100 value 40.760737
## final value 40.760737
## stopped after 100 iterations
## # weights: 48
## initial value 119.115762
## iter 10 value 56.209505
## iter 20 value 41.238029
## iter 30 value 40.336536
## iter 40 value 39.901453
## iter 50 value 39.714537
## iter 60 value 39.667178
## iter 70 value 39.630738
## iter 80 value 39.620657
## iter 90 value 39.607863
## iter 100 value 39.590802
## final value 39.590802
## stopped after 100 iterations
## # weights: 48
## initial value 130.897287
## iter 10 value 53.027356
## iter 20 value 42.559199
## iter 30 value 40.622241
## iter 40 value 40.400188
## iter 50 value 40.304260
## iter 60 value 40.183203
## iter 70 value 40.167323
## iter 80 value 40.155112
## iter 90 value 40.154483
## iter 100 value 40.153992
## final value 40.153992
## stopped after 100 iterations
## # weights: 48
## initial value 123.122285
## iter 10 value 67.655675
## iter 20 value 44.345506
## iter 30 value 42.713675
## iter 40 value 42.017506
## iter 50 value 41.814947
## iter 60 value 41.584806
## iter 70 value 41.437306
## iter 80 value 41.348463
## iter 90 value 41.309855
## iter 100 value 41.308768
## final value 41.308768
## stopped after 100 iterations
## # weights: 48
## initial value 118.416998
## iter 10 value 63.970825
## iter 20 value 42.670465
## iter 30 value 40.826963
## iter 40 value 40.605001
## iter 50 value 40.277398
## iter 60 value 39.770965
## iter 70 value 39.584432
## iter 80 value 39.493733
## iter 90 value 39.482257
## iter 100 value 39.462947
## final value 39.462947
## stopped after 100 iterations
## # weights: 48
## initial value 127.528706
## iter 10 value 64.118275
## iter 20 value 44.475935
## iter 30 value 42.144677
## iter 40 value 41.853231
## iter 50 value 41.606289
## iter 60 value 41.485717
## iter 70 value 41.418521
## iter 80 value 41.306789
## iter 90 value 41.262549
## iter 100 value 41.224655
## final value 41.224655
## stopped after 100 iterations
## # weights: 48
## initial value 121.746165
## iter 10 value 63.257160
## iter 20 value 43.051466
## iter 30 value 41.707390
## iter 40 value 41.299814
## iter 50 value 41.145229
## iter 60 value 40.977565
## iter 70 value 40.947271
## iter 80 value 40.927952
## iter 90 value 40.918022
## iter 100 value 40.913393
## final value 40.913393
## stopped after 100 iterations
## # weights: 48
## initial value 122.750816
## iter 10 value 71.004092
## iter 20 value 43.708933
## iter 30 value 41.914597
## iter 40 value 41.693289
## iter 50 value 41.641296
## iter 60 value 41.611029
## iter 70 value 41.606648
## iter 80 value 41.605759
## iter 90 value 41.605538
## iter 100 value 41.605441
## final value 41.605441
## stopped after 100 iterations
## # weights: 48
## initial value 117.559593
## iter 10 value 52.455459
## iter 20 value 40.866633
## iter 30 value 40.498184
## iter 40 value 40.302594
## iter 50 value 40.113216
## iter 60 value 40.067041
## iter 70 value 40.063129
## iter 80 value 40.060526
## iter 90 value 40.059828
## iter 100 value 40.059552
## final value 40.059552
## stopped after 100 iterations
## # weights: 48
## initial value 146.339766
## iter 10 value 53.373168
## iter 20 value 38.558408
## iter 30 value 37.462619
## iter 40 value 37.155821
## iter 50 value 37.055768
## iter 60 value 36.986262
## iter 70 value 36.953976
## iter 80 value 36.945122
## iter 90 value 36.941194
## iter 100 value 36.936951
## final value 36.936951
## stopped after 100 iterations
## # weights: 48
## initial value 130.705051
## iter 10 value 69.345579
## iter 20 value 44.606554
## iter 30 value 41.992484
## iter 40 value 41.751945
## iter 50 value 41.604595
## iter 60 value 41.524984
## iter 70 value 41.502556
## iter 80 value 41.469396
## iter 90 value 41.441855
## iter 100 value 41.429631
## final value 41.429631
## stopped after 100 iterations
## # weights: 48
## initial value 129.624311
## iter 10 value 56.088685
## iter 20 value 41.676062
## iter 30 value 41.192531
## iter 40 value 41.025285
## iter 50 value 40.997447
## iter 60 value 40.975515
## iter 70 value 40.962604
## iter 80 value 40.949203
## iter 90 value 40.926481
## iter 100 value 40.920937
## final value 40.920937
## stopped after 100 iterations
## # weights: 48
## initial value 118.491349
## iter 10 value 73.442991
## iter 20 value 44.305618
## iter 30 value 40.895519
## iter 40 value 40.573222
## iter 50 value 40.477612
## iter 60 value 40.389450
## iter 70 value 40.331208
## iter 80 value 40.215184
## iter 90 value 40.167455
## iter 100 value 40.160906
## final value 40.160906
## stopped after 100 iterations
## # weights: 48
## initial value 144.075691
## iter 10 value 55.644713
## iter 20 value 43.355376
## iter 30 value 42.257441
## iter 40 value 41.844741
## iter 50 value 41.371830
## iter 60 value 41.060729
## iter 70 value 40.996510
## iter 80 value 40.971362
## iter 90 value 40.965840
## iter 100 value 40.965036
## final value 40.965036
## stopped after 100 iterations
## # weights: 48
## initial value 131.035273
## iter 10 value 61.013546
## iter 20 value 43.184780
## iter 30 value 40.521747
## iter 40 value 39.678596
## iter 50 value 39.252086
## iter 60 value 39.123153
## iter 70 value 38.991217
## iter 80 value 38.965582
## iter 90 value 38.953254
## iter 100 value 38.949211
## final value 38.949211
## stopped after 100 iterations
## # weights: 48
## initial value 137.896771
## iter 10 value 52.770809
## iter 20 value 40.361107
## iter 30 value 39.850904
## iter 40 value 39.616298
## iter 50 value 39.588278
## iter 60 value 39.576509
## iter 70 value 39.569575
## iter 80 value 39.567279
## iter 90 value 39.567116
## final value 39.567082
## converged
## # weights: 48
## initial value 134.341907
## iter 10 value 59.899140
## iter 20 value 43.645890
## iter 30 value 41.578935
## iter 40 value 41.325816
## iter 50 value 41.304216
## iter 60 value 41.299096
## iter 70 value 41.295931
## iter 80 value 41.289423
## iter 90 value 41.282270
## iter 100 value 41.278787
## final value 41.278787
## stopped after 100 iterations
## # weights: 48
## initial value 120.617297
## iter 10 value 60.739317
## iter 20 value 42.617561
## iter 30 value 41.328669
## iter 40 value 41.118161
## iter 50 value 41.076685
## iter 60 value 41.067207
## iter 70 value 41.066156
## iter 80 value 41.065786
## iter 90 value 41.065769
## final value 41.065768
## converged
## # weights: 48
## initial value 117.908536
## iter 10 value 91.355010
## iter 20 value 44.629226
## iter 30 value 39.653235
## iter 40 value 38.657849
## iter 50 value 38.220043
## iter 60 value 38.028714
## iter 70 value 38.002329
## iter 80 value 37.987862
## iter 90 value 37.973170
## iter 100 value 37.963733
## final value 37.963733
## stopped after 100 iterations
## # weights: 48
## initial value 120.579740
## iter 10 value 63.730536
## iter 20 value 41.420021
## iter 30 value 39.295561
## iter 40 value 38.884809
## iter 50 value 38.635810
## iter 60 value 38.571727
## iter 70 value 38.537371
## iter 80 value 38.526442
## iter 90 value 38.522715
## iter 100 value 38.520349
## final value 38.520349
## stopped after 100 iterations
## # weights: 48
## initial value 176.575288
## iter 10 value 71.050208
## iter 20 value 48.445657
## iter 30 value 42.594194
## iter 40 value 40.626706
## iter 50 value 40.528974
## iter 60 value 40.494885
## iter 70 value 40.483579
## iter 80 value 40.444599
## iter 90 value 40.426141
## iter 100 value 40.421336
## final value 40.421336
## stopped after 100 iterations
## # weights: 48
## initial value 129.466423
## iter 10 value 60.939954
## iter 20 value 42.629215
## iter 30 value 40.924746
## iter 40 value 40.292077
## iter 50 value 40.160049
## iter 60 value 40.050748
## iter 70 value 39.961300
## iter 80 value 39.955378
## iter 90 value 39.952257
## iter 100 value 39.950562
## final value 39.950562
## stopped after 100 iterations
## # weights: 48
## initial value 120.035552
## iter 10 value 78.230575
## iter 20 value 46.853134
## iter 30 value 43.632448
## iter 40 value 42.561550
## iter 50 value 41.719349
## iter 60 value 41.466241
## iter 70 value 41.298185
## iter 80 value 41.235033
## iter 90 value 41.200773
## iter 100 value 41.193374
## final value 41.193374
## stopped after 100 iterations
## # weights: 48
## initial value 120.277655
## iter 10 value 55.490600
## iter 20 value 42.056794
## iter 30 value 41.116172
## iter 40 value 41.042276
## iter 50 value 40.981160
## iter 60 value 40.972625
## iter 70 value 40.971607
## iter 80 value 40.971515
## final value 40.971514
## converged
## # weights: 48
## initial value 121.487252
## iter 10 value 63.661197
## iter 20 value 46.877524
## iter 30 value 43.235257
## iter 40 value 41.665371
## iter 50 value 40.823680
## iter 60 value 40.406409
## iter 70 value 40.329101
## iter 80 value 40.168279
## iter 90 value 40.124783
## iter 100 value 40.109128
## final value 40.109128
## stopped after 100 iterations
## # weights: 48
## initial value 136.522694
## iter 10 value 54.616873
## iter 20 value 40.582081
## iter 30 value 39.319287
## iter 40 value 39.147393
## iter 50 value 39.101662
## iter 60 value 39.003542
## iter 70 value 38.932923
## iter 80 value 38.918943
## iter 90 value 38.916704
## iter 100 value 38.912812
## final value 38.912812
## stopped after 100 iterations
## # weights: 48
## initial value 162.658178
## iter 10 value 62.229686
## iter 20 value 47.807623
## iter 30 value 43.428783
## iter 40 value 41.150281
## iter 50 value 40.573627
## iter 60 value 40.432166
## iter 70 value 40.368214
## iter 80 value 40.347240
## iter 90 value 40.334249
## iter 100 value 40.315339
## final value 40.315339
## stopped after 100 iterations
## # weights: 48
## initial value 131.972517
## iter 10 value 74.146543
## iter 20 value 41.806862
## iter 30 value 41.283674
## iter 40 value 41.215412
## iter 50 value 41.202151
## iter 60 value 41.198231
## iter 70 value 41.184424
## iter 80 value 41.165953
## iter 90 value 41.153373
## iter 100 value 41.153008
## final value 41.153008
## stopped after 100 iterations
## # weights: 30
## initial value 141.201534
## iter 10 value 70.047942
## iter 20 value 29.489155
## iter 30 value 27.848957
## iter 40 value 27.618085
## iter 50 value 27.411667
## iter 60 value 27.234154
## iter 70 value 27.209475
## iter 80 value 27.200663
## iter 90 value 27.198864
## iter 100 value 27.198837
## final value 27.198837
## stopped after 100 iterations
## # weights: 30
## initial value 129.142792
## iter 10 value 52.470540
## iter 20 value 34.571732
## iter 30 value 29.318636
## iter 40 value 28.416343
## iter 50 value 28.243984
## iter 60 value 28.134437
## iter 70 value 28.004937
## iter 80 value 27.930838
## iter 90 value 27.826067
## iter 100 value 27.802981
## final value 27.802981
## stopped after 100 iterations
## # weights: 30
## initial value 130.618883
## iter 10 value 46.161005
## iter 20 value 28.411599
## iter 30 value 27.807845
## iter 40 value 27.669707
## iter 50 value 27.610591
## iter 60 value 27.595817
## iter 70 value 27.576839
## iter 80 value 27.558577
## iter 90 value 27.554325
## iter 100 value 27.548345
## final value 27.548345
## stopped after 100 iterations
## # weights: 30
## initial value 121.546756
## iter 10 value 66.596119
## iter 20 value 29.710703
## iter 30 value 28.297321
## iter 40 value 28.152700
## iter 50 value 28.026674
## iter 60 value 27.829813
## iter 70 value 27.587600
## iter 80 value 27.509258
## iter 90 value 27.501914
## iter 100 value 27.501755
## final value 27.501755
## stopped after 100 iterations
## # weights: 30
## initial value 120.132597
## iter 10 value 54.608702
## iter 20 value 29.371826
## iter 30 value 26.361154
## iter 40 value 26.099790
## iter 50 value 25.901575
## iter 60 value 25.764088
## iter 70 value 25.739336
## iter 80 value 25.371227
## iter 90 value 25.148592
## iter 100 value 25.062364
## final value 25.062364
## stopped after 100 iterations
## # weights: 30
## initial value 119.082670
## iter 10 value 63.439873
## iter 20 value 32.639391
## iter 30 value 25.740533
## iter 40 value 24.704992
## iter 50 value 24.368049
## iter 60 value 24.282169
## iter 70 value 24.191204
## iter 80 value 24.130710
## iter 90 value 24.114034
## iter 100 value 24.113752
## final value 24.113752
## stopped after 100 iterations
## # weights: 30
## initial value 127.189782
## iter 10 value 68.310293
## iter 20 value 26.384123
## iter 30 value 25.863993
## iter 40 value 25.734825
## iter 50 value 25.574686
## iter 60 value 25.490845
## iter 70 value 25.373064
## iter 80 value 25.334448
## iter 90 value 25.327469
## iter 100 value 25.326967
## final value 25.326967
## stopped after 100 iterations
## # weights: 30
## initial value 131.116090
## iter 10 value 38.827336
## iter 20 value 30.217252
## iter 30 value 28.373478
## iter 40 value 27.473924
## iter 50 value 27.357555
## iter 60 value 27.270470
## iter 70 value 27.215554
## iter 80 value 27.172178
## iter 90 value 27.164199
## iter 100 value 27.164120
## final value 27.164120
## stopped after 100 iterations
## # weights: 30
## initial value 145.536437
## iter 10 value 61.606982
## iter 20 value 30.533035
## iter 30 value 28.746515
## iter 40 value 28.495567
## iter 50 value 27.965970
## iter 60 value 27.413561
## iter 70 value 27.051280
## iter 80 value 27.028344
## iter 90 value 27.026825
## iter 100 value 27.026772
## final value 27.026772
## stopped after 100 iterations
## # weights: 30
## initial value 116.572119
## iter 10 value 45.321570
## iter 20 value 27.245570
## iter 30 value 26.516506
## iter 40 value 26.193419
## iter 50 value 26.128265
## iter 60 value 26.091102
## iter 70 value 26.080227
## iter 80 value 26.079416
## final value 26.079403
## converged
## # weights: 30
## initial value 120.302126
## iter 10 value 67.479292
## iter 20 value 30.162002
## iter 30 value 27.434894
## iter 40 value 27.339302
## iter 50 value 27.303668
## iter 60 value 27.223159
## iter 70 value 27.219201
## iter 80 value 27.218787
## final value 27.218768
## converged
## # weights: 30
## initial value 136.728358
## iter 10 value 56.220502
## iter 20 value 32.419584
## iter 30 value 29.901954
## iter 40 value 28.569874
## iter 50 value 28.084460
## iter 60 value 27.903609
## iter 70 value 27.718230
## iter 80 value 27.667889
## iter 90 value 27.665276
## final value 27.665214
## converged
## # weights: 30
## initial value 124.240197
## iter 10 value 81.747942
## iter 20 value 29.770851
## iter 30 value 28.135156
## iter 40 value 27.857973
## iter 50 value 27.585202
## iter 60 value 27.567997
## iter 70 value 27.566343
## iter 80 value 27.565838
## iter 90 value 27.565820
## final value 27.565819
## converged
## # weights: 30
## initial value 117.612806
## iter 10 value 52.137620
## iter 20 value 32.388367
## iter 30 value 29.737452
## iter 40 value 29.003000
## iter 50 value 28.718818
## iter 60 value 28.522405
## iter 70 value 28.079180
## iter 80 value 27.498643
## iter 90 value 27.150150
## iter 100 value 27.081395
## final value 27.081395
## stopped after 100 iterations
## # weights: 30
## initial value 136.580517
## iter 10 value 53.761612
## iter 20 value 31.084329
## iter 30 value 27.740502
## iter 40 value 27.276356
## iter 50 value 27.099873
## iter 60 value 26.873774
## iter 70 value 26.836105
## iter 80 value 26.500794
## iter 90 value 26.435151
## iter 100 value 26.432899
## final value 26.432899
## stopped after 100 iterations
## # weights: 30
## initial value 127.190811
## iter 10 value 52.711898
## iter 20 value 29.672479
## iter 30 value 28.261517
## iter 40 value 27.513188
## iter 50 value 26.998363
## iter 60 value 26.885859
## iter 70 value 26.876381
## iter 80 value 26.874538
## final value 26.874514
## converged
## # weights: 30
## initial value 119.320343
## iter 10 value 65.508405
## iter 20 value 27.932856
## iter 30 value 25.929617
## iter 40 value 25.666448
## iter 50 value 25.655262
## iter 60 value 25.646119
## iter 70 value 25.640138
## iter 80 value 25.632790
## iter 90 value 25.628985
## iter 100 value 25.628884
## final value 25.628884
## stopped after 100 iterations
## # weights: 30
## initial value 120.595786
## iter 10 value 60.951458
## iter 20 value 32.081181
## iter 30 value 27.974203
## iter 40 value 24.776181
## iter 50 value 24.573824
## iter 60 value 24.460630
## iter 70 value 24.353106
## iter 80 value 24.321634
## iter 90 value 24.320245
## final value 24.320225
## converged
## # weights: 30
## initial value 125.816132
## iter 10 value 68.325358
## iter 20 value 29.588250
## iter 30 value 28.260654
## iter 40 value 28.094881
## iter 50 value 27.817677
## iter 60 value 27.671595
## iter 70 value 27.667096
## iter 80 value 27.665327
## iter 90 value 27.665145
## final value 27.665143
## converged
## # weights: 30
## initial value 151.023857
## iter 10 value 118.211770
## iter 20 value 50.975666
## iter 30 value 29.847745
## iter 40 value 29.148608
## iter 50 value 28.592812
## iter 60 value 28.069051
## iter 70 value 28.025374
## iter 80 value 28.004070
## iter 90 value 27.993235
## iter 100 value 27.992902
## final value 27.992902
## stopped after 100 iterations
## # weights: 30
## initial value 118.770235
## iter 10 value 68.368620
## iter 20 value 32.747362
## iter 30 value 30.248182
## iter 40 value 29.190561
## iter 50 value 28.800671
## iter 60 value 28.702121
## iter 70 value 28.696609
## iter 80 value 28.678183
## iter 90 value 28.648176
## iter 100 value 28.313023
## final value 28.313023
## stopped after 100 iterations
## # weights: 30
## initial value 122.762519
## iter 10 value 40.928506
## iter 20 value 24.535663
## iter 30 value 22.304991
## iter 40 value 21.672899
## iter 50 value 21.515966
## iter 60 value 21.453184
## iter 70 value 21.361328
## iter 80 value 21.315978
## iter 90 value 21.302925
## iter 100 value 21.302752
## final value 21.302752
## stopped after 100 iterations
## # weights: 30
## initial value 117.341327
## iter 10 value 31.848299
## iter 20 value 26.363358
## iter 30 value 25.935356
## iter 40 value 25.864424
## iter 50 value 25.742500
## iter 60 value 25.629758
## iter 70 value 25.614361
## iter 80 value 25.611339
## iter 90 value 25.611199
## final value 25.611188
## converged
## # weights: 30
## initial value 118.442710
## iter 10 value 72.047626
## iter 20 value 43.754896
## iter 30 value 36.344235
## iter 40 value 32.439240
## iter 50 value 29.881022
## iter 60 value 28.594025
## iter 70 value 28.020052
## iter 80 value 27.948379
## iter 90 value 27.946633
## final value 27.946582
## converged
## # weights: 30
## initial value 124.668894
## iter 10 value 54.280387
## iter 20 value 31.269409
## iter 30 value 28.513532
## iter 40 value 27.877792
## iter 50 value 27.780470
## iter 60 value 27.678582
## iter 70 value 27.395207
## iter 80 value 27.207714
## iter 90 value 27.001867
## iter 100 value 26.814734
## final value 26.814734
## stopped after 100 iterations
## # weights: 30
## initial value 135.138533
## iter 10 value 56.293096
## iter 20 value 32.231286
## iter 30 value 28.842331
## iter 40 value 27.108004
## iter 50 value 26.709522
## iter 60 value 26.658144
## iter 70 value 26.529388
## iter 80 value 26.376274
## iter 90 value 26.348163
## iter 100 value 26.347983
## final value 26.347983
## stopped after 100 iterations
## # weights: 30
## initial value 141.410689
## iter 10 value 71.518966
## iter 20 value 32.879815
## iter 30 value 29.577746
## iter 40 value 28.783749
## iter 50 value 28.682272
## iter 60 value 28.198129
## iter 70 value 28.058810
## iter 80 value 27.900495
## iter 90 value 27.897368
## iter 100 value 27.897293
## final value 27.897293
## stopped after 100 iterations
## # weights: 30
## initial value 125.223775
## iter 10 value 64.904437
## iter 20 value 32.617576
## iter 30 value 29.993794
## iter 40 value 28.907463
## iter 50 value 28.545114
## iter 60 value 28.235838
## iter 70 value 28.126261
## iter 80 value 28.083519
## iter 90 value 27.597802
## iter 100 value 26.565256
## final value 26.565256
## stopped after 100 iterations
## # weights: 30
## initial value 124.415860
## iter 10 value 35.237337
## iter 20 value 29.659748
## iter 30 value 28.353670
## iter 40 value 27.870103
## iter 50 value 27.758787
## iter 60 value 27.581332
## iter 70 value 27.453391
## iter 80 value 27.421283
## iter 90 value 27.417541
## final value 27.417501
## converged
## # weights: 30
## initial value 122.573010
## iter 10 value 53.002577
## iter 20 value 28.898553
## iter 30 value 28.306257
## iter 40 value 28.060214
## iter 50 value 27.979217
## iter 60 value 27.737981
## iter 70 value 27.618288
## iter 80 value 27.589441
## iter 90 value 27.587832
## final value 27.587816
## converged
## # weights: 30
## initial value 121.660217
## iter 10 value 31.904293
## iter 20 value 24.721653
## iter 30 value 24.366027
## iter 40 value 24.289601
## iter 50 value 24.194094
## iter 60 value 24.040179
## iter 70 value 23.912077
## iter 80 value 23.894151
## iter 90 value 23.893206
## iter 100 value 23.893164
## final value 23.893164
## stopped after 100 iterations
## # weights: 30
## initial value 121.424812
## iter 10 value 38.932022
## iter 20 value 29.509755
## iter 30 value 28.510241
## iter 40 value 28.182259
## iter 50 value 28.022160
## iter 60 value 27.936268
## iter 70 value 27.861965
## iter 80 value 27.851510
## iter 90 value 27.851129
## final value 27.851105
## converged
## # weights: 30
## initial value 130.536230
## iter 10 value 38.105669
## iter 20 value 30.349688
## iter 30 value 27.752503
## iter 40 value 27.377585
## iter 50 value 26.849197
## iter 60 value 26.733534
## iter 70 value 26.688019
## iter 80 value 26.658796
## iter 90 value 26.657035
## final value 26.656995
## converged
## # weights: 30
## initial value 118.476825
## iter 10 value 78.936696
## iter 20 value 36.076542
## iter 30 value 30.306446
## iter 40 value 28.958333
## iter 50 value 28.729760
## iter 60 value 28.610550
## iter 70 value 28.424444
## iter 80 value 28.226173
## iter 90 value 27.923565
## iter 100 value 27.826221
## final value 27.826221
## stopped after 100 iterations
## # weights: 30
## initial value 120.132645
## iter 10 value 44.785166
## iter 20 value 26.968637
## iter 30 value 25.364383
## iter 40 value 24.770151
## iter 50 value 24.467560
## iter 60 value 24.329609
## iter 70 value 24.218821
## iter 80 value 24.210772
## iter 90 value 24.210403
## final value 24.210399
## converged
## # weights: 30
## initial value 120.140795
## iter 10 value 54.760958
## iter 20 value 29.981688
## iter 30 value 27.167476
## iter 40 value 26.628830
## iter 50 value 26.472856
## iter 60 value 26.305855
## iter 70 value 26.201055
## iter 80 value 26.171348
## iter 90 value 26.168420
## final value 26.168349
## converged
## # weights: 30
## initial value 133.259976
## iter 10 value 56.123912
## iter 20 value 29.420737
## iter 30 value 28.478036
## iter 40 value 28.200118
## iter 50 value 28.154010
## iter 60 value 28.111986
## iter 70 value 28.100895
## iter 80 value 28.099090
## iter 90 value 28.098906
## final value 28.098904
## converged
## # weights: 30
## initial value 118.560855
## iter 10 value 69.135665
## iter 20 value 31.041638
## iter 30 value 29.779602
## iter 40 value 29.194251
## iter 50 value 29.009134
## iter 60 value 28.996958
## iter 70 value 28.984957
## iter 80 value 28.980970
## iter 90 value 28.975621
## final value 28.975531
## converged
## # weights: 30
## initial value 131.895244
## iter 10 value 55.607005
## iter 20 value 35.057405
## iter 30 value 29.244076
## iter 40 value 28.345943
## iter 50 value 28.255235
## iter 60 value 28.110424
## iter 70 value 27.923273
## iter 80 value 27.796302
## iter 90 value 27.761910
## iter 100 value 27.755043
## final value 27.755043
## stopped after 100 iterations
## # weights: 30
## initial value 126.158432
## iter 10 value 75.496678
## iter 20 value 30.490449
## iter 30 value 27.766036
## iter 40 value 27.578558
## iter 50 value 27.545764
## iter 60 value 27.509711
## iter 70 value 27.461265
## iter 80 value 27.445118
## iter 90 value 27.444057
## final value 27.444057
## converged
## # weights: 30
## initial value 128.215189
## iter 10 value 46.550293
## iter 20 value 29.167737
## iter 30 value 28.720841
## iter 40 value 28.547368
## iter 50 value 28.477959
## iter 60 value 28.127043
## iter 70 value 27.442518
## iter 80 value 27.318558
## iter 90 value 27.315921
## iter 100 value 27.315111
## final value 27.315111
## stopped after 100 iterations
## # weights: 30
## initial value 133.397945
## iter 10 value 79.321237
## iter 20 value 33.970433
## iter 30 value 30.476469
## iter 40 value 28.297827
## iter 50 value 27.694653
## iter 60 value 27.637833
## iter 70 value 27.465562
## iter 80 value 27.434320
## iter 90 value 27.433500
## final value 27.433452
## converged
## # weights: 30
## initial value 124.179717
## iter 10 value 44.201282
## iter 20 value 29.935251
## iter 30 value 28.622905
## iter 40 value 28.120073
## iter 50 value 27.906940
## iter 60 value 27.769440
## iter 70 value 27.700017
## iter 80 value 27.689013
## iter 90 value 27.688023
## iter 100 value 26.886547
## final value 26.886547
## stopped after 100 iterations
## # weights: 30
## initial value 128.383962
## iter 10 value 36.716937
## iter 20 value 29.467144
## iter 30 value 28.519067
## iter 40 value 28.198264
## iter 50 value 28.152903
## iter 60 value 28.112347
## iter 70 value 28.010424
## iter 80 value 27.100171
## iter 90 value 26.742180
## iter 100 value 26.736027
## final value 26.736027
## stopped after 100 iterations
## # weights: 30
## initial value 120.124051
## iter 10 value 46.196698
## iter 20 value 25.488673
## iter 30 value 23.665260
## iter 40 value 22.228016
## iter 50 value 21.458047
## iter 60 value 21.202701
## iter 70 value 21.026324
## iter 80 value 21.011577
## iter 90 value 21.010665
## final value 21.010646
## converged
## # weights: 30
## initial value 127.210255
## iter 10 value 64.256016
## iter 20 value 28.674109
## iter 30 value 28.039183
## iter 40 value 27.683980
## iter 50 value 27.513637
## iter 60 value 27.376127
## iter 70 value 27.339880
## iter 80 value 27.324692
## iter 90 value 27.324494
## final value 27.324482
## converged
## # weights: 30
## initial value 125.662425
## iter 10 value 41.919718
## iter 20 value 28.397456
## iter 30 value 28.030458
## iter 40 value 27.608255
## iter 50 value 27.466932
## iter 60 value 27.409187
## iter 70 value 27.371909
## iter 80 value 27.365107
## iter 90 value 27.364559
## final value 27.364557
## converged
## # weights: 30
## initial value 126.762387
## iter 10 value 54.595246
## iter 20 value 28.895971
## iter 30 value 28.114348
## iter 40 value 27.694136
## iter 50 value 27.601739
## iter 60 value 27.575514
## iter 70 value 27.573959
## iter 80 value 27.573168
## final value 27.573155
## converged
## # weights: 30
## initial value 129.004794
## iter 10 value 71.078553
## iter 20 value 25.316123
## iter 30 value 23.890762
## iter 40 value 23.770080
## iter 50 value 23.753825
## iter 60 value 23.748293
## iter 70 value 23.746475
## iter 80 value 23.746241
## final value 23.746237
## converged
## # weights: 30
## initial value 130.176932
## iter 10 value 44.132367
## iter 20 value 28.623470
## iter 30 value 27.917590
## iter 40 value 27.699900
## iter 50 value 27.693456
## iter 60 value 27.692763
## iter 70 value 27.691467
## iter 80 value 27.689714
## iter 90 value 27.689429
## final value 27.689425
## converged
## # weights: 30
## initial value 120.656787
## iter 10 value 43.676398
## iter 20 value 28.784068
## iter 30 value 27.402869
## iter 40 value 27.218828
## iter 50 value 27.187995
## iter 60 value 27.184623
## iter 70 value 27.184437
## final value 27.184396
## converged
## # weights: 30
## initial value 135.486814
## iter 10 value 59.013309
## iter 20 value 29.824172
## iter 30 value 24.831212
## iter 40 value 24.392011
## iter 50 value 24.119246
## iter 60 value 24.041733
## iter 70 value 24.020895
## iter 80 value 24.017501
## iter 90 value 23.853498
## iter 100 value 23.485866
## final value 23.485866
## stopped after 100 iterations
## # weights: 30
## initial value 131.847985
## iter 10 value 53.873978
## iter 20 value 29.979387
## iter 30 value 28.119380
## iter 40 value 27.723856
## iter 50 value 27.534300
## iter 60 value 27.454100
## iter 70 value 27.408590
## iter 80 value 27.395344
## iter 90 value 27.395144
## final value 27.395137
## converged
## # weights: 30
## initial value 122.131430
## iter 10 value 48.254696
## iter 20 value 27.787609
## iter 30 value 26.882328
## iter 40 value 26.628640
## iter 50 value 26.481572
## iter 60 value 26.439005
## iter 70 value 26.422661
## iter 80 value 26.419093
## iter 90 value 26.418967
## final value 26.418960
## converged
## # weights: 30
## initial value 134.006230
## iter 10 value 37.379385
## iter 20 value 28.148160
## iter 30 value 26.971614
## iter 40 value 26.421726
## iter 50 value 26.039392
## iter 60 value 25.950703
## iter 70 value 25.870509
## iter 80 value 25.835476
## iter 90 value 25.830743
## iter 100 value 25.830707
## final value 25.830707
## stopped after 100 iterations
## # weights: 30
## initial value 129.288460
## iter 10 value 44.088930
## iter 20 value 27.619333
## iter 30 value 25.405659
## iter 40 value 24.603858
## iter 50 value 24.532409
## iter 60 value 24.472400
## iter 70 value 24.394508
## iter 80 value 24.291436
## iter 90 value 24.269405
## iter 100 value 24.269127
## final value 24.269127
## stopped after 100 iterations
## # weights: 30
## initial value 118.377653
## iter 10 value 34.850786
## iter 20 value 26.326124
## iter 30 value 25.432441
## iter 40 value 25.177448
## iter 50 value 25.166581
## iter 60 value 25.166289
## iter 70 value 25.166245
## iter 80 value 25.166206
## final value 25.166205
## converged
## # weights: 30
## initial value 118.567276
## iter 10 value 53.100413
## iter 20 value 29.332236
## iter 30 value 27.386537
## iter 40 value 27.340589
## iter 50 value 27.309419
## iter 60 value 27.306967
## iter 70 value 27.304099
## iter 80 value 27.303744
## final value 27.303731
## converged
## # weights: 30
## initial value 124.084235
## iter 10 value 54.183689
## iter 20 value 29.056898
## iter 30 value 28.343169
## iter 40 value 27.342144
## iter 50 value 26.852811
## iter 60 value 26.783778
## iter 70 value 26.727453
## iter 80 value 26.718491
## iter 90 value 26.718207
## final value 26.718200
## converged
## # weights: 30
## initial value 130.858126
## iter 10 value 41.332674
## iter 20 value 28.442033
## iter 30 value 27.178199
## iter 40 value 26.922678
## iter 50 value 26.904879
## iter 60 value 26.902705
## iter 70 value 26.902226
## iter 80 value 26.902119
## final value 26.902113
## converged
## # weights: 30
## initial value 119.697318
## iter 10 value 37.620597
## iter 20 value 30.787674
## iter 30 value 28.698497
## iter 40 value 28.067722
## iter 50 value 27.964680
## iter 60 value 27.933132
## iter 70 value 27.918190
## iter 80 value 27.909185
## iter 90 value 27.883140
## iter 100 value 27.875712
## final value 27.875712
## stopped after 100 iterations
## # weights: 30
## initial value 127.399331
## iter 10 value 62.263154
## iter 20 value 34.020715
## iter 30 value 29.929858
## iter 40 value 27.978639
## iter 50 value 27.307245
## iter 60 value 27.199618
## iter 70 value 27.143798
## iter 80 value 27.122489
## iter 90 value 27.116388
## iter 100 value 27.113244
## final value 27.113244
## stopped after 100 iterations
## # weights: 30
## initial value 124.280436
## iter 10 value 72.732940
## iter 20 value 30.344406
## iter 30 value 29.274680
## iter 40 value 28.025356
## iter 50 value 27.797025
## iter 60 value 27.616092
## iter 70 value 27.587815
## iter 80 value 27.582285
## iter 90 value 27.565537
## iter 100 value 27.552777
## final value 27.552777
## stopped after 100 iterations
## # weights: 30
## initial value 124.679072
## iter 10 value 38.099814
## iter 20 value 28.656109
## iter 30 value 27.530292
## iter 40 value 26.581273
## iter 50 value 26.300348
## iter 60 value 26.205119
## iter 70 value 26.195519
## iter 80 value 26.195116
## final value 26.195111
## converged
## # weights: 30
## initial value 134.894705
## iter 10 value 71.801582
## iter 20 value 29.044227
## iter 30 value 27.516288
## iter 40 value 27.284562
## iter 50 value 27.168693
## iter 60 value 26.994476
## iter 70 value 26.942099
## iter 80 value 26.926167
## iter 90 value 26.925227
## iter 100 value 26.925153
## final value 26.925153
## stopped after 100 iterations
## # weights: 30
## initial value 120.043999
## iter 10 value 51.069979
## iter 20 value 30.401918
## iter 30 value 29.741106
## iter 40 value 29.126219
## iter 50 value 28.606086
## iter 60 value 28.164648
## iter 70 value 28.026292
## iter 80 value 27.828658
## iter 90 value 27.810102
## iter 100 value 27.809992
## final value 27.809992
## stopped after 100 iterations
## # weights: 30
## initial value 127.287514
## iter 10 value 67.848712
## iter 20 value 36.180717
## iter 30 value 29.617629
## iter 40 value 26.459265
## iter 50 value 25.829884
## iter 60 value 25.719866
## iter 70 value 25.329949
## iter 80 value 25.261594
## iter 90 value 25.260418
## iter 100 value 25.260392
## final value 25.260392
## stopped after 100 iterations
## # weights: 30
## initial value 125.336988
## iter 10 value 42.025581
## iter 20 value 29.530470
## iter 30 value 28.718478
## iter 40 value 28.370525
## iter 50 value 28.159508
## iter 60 value 27.921697
## iter 70 value 27.823365
## iter 80 value 27.807760
## iter 90 value 27.807597
## final value 27.807588
## converged
## # weights: 30
## initial value 116.610117
## iter 10 value 49.778977
## iter 20 value 28.988408
## iter 30 value 27.936231
## iter 40 value 27.619748
## iter 50 value 27.544710
## iter 60 value 27.541108
## iter 70 value 27.539846
## iter 80 value 27.539092
## iter 90 value 27.538197
## final value 27.538124
## converged
## # weights: 30
## initial value 127.633563
## iter 10 value 54.271162
## iter 20 value 36.511987
## iter 30 value 30.208358
## iter 40 value 29.017273
## iter 50 value 28.573789
## iter 60 value 28.339968
## iter 70 value 28.197581
## iter 80 value 27.976418
## iter 90 value 27.956794
## iter 100 value 27.956647
## final value 27.956647
## stopped after 100 iterations
## # weights: 30
## initial value 125.698461
## iter 10 value 92.607298
## iter 20 value 33.307044
## iter 30 value 29.151419
## iter 40 value 28.016725
## iter 50 value 27.370070
## iter 60 value 27.197872
## iter 70 value 27.048857
## iter 80 value 26.870242
## iter 90 value 26.815771
## iter 100 value 26.808184
## final value 26.808184
## stopped after 100 iterations
## # weights: 30
## initial value 122.609092
## iter 10 value 64.300114
## iter 20 value 25.175601
## iter 30 value 23.526061
## iter 40 value 22.863537
## iter 50 value 22.743937
## iter 60 value 22.692409
## iter 70 value 22.639744
## iter 80 value 22.622920
## iter 90 value 22.622353
## iter 100 value 22.622280
## final value 22.622280
## stopped after 100 iterations
## # weights: 30
## initial value 126.233684
## iter 10 value 44.195415
## iter 20 value 31.677895
## iter 30 value 29.337544
## iter 40 value 28.770125
## iter 50 value 28.574652
## iter 60 value 28.381782
## iter 70 value 28.120974
## iter 80 value 27.945432
## iter 90 value 27.940081
## final value 27.940023
## converged
## # weights: 30
## initial value 120.667459
## iter 10 value 105.286884
## iter 20 value 46.620944
## iter 30 value 32.421812
## iter 40 value 30.207957
## iter 50 value 29.906969
## iter 60 value 29.816539
## iter 70 value 29.161490
## iter 80 value 28.015662
## iter 90 value 27.656603
## iter 100 value 27.602750
## final value 27.602750
## stopped after 100 iterations
## # weights: 30
## initial value 120.535954
## iter 10 value 45.343130
## iter 20 value 29.002214
## iter 30 value 27.141658
## iter 40 value 26.677342
## iter 50 value 26.525653
## iter 60 value 26.482728
## iter 70 value 26.479415
## iter 80 value 26.479333
## iter 80 value 26.479333
## iter 80 value 26.479333
## final value 26.479333
## converged
## # weights: 30
## initial value 127.734477
## iter 10 value 68.525098
## iter 20 value 32.316774
## iter 30 value 29.780718
## iter 40 value 28.813278
## iter 50 value 28.326249
## iter 60 value 28.171004
## iter 70 value 27.951116
## iter 80 value 27.685502
## iter 90 value 27.663610
## iter 100 value 27.663472
## final value 27.663472
## stopped after 100 iterations
## # weights: 30
## initial value 128.753795
## iter 10 value 52.039313
## iter 20 value 32.012178
## iter 30 value 26.417392
## iter 40 value 25.149903
## iter 50 value 24.728869
## iter 60 value 24.573305
## iter 70 value 24.546228
## iter 80 value 24.537314
## iter 90 value 24.536733
## final value 24.536711
## converged
## # weights: 30
## initial value 123.511563
## iter 10 value 48.880666
## iter 20 value 27.407043
## iter 30 value 26.119802
## iter 40 value 25.858727
## iter 50 value 25.735930
## iter 60 value 25.688459
## iter 70 value 25.661809
## iter 80 value 25.651740
## iter 90 value 25.651063
## final value 25.651046
## converged
## # weights: 30
## initial value 140.942507
## iter 10 value 73.871121
## iter 20 value 31.693117
## iter 30 value 27.365075
## iter 40 value 26.963171
## iter 50 value 26.847638
## iter 60 value 26.787216
## iter 70 value 26.622791
## iter 80 value 26.580182
## iter 90 value 26.577978
## final value 26.577957
## converged
## # weights: 30
## initial value 120.664370
## iter 10 value 56.472342
## iter 20 value 31.833864
## iter 30 value 29.227919
## iter 40 value 28.306866
## iter 50 value 27.959431
## iter 60 value 27.869330
## iter 70 value 27.780161
## iter 80 value 27.740338
## iter 90 value 27.726895
## iter 100 value 27.726508
## final value 27.726508
## stopped after 100 iterations
## # weights: 30
## initial value 122.314841
## iter 10 value 41.512523
## iter 20 value 24.330779
## iter 30 value 24.123437
## iter 40 value 24.069077
## iter 50 value 23.945316
## iter 60 value 23.863585
## iter 70 value 23.822509
## iter 80 value 23.802159
## iter 90 value 23.799340
## final value 23.799312
## converged
## # weights: 30
## initial value 127.036091
## iter 10 value 50.840507
## iter 20 value 33.967624
## iter 30 value 29.678268
## iter 40 value 25.767672
## iter 50 value 24.815116
## iter 60 value 24.428635
## iter 70 value 24.182914
## iter 80 value 24.045857
## iter 90 value 23.974219
## iter 100 value 23.969006
## final value 23.969006
## stopped after 100 iterations
## # weights: 30
## initial value 128.160451
## iter 10 value 90.718262
## iter 20 value 32.719326
## iter 30 value 29.090725
## iter 40 value 28.426025
## iter 50 value 28.171612
## iter 60 value 27.750153
## iter 70 value 27.426266
## iter 80 value 27.200922
## iter 90 value 27.177825
## iter 100 value 27.177702
## final value 27.177702
## stopped after 100 iterations
## # weights: 30
## initial value 140.604866
## iter 10 value 80.400864
## iter 20 value 29.831054
## iter 30 value 27.257796
## iter 40 value 26.836554
## iter 50 value 26.792634
## iter 60 value 26.779719
## iter 70 value 26.771870
## iter 80 value 26.769119
## iter 90 value 26.768228
## final value 26.768204
## converged
## # weights: 30
## initial value 140.115007
## iter 10 value 51.670077
## iter 20 value 35.581569
## iter 30 value 32.404158
## iter 40 value 29.216542
## iter 50 value 28.483789
## iter 60 value 28.066025
## iter 70 value 27.896457
## iter 80 value 27.842763
## iter 90 value 27.839686
## final value 27.839677
## converged
## # weights: 30
## initial value 134.303430
## iter 10 value 55.326524
## iter 20 value 34.047974
## iter 30 value 31.600447
## iter 40 value 28.855922
## iter 50 value 27.983398
## iter 60 value 27.418309
## iter 70 value 27.318305
## iter 80 value 27.311056
## iter 90 value 27.310903
## final value 27.310899
## converged
## # weights: 30
## initial value 120.473797
## iter 10 value 64.999579
## iter 20 value 29.770083
## iter 30 value 28.038091
## iter 40 value 26.894921
## iter 50 value 26.511942
## iter 60 value 26.355201
## iter 70 value 26.176993
## iter 80 value 26.140575
## iter 90 value 26.139610
## iter 100 value 26.139591
## final value 26.139591
## stopped after 100 iterations
## # weights: 30
## initial value 126.395283
## iter 10 value 38.284921
## iter 20 value 25.936644
## iter 30 value 25.216257
## iter 40 value 25.014963
## iter 50 value 24.951209
## iter 60 value 24.913208
## iter 70 value 24.907414
## iter 80 value 24.905327
## iter 90 value 24.905242
## final value 24.905241
## converged
## # weights: 30
## initial value 130.448941
## iter 10 value 54.850150
## iter 20 value 34.843193
## iter 30 value 32.283718
## iter 40 value 29.919314
## iter 50 value 27.410096
## iter 60 value 26.377201
## iter 70 value 26.193234
## iter 80 value 26.178508
## iter 90 value 26.178264
## final value 26.178254
## converged
## # weights: 30
## initial value 123.093853
## iter 10 value 49.458346
## iter 20 value 29.932326
## iter 30 value 28.841467
## iter 40 value 28.253445
## iter 50 value 27.879833
## iter 60 value 27.834340
## iter 70 value 27.827759
## iter 80 value 27.820287
## iter 90 value 27.817752
## iter 100 value 27.817651
## final value 27.817651
## stopped after 100 iterations
## # weights: 21
## initial value 129.438148
## iter 10 value 77.258554
## iter 20 value 61.818107
## iter 30 value 53.525969
## iter 40 value 51.253019
## iter 50 value 51.224362
## final value 51.224159
## converged
## # weights: 21
## initial value 147.709286
## iter 10 value 111.326042
## iter 20 value 59.533183
## iter 30 value 54.849227
## iter 40 value 53.613480
## iter 50 value 53.475450
## final value 53.469252
## converged
## # weights: 21
## initial value 138.592310
## iter 10 value 83.561929
## iter 20 value 51.957567
## iter 30 value 50.008714
## iter 40 value 49.671713
## final value 49.667879
## converged
## # weights: 21
## initial value 130.082160
## iter 10 value 76.325343
## iter 20 value 58.748322
## iter 30 value 54.136035
## iter 40 value 53.381435
## iter 50 value 53.292886
## final value 53.292515
## converged
## # weights: 21
## initial value 120.177561
## iter 10 value 78.660345
## iter 20 value 51.502780
## iter 30 value 49.347390
## iter 40 value 49.085103
## final value 49.082217
## converged
## # weights: 21
## initial value 121.903146
## iter 10 value 68.005977
## iter 20 value 55.115873
## iter 30 value 49.385418
## iter 40 value 48.316689
## iter 50 value 48.297223
## final value 48.297037
## converged
## # weights: 21
## initial value 117.679542
## iter 10 value 53.650220
## iter 20 value 48.431375
## iter 30 value 48.201772
## iter 40 value 48.174951
## iter 40 value 48.174951
## iter 40 value 48.174951
## final value 48.174951
## converged
## # weights: 21
## initial value 124.767325
## iter 10 value 77.403387
## iter 20 value 54.407520
## iter 30 value 53.730032
## final value 53.686066
## converged
## # weights: 21
## initial value 141.356554
## iter 10 value 71.613067
## iter 20 value 56.462079
## iter 30 value 52.976942
## iter 40 value 52.398258
## iter 50 value 52.260417
## final value 52.260256
## converged
## # weights: 21
## initial value 125.688469
## iter 10 value 78.008599
## iter 20 value 53.327331
## iter 30 value 51.792559
## iter 40 value 51.584589
## final value 51.584523
## converged
## # weights: 21
## initial value 126.459357
## iter 10 value 66.344836
## iter 20 value 51.972709
## iter 30 value 50.364926
## iter 40 value 50.311108
## final value 50.310726
## converged
## # weights: 21
## initial value 125.247802
## iter 10 value 58.690288
## iter 20 value 51.269886
## iter 30 value 50.639809
## iter 40 value 50.631319
## final value 50.631226
## converged
## # weights: 21
## initial value 118.299597
## iter 10 value 62.437974
## iter 20 value 50.706014
## iter 30 value 50.670456
## iter 40 value 50.668094
## final value 50.668057
## converged
## # weights: 21
## initial value 132.312016
## iter 10 value 73.654358
## iter 20 value 55.924336
## iter 30 value 53.812804
## iter 40 value 53.571301
## final value 53.552291
## converged
## # weights: 21
## initial value 122.483704
## iter 10 value 64.687215
## iter 20 value 53.403454
## iter 30 value 52.346202
## iter 40 value 52.273040
## final value 52.272679
## converged
## # weights: 21
## initial value 123.522991
## iter 10 value 64.597370
## iter 20 value 53.944259
## iter 30 value 49.838215
## iter 40 value 49.804762
## final value 49.804632
## converged
## # weights: 21
## initial value 118.648167
## iter 10 value 73.628280
## iter 20 value 54.132144
## iter 30 value 52.473393
## iter 40 value 52.469527
## final value 52.469513
## converged
## # weights: 21
## initial value 119.541205
## iter 10 value 72.880789
## iter 20 value 54.197399
## iter 30 value 52.085787
## iter 40 value 51.809591
## iter 50 value 51.805351
## final value 51.805348
## converged
## # weights: 21
## initial value 124.621059
## iter 10 value 71.008882
## iter 20 value 60.420156
## iter 30 value 57.634143
## iter 40 value 54.797589
## iter 50 value 54.175717
## iter 60 value 54.112674
## final value 54.112669
## converged
## # weights: 21
## initial value 127.098404
## iter 10 value 72.181399
## iter 20 value 54.660254
## iter 30 value 54.186699
## iter 40 value 54.136345
## final value 54.133969
## converged
## # weights: 21
## initial value 126.942281
## iter 10 value 64.356572
## iter 20 value 53.341144
## iter 30 value 52.497638
## iter 40 value 52.460595
## final value 52.460593
## converged
## # weights: 21
## initial value 120.209158
## iter 10 value 59.037989
## iter 20 value 49.921386
## iter 30 value 45.741225
## iter 40 value 45.367911
## final value 45.365459
## converged
## # weights: 21
## initial value 121.196088
## iter 10 value 64.076212
## iter 20 value 53.439609
## iter 30 value 52.049601
## iter 40 value 51.647451
## iter 50 value 51.573924
## final value 51.573727
## converged
## # weights: 21
## initial value 138.746709
## iter 10 value 96.767335
## iter 20 value 56.464676
## iter 30 value 52.928043
## iter 40 value 52.893107
## final value 52.893095
## converged
## # weights: 21
## initial value 144.827611
## iter 10 value 105.857156
## iter 20 value 54.461438
## iter 30 value 50.061503
## iter 40 value 49.925062
## final value 49.919940
## converged
## # weights: 21
## initial value 122.594433
## iter 10 value 86.922917
## iter 20 value 52.715857
## iter 30 value 51.669537
## iter 40 value 51.640422
## final value 51.639275
## converged
## # weights: 21
## initial value 122.266458
## iter 10 value 60.418246
## iter 20 value 51.477149
## iter 30 value 51.274136
## iter 40 value 51.228259
## final value 51.225925
## converged
## # weights: 21
## initial value 122.105764
## iter 10 value 82.206039
## iter 20 value 49.523141
## iter 30 value 49.251160
## iter 40 value 49.250921
## final value 49.250920
## converged
## # weights: 21
## initial value 125.125197
## iter 10 value 88.085238
## iter 20 value 56.493022
## iter 30 value 51.445612
## iter 40 value 50.385997
## iter 50 value 50.385561
## final value 50.385533
## converged
## # weights: 21
## initial value 118.827299
## iter 10 value 65.652523
## iter 20 value 51.671986
## iter 30 value 51.439940
## iter 40 value 51.432012
## iter 40 value 51.432011
## iter 40 value 51.432011
## final value 51.432011
## converged
## # weights: 21
## initial value 135.586154
## iter 10 value 95.010308
## iter 20 value 48.951655
## iter 30 value 47.848713
## iter 40 value 47.846510
## final value 47.846506
## converged
## # weights: 21
## initial value 124.195699
## iter 10 value 72.124298
## iter 20 value 54.024724
## iter 30 value 50.982302
## iter 40 value 50.333708
## iter 50 value 50.321414
## final value 50.321380
## converged
## # weights: 21
## initial value 128.396284
## iter 10 value 67.329903
## iter 20 value 50.724243
## iter 30 value 49.433713
## iter 40 value 49.386089
## final value 49.385958
## converged
## # weights: 21
## initial value 125.033188
## iter 10 value 85.203716
## iter 20 value 50.948424
## iter 30 value 50.251710
## iter 40 value 50.171679
## iter 50 value 50.166282
## final value 50.166226
## converged
## # weights: 21
## initial value 124.719944
## iter 10 value 67.346809
## iter 20 value 53.864736
## iter 30 value 48.700655
## iter 40 value 47.551795
## iter 50 value 47.511282
## final value 47.510220
## converged
## # weights: 21
## initial value 117.849314
## iter 10 value 72.612973
## iter 20 value 52.062080
## iter 30 value 51.539615
## iter 40 value 51.530705
## iter 40 value 51.530705
## iter 40 value 51.530705
## final value 51.530705
## converged
## # weights: 21
## initial value 135.631044
## iter 10 value 99.080630
## iter 20 value 56.907250
## iter 30 value 54.131823
## iter 40 value 53.844893
## final value 53.843886
## converged
## # weights: 21
## initial value 122.440662
## iter 10 value 70.394292
## iter 20 value 58.566762
## iter 30 value 52.145305
## iter 40 value 51.302782
## iter 50 value 51.079922
## iter 60 value 51.063638
## final value 51.063637
## converged
## # weights: 21
## initial value 119.412082
## iter 10 value 58.873267
## iter 20 value 49.970780
## iter 30 value 49.936797
## final value 49.936661
## converged
## # weights: 21
## initial value 124.094744
## iter 10 value 86.527614
## iter 20 value 55.479772
## iter 30 value 53.468385
## iter 40 value 53.334425
## final value 53.333977
## converged
## # weights: 21
## initial value 133.394490
## iter 10 value 92.422216
## iter 20 value 60.655998
## iter 30 value 53.406868
## iter 40 value 50.688838
## iter 50 value 50.640776
## iter 60 value 50.565947
## final value 50.565936
## converged
## # weights: 21
## initial value 117.193024
## iter 10 value 57.734292
## iter 20 value 53.244338
## iter 30 value 53.147797
## final value 53.147773
## converged
## # weights: 21
## initial value 121.835219
## iter 10 value 72.234187
## iter 20 value 55.652592
## iter 30 value 50.062074
## iter 40 value 48.874317
## iter 50 value 48.817496
## final value 48.817241
## converged
## # weights: 21
## initial value 122.209718
## iter 10 value 68.155242
## iter 20 value 58.455949
## iter 30 value 54.260652
## iter 40 value 52.348628
## iter 50 value 52.058252
## iter 60 value 52.051909
## iter 60 value 52.051909
## iter 60 value 52.051909
## final value 52.051909
## converged
## # weights: 21
## initial value 128.427062
## iter 10 value 77.417260
## iter 20 value 49.686476
## iter 30 value 49.009082
## iter 40 value 48.806845
## iter 50 value 48.787336
## final value 48.786690
## converged
## # weights: 21
## initial value 125.494332
## iter 10 value 64.629754
## iter 20 value 53.425296
## iter 30 value 52.892188
## iter 40 value 52.874173
## final value 52.874121
## converged
## # weights: 21
## initial value 133.577326
## iter 10 value 70.087625
## iter 20 value 58.051468
## iter 30 value 53.643231
## iter 40 value 50.407329
## iter 50 value 49.820334
## iter 60 value 49.773107
## final value 49.773102
## converged
## # weights: 21
## initial value 158.891991
## iter 10 value 87.311882
## iter 20 value 59.876858
## iter 30 value 55.710399
## iter 40 value 50.605269
## iter 50 value 49.647152
## final value 49.633185
## converged
## # weights: 21
## initial value 121.753625
## iter 10 value 60.802235
## iter 20 value 54.927182
## iter 30 value 51.597125
## iter 40 value 50.887758
## final value 50.856925
## converged
## # weights: 21
## initial value 120.452497
## iter 10 value 60.742746
## iter 20 value 54.697317
## iter 30 value 53.931247
## iter 40 value 53.891622
## final value 53.891614
## converged
## # weights: 21
## initial value 123.668965
## iter 10 value 81.085610
## iter 20 value 52.429349
## iter 30 value 50.668619
## iter 40 value 50.284071
## iter 50 value 50.173589
## final value 50.170389
## converged
## # weights: 21
## initial value 122.661323
## iter 10 value 63.456057
## iter 20 value 48.551800
## iter 30 value 48.452144
## iter 40 value 48.443245
## final value 48.443139
## converged
## # weights: 21
## initial value 128.520001
## iter 10 value 63.087982
## iter 20 value 59.373590
## iter 30 value 54.895000
## iter 40 value 51.173321
## iter 50 value 50.758523
## iter 60 value 50.703163
## final value 50.703152
## converged
## # weights: 21
## initial value 131.007917
## iter 10 value 85.535903
## iter 20 value 55.607936
## iter 30 value 50.715015
## iter 40 value 49.207370
## iter 50 value 49.199055
## iter 60 value 49.198676
## iter 60 value 49.198675
## iter 60 value 49.198675
## final value 49.198675
## converged
## # weights: 21
## initial value 119.151467
## iter 10 value 54.090693
## iter 20 value 50.010543
## iter 30 value 49.911082
## final value 49.910382
## converged
## # weights: 21
## initial value 125.717304
## iter 10 value 59.982377
## iter 20 value 50.382011
## iter 30 value 49.262305
## iter 40 value 49.193296
## final value 49.193145
## converged
## # weights: 21
## initial value 118.499412
## iter 10 value 86.097217
## iter 20 value 58.467633
## iter 30 value 56.597336
## iter 40 value 53.551566
## iter 50 value 48.019201
## iter 60 value 47.927419
## final value 47.927261
## converged
## # weights: 21
## initial value 125.736552
## iter 10 value 99.252204
## iter 20 value 69.849758
## iter 30 value 52.110628
## iter 40 value 49.605435
## iter 50 value 49.425045
## final value 49.422176
## converged
## # weights: 21
## initial value 118.228406
## iter 10 value 61.842143
## iter 20 value 54.321910
## iter 30 value 52.862708
## iter 40 value 52.805860
## final value 52.803763
## converged
## # weights: 21
## initial value 121.057996
## iter 10 value 57.565085
## iter 20 value 49.752457
## iter 30 value 49.283447
## iter 40 value 49.266997
## final value 49.266911
## converged
## # weights: 21
## initial value 128.243847
## iter 10 value 88.924018
## iter 20 value 51.348792
## iter 30 value 50.359706
## iter 40 value 50.349398
## iter 40 value 50.349398
## iter 40 value 50.349398
## final value 50.349398
## converged
## # weights: 21
## initial value 116.907768
## iter 10 value 83.048451
## iter 20 value 53.301171
## iter 30 value 52.936581
## iter 40 value 52.932996
## final value 52.932992
## converged
## # weights: 21
## initial value 125.624839
## iter 10 value 101.604498
## iter 20 value 60.235709
## iter 30 value 51.436581
## iter 40 value 50.872704
## iter 50 value 50.583666
## iter 60 value 50.570665
## final value 50.570664
## converged
## # weights: 21
## initial value 118.930904
## iter 10 value 83.247891
## iter 20 value 51.862112
## iter 30 value 51.718983
## iter 40 value 51.705349
## final value 51.705344
## converged
## # weights: 21
## initial value 119.149611
## iter 10 value 65.608104
## iter 20 value 49.969376
## iter 30 value 49.298746
## iter 40 value 49.288206
## final value 49.288204
## converged
## # weights: 21
## initial value 122.067660
## iter 10 value 82.035242
## iter 20 value 54.911596
## iter 30 value 51.610184
## iter 40 value 51.010333
## iter 50 value 50.920263
## iter 60 value 50.917090
## final value 50.917089
## converged
## # weights: 21
## initial value 130.482681
## iter 10 value 92.514344
## iter 20 value 54.034248
## iter 30 value 52.570836
## iter 40 value 52.555822
## final value 52.555817
## converged
## # weights: 21
## initial value 126.694672
## iter 10 value 103.536380
## iter 20 value 56.107576
## iter 30 value 53.931592
## iter 40 value 53.651287
## iter 50 value 53.609687
## final value 53.609682
## converged
## # weights: 21
## initial value 149.174512
## iter 10 value 110.128566
## iter 20 value 62.940731
## iter 30 value 54.227138
## iter 40 value 53.323968
## iter 50 value 53.162693
## final value 53.161680
## converged
## # weights: 21
## initial value 122.799622
## iter 10 value 75.303689
## iter 20 value 57.527784
## iter 30 value 54.303820
## iter 40 value 53.960998
## final value 53.960971
## converged
## # weights: 21
## initial value 138.806927
## iter 10 value 75.688541
## iter 20 value 50.374026
## iter 30 value 50.013187
## iter 40 value 49.956361
## iter 50 value 49.716569
## iter 60 value 49.708118
## final value 49.708117
## converged
## # weights: 21
## initial value 136.434923
## iter 10 value 68.788798
## iter 20 value 50.708212
## iter 30 value 47.030528
## iter 40 value 47.025030
## final value 47.024735
## converged
## # weights: 21
## initial value 121.848773
## iter 10 value 60.309896
## iter 20 value 52.119753
## iter 30 value 51.558973
## iter 40 value 51.210342
## iter 50 value 51.201821
## final value 51.201808
## converged
## # weights: 21
## initial value 118.309452
## iter 10 value 86.465387
## iter 20 value 57.074816
## iter 30 value 56.322613
## iter 40 value 55.077319
## iter 50 value 50.526000
## iter 60 value 49.744284
## final value 49.744133
## converged
## # weights: 21
## initial value 121.970703
## iter 10 value 63.468932
## iter 20 value 49.518616
## iter 30 value 49.017351
## iter 40 value 49.016263
## iter 40 value 49.016262
## iter 40 value 49.016262
## final value 49.016262
## converged
## # weights: 21
## initial value 121.466683
## iter 10 value 66.534543
## iter 20 value 53.214356
## iter 30 value 52.771275
## iter 40 value 52.739959
## iter 40 value 52.739959
## iter 40 value 52.739959
## final value 52.739959
## converged
## # weights: 21
## initial value 121.969810
## iter 10 value 87.490832
## iter 20 value 52.764185
## iter 30 value 49.564168
## iter 40 value 48.340371
## iter 50 value 48.229141
## final value 48.226779
## converged
## # weights: 21
## initial value 126.460342
## iter 10 value 90.659200
## iter 20 value 56.076740
## iter 30 value 53.447725
## iter 40 value 52.219710
## iter 50 value 52.040884
## final value 52.040747
## converged
## # weights: 21
## initial value 120.922084
## iter 10 value 76.691548
## iter 20 value 59.429948
## iter 30 value 54.689888
## iter 40 value 53.439751
## iter 50 value 53.391954
## final value 53.391904
## converged
## # weights: 21
## initial value 129.490169
## iter 10 value 63.543894
## iter 20 value 50.446674
## iter 30 value 50.295431
## iter 40 value 50.262339
## final value 50.261678
## converged
## # weights: 21
## initial value 116.590511
## iter 10 value 66.480721
## iter 20 value 52.837203
## iter 30 value 50.688900
## iter 40 value 50.568424
## final value 50.568413
## converged
## # weights: 21
## initial value 130.595975
## iter 10 value 108.821653
## iter 20 value 55.333848
## iter 30 value 48.886892
## iter 40 value 48.034861
## iter 50 value 48.025576
## final value 48.025493
## converged
## # weights: 21
## initial value 124.473612
## iter 10 value 65.436843
## iter 20 value 50.350436
## iter 30 value 49.408287
## iter 40 value 49.402166
## final value 49.402163
## converged
## # weights: 21
## initial value 133.555629
## iter 10 value 64.960708
## iter 20 value 50.980930
## iter 30 value 49.120035
## iter 40 value 48.848900
## iter 50 value 48.827367
## final value 48.827324
## converged
## # weights: 21
## initial value 126.186927
## iter 10 value 101.273205
## iter 20 value 58.181129
## iter 30 value 52.957398
## iter 40 value 50.434206
## iter 50 value 50.254385
## final value 50.254370
## converged
## # weights: 21
## initial value 124.190630
## iter 10 value 88.045610
## iter 20 value 54.843640
## iter 30 value 50.731146
## iter 40 value 50.034189
## iter 50 value 49.965805
## iter 60 value 49.953529
## iter 60 value 49.953529
## iter 60 value 49.953529
## final value 49.953529
## converged
## # weights: 21
## initial value 118.123590
## iter 10 value 82.578479
## iter 20 value 56.991665
## iter 30 value 55.494868
## iter 40 value 52.390085
## iter 50 value 52.056525
## final value 52.046346
## converged
## # weights: 21
## initial value 129.597725
## iter 10 value 67.396676
## iter 20 value 51.640371
## iter 30 value 48.068830
## iter 40 value 47.959567
## final value 47.959562
## converged
## # weights: 21
## initial value 117.422151
## iter 10 value 57.419036
## iter 20 value 50.065325
## iter 30 value 50.002343
## final value 50.000908
## converged
## # weights: 21
## initial value 127.893478
## iter 10 value 69.060388
## iter 20 value 56.277197
## iter 30 value 50.847947
## iter 40 value 50.143357
## iter 50 value 50.142408
## final value 50.142397
## converged
## # weights: 30
## initial value 120.339254
## iter 10 value 60.893288
## iter 20 value 39.620921
## iter 30 value 38.027964
## iter 40 value 37.756699
## iter 50 value 37.552316
## iter 60 value 37.005235
## iter 70 value 36.978717
## iter 80 value 36.976585
## final value 36.976516
## converged
## # weights: 30
## initial value 130.854458
## iter 10 value 86.779897
## iter 20 value 42.141000
## iter 30 value 38.663027
## iter 40 value 37.580039
## iter 50 value 37.404296
## iter 60 value 37.330111
## iter 70 value 37.309913
## iter 80 value 37.308084
## final value 37.308070
## converged
## # weights: 30
## initial value 131.001443
## iter 10 value 54.841520
## iter 20 value 43.675342
## iter 30 value 38.659811
## iter 40 value 37.292066
## iter 50 value 36.916086
## iter 60 value 36.861659
## iter 70 value 36.843229
## iter 80 value 36.839700
## final value 36.839634
## converged
## # weights: 30
## initial value 154.351968
## iter 10 value 76.965654
## iter 20 value 39.264294
## iter 30 value 37.354104
## iter 40 value 37.000615
## iter 50 value 36.887058
## iter 60 value 36.845229
## iter 70 value 36.844488
## iter 80 value 36.844453
## final value 36.844451
## converged
## # weights: 30
## initial value 131.615788
## iter 10 value 62.815465
## iter 20 value 41.262747
## iter 30 value 37.580510
## iter 40 value 36.708353
## iter 50 value 35.869271
## iter 60 value 35.666967
## iter 70 value 35.357118
## iter 80 value 34.925550
## iter 90 value 34.858539
## iter 100 value 34.856419
## final value 34.856419
## stopped after 100 iterations
## # weights: 30
## initial value 120.349336
## iter 10 value 63.788899
## iter 20 value 36.296333
## iter 30 value 34.723787
## iter 40 value 34.551623
## iter 50 value 34.463740
## iter 60 value 34.377246
## iter 70 value 34.358694
## iter 80 value 34.357954
## final value 34.357949
## converged
## # weights: 30
## initial value 117.206868
## iter 10 value 66.504846
## iter 20 value 38.196304
## iter 30 value 35.768646
## iter 40 value 35.313676
## iter 50 value 35.260382
## iter 60 value 35.111573
## iter 70 value 35.085322
## iter 80 value 35.078907
## iter 90 value 35.078622
## iter 90 value 35.078621
## iter 90 value 35.078621
## final value 35.078621
## converged
## # weights: 30
## initial value 124.570628
## iter 10 value 65.879178
## iter 20 value 39.613242
## iter 30 value 37.883325
## iter 40 value 37.081889
## iter 50 value 36.935158
## iter 60 value 36.902783
## iter 70 value 36.898238
## iter 80 value 36.897906
## final value 36.897904
## converged
## # weights: 30
## initial value 126.319712
## iter 10 value 63.933387
## iter 20 value 37.691705
## iter 30 value 37.428208
## iter 40 value 36.822237
## iter 50 value 36.364045
## iter 60 value 36.246499
## iter 70 value 36.236841
## iter 80 value 36.232965
## iter 90 value 36.232853
## final value 36.232849
## converged
## # weights: 30
## initial value 116.459320
## iter 10 value 53.469073
## iter 20 value 36.573642
## iter 30 value 35.540563
## iter 40 value 35.463526
## iter 50 value 35.460959
## iter 60 value 35.460553
## final value 35.460548
## converged
## # weights: 30
## initial value 120.480534
## iter 10 value 61.853657
## iter 20 value 44.877038
## iter 30 value 39.952873
## iter 40 value 37.254857
## iter 50 value 37.010493
## iter 60 value 36.583303
## iter 70 value 36.353959
## iter 80 value 36.338705
## iter 90 value 36.338299
## final value 36.338297
## converged
## # weights: 30
## initial value 118.298327
## iter 10 value 56.584873
## iter 20 value 38.259621
## iter 30 value 37.084690
## iter 40 value 36.975004
## iter 50 value 36.908469
## iter 60 value 36.897006
## iter 70 value 36.895623
## iter 80 value 36.895534
## iter 80 value 36.895534
## iter 80 value 36.895534
## final value 36.895534
## converged
## # weights: 30
## initial value 137.995038
## iter 10 value 65.794381
## iter 20 value 37.656516
## iter 30 value 37.145463
## iter 40 value 36.987389
## iter 50 value 36.968149
## iter 60 value 36.965377
## final value 36.965341
## converged
## # weights: 30
## initial value 141.923179
## iter 10 value 58.938404
## iter 20 value 41.197806
## iter 30 value 37.839236
## iter 40 value 36.862375
## iter 50 value 36.654744
## iter 60 value 36.631370
## iter 70 value 36.626967
## iter 80 value 36.625319
## final value 36.625275
## converged
## # weights: 30
## initial value 125.770823
## iter 10 value 54.329696
## iter 20 value 43.279971
## iter 30 value 38.351412
## iter 40 value 36.447197
## iter 50 value 36.088470
## iter 60 value 35.960027
## iter 70 value 35.906586
## iter 80 value 35.855600
## iter 90 value 35.854404
## final value 35.854397
## converged
## # weights: 30
## initial value 123.914849
## iter 10 value 68.165487
## iter 20 value 42.236125
## iter 30 value 38.145337
## iter 40 value 37.417815
## iter 50 value 37.293380
## iter 60 value 36.879985
## iter 70 value 36.626998
## iter 80 value 36.601648
## iter 90 value 36.601338
## final value 36.601336
## converged
## # weights: 30
## initial value 129.972470
## iter 10 value 50.511977
## iter 20 value 41.265412
## iter 30 value 38.715899
## iter 40 value 37.246901
## iter 50 value 36.013832
## iter 60 value 35.597544
## iter 70 value 35.411671
## iter 80 value 35.399134
## iter 90 value 35.398954
## final value 35.398951
## converged
## # weights: 30
## initial value 117.120612
## iter 10 value 67.781107
## iter 20 value 39.621060
## iter 30 value 38.897060
## iter 40 value 37.790392
## iter 50 value 37.433907
## iter 60 value 37.141460
## iter 70 value 35.286633
## iter 80 value 34.724910
## iter 90 value 34.713013
## iter 100 value 34.712944
## final value 34.712944
## stopped after 100 iterations
## # weights: 30
## initial value 129.593018
## iter 10 value 51.511865
## iter 20 value 40.349580
## iter 30 value 37.707720
## iter 40 value 37.469010
## iter 50 value 37.387755
## iter 60 value 37.354960
## iter 70 value 37.354491
## final value 37.354464
## converged
## # weights: 30
## initial value 117.497414
## iter 10 value 53.815470
## iter 20 value 40.953119
## iter 30 value 39.172783
## iter 40 value 38.185280
## iter 50 value 37.870597
## iter 60 value 37.695265
## iter 70 value 37.661143
## iter 80 value 37.653135
## iter 90 value 37.652867
## final value 37.652865
## converged
## # weights: 30
## initial value 139.818456
## iter 10 value 60.471966
## iter 20 value 43.547069
## iter 30 value 40.165252
## iter 40 value 38.814169
## iter 50 value 37.386935
## iter 60 value 37.230538
## iter 70 value 36.749001
## iter 80 value 36.555201
## iter 90 value 36.519133
## iter 100 value 36.506046
## final value 36.506046
## stopped after 100 iterations
## # weights: 30
## initial value 122.117559
## iter 10 value 62.826191
## iter 20 value 33.196986
## iter 30 value 32.391404
## iter 40 value 32.206995
## iter 50 value 31.852584
## iter 60 value 31.622773
## iter 70 value 31.569358
## iter 80 value 31.564216
## iter 90 value 31.563906
## final value 31.563905
## converged
## # weights: 30
## initial value 142.961557
## iter 10 value 71.570444
## iter 20 value 36.247812
## iter 30 value 35.385816
## iter 40 value 35.132665
## iter 50 value 35.015247
## iter 60 value 34.985431
## iter 70 value 34.979419
## iter 80 value 34.978523
## iter 90 value 34.978488
## iter 90 value 34.978487
## iter 90 value 34.978487
## final value 34.978487
## converged
## # weights: 30
## initial value 117.875154
## iter 10 value 66.589284
## iter 20 value 39.057631
## iter 30 value 37.836772
## iter 40 value 37.425768
## iter 50 value 37.250668
## iter 60 value 37.229925
## iter 70 value 37.210353
## iter 80 value 37.208771
## iter 90 value 37.208724
## iter 90 value 37.208723
## iter 90 value 37.208723
## final value 37.208723
## converged
## # weights: 30
## initial value 117.892162
## iter 10 value 63.620062
## iter 20 value 41.699504
## iter 30 value 40.677199
## iter 40 value 40.269740
## iter 50 value 40.152977
## iter 60 value 40.125394
## iter 70 value 40.110337
## iter 80 value 39.904863
## iter 90 value 36.968860
## iter 100 value 36.709902
## final value 36.709902
## stopped after 100 iterations
## # weights: 30
## initial value 119.765807
## iter 10 value 66.974292
## iter 20 value 39.284933
## iter 30 value 37.392691
## iter 40 value 36.209447
## iter 50 value 35.783646
## iter 60 value 35.753113
## iter 70 value 35.746454
## iter 80 value 35.744211
## final value 35.744193
## converged
## # weights: 30
## initial value 120.498036
## iter 10 value 53.303900
## iter 20 value 43.308659
## iter 30 value 41.305902
## iter 40 value 40.378894
## iter 50 value 40.222329
## iter 60 value 40.135382
## iter 70 value 39.085301
## iter 80 value 37.442674
## iter 90 value 37.421822
## iter 100 value 37.421545
## final value 37.421545
## stopped after 100 iterations
## # weights: 30
## initial value 119.656508
## iter 10 value 66.644287
## iter 20 value 43.412577
## iter 30 value 40.914873
## iter 40 value 39.386795
## iter 50 value 39.212354
## iter 60 value 38.943877
## iter 70 value 37.792290
## iter 80 value 36.282651
## iter 90 value 36.018025
## iter 100 value 36.016781
## final value 36.016781
## stopped after 100 iterations
## # weights: 30
## initial value 128.406697
## iter 10 value 64.260820
## iter 20 value 41.249227
## iter 30 value 37.746516
## iter 40 value 37.526424
## iter 50 value 37.359356
## iter 60 value 36.878669
## iter 70 value 36.846400
## iter 80 value 36.844462
## final value 36.844445
## converged
## # weights: 30
## initial value 138.680041
## iter 10 value 60.975523
## iter 20 value 39.291040
## iter 30 value 37.987345
## iter 40 value 37.543781
## iter 50 value 37.269579
## iter 60 value 37.260501
## iter 70 value 37.254905
## iter 80 value 37.252767
## final value 37.252718
## converged
## # weights: 30
## initial value 132.152467
## iter 10 value 60.133156
## iter 20 value 37.557631
## iter 30 value 35.219310
## iter 40 value 34.450927
## iter 50 value 34.311178
## iter 60 value 34.216878
## iter 70 value 34.190705
## iter 80 value 34.175443
## iter 90 value 34.175184
## final value 34.175184
## converged
## # weights: 30
## initial value 125.769288
## iter 10 value 52.337850
## iter 20 value 43.072210
## iter 30 value 37.680537
## iter 40 value 37.469866
## iter 50 value 37.337721
## iter 60 value 37.332377
## iter 70 value 37.331250
## iter 80 value 37.330873
## final value 37.330867
## converged
## # weights: 30
## initial value 125.004060
## iter 10 value 47.599186
## iter 20 value 40.926248
## iter 30 value 37.160842
## iter 40 value 36.710540
## iter 50 value 36.363550
## iter 60 value 36.328854
## iter 70 value 36.325565
## iter 80 value 36.323686
## iter 90 value 36.323628
## final value 36.323627
## converged
## # weights: 30
## initial value 120.119911
## iter 10 value 85.230865
## iter 20 value 41.821081
## iter 30 value 39.343307
## iter 40 value 37.398151
## iter 50 value 37.300408
## iter 60 value 37.243244
## iter 70 value 37.217932
## iter 80 value 37.206147
## iter 90 value 37.206045
## iter 90 value 37.206045
## iter 90 value 37.206045
## final value 37.206045
## converged
## # weights: 30
## initial value 125.363381
## iter 10 value 51.194393
## iter 20 value 36.551524
## iter 30 value 34.615218
## iter 40 value 34.410645
## iter 50 value 34.328562
## iter 60 value 34.311228
## iter 70 value 34.310913
## iter 80 value 34.308553
## iter 90 value 34.272628
## iter 100 value 34.084399
## final value 34.084399
## stopped after 100 iterations
## # weights: 30
## initial value 120.502523
## iter 10 value 62.663637
## iter 20 value 39.414317
## iter 30 value 36.558514
## iter 40 value 36.334616
## iter 50 value 36.151324
## iter 60 value 35.566403
## iter 70 value 35.542731
## iter 80 value 35.541217
## iter 90 value 35.541182
## iter 90 value 35.541181
## iter 90 value 35.541181
## final value 35.541181
## converged
## # weights: 30
## initial value 119.810827
## iter 10 value 51.833195
## iter 20 value 41.345228
## iter 30 value 38.744824
## iter 40 value 38.037188
## iter 50 value 37.753319
## iter 60 value 37.671581
## iter 70 value 37.666903
## final value 37.666818
## converged
## # weights: 30
## initial value 121.673189
## iter 10 value 57.911122
## iter 20 value 41.086042
## iter 30 value 37.625790
## iter 40 value 37.244607
## iter 50 value 36.986090
## iter 60 value 36.979068
## iter 70 value 36.977398
## iter 80 value 36.977056
## final value 36.977052
## converged
## # weights: 30
## initial value 117.057031
## iter 10 value 60.298651
## iter 20 value 39.141771
## iter 30 value 37.605709
## iter 40 value 37.183280
## iter 50 value 37.115439
## iter 60 value 37.092959
## final value 37.092862
## converged
## # weights: 30
## initial value 119.001603
## iter 10 value 63.645252
## iter 20 value 39.515569
## iter 30 value 38.622041
## iter 40 value 37.715926
## iter 50 value 37.137826
## iter 60 value 37.086584
## iter 70 value 37.076559
## iter 80 value 37.075463
## iter 90 value 37.075428
## iter 90 value 37.075427
## iter 90 value 37.075427
## final value 37.075427
## converged
## # weights: 30
## initial value 119.000887
## iter 10 value 57.884471
## iter 20 value 44.714169
## iter 30 value 39.059603
## iter 40 value 37.633375
## iter 50 value 37.096956
## iter 60 value 36.971254
## iter 70 value 36.872358
## iter 80 value 36.863135
## iter 90 value 36.863070
## final value 36.863069
## converged
## # weights: 30
## initial value 117.464229
## iter 10 value 81.456900
## iter 20 value 45.345285
## iter 30 value 38.157095
## iter 40 value 37.291803
## iter 50 value 37.186225
## iter 60 value 37.086289
## iter 70 value 36.851088
## iter 80 value 36.803641
## iter 90 value 36.802326
## final value 36.802323
## converged
## # weights: 30
## initial value 124.820566
## iter 10 value 71.991443
## iter 20 value 37.486429
## iter 30 value 36.254226
## iter 40 value 36.076262
## iter 50 value 35.706444
## iter 60 value 35.680892
## iter 70 value 35.679450
## iter 80 value 35.659604
## iter 90 value 35.643930
## iter 100 value 35.642750
## final value 35.642750
## stopped after 100 iterations
## # weights: 30
## initial value 142.478602
## iter 10 value 72.804783
## iter 20 value 37.005355
## iter 30 value 36.172197
## iter 40 value 36.095620
## iter 50 value 36.075284
## iter 60 value 36.072691
## final value 36.072543
## converged
## # weights: 30
## initial value 124.259269
## iter 10 value 67.750120
## iter 20 value 36.496174
## iter 30 value 34.316286
## iter 40 value 32.732384
## iter 50 value 31.715274
## iter 60 value 31.504055
## iter 70 value 31.306278
## iter 80 value 31.273257
## iter 90 value 31.272708
## final value 31.272705
## converged
## # weights: 30
## initial value 125.335290
## iter 10 value 78.275212
## iter 20 value 37.937664
## iter 30 value 37.072037
## iter 40 value 36.893351
## iter 50 value 36.863734
## iter 60 value 36.806682
## iter 70 value 36.784682
## iter 80 value 36.778753
## iter 90 value 36.778172
## final value 36.778155
## converged
## # weights: 30
## initial value 120.300314
## iter 10 value 64.912401
## iter 20 value 41.721976
## iter 30 value 37.574898
## iter 40 value 37.024393
## iter 50 value 36.952910
## iter 60 value 36.938327
## iter 70 value 36.924115
## iter 80 value 36.921689
## final value 36.921610
## converged
## # weights: 30
## initial value 121.270682
## iter 10 value 59.520403
## iter 20 value 43.168258
## iter 30 value 40.309331
## iter 40 value 39.891380
## iter 50 value 39.761138
## iter 60 value 39.737551
## iter 70 value 39.724100
## iter 80 value 39.612379
## iter 90 value 37.503376
## iter 100 value 36.882799
## final value 36.882799
## stopped after 100 iterations
## # weights: 30
## initial value 118.075508
## iter 10 value 75.147181
## iter 20 value 43.512259
## iter 30 value 38.057162
## iter 40 value 35.905025
## iter 50 value 35.650419
## iter 60 value 35.575839
## iter 70 value 35.283240
## iter 80 value 35.116573
## iter 90 value 35.085498
## iter 100 value 34.726824
## final value 34.726824
## stopped after 100 iterations
## # weights: 30
## initial value 123.367008
## iter 10 value 65.261082
## iter 20 value 41.234873
## iter 30 value 40.584556
## iter 40 value 39.355787
## iter 50 value 38.312972
## iter 60 value 37.827421
## iter 70 value 37.347007
## iter 80 value 37.225051
## iter 90 value 37.216251
## final value 37.216215
## converged
## # weights: 30
## initial value 145.483782
## iter 10 value 73.020774
## iter 20 value 42.716023
## iter 30 value 38.169782
## iter 40 value 36.940889
## iter 50 value 36.519991
## iter 60 value 36.457132
## iter 70 value 36.418597
## iter 80 value 36.366023
## iter 90 value 36.359298
## final value 36.359272
## converged
## # weights: 30
## initial value 129.944369
## iter 10 value 68.182615
## iter 20 value 39.012461
## iter 30 value 35.405819
## iter 40 value 34.703633
## iter 50 value 34.214121
## iter 60 value 33.917455
## iter 70 value 33.721738
## iter 80 value 33.634637
## iter 90 value 33.631515
## final value 33.631508
## converged
## # weights: 30
## initial value 118.155976
## iter 10 value 58.367753
## iter 20 value 38.467294
## iter 30 value 37.613289
## iter 40 value 37.340169
## iter 50 value 37.063110
## iter 60 value 36.967246
## iter 70 value 36.940133
## iter 80 value 36.927898
## iter 90 value 36.927457
## final value 36.927455
## converged
## # weights: 30
## initial value 124.636966
## iter 10 value 59.163793
## iter 20 value 44.174769
## iter 30 value 37.755978
## iter 40 value 36.772405
## iter 50 value 36.413092
## iter 60 value 36.184868
## iter 70 value 36.131554
## iter 80 value 36.085882
## iter 90 value 36.085501
## final value 36.085495
## converged
## # weights: 30
## initial value 129.075228
## iter 10 value 55.749811
## iter 20 value 37.244974
## iter 30 value 36.430480
## iter 40 value 35.903200
## iter 50 value 35.713945
## iter 60 value 35.691470
## iter 70 value 35.690796
## iter 80 value 35.690700
## final value 35.690697
## converged
## # weights: 30
## initial value 137.403673
## iter 10 value 58.215211
## iter 20 value 39.688110
## iter 30 value 35.969496
## iter 40 value 34.981577
## iter 50 value 34.874241
## iter 60 value 34.687827
## iter 70 value 34.594154
## iter 80 value 34.521178
## iter 90 value 34.518190
## final value 34.518177
## converged
## # weights: 30
## initial value 132.368949
## iter 10 value 72.643698
## iter 20 value 37.082779
## iter 30 value 35.707850
## iter 40 value 35.336649
## iter 50 value 34.918570
## iter 60 value 34.858788
## iter 70 value 34.849490
## iter 80 value 34.848546
## final value 34.848534
## converged
## # weights: 30
## initial value 117.334027
## iter 10 value 62.653827
## iter 20 value 38.931238
## iter 30 value 37.522688
## iter 40 value 37.022255
## iter 50 value 36.689176
## iter 60 value 36.621092
## iter 70 value 36.605334
## iter 80 value 36.604356
## final value 36.604344
## converged
## # weights: 30
## initial value 119.759879
## iter 10 value 68.667938
## iter 20 value 39.145343
## iter 30 value 36.658738
## iter 40 value 36.544283
## iter 50 value 36.388292
## iter 60 value 36.363856
## iter 70 value 36.358885
## iter 80 value 36.357567
## final value 36.357564
## converged
## # weights: 30
## initial value 150.250331
## iter 10 value 88.545790
## iter 20 value 46.220392
## iter 30 value 38.876410
## iter 40 value 37.040634
## iter 50 value 36.361151
## iter 60 value 36.330020
## iter 70 value 36.322810
## iter 80 value 36.322415
## final value 36.322401
## converged
## # weights: 30
## initial value 122.046570
## iter 10 value 67.603972
## iter 20 value 38.913511
## iter 30 value 37.698220
## iter 40 value 37.480131
## iter 50 value 37.433636
## iter 60 value 37.415202
## iter 70 value 37.410516
## iter 80 value 37.409494
## final value 37.409451
## converged
## # weights: 30
## initial value 133.085922
## iter 10 value 59.356064
## iter 20 value 44.685379
## iter 30 value 39.433842
## iter 40 value 37.770132
## iter 50 value 37.432650
## iter 60 value 36.876763
## iter 70 value 36.684710
## iter 80 value 36.674741
## iter 90 value 36.674722
## final value 36.674722
## converged
## # weights: 30
## initial value 133.053301
## iter 10 value 59.133098
## iter 20 value 43.528378
## iter 30 value 38.914982
## iter 40 value 37.948412
## iter 50 value 37.650172
## iter 60 value 37.104603
## iter 70 value 36.901096
## iter 80 value 36.837613
## iter 90 value 36.836268
## final value 36.836265
## converged
## # weights: 30
## initial value 118.431494
## iter 10 value 62.785260
## iter 20 value 38.748242
## iter 30 value 36.782072
## iter 40 value 35.862978
## iter 50 value 35.627865
## iter 60 value 35.594282
## iter 70 value 35.586224
## iter 80 value 35.584365
## final value 35.584347
## converged
## # weights: 30
## initial value 132.335392
## iter 10 value 51.975991
## iter 20 value 37.063918
## iter 30 value 36.057184
## iter 40 value 35.920771
## iter 50 value 35.902805
## iter 60 value 35.880068
## iter 70 value 35.878992
## iter 80 value 35.878886
## final value 35.878883
## converged
## # weights: 30
## initial value 119.153042
## iter 10 value 61.827232
## iter 20 value 38.877937
## iter 30 value 37.616416
## iter 40 value 37.391250
## iter 50 value 37.297722
## iter 60 value 37.267955
## iter 70 value 37.265517
## iter 80 value 37.265206
## final value 37.265205
## converged
## # weights: 30
## initial value 138.102565
## iter 10 value 52.634314
## iter 20 value 36.488473
## iter 30 value 35.567541
## iter 40 value 35.364792
## iter 50 value 35.328809
## iter 60 value 35.323300
## iter 70 value 35.322868
## iter 80 value 35.322750
## final value 35.322750
## converged
## # weights: 30
## initial value 131.491449
## iter 10 value 56.893399
## iter 20 value 42.170871
## iter 30 value 38.818535
## iter 40 value 38.479513
## iter 50 value 37.758567
## iter 60 value 37.343841
## iter 70 value 37.262280
## iter 80 value 37.255274
## iter 90 value 37.255026
## final value 37.255024
## converged
## # weights: 30
## initial value 131.654114
## iter 10 value 73.395235
## iter 20 value 38.737565
## iter 30 value 37.685479
## iter 40 value 37.335409
## iter 50 value 37.057064
## iter 60 value 37.024316
## iter 70 value 36.978002
## iter 80 value 36.968316
## iter 90 value 36.967565
## final value 36.967563
## converged
## # weights: 30
## initial value 129.127524
## iter 10 value 63.006505
## iter 20 value 46.951770
## iter 30 value 43.874474
## iter 40 value 39.317074
## iter 50 value 37.805933
## iter 60 value 37.682471
## iter 70 value 37.625571
## iter 80 value 37.620709
## iter 90 value 37.620614
## final value 37.620613
## converged
## # weights: 30
## initial value 121.674446
## iter 10 value 57.420859
## iter 20 value 39.085781
## iter 30 value 37.232132
## iter 40 value 36.907616
## iter 50 value 36.505025
## iter 60 value 36.274081
## iter 70 value 36.117047
## iter 80 value 36.085774
## final value 36.085049
## converged
## # weights: 30
## initial value 119.323811
## iter 10 value 63.496636
## iter 20 value 43.041457
## iter 30 value 34.972210
## iter 40 value 33.895679
## iter 50 value 33.522430
## iter 60 value 32.895309
## iter 70 value 32.748600
## iter 80 value 32.718116
## iter 90 value 32.717798
## final value 32.717797
## converged
## # weights: 30
## initial value 117.861026
## iter 10 value 72.795576
## iter 20 value 41.861815
## iter 30 value 39.903338
## iter 40 value 38.550605
## iter 50 value 37.941792
## iter 60 value 37.674417
## iter 70 value 37.512828
## iter 80 value 37.497384
## iter 90 value 37.496958
## final value 37.496956
## converged
## # weights: 30
## initial value 133.721600
## iter 10 value 45.567633
## iter 20 value 37.713936
## iter 30 value 37.264975
## iter 40 value 36.940970
## iter 50 value 36.879799
## iter 60 value 36.875633
## iter 70 value 36.873807
## final value 36.873708
## converged
## # weights: 30
## initial value 119.089293
## iter 10 value 64.040137
## iter 20 value 40.291830
## iter 30 value 37.470391
## iter 40 value 37.115383
## iter 50 value 36.581757
## iter 60 value 36.184698
## iter 70 value 36.037563
## iter 80 value 36.009360
## iter 90 value 36.008882
## final value 36.008879
## converged
## # weights: 30
## initial value 127.282201
## iter 10 value 56.435811
## iter 20 value 39.579832
## iter 30 value 37.946459
## iter 40 value 37.278167
## iter 50 value 37.087577
## iter 60 value 37.025674
## iter 70 value 36.947783
## iter 80 value 36.936257
## iter 90 value 36.936029
## final value 36.936025
## converged
## # weights: 30
## initial value 118.156123
## iter 10 value 50.297544
## iter 20 value 36.265240
## iter 30 value 35.398280
## iter 40 value 34.825090
## iter 50 value 34.706341
## iter 60 value 34.686402
## iter 70 value 34.682052
## iter 80 value 34.680708
## final value 34.680669
## converged
## # weights: 30
## initial value 128.612458
## iter 10 value 51.419473
## iter 20 value 38.300460
## iter 30 value 36.910132
## iter 40 value 36.247732
## iter 50 value 35.661775
## iter 60 value 35.546868
## iter 70 value 35.492424
## iter 80 value 35.483845
## iter 90 value 35.483589
## final value 35.483589
## converged
## # weights: 30
## initial value 145.076874
## iter 10 value 60.202754
## iter 20 value 37.682762
## iter 30 value 36.355069
## iter 40 value 36.269210
## iter 50 value 36.261699
## iter 60 value 36.258976
## iter 70 value 36.258574
## iter 80 value 36.258382
## final value 36.258379
## converged
## # weights: 30
## initial value 123.705616
## iter 10 value 61.940962
## iter 20 value 42.404325
## iter 30 value 39.200814
## iter 40 value 38.486025
## iter 50 value 37.499359
## iter 60 value 37.239510
## iter 70 value 37.219049
## iter 80 value 37.160583
## iter 90 value 37.132821
## iter 100 value 37.132227
## final value 37.132227
## stopped after 100 iterations
## # weights: 30
## initial value 119.996013
## iter 10 value 59.097293
## iter 20 value 38.561953
## iter 30 value 35.150558
## iter 40 value 34.126263
## iter 50 value 33.847482
## iter 60 value 33.799754
## iter 70 value 33.772796
## iter 80 value 33.763939
## final value 33.763874
## converged
## # weights: 30
## initial value 144.845485
## iter 10 value 72.876831
## iter 20 value 39.348624
## iter 30 value 35.333789
## iter 40 value 34.700935
## iter 50 value 34.363961
## iter 60 value 34.296645
## iter 70 value 34.228290
## iter 80 value 34.226231
## final value 34.225869
## converged
## # weights: 30
## initial value 126.453323
## iter 10 value 65.277192
## iter 20 value 41.461668
## iter 30 value 38.048518
## iter 40 value 37.269960
## iter 50 value 36.932206
## iter 60 value 36.744522
## iter 70 value 36.597174
## iter 80 value 36.581084
## iter 90 value 36.580428
## final value 36.580426
## converged
## # weights: 30
## initial value 119.876210
## iter 10 value 78.182224
## iter 20 value 36.965803
## iter 30 value 36.014286
## iter 40 value 35.954103
## iter 50 value 35.899832
## iter 60 value 35.888001
## final value 35.887937
## converged
## # weights: 30
## initial value 117.432132
## iter 10 value 58.570908
## iter 20 value 44.598387
## iter 30 value 40.525787
## iter 40 value 40.158987
## iter 50 value 40.105442
## iter 60 value 40.053081
## iter 70 value 39.809927
## iter 80 value 37.689602
## iter 90 value 37.371116
## iter 100 value 37.349651
## final value 37.349651
## stopped after 100 iterations
## # weights: 30
## initial value 128.325078
## iter 10 value 78.693396
## iter 20 value 38.774438
## iter 30 value 37.842160
## iter 40 value 37.351328
## iter 50 value 37.093990
## iter 60 value 36.938378
## iter 70 value 36.921159
## iter 80 value 36.921070
## final value 36.921058
## converged
## # weights: 30
## initial value 128.273583
## iter 10 value 71.223330
## iter 20 value 40.200726
## iter 30 value 37.293092
## iter 40 value 37.083396
## iter 50 value 37.013059
## iter 60 value 36.936372
## iter 70 value 36.767691
## iter 80 value 36.114023
## iter 90 value 35.906317
## iter 100 value 35.876343
## final value 35.876343
## stopped after 100 iterations
## # weights: 30
## initial value 122.097491
## iter 10 value 56.735661
## iter 20 value 37.021982
## iter 30 value 34.787634
## iter 40 value 34.670374
## iter 50 value 34.653856
## iter 60 value 34.651718
## iter 70 value 34.651112
## iter 80 value 34.650737
## final value 34.650735
## converged
## # weights: 30
## initial value 122.789812
## iter 10 value 50.565141
## iter 20 value 39.668235
## iter 30 value 36.994610
## iter 40 value 36.460194
## iter 50 value 36.049295
## iter 60 value 36.021006
## iter 70 value 36.014133
## iter 80 value 36.012602
## final value 36.012575
## converged
## # weights: 30
## initial value 168.069840
## iter 10 value 73.051399
## iter 20 value 40.704712
## iter 30 value 38.316334
## iter 40 value 37.549781
## iter 50 value 37.359064
## iter 60 value 37.273705
## final value 37.269340
## converged
## # weights: 30
## initial value 125.682962
## iter 10 value 56.129691
## iter 20 value 42.268209
## iter 30 value 39.413565
## iter 40 value 38.741375
## iter 50 value 38.415387
## iter 60 value 38.401703
## iter 70 value 38.400559
## iter 80 value 38.400350
## final value 38.400346
## converged
## # weights: 30
## initial value 121.393265
## iter 10 value 56.448284
## iter 20 value 39.633747
## iter 30 value 38.800047
## iter 40 value 38.662666
## iter 50 value 38.631947
## iter 60 value 38.630023
## final value 38.630005
## converged
## # weights: 30
## initial value 131.229178
## iter 10 value 61.499021
## iter 20 value 39.811833
## iter 30 value 38.582735
## iter 40 value 38.410987
## iter 50 value 38.318628
## iter 60 value 38.246458
## iter 70 value 38.204637
## iter 80 value 38.204056
## final value 38.204023
## converged
## # weights: 30
## initial value 130.756211
## iter 10 value 87.061030
## iter 20 value 42.422009
## iter 30 value 38.404077
## iter 40 value 38.251938
## iter 50 value 38.214904
## iter 60 value 38.165048
## iter 70 value 38.154589
## iter 80 value 38.138205
## iter 90 value 38.135676
## final value 38.135665
## converged
## # weights: 30
## initial value 129.592957
## iter 10 value 50.287395
## iter 20 value 37.985610
## iter 30 value 36.954943
## iter 40 value 36.415601
## iter 50 value 36.363600
## iter 60 value 36.358330
## iter 70 value 36.356424
## iter 80 value 36.356162
## final value 36.356158
## converged
## # weights: 30
## initial value 135.314860
## iter 10 value 58.504663
## iter 20 value 40.129895
## iter 30 value 38.700143
## iter 40 value 37.784098
## iter 50 value 36.251819
## iter 60 value 35.932512
## iter 70 value 35.869798
## iter 80 value 35.858215
## final value 35.858085
## converged
## # weights: 30
## initial value 143.035072
## iter 10 value 79.201963
## iter 20 value 39.794404
## iter 30 value 37.479277
## iter 40 value 37.000889
## iter 50 value 36.626831
## iter 60 value 36.555182
## iter 70 value 36.500502
## iter 80 value 36.488492
## iter 90 value 36.488191
## iter 90 value 36.488191
## iter 90 value 36.488191
## final value 36.488191
## converged
## # weights: 30
## initial value 127.032102
## iter 10 value 58.791693
## iter 20 value 43.319454
## iter 30 value 40.272705
## iter 40 value 39.414215
## iter 50 value 38.896146
## iter 60 value 38.633147
## iter 70 value 38.343232
## iter 80 value 38.265567
## iter 90 value 38.264505
## final value 38.264502
## converged
## # weights: 30
## initial value 138.163845
## iter 10 value 67.893888
## iter 20 value 40.123019
## iter 30 value 39.125923
## iter 40 value 38.374056
## iter 50 value 37.753710
## iter 60 value 37.682005
## iter 70 value 37.657841
## iter 80 value 37.655205
## iter 90 value 37.655136
## iter 90 value 37.655136
## iter 90 value 37.655136
## final value 37.655136
## converged
## # weights: 30
## initial value 120.127110
## iter 10 value 66.457808
## iter 20 value 39.451504
## iter 30 value 37.178946
## iter 40 value 36.998451
## iter 50 value 36.955597
## iter 60 value 36.904348
## iter 70 value 36.903607
## iter 80 value 36.903384
## final value 36.903383
## converged
## # weights: 30
## initial value 123.639897
## iter 10 value 96.952122
## iter 20 value 43.680558
## iter 30 value 40.632440
## iter 40 value 38.896785
## iter 50 value 38.328509
## iter 60 value 38.021843
## iter 70 value 37.776791
## iter 80 value 37.683380
## iter 90 value 37.681403
## final value 37.681399
## converged
## # weights: 30
## initial value 131.408366
## iter 10 value 60.211156
## iter 20 value 45.831299
## iter 30 value 44.703546
## iter 40 value 40.464747
## iter 50 value 38.889754
## iter 60 value 38.514250
## iter 70 value 38.369888
## iter 80 value 38.309063
## iter 90 value 38.308680
## final value 38.308679
## converged
## # weights: 30
## initial value 119.114746
## iter 10 value 53.654505
## iter 20 value 39.876215
## iter 30 value 38.958971
## iter 40 value 38.603959
## iter 50 value 38.408914
## iter 60 value 38.342335
## iter 70 value 38.338418
## iter 80 value 38.338212
## final value 38.338205
## converged
## # weights: 30
## initial value 138.509135
## iter 10 value 50.493964
## iter 20 value 41.451474
## iter 30 value 39.056189
## iter 40 value 38.485973
## iter 50 value 38.148059
## iter 60 value 38.127498
## iter 70 value 38.118498
## iter 80 value 38.117802
## final value 38.117787
## converged
## # weights: 30
## initial value 130.216940
## iter 10 value 60.859555
## iter 20 value 42.410374
## iter 30 value 40.458001
## iter 40 value 39.821066
## iter 50 value 38.682894
## iter 60 value 37.513650
## iter 70 value 37.282430
## iter 80 value 37.237597
## iter 90 value 37.234906
## final value 37.234890
## converged
## # weights: 30
## initial value 147.320484
## iter 10 value 70.147491
## iter 20 value 41.519464
## iter 30 value 38.666913
## iter 40 value 38.333930
## iter 50 value 38.095175
## iter 60 value 38.053630
## iter 70 value 38.026474
## iter 80 value 38.021006
## final value 38.020869
## converged
## # weights: 30
## initial value 117.096995
## iter 10 value 50.250986
## iter 20 value 39.106928
## iter 30 value 37.395990
## iter 40 value 37.054679
## iter 50 value 36.887731
## iter 60 value 36.884076
## iter 70 value 36.883758
## final value 36.883736
## converged
## # weights: 30
## initial value 131.595322
## iter 10 value 54.939590
## iter 20 value 37.197915
## iter 30 value 36.461189
## iter 40 value 36.326299
## iter 50 value 36.270256
## iter 60 value 36.265133
## iter 70 value 36.263908
## iter 80 value 36.263424
## final value 36.263413
## converged
## # weights: 30
## initial value 127.417458
## iter 10 value 68.729487
## iter 20 value 44.805794
## iter 30 value 41.739106
## iter 40 value 40.315054
## iter 50 value 39.999814
## iter 60 value 39.808490
## iter 70 value 39.556387
## iter 80 value 38.871077
## iter 90 value 38.766163
## iter 100 value 38.761562
## final value 38.761562
## stopped after 100 iterations
## # weights: 30
## initial value 130.001917
## iter 10 value 79.808692
## iter 20 value 42.246474
## iter 30 value 39.778065
## iter 40 value 39.426744
## iter 50 value 39.126249
## iter 60 value 39.028175
## iter 70 value 39.027265
## iter 80 value 39.024921
## iter 90 value 39.024787
## final value 39.024785
## converged
## # weights: 30
## initial value 121.149826
## iter 10 value 56.095290
## iter 20 value 41.624830
## iter 30 value 38.117743
## iter 40 value 37.959053
## iter 50 value 37.899352
## iter 60 value 37.892136
## iter 70 value 37.879342
## iter 80 value 37.873816
## iter 90 value 37.873586
## iter 90 value 37.873585
## iter 90 value 37.873585
## final value 37.873585
## converged
## # weights: 30
## initial value 143.309791
## iter 10 value 79.983332
## iter 20 value 34.718570
## iter 30 value 34.058557
## iter 40 value 33.217074
## iter 50 value 33.063748
## iter 60 value 33.000062
## iter 70 value 32.994245
## iter 80 value 32.990284
## iter 90 value 32.990163
## final value 32.990162
## converged
## # weights: 30
## initial value 123.440503
## iter 10 value 54.684825
## iter 20 value 40.157328
## iter 30 value 39.557548
## iter 40 value 39.455562
## iter 50 value 39.409722
## iter 60 value 39.403850
## iter 70 value 39.402227
## iter 80 value 39.339537
## iter 90 value 39.237185
## iter 100 value 37.296391
## final value 37.296391
## stopped after 100 iterations
## # weights: 30
## initial value 116.924124
## iter 10 value 56.650251
## iter 20 value 40.098047
## iter 30 value 38.993002
## iter 40 value 38.678774
## iter 50 value 38.596890
## iter 60 value 38.575223
## iter 70 value 38.571534
## iter 80 value 38.571188
## final value 38.571184
## converged
## # weights: 30
## initial value 120.140389
## iter 10 value 56.948433
## iter 20 value 44.431675
## iter 30 value 39.532584
## iter 40 value 38.254130
## iter 50 value 38.134018
## iter 60 value 38.119795
## iter 70 value 38.118687
## iter 80 value 38.118384
## final value 38.118383
## converged
## # weights: 30
## initial value 126.529975
## iter 10 value 45.479183
## iter 20 value 37.711153
## iter 30 value 37.275555
## iter 40 value 37.191320
## iter 50 value 37.151866
## iter 60 value 37.126264
## iter 70 value 37.122581
## iter 80 value 37.121939
## final value 37.121927
## converged
## # weights: 30
## initial value 119.777406
## iter 10 value 54.298642
## iter 20 value 40.291368
## iter 30 value 38.899042
## iter 40 value 38.815583
## iter 50 value 38.807331
## iter 60 value 38.806695
## final value 38.806689
## converged
## # weights: 30
## initial value 120.062502
## iter 10 value 68.611073
## iter 20 value 42.471767
## iter 30 value 39.749550
## iter 40 value 38.828029
## iter 50 value 38.291511
## iter 60 value 38.039380
## iter 70 value 37.585426
## iter 80 value 37.497191
## iter 90 value 37.495764
## final value 37.495763
## converged
## # weights: 30
## initial value 149.646302
## iter 10 value 65.840257
## iter 20 value 44.303739
## iter 30 value 41.399422
## iter 40 value 40.572296
## iter 50 value 40.160042
## iter 60 value 38.683323
## iter 70 value 38.337297
## iter 80 value 38.211251
## iter 90 value 38.210212
## final value 38.210210
## converged
## # weights: 30
## initial value 122.619236
## iter 10 value 52.608952
## iter 20 value 39.355295
## iter 30 value 38.871412
## iter 40 value 38.780793
## iter 50 value 38.775865
## iter 60 value 38.759561
## iter 70 value 38.751377
## iter 80 value 38.749554
## final value 38.749550
## converged
## # weights: 30
## initial value 123.004102
## iter 10 value 59.558228
## iter 20 value 38.495740
## iter 30 value 36.846121
## iter 40 value 36.053291
## iter 50 value 35.813888
## iter 60 value 35.684853
## iter 70 value 35.607262
## iter 80 value 35.593802
## iter 90 value 35.593319
## final value 35.593317
## converged
## # weights: 30
## initial value 122.740707
## iter 10 value 63.044632
## iter 20 value 43.783038
## iter 30 value 40.817888
## iter 40 value 39.134187
## iter 50 value 38.800882
## iter 60 value 38.721883
## iter 70 value 38.718230
## iter 80 value 38.717402
## final value 38.717384
## converged
## # weights: 30
## initial value 117.219467
## iter 10 value 49.841068
## iter 20 value 40.217931
## iter 30 value 38.424124
## iter 40 value 37.803024
## iter 50 value 37.692790
## iter 60 value 37.665035
## iter 70 value 37.662094
## iter 80 value 37.661597
## final value 37.661597
## converged
## # weights: 30
## initial value 120.513868
## iter 10 value 64.160480
## iter 20 value 39.713527
## iter 30 value 38.763713
## iter 40 value 38.633568
## iter 50 value 38.602261
## iter 60 value 38.590592
## iter 70 value 38.588721
## iter 80 value 38.588239
## final value 38.588224
## converged
## # weights: 30
## initial value 132.729790
## iter 10 value 47.845351
## iter 20 value 36.425960
## iter 30 value 35.854012
## iter 40 value 35.625400
## iter 50 value 35.581384
## iter 60 value 35.576184
## iter 70 value 35.573871
## iter 80 value 35.573661
## final value 35.573654
## converged
## # weights: 30
## initial value 134.044882
## iter 10 value 48.166096
## iter 20 value 38.392819
## iter 30 value 37.715099
## iter 40 value 37.241784
## iter 50 value 37.044977
## iter 60 value 36.995679
## iter 70 value 36.972639
## iter 80 value 36.971808
## final value 36.971784
## converged
## # weights: 30
## initial value 122.193064
## iter 10 value 63.868118
## iter 20 value 46.136736
## iter 30 value 42.794340
## iter 40 value 39.972916
## iter 50 value 39.269897
## iter 60 value 39.109882
## iter 70 value 39.057093
## iter 80 value 39.008583
## iter 90 value 39.006140
## final value 39.006138
## converged
## # weights: 30
## initial value 118.960497
## iter 10 value 57.460726
## iter 20 value 42.054291
## iter 30 value 41.509228
## iter 40 value 41.362533
## iter 50 value 40.942811
## iter 60 value 39.003761
## iter 70 value 38.361697
## iter 80 value 38.344513
## iter 90 value 38.343887
## final value 38.343878
## converged
## # weights: 30
## initial value 123.012942
## iter 10 value 69.587485
## iter 20 value 42.066116
## iter 30 value 40.409099
## iter 40 value 39.525790
## iter 50 value 38.817036
## iter 60 value 38.598544
## iter 70 value 38.491813
## iter 80 value 38.464756
## iter 90 value 38.464049
## final value 38.464042
## converged
## # weights: 30
## initial value 125.297176
## iter 10 value 67.310004
## iter 20 value 40.743440
## iter 30 value 38.714790
## iter 40 value 38.473235
## iter 50 value 38.446906
## iter 60 value 38.428415
## iter 70 value 38.412994
## iter 80 value 38.407974
## final value 38.407889
## converged
## # weights: 30
## initial value 122.262414
## iter 10 value 64.789228
## iter 20 value 42.104088
## iter 30 value 39.517493
## iter 40 value 38.416573
## iter 50 value 38.279743
## iter 60 value 38.232830
## iter 70 value 38.178154
## iter 80 value 38.165955
## iter 90 value 38.165119
## final value 38.165116
## converged
## # weights: 30
## initial value 120.965373
## iter 10 value 64.677397
## iter 20 value 43.776949
## iter 30 value 41.861960
## iter 40 value 40.367101
## iter 50 value 39.153233
## iter 60 value 38.211617
## iter 70 value 38.164212
## iter 80 value 38.163165
## final value 38.163140
## converged
## # weights: 30
## initial value 117.726491
## iter 10 value 56.029161
## iter 20 value 44.076838
## iter 30 value 39.885458
## iter 40 value 38.048922
## iter 50 value 37.162805
## iter 60 value 37.055171
## iter 70 value 37.046885
## iter 80 value 37.044082
## final value 37.043983
## converged
## # weights: 30
## initial value 136.868181
## iter 10 value 67.610103
## iter 20 value 38.690758
## iter 30 value 37.730171
## iter 40 value 37.688115
## iter 50 value 37.684537
## iter 60 value 37.662729
## iter 70 value 37.520187
## iter 80 value 37.460826
## iter 90 value 37.456466
## final value 37.456436
## converged
## # weights: 30
## initial value 130.764124
## iter 10 value 69.549870
## iter 20 value 41.852286
## iter 30 value 39.691205
## iter 40 value 34.780425
## iter 50 value 33.330263
## iter 60 value 33.000875
## iter 70 value 32.891456
## iter 80 value 32.829224
## iter 90 value 32.828461
## iter 90 value 32.828461
## iter 90 value 32.828461
## final value 32.828461
## converged
## # weights: 30
## initial value 126.329838
## iter 10 value 68.340049
## iter 20 value 42.100782
## iter 30 value 38.732315
## iter 40 value 38.395095
## iter 50 value 38.323073
## iter 60 value 38.185288
## iter 70 value 38.157875
## iter 80 value 38.150668
## iter 90 value 38.150535
## iter 90 value 38.150535
## iter 90 value 38.150535
## final value 38.150535
## converged
## # weights: 30
## initial value 144.474697
## iter 10 value 86.353494
## iter 20 value 41.795871
## iter 30 value 39.435652
## iter 40 value 38.584452
## iter 50 value 38.322694
## iter 60 value 38.266889
## iter 70 value 38.236464
## iter 80 value 38.221493
## iter 90 value 38.220224
## final value 38.220222
## converged
## # weights: 30
## initial value 148.599355
## iter 10 value 83.915186
## iter 20 value 47.183089
## iter 30 value 39.625936
## iter 40 value 38.660465
## iter 50 value 38.487484
## iter 60 value 38.474826
## iter 70 value 38.445929
## iter 80 value 38.320076
## iter 90 value 38.306875
## final value 38.306845
## converged
## # weights: 30
## initial value 125.205812
## iter 10 value 54.991260
## iter 20 value 39.040417
## iter 30 value 37.109889
## iter 40 value 35.913379
## iter 50 value 35.787684
## iter 60 value 35.561966
## iter 70 value 35.501852
## iter 80 value 35.474476
## iter 90 value 35.474173
## final value 35.474172
## converged
## # weights: 30
## initial value 133.547491
## iter 10 value 77.365933
## iter 20 value 42.522960
## iter 30 value 40.696153
## iter 40 value 39.958450
## iter 50 value 39.119730
## iter 60 value 38.958325
## iter 70 value 38.770030
## iter 80 value 38.612129
## iter 90 value 38.607985
## final value 38.607983
## converged
## # weights: 30
## initial value 131.968749
## iter 10 value 58.691161
## iter 20 value 40.312963
## iter 30 value 38.018569
## iter 40 value 37.787350
## iter 50 value 37.744369
## iter 60 value 37.740518
## iter 70 value 37.740039
## final value 37.739996
## converged
## # weights: 30
## initial value 136.863880
## iter 10 value 44.014113
## iter 20 value 38.283098
## iter 30 value 37.895715
## iter 40 value 37.866678
## iter 50 value 37.396701
## iter 60 value 35.487565
## iter 70 value 35.120250
## iter 80 value 35.077534
## iter 90 value 35.077202
## final value 35.077195
## converged
## # weights: 30
## initial value 127.484769
## iter 10 value 61.899505
## iter 20 value 42.602114
## iter 30 value 41.556089
## iter 40 value 41.260529
## iter 50 value 41.179963
## iter 60 value 41.017030
## iter 70 value 39.365783
## iter 80 value 38.247855
## iter 90 value 38.242452
## iter 100 value 38.242090
## final value 38.242090
## stopped after 100 iterations
## # weights: 30
## initial value 126.193371
## iter 10 value 62.293701
## iter 20 value 45.906152
## iter 30 value 42.790185
## iter 40 value 38.617701
## iter 50 value 38.118495
## iter 60 value 37.859974
## iter 70 value 37.516550
## iter 80 value 37.462220
## iter 90 value 37.440660
## final value 37.439716
## converged
## # weights: 30
## initial value 119.265406
## iter 10 value 57.873691
## iter 20 value 41.964155
## iter 30 value 38.488187
## iter 40 value 37.739252
## iter 50 value 37.380381
## iter 60 value 37.123834
## iter 70 value 37.107730
## iter 80 value 37.105370
## final value 37.105331
## converged
## # weights: 30
## initial value 123.272147
## iter 10 value 69.508198
## iter 20 value 40.989808
## iter 30 value 37.368463
## iter 40 value 36.470382
## iter 50 value 36.037807
## iter 60 value 36.015553
## iter 70 value 36.014143
## iter 80 value 36.012196
## iter 90 value 36.012140
## final value 36.012139
## converged
## # weights: 30
## initial value 124.789912
## iter 10 value 58.534767
## iter 20 value 40.484080
## iter 30 value 39.279852
## iter 40 value 39.185304
## iter 50 value 39.113964
## iter 60 value 39.076179
## iter 70 value 38.624777
## iter 80 value 36.504828
## iter 90 value 36.166117
## iter 100 value 36.165424
## final value 36.165424
## stopped after 100 iterations
## # weights: 30
## initial value 129.995967
## iter 10 value 59.222292
## iter 20 value 43.125238
## iter 30 value 38.619086
## iter 40 value 38.280688
## iter 50 value 38.046137
## iter 60 value 38.003364
## iter 70 value 37.978039
## iter 80 value 37.974665
## iter 90 value 37.974580
## iter 90 value 37.974579
## iter 90 value 37.974579
## final value 37.974579
## converged
## # weights: 30
## initial value 125.876702
## iter 10 value 53.727254
## iter 20 value 40.132653
## iter 30 value 38.807197
## iter 40 value 37.854525
## iter 50 value 37.801231
## iter 60 value 37.786300
## iter 70 value 37.779701
## iter 80 value 37.778766
## final value 37.778761
## converged
## # weights: 30
## initial value 147.430558
## iter 10 value 63.700814
## iter 20 value 40.664702
## iter 30 value 38.113520
## iter 40 value 37.952269
## iter 50 value 37.755394
## iter 60 value 37.710326
## iter 70 value 37.701438
## iter 80 value 37.701254
## final value 37.701248
## converged
## # weights: 30
## initial value 193.226656
## iter 10 value 60.978621
## iter 20 value 41.120756
## iter 30 value 40.385264
## iter 40 value 39.380697
## iter 50 value 38.950378
## iter 60 value 38.822641
## iter 70 value 38.799352
## iter 80 value 38.798049
## final value 38.798034
## converged
## # weights: 30
## initial value 132.278494
## iter 10 value 68.083146
## iter 20 value 40.204630
## iter 30 value 39.355683
## iter 40 value 39.033037
## iter 50 value 38.876195
## iter 60 value 38.686444
## iter 70 value 38.246375
## iter 80 value 38.073262
## iter 90 value 38.070775
## final value 38.070729
## converged
## # weights: 30
## initial value 148.773795
## iter 10 value 63.788168
## iter 20 value 40.845533
## iter 30 value 38.996837
## iter 40 value 38.441833
## iter 50 value 38.216917
## iter 60 value 38.188944
## iter 70 value 38.178782
## iter 80 value 38.176185
## final value 38.176145
## converged
## # weights: 30
## initial value 138.378222
## iter 10 value 66.980137
## iter 20 value 40.546213
## iter 30 value 38.363026
## iter 40 value 37.439212
## iter 50 value 37.276356
## iter 60 value 37.158343
## iter 70 value 37.016070
## iter 80 value 36.978696
## iter 90 value 36.977377
## final value 36.977369
## converged
## # weights: 30
## initial value 125.523794
## iter 10 value 53.821905
## iter 20 value 39.155143
## iter 30 value 38.520627
## iter 40 value 38.196125
## iter 50 value 37.867106
## iter 60 value 37.419779
## iter 70 value 37.386750
## iter 80 value 37.381119
## final value 37.381060
## converged
## # weights: 30
## initial value 129.169934
## iter 10 value 71.692590
## iter 20 value 47.540736
## iter 30 value 40.218558
## iter 40 value 39.671405
## iter 50 value 39.392699
## iter 60 value 39.105466
## iter 70 value 38.732324
## iter 80 value 38.684526
## iter 90 value 38.683811
## final value 38.683805
## converged
## # weights: 30
## initial value 136.039254
## iter 10 value 68.161177
## iter 20 value 44.479887
## iter 30 value 39.002827
## iter 40 value 37.423342
## iter 50 value 36.929281
## iter 60 value 36.784760
## iter 70 value 36.777564
## iter 80 value 36.776609
## final value 36.776596
## converged
## # weights: 30
## initial value 119.249453
## iter 10 value 61.889671
## iter 20 value 40.330985
## iter 30 value 39.573349
## iter 40 value 39.387901
## iter 50 value 39.249258
## iter 60 value 38.683718
## iter 70 value 38.662949
## iter 80 value 38.660734
## final value 38.660676
## converged
## # weights: 30
## initial value 118.655963
## iter 10 value 68.857375
## iter 20 value 41.834801
## iter 30 value 39.080857
## iter 40 value 38.428594
## iter 50 value 38.377705
## iter 60 value 38.353297
## iter 70 value 38.351841
## iter 80 value 38.351689
## final value 38.351685
## converged
## # weights: 30
## initial value 119.108765
## iter 10 value 59.506213
## iter 20 value 39.897592
## iter 30 value 39.222473
## iter 40 value 39.118825
## iter 50 value 39.046823
## iter 60 value 39.034002
## iter 70 value 39.030961
## iter 80 value 39.029949
## final value 39.029927
## converged
## # weights: 30
## initial value 126.098593
## iter 10 value 70.508099
## iter 20 value 42.619102
## iter 30 value 40.593252
## iter 40 value 40.407478
## iter 50 value 40.403599
## iter 60 value 40.371165
## iter 70 value 40.334257
## iter 80 value 40.252783
## iter 90 value 38.120917
## iter 100 value 37.449530
## final value 37.449530
## stopped after 100 iterations
## # weights: 30
## initial value 126.334493
## iter 10 value 74.583819
## iter 20 value 36.413749
## iter 30 value 34.836488
## iter 40 value 34.369977
## iter 50 value 34.328451
## iter 60 value 34.279059
## iter 70 value 34.262892
## iter 80 value 34.254112
## final value 34.254042
## converged
## # weights: 30
## initial value 119.970325
## iter 10 value 62.831785
## iter 20 value 42.793772
## iter 30 value 41.054111
## iter 40 value 39.943439
## iter 50 value 39.395148
## iter 60 value 38.943392
## iter 70 value 38.889420
## iter 80 value 38.881349
## iter 90 value 38.881208
## iter 90 value 38.881208
## iter 90 value 38.881208
## final value 38.881208
## converged
## # weights: 30
## initial value 131.276296
## iter 10 value 59.644807
## iter 20 value 44.649055
## iter 30 value 40.723315
## iter 40 value 39.177271
## iter 50 value 38.232460
## iter 60 value 38.195731
## iter 70 value 38.164909
## iter 80 value 38.159969
## final value 38.159959
## converged
## # weights: 30
## initial value 126.699019
## iter 10 value 50.073763
## iter 20 value 39.358688
## iter 30 value 38.395911
## iter 40 value 37.728635
## iter 50 value 37.614045
## iter 60 value 37.486035
## iter 70 value 37.453280
## iter 80 value 37.450929
## final value 37.450884
## converged
## # weights: 30
## initial value 122.195208
## iter 10 value 78.925406
## iter 20 value 43.840739
## iter 30 value 41.515163
## iter 40 value 39.668708
## iter 50 value 38.858550
## iter 60 value 38.500527
## iter 70 value 38.376130
## iter 80 value 38.362626
## iter 90 value 38.362557
## iter 90 value 38.362557
## iter 90 value 38.362557
## final value 38.362557
## converged
## # weights: 30
## initial value 124.497515
## iter 10 value 65.687641
## iter 20 value 37.887723
## iter 30 value 36.776830
## iter 40 value 36.168197
## iter 50 value 36.142401
## iter 60 value 36.141814
## iter 70 value 36.141474
## iter 80 value 36.141372
## final value 36.141371
## converged
## # weights: 30
## initial value 128.403061
## iter 10 value 92.748576
## iter 20 value 52.059726
## iter 30 value 42.874503
## iter 40 value 39.738097
## iter 50 value 38.176629
## iter 60 value 37.522090
## iter 70 value 37.106435
## iter 80 value 36.816243
## iter 90 value 36.810656
## iter 100 value 36.810313
## final value 36.810313
## stopped after 100 iterations
## # weights: 30
## initial value 134.603370
## iter 10 value 79.146680
## iter 20 value 46.156806
## iter 30 value 44.944055
## iter 40 value 40.862547
## iter 50 value 38.657240
## iter 60 value 38.083115
## iter 70 value 37.801271
## iter 80 value 37.733469
## iter 90 value 37.731833
## final value 37.731823
## converged
## # weights: 30
## initial value 125.329728
## iter 10 value 49.840054
## iter 20 value 41.421123
## iter 30 value 38.627951
## iter 40 value 38.573530
## iter 50 value 38.555614
## iter 60 value 38.536705
## iter 70 value 38.531808
## iter 80 value 38.530746
## final value 38.530723
## converged
## # weights: 30
## initial value 124.639734
## iter 10 value 57.067483
## iter 20 value 38.222528
## iter 30 value 36.515865
## iter 40 value 35.956154
## iter 50 value 35.594897
## iter 60 value 35.398516
## iter 70 value 35.312575
## iter 80 value 35.271824
## iter 90 value 35.271469
## iter 90 value 35.271468
## iter 90 value 35.271468
## final value 35.271468
## converged
## # weights: 30
## initial value 127.642263
## iter 10 value 63.143783
## iter 20 value 39.038166
## iter 30 value 37.704306
## iter 40 value 37.205903
## iter 50 value 36.502827
## iter 60 value 35.853345
## iter 70 value 35.697095
## iter 80 value 35.649946
## iter 90 value 35.649070
## iter 90 value 35.649070
## iter 90 value 35.649070
## final value 35.649070
## converged
## # weights: 30
## initial value 119.566554
## iter 10 value 57.528368
## iter 20 value 42.923411
## iter 30 value 41.625627
## iter 40 value 41.321784
## iter 50 value 41.289634
## iter 60 value 41.279802
## iter 70 value 41.270024
## iter 80 value 41.233128
## iter 90 value 40.716187
## iter 100 value 38.280790
## final value 38.280790
## stopped after 100 iterations
## # weights: 30
## initial value 140.525133
## iter 10 value 81.730471
## iter 20 value 44.213259
## iter 30 value 39.584495
## iter 40 value 38.144013
## iter 50 value 37.328141
## iter 60 value 37.199368
## iter 70 value 37.198340
## iter 80 value 37.198029
## final value 37.198001
## converged
## # weights: 30
## initial value 144.499716
## iter 10 value 73.296660
## iter 20 value 41.039992
## iter 30 value 40.118325
## iter 40 value 39.939781
## iter 50 value 39.886232
## iter 60 value 39.877894
## iter 70 value 39.843440
## iter 80 value 39.185564
## iter 90 value 38.825665
## iter 100 value 38.774112
## final value 38.774112
## stopped after 100 iterations
## # weights: 30
## initial value 136.875901
## iter 10 value 56.565069
## iter 20 value 40.056679
## iter 30 value 38.422201
## iter 40 value 38.388799
## iter 50 value 38.378652
## iter 60 value 38.375820
## iter 70 value 38.373554
## iter 80 value 38.313345
## iter 90 value 38.269467
## iter 100 value 38.267351
## final value 38.267351
## stopped after 100 iterations
## # weights: 30
## initial value 121.333569
## iter 10 value 77.091458
## iter 20 value 40.094472
## iter 30 value 38.548829
## iter 40 value 38.180275
## iter 50 value 37.485174
## iter 60 value 37.233070
## iter 70 value 37.226952
## iter 80 value 37.226369
## final value 37.226354
## converged
## # weights: 30
## initial value 119.785157
## iter 10 value 66.077056
## iter 20 value 40.071875
## iter 30 value 37.222345
## iter 40 value 36.359040
## iter 50 value 36.227486
## iter 60 value 36.113299
## iter 70 value 36.076793
## iter 80 value 36.064644
## final value 36.064567
## converged
## # weights: 30
## initial value 121.064262
## iter 10 value 47.500833
## iter 20 value 41.042111
## iter 30 value 38.184166
## iter 40 value 37.540962
## iter 50 value 37.378335
## iter 60 value 37.373753
## iter 70 value 37.371327
## iter 80 value 37.370855
## final value 37.370844
## converged
## # weights: 30
## initial value 124.331104
## iter 10 value 80.235294
## iter 20 value 40.256647
## iter 30 value 38.982335
## iter 40 value 38.671195
## iter 50 value 38.646366
## iter 60 value 38.645840
## final value 38.645789
## converged
toc()## 8.69 sec elapsed
lrn_tune## Tune result:
## Op. pars: size=5; decay=0.0661
## mmce.test.mean=0.0515873
Here, you will evaluate the results of a hyperparameter tuning run for a decision tree trained with the rpart package. The knowledge_train_data dataset has already been loaded for you, as have the packages mlr and tidyverse. And the following code has also been run:
task <- makeClassifTask(data = knowledge_train_data,
target = "UNS")
lrn <- makeLearner(cl = "classif.rpart", fix.factors.prediction = TRUE)
param_set <- makeParamSet(
makeIntegerParam("minsplit", lower = 1, upper = 30),
makeIntegerParam("minbucket", lower = 1, upper = 30),
makeIntegerParam("maxdepth", lower = 3, upper = 10)
)
ctrl_random <- makeTuneControlRandom(maxit = 10)# Create holdout sampling
holdout <- makeResampleDesc("Holdout")
# Perform tuning
lrn_tune <- tuneParams(learner = lrn, task = task, resampling = holdout,
control = ctrl_random, par.set = param_set)
# Generate hyperparameter effect data
hyperpar_effects <- generateHyperParsEffectData(lrn_tune, partial.dep = TRUE)
# Plot hyperparameter effects
plotHyperParsEffect(hyperpar_effects,
partial.dep.learn = "regr.glm",
x = "minsplit", y = "mmce.test.mean", z = "maxdepth",
plot.type = "line")
Now, you are going to define performance measures. The knowledge_train_data dataset has already been loaded for you, as have the packages mlr and tidyverse. And the following code has also been run:
task <- makeClassifTask(data = knowledge_train_data,
target = "UNS")
lrn <- makeLearner(cl = "classif.nnet", fix.factors.prediction = TRUE)
param_set <- makeParamSet(
makeIntegerParam("size", lower = 1, upper = 5),
makeIntegerParam("maxit", lower = 1, upper = 300),
makeNumericParam("decay", lower = 0.0001, upper = 1)
)
ctrl_random <- makeTuneControlRandom(maxit = 10)
# Create holdout sampling
holdout <- makeResampleDesc("Holdout", predict = "both")
# Perform tuning
lrn_tune <- tuneParams(learner = lrn,
task = task,
resampling = holdout,
control = ctrl_random,
par.set = param_set,
measures = list(acc,
setAggregation(acc, train.mean), mmce,
setAggregation(mmce, train.mean)))## # weights: 30
## initial value 87.272848
## iter 10 value 50.080837
## iter 20 value 39.226655
## iter 30 value 34.860127
## iter 40 value 33.690721
## iter 50 value 33.611310
## iter 60 value 33.604540
## iter 70 value 33.600858
## final value 33.600847
## stopped after 71 iterations
## # weights: 21
## initial value 88.902697
## iter 10 value 80.260410
## iter 20 value 61.584441
## iter 30 value 61.504808
## iter 40 value 61.480643
## iter 50 value 61.282290
## final value 61.277290
## converged
## # weights: 21
## initial value 96.348339
## iter 10 value 70.275186
## iter 20 value 67.270551
## final value 67.262776
## converged
## # weights: 12
## initial value 91.251304
## iter 10 value 37.937297
## iter 20 value 29.793187
## final value 29.792070
## converged
## # weights: 39
## initial value 98.279960
## iter 10 value 83.240200
## iter 20 value 82.706523
## iter 30 value 82.701247
## iter 30 value 82.701247
## iter 30 value 82.701247
## final value 82.701247
## converged
## # weights: 30
## initial value 106.676752
## iter 10 value 81.161274
## iter 20 value 80.071702
## final value 80.069657
## converged
## # weights: 39
## initial value 97.883307
## iter 10 value 68.323590
## iter 20 value 56.952200
## iter 30 value 56.236543
## iter 40 value 55.780078
## iter 50 value 55.757940
## iter 60 value 55.753301
## iter 70 value 55.753064
## final value 55.753055
## converged
## # weights: 30
## initial value 94.280277
## iter 10 value 62.761484
## iter 20 value 61.143259
## iter 30 value 61.049599
## iter 40 value 60.884499
## final value 60.881375
## converged
## # weights: 48
## initial value 127.942963
## iter 10 value 80.782113
## iter 20 value 77.909563
## iter 30 value 77.552767
## iter 40 value 77.537457
## final value 77.537455
## converged
## # weights: 21
## initial value 109.226732
## iter 10 value 86.342794
## iter 20 value 85.267567
## iter 30 value 84.212476
## final value 84.212080
## converged
And finally, you are going to set specific hyperparameters, which you might have found by examining your tuning results from before, The knowledge_train_data dataset has already been loaded for you, as have the packages mlr and tidyverse. And the following code has also been run:
task <- makeClassifTask(data = knowledge_train_data,
target = "UNS")
lrn <- makeLearner(cl = "classif.nnet", fix.factors.prediction = TRUE)
# Set hyperparameters
lrn_best <- setHyperPars(lrn, par.vals = list(size = 1,
maxit = 150,
decay = 0))
# Train model
model_best <- train(lrn_best, task)## # weights: 12
## initial value 135.767196
## iter 10 value 64.040324
## iter 20 value 23.380580
## iter 30 value 14.234143
## iter 40 value 13.783268
## iter 50 value 13.214890
## iter 60 value 13.168267
## iter 70 value 13.158141
## iter 80 value 13.148265
## iter 90 value 13.145406
## iter 100 value 13.139159
## iter 110 value 13.123559
## iter 120 value 13.121204
## iter 130 value 13.115873
## iter 140 value 13.114882
## iter 150 value 13.113746
## final value 13.113746
## stopped after 150 iterations
model_best## Model for learner.id=classif.nnet; learner.class=classif.nnet
## Trained on: task.id = knowledge_train_data; obs = 120; features = 5
## Hyperparameters: size=1,maxit=150,decay=0
In order to train models with h2o, you need to prepare the data according to h2o’s specific needs. Here, you will go over a common data preparation workflow in h2o. The h2o library has already been loaded for you, as has the seeds_train_data object. This chapter uses functions that can take some time to run, so don’t be surprised if it takes a little longer than usual to submit your answer. On rare occurrences, you may get a server error. If this is the case, just reload the page
library(h2o)
seeds_train_data <- fread("seeds_train_data.csv")
# Initialise h2o cluster
h2o.init()##
## H2O is not running yet, starting it now...
##
## Note: In case of errors look at the following log files:
## C:\Users\mmburu\AppData\Local\Temp\RtmpaWVZxC/h2o_mmburu_started_from_r.out
## C:\Users\mmburu\AppData\Local\Temp\RtmpaWVZxC/h2o_mmburu_started_from_r.err
##
##
## Starting H2O JVM and connecting: Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 3 seconds 374 milliseconds
## H2O cluster timezone: Africa/Nairobi
## H2O data parsing timezone: UTC
## H2O cluster version: 3.22.1.1
## H2O cluster version age: 1 year, 2 months and 23 days !!!
## H2O cluster name: H2O_started_from_R_mmburu_owx177
## H2O cluster total nodes: 1
## H2O cluster total memory: 3.97 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## H2O API Extensions: Algos, AutoML, Core V3, Core V4
## R Version: R version 3.5.3 (2019-03-11)
# Convert data to h2o frame
seeds_train_data_hf <- as.h2o(seeds_train_data)##
|
| | 0%
|
|=================================================================| 100%
# Identify target and features
y <- "seed_type"
x <- setdiff(colnames(seeds_train_data_hf), y)
# Split data into train & validation sets
sframe <- h2o.splitFrame(seeds_train_data_hf, seed = 42)
train <- sframe[[1]]
valid <- sframe[[2]]
# Calculate ratio of the target variable in the training set
summary(train$seed_type, exact_quantiles = TRUE)## seed_type
## Min. :1
## 1st Qu.:1
## Median :2
## Mean :2
## 3rd Qu.:3
## Max. :3
In the last exercise, you successfully prepared data for modeling with h2o. Now, you can use this data to train a model. The h2o library has already been loaded for you, as has the seeds_train_data object and the following code has been run:
h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 5 seconds 609 milliseconds
## H2O cluster timezone: Africa/Nairobi
## H2O data parsing timezone: UTC
## H2O cluster version: 3.22.1.1
## H2O cluster version age: 1 year, 2 months and 23 days !!!
## H2O cluster name: H2O_started_from_R_mmburu_owx177
## H2O cluster total nodes: 1
## H2O cluster total memory: 3.97 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## H2O API Extensions: Algos, AutoML, Core V3, Core V4
## R Version: R version 3.5.3 (2019-03-11)
seeds_train_data_hf <- as.h2o(seeds_train_data)##
|
| | 0%
|
|=================================================================| 100%
y <- "seed_type"
x <- setdiff(colnames(seeds_train_data_hf), y)
seeds_train_data_hf[, y] <- as.factor(seeds_train_data_hf[, y])
sframe <- h2o.splitFrame(seeds_train_data_hf, seed = 42)
train <- sframe[[1]]
valid <- sframe[[2]]
# Train random forest model
rf_model <- h2o.randomForest(x = x,
y = y,
training_frame = train,
validation_frame = valid)##
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# Calculate model performance
perf <- h2o.performance(rf_model, valid = TRUE)
# Extract confusion matrix
h2o.confusionMatrix(perf)## Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
## 1 2 3 Error Rate
## 1 9 0 0 0.0000 = 0 / 9
## 2 0 5 0 0.0000 = 0 / 5
## 3 1 0 8 0.1111 = 1 / 9
## Totals 10 5 8 0.0435 = 1 / 23
# Extract logloss
h2o.logloss(perf)## [1] 0.1850259
Now that you successfully trained a Random Forest model with h2o, you can apply the same concepts to training all other algorithms, like Deep Learning. In this exercise, you are going to apply a grid search to tune a model. The h2o library has already been loaded and initialized for you.
# Define hyperparameters
dl_params <- list(hidden = list(c(50, 50), c(100, 100)),
epochs = c(5, 10, 15),
rate = c(0.001, 0.005, 0.01))Next, you will use random search. The h2o library and seeds_train_data have already been loaded for you and the following code has been run:
h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 7 seconds 705 milliseconds
## H2O cluster timezone: Africa/Nairobi
## H2O data parsing timezone: UTC
## H2O cluster version: 3.22.1.1
## H2O cluster version age: 1 year, 2 months and 23 days !!!
## H2O cluster name: H2O_started_from_R_mmburu_owx177
## H2O cluster total nodes: 1
## H2O cluster total memory: 3.97 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## H2O API Extensions: Algos, AutoML, Core V3, Core V4
## R Version: R version 3.5.3 (2019-03-11)
seeds_train_data_hf <- as.h2o(seeds_train_data)##
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y <- "seed_type"
x <- setdiff(colnames(seeds_train_data_hf), y)
seeds_train_data_hf[, y] <- as.factor(seeds_train_data_hf[, y])
sframe <- h2o.splitFrame(seeds_train_data_hf, seed = 42)
train <- sframe[[1]]
valid <- sframe[[2]]
dl_params <- list(hidden = list(c(50, 50), c(100, 100)),
epochs = c(5, 10, 15),
rate = c(0.001, 0.005, 0.01))
# Define search criteria
search_criteria <- list(strategy = "RandomDiscrete",
max_runtime_secs = 10, # this is way too short & only used to keep runtime short!
seed = 42)
# Train with random search
dl_grid <- h2o.grid("deeplearning",
grid_id = "dl_grid",
x = x,
y = y,
training_frame = train,
validation_frame = valid,
seed = 42,
hyper_params = dl_params,
search_criteria = search_criteria)##
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In random search, you can also define stopping criteria instead of a maximum runtime. The h2o library and seeds_train_data has already been loaded and initialized for you.
# Define early stopping
stopping_params <- list(strategy = "RandomDiscrete",
stopping_metric = "misclassification",
stopping_rounds = 2,
stopping_tolerance = 0.1,
seed = 42)A very convenient functionality of h2o is automatic machine learning (AutoML). The h2o library and seeds_train_data have already been loaded for you and the following code has been run
h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 18 seconds 621 milliseconds
## H2O cluster timezone: Africa/Nairobi
## H2O data parsing timezone: UTC
## H2O cluster version: 3.22.1.1
## H2O cluster version age: 1 year, 2 months and 23 days !!!
## H2O cluster name: H2O_started_from_R_mmburu_owx177
## H2O cluster total nodes: 1
## H2O cluster total memory: 3.97 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## H2O API Extensions: Algos, AutoML, Core V3, Core V4
## R Version: R version 3.5.3 (2019-03-11)
seeds_train_data_hf <- as.h2o(seeds_train_data)##
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y <- "seed_type"
x <- setdiff(colnames(seeds_train_data_hf), y)
seeds_train_data_hf[, y] <- as.factor(seeds_train_data_hf[, y])
sframe <- h2o.splitFrame(seeds_train_data_hf, seed = 42)
train <- sframe[[1]]
valid <- sframe[[2]]
# Run automatic machine learning
automl_model <- h2o.automl(x = x,
y = y,
training_frame = train,
max_runtime_secs = 10,
sort_metric = "mean_per_class_error",
nfolds = 3,
seed = 42)##
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# Run automatic machine learning
automl_model <- h2o.automl(x = x,
y = y,
training_frame = train,
max_runtime_secs = 10,
sort_metric = "mean_per_class_error",
validation_frame= valid,
seed = 42)##
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Correct! With training_frame + validation_frame, training data is used as is, validation set is split 50/50 into validation and leaderboard data.’
##Extract h2o models and evaluate performance In this final exercise, you will extract the best model from the AutoML leaderboard. The h2o library and test data has been loaded and the following code has been run:
seeds_data <- read_csv("seeds_data.csv")
seeds_data_hf <- as.h2o(seeds_data)##
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y <- "seed_type"
x <- setdiff(colnames(seeds_data ), y)
seeds_data_hf[, y] <- as.factor(seeds_data_hf[, y])
automl_model <- h2o.automl(x = x,
y = y,
training_frame = seeds_data_hf,
nfolds = 3,
max_runtime_secs = 60,
sort_metric = "mean_per_class_error",
seed = 42)##
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# Extract the leaderboard
lb <- automl_model@leaderboard
head(lb)## model_id mean_per_class_error
## 1 DeepLearning_grid_1_AutoML_20200323_111633_model_2 0.01333333
## 2 DeepLearning_grid_1_AutoML_20200323_111633_model_1 0.02000000
## 3 GLM_grid_1_AutoML_20200323_111633_model_1 0.02000000
## 4 GBM_grid_1_AutoML_20200323_111633_model_56 0.02666667
## 5 StackedEnsemble_AllModels_AutoML_20200323_111633 0.02666667
## 6 GBM_grid_1_AutoML_20200323_111633_model_36 0.02666667
## logloss rmse mse
## 1 0.09168983 0.1463798 0.02142703
## 2 0.12283276 0.1473739 0.02171907
## 3 0.06254323 0.1346297 0.01812515
## 4 0.17577651 0.1553925 0.02414684
## 5 0.19196602 0.1996250 0.03985013
## 6 0.16683505 0.1554899 0.02417711
# Assign best model new object name
aml_leader <- automl_model@leader
# Look at best model
summary(aml_leader)## Model Details:
## ==============
##
## H2OMultinomialModel: deeplearning
## Model Key: DeepLearning_grid_1_AutoML_20200323_111633_model_2
## Status of Neuron Layers: predicting seed_type, 3-class classification, multinomial distribution, CrossEntropy loss, 2,203 weights/biases, 31.6 KB, 1,059 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate
## 1 1 7 Input 5.00 % NA NA NA
## 2 2 200 RectifierDropout 50.00 % 0.000000 0.000000 0.013774
## 3 3 3 Softmax NA 0.000000 0.000000 0.011778
## rate_rms momentum mean_weight weight_rms mean_bias bias_rms
## 1 NA NA NA NA NA NA
## 2 0.010542 0.000000 -0.001520 0.113655 0.483052 0.048652
## 3 0.004961 0.000000 -0.005678 0.401439 -0.018148 0.064015
##
## H2OMultinomialMetrics: deeplearning
## ** Reported on training data. **
## ** Metrics reported on full training frame **
##
## Training Set Metrics:
## =====================
##
## Extract training frame with `h2o.getFrame("automl_training_RTMP_sid_90bf_40")`
## MSE: (Extract with `h2o.mse`) 0.01514625
## RMSE: (Extract with `h2o.rmse`) 0.1230701
## Logloss: (Extract with `h2o.logloss`) 0.05587419
## Mean Per-Class Error: 0.01333333
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,train = TRUE)`)
## =========================================================================
## Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
## 1 2 3 Error Rate
## 1 49 1 0 0.0200 = 1 / 50
## 2 0 50 0 0.0000 = 0 / 50
## 3 1 0 49 0.0200 = 1 / 50
## Totals 50 51 49 0.0133 = 2 / 150
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,train = TRUE)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.986667
## 2 2 1.000000
## 3 3 1.000000
##
##
##
## H2OMultinomialMetrics: deeplearning
## ** Reported on cross-validation data. **
## ** 3-fold cross-validation on training data (Metrics computed for combined holdout predictions) **
##
## Cross-Validation Set Metrics:
## =====================
##
## Extract cross-validation frame with `h2o.getFrame("automl_training_RTMP_sid_90bf_40")`
## MSE: (Extract with `h2o.mse`) 0.02142703
## RMSE: (Extract with `h2o.rmse`) 0.1463798
## Logloss: (Extract with `h2o.logloss`) 0.09168983
## Mean Per-Class Error: 0.01333333
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,xval = TRUE)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.986667
## 2 2 1.000000
## 3 3 1.000000
##
##
## Cross-Validation Metrics Summary:
## mean sd cv_1_valid cv_2_valid
## accuracy 0.81333333 0.04807402 0.72 0.84
## err 0.18666667 0.04807402 0.28 0.16
## err_count 9.333333 2.4037008 14.0 8.0
## logloss 0.4449608 0.07512626 0.5629788 0.30541807
## max_per_class_error 0.40318626 0.08144671 0.5625 0.29411766
## mean_per_class_accuracy 0.8120915 0.0503612 0.7144608 0.8394608
## mean_per_class_error 0.1879085 0.0503612 0.2855392 0.16053921
## mse 0.13301021 0.028452428 0.18809272 0.09309722
## r2 0.79975855 0.044137318 0.7148382 0.8630923
## rmse 0.3606981 0.038125448 0.43369657 0.30511835
## cv_3_valid
## accuracy 0.88
## err 0.12
## err_count 6.0
## logloss 0.4664856
## max_per_class_error 0.3529412
## mean_per_class_accuracy 0.88235295
## mean_per_class_error 0.11764706
## mse 0.11784071
## r2 0.8213452
## rmse 0.34327933
##
## Scoring History:
## timestamp duration training_speed epochs iterations
## 1 2020-03-23 11:17:10 0.000 sec NA 0.00000 0
## 2 2020-03-23 11:17:10 6.610 sec 31333 obs/sec 0.62667 1
## 3 2020-03-23 11:17:10 6.627 sec 55736 obs/sec 7.06000 11
## samples training_rmse training_logloss training_r2
## 1 0.000000 NA NA NA
## 2 94.000000 0.21226 0.16692 0.93242
## 3 1059.000000 0.12307 0.05587 0.97728
## training_classification_error
## 1 NA
## 2 0.06000
## 3 0.01333
##
## Variable Importances: (Extract with `h2o.varimp`)
## =================================================
##
## Variable Importances:
## variable relative_importance scaled_importance percentage
## 1 kernel_length 1.000000 1.000000 0.151976
## 2 kernel_groove 0.978019 0.978019 0.148635
## 3 perimeter 0.975993 0.975993 0.148327
## 4 kernel_width 0.927169 0.927169 0.140907
## 5 compactness 0.924722 0.924722 0.140535
## 6 area 0.889509 0.889509 0.135184
## 7 asymmetry 0.884593 0.884593 0.134437
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