cap15/CH15EJ4-Stacked.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; clear: both;\">\n",
    "<div style=\"float: left; width: 50%;\">\n",
    "<img src=\"http://www.uoc.edu/portal/_resources/common/imatges/marca_UOC/UOC_Masterbrand.jpg\" align=\"left\">\n",
    "</div>\n",
    "<div style=\"float: right; width: 50%;\">\n",
    "<p style=\"margin: 0; padding-top: 22px; text-align:right;\"><a href=\"http://www.editorialuoc.cat/mineria-de-datos\" target=\"_blank\">Minería de datos: Modelos y algoritmos</a></p>\n",
    "<p style=\"margin: 0; text-align:right;\">Estudios de Informática, Multimedia y Telecomunicación</p>\n",
    "<p style=\"margin: 0; text-align:right; padding-button: 100px;\">Universitat Oberta de Catalunya (<b>UOC</b>)</p>\n",
    "</div>\n",
    "</div>\n",
    "<div style=\"width:100%;\">&nbsp;</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ea6cb8e6-4d3d-45c8-abc9-44f65205068f"
    }
   },
   "source": [
    "# CH15EJ4 *Stacked*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este ejemplo se muestra como generar un clasificador arbol de decisión y como mejorarlo con la técnica de *Stacking*. Trabajaremos con el paquete R neuralnet y con el juego de datos [Breast Cancer Wisconsin (Diagnostic) Data Set](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28diagnostic%29)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Instalar los packages necesarios "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: rpart\n",
      "\n",
      "Loading required package: caret\n",
      "\n",
      "Loading required package: lattice\n",
      "\n",
      "Loading required package: ggplot2\n",
      "\n",
      "Loading required package: randomForest\n",
      "\n",
      "randomForest 4.6-14\n",
      "\n",
      "Type rfNews() to see new features/changes/bug fixes.\n",
      "\n",
      "\n",
      "Attaching package: ‘randomForest’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    margin\n",
      "\n",
      "\n",
      "Loading required package: caretEnsemble\n",
      "\n",
      "\n",
      "Attaching package: ‘caretEnsemble’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    autoplot\n",
      "\n",
      "\n",
      "Loading required package: C50\n",
      "\n",
      "Loading required package: plyr\n",
      "\n",
      "Loading required package: kernlab\n",
      "\n",
      "\n",
      "Attaching package: ‘kernlab’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    alpha\n",
      "\n",
      "\n",
      "Loading required package: nnet\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if(!require(rpart)){\n",
    "    install.packages('rpart',repos='http://cran.es.r-project.org')\n",
    "    require(rpart)\n",
    "}\n",
    "if(!require(caret)){\n",
    "    install.packages('caret',repos='http://cran.es.r-project.org')\n",
    "    require(caret)\n",
    "}\n",
    "if(!require(randomForest)){\n",
    "    install.packages('randomForest',repos='http://cran.es.r-project.org')\n",
    "    require(ramdomForest)\n",
    "}\n",
    "if(!require(caretEnsemble)){\n",
    "    install.packages('caretEnsemble',repos='http://cran.es.r-project.org')\n",
    "    require(caretEnsemble)\n",
    "}\n",
    "if(!require(C50)){\n",
    "    install.packages('C50',repos='http://cran.es.r-project.org')\n",
    "    require(C50)\n",
    "}\n",
    "if(!require(plyr)){\n",
    "    install.packages('plyr',repos='http://cran.es.r-project.org')\n",
    "    require(plyr)\n",
    "}\n",
    "if(!require(kernlab)){\n",
    "    install.packages('kernlab',repos='http://cran.es.r-project.org')\n",
    "    require(kernlab)\n",
    "}\n",
    "if(!require(nnet)){\n",
    "    install.packages('nnet',repos='http://cran.es.r-project.org')\n",
    "    require(nnet)\n",
    "}\n",
    "set.seed(17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "fafb1f54-b4ab-4c2e-bc0d-c723f2c6814d"
    }
   },
   "source": [
    "## 2. Carga de los datos\n",
    "\n",
    "Leemos el fichero de datos y vemos que consiste en 32 variables, y en la documentación vemos que estas corresponden a:\n",
    "\n",
    "- 1: Identificador de la muestra\n",
    "- 2: Diagnóstico (M = maligno, B = benigno)\n",
    "- 3-32: DIferentes medidas del tamaño y forma del núcleo de las células"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbpresent": {
     "id": "352d9955-18b6-49b7-a343-102b723bf3cd"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>569</li><li>32</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 569\n",
       "\\item 32\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 569\n",
       "2. 32\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 569  32"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 31</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th><th scope=col>V11</th><th scope=col>⋯</th><th scope=col>V23</th><th scope=col>V24</th><th scope=col>V25</th><th scope=col>V26</th><th scope=col>V27</th><th scope=col>V28</th><th scope=col>V29</th><th scope=col>V30</th><th scope=col>V31</th><th scope=col>V32</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>M</td><td>17.99</td><td>10.38</td><td>122.80</td><td>1001.0</td><td>0.11840</td><td>0.27760</td><td>0.3001</td><td>0.14710</td><td>0.2419</td><td>⋯</td><td>25.38</td><td>17.33</td><td>184.60</td><td>2019.0</td><td>0.1622</td><td>0.6656</td><td>0.7119</td><td>0.2654</td><td>0.4601</td><td>0.11890</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>M</td><td>20.57</td><td>17.77</td><td>132.90</td><td>1326.0</td><td>0.08474</td><td>0.07864</td><td>0.0869</td><td>0.07017</td><td>0.1812</td><td>⋯</td><td>24.99</td><td>23.41</td><td>158.80</td><td>1956.0</td><td>0.1238</td><td>0.1866</td><td>0.2416</td><td>0.1860</td><td>0.2750</td><td>0.08902</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>M</td><td>19.69</td><td>21.25</td><td>130.00</td><td>1203.0</td><td>0.10960</td><td>0.15990</td><td>0.1974</td><td>0.12790</td><td>0.2069</td><td>⋯</td><td>23.57</td><td>25.53</td><td>152.50</td><td>1709.0</td><td>0.1444</td><td>0.4245</td><td>0.4504</td><td>0.2430</td><td>0.3613</td><td>0.08758</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>M</td><td>11.42</td><td>20.38</td><td> 77.58</td><td> 386.1</td><td>0.14250</td><td>0.28390</td><td>0.2414</td><td>0.10520</td><td>0.2597</td><td>⋯</td><td>14.91</td><td>26.50</td><td> 98.87</td><td> 567.7</td><td>0.2098</td><td>0.8663</td><td>0.6869</td><td>0.2575</td><td>0.6638</td><td>0.17300</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>M</td><td>20.29</td><td>14.34</td><td>135.10</td><td>1297.0</td><td>0.10030</td><td>0.13280</td><td>0.1980</td><td>0.10430</td><td>0.1809</td><td>⋯</td><td>22.54</td><td>16.67</td><td>152.20</td><td>1575.0</td><td>0.1374</td><td>0.2050</td><td>0.4000</td><td>0.1625</td><td>0.2364</td><td>0.07678</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>M</td><td>12.45</td><td>15.70</td><td> 82.57</td><td> 477.1</td><td>0.12780</td><td>0.17000</td><td>0.1578</td><td>0.08089</td><td>0.2087</td><td>⋯</td><td>15.47</td><td>23.75</td><td>103.40</td><td> 741.6</td><td>0.1791</td><td>0.5249</td><td>0.5355</td><td>0.1741</td><td>0.3985</td><td>0.12440</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 31\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & ⋯ & V23 & V24 & V25 & V26 & V27 & V28 & V29 & V30 & V31 & V32\\\\\n",
       "  & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & M & 17.99 & 10.38 & 122.80 & 1001.0 & 0.11840 & 0.27760 & 0.3001 & 0.14710 & 0.2419 & ⋯ & 25.38 & 17.33 & 184.60 & 2019.0 & 0.1622 & 0.6656 & 0.7119 & 0.2654 & 0.4601 & 0.11890\\\\\n",
       "\t2 & M & 20.57 & 17.77 & 132.90 & 1326.0 & 0.08474 & 0.07864 & 0.0869 & 0.07017 & 0.1812 & ⋯ & 24.99 & 23.41 & 158.80 & 1956.0 & 0.1238 & 0.1866 & 0.2416 & 0.1860 & 0.2750 & 0.08902\\\\\n",
       "\t3 & M & 19.69 & 21.25 & 130.00 & 1203.0 & 0.10960 & 0.15990 & 0.1974 & 0.12790 & 0.2069 & ⋯ & 23.57 & 25.53 & 152.50 & 1709.0 & 0.1444 & 0.4245 & 0.4504 & 0.2430 & 0.3613 & 0.08758\\\\\n",
       "\t4 & M & 11.42 & 20.38 &  77.58 &  386.1 & 0.14250 & 0.28390 & 0.2414 & 0.10520 & 0.2597 & ⋯ & 14.91 & 26.50 &  98.87 &  567.7 & 0.2098 & 0.8663 & 0.6869 & 0.2575 & 0.6638 & 0.17300\\\\\n",
       "\t5 & M & 20.29 & 14.34 & 135.10 & 1297.0 & 0.10030 & 0.13280 & 0.1980 & 0.10430 & 0.1809 & ⋯ & 22.54 & 16.67 & 152.20 & 1575.0 & 0.1374 & 0.2050 & 0.4000 & 0.1625 & 0.2364 & 0.07678\\\\\n",
       "\t6 & M & 12.45 & 15.70 &  82.57 &  477.1 & 0.12780 & 0.17000 & 0.1578 & 0.08089 & 0.2087 & ⋯ & 15.47 & 23.75 & 103.40 &  741.6 & 0.1791 & 0.5249 & 0.5355 & 0.1741 & 0.3985 & 0.12440\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 31\n",
       "\n",
       "| <!--/--> | V2 &lt;fct&gt; | V3 &lt;dbl&gt; | V4 &lt;dbl&gt; | V5 &lt;dbl&gt; | V6 &lt;dbl&gt; | V7 &lt;dbl&gt; | V8 &lt;dbl&gt; | V9 &lt;dbl&gt; | V10 &lt;dbl&gt; | V11 &lt;dbl&gt; | ⋯ ⋯ | V23 &lt;dbl&gt; | V24 &lt;dbl&gt; | V25 &lt;dbl&gt; | V26 &lt;dbl&gt; | V27 &lt;dbl&gt; | V28 &lt;dbl&gt; | V29 &lt;dbl&gt; | V30 &lt;dbl&gt; | V31 &lt;dbl&gt; | V32 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | M | 17.99 | 10.38 | 122.80 | 1001.0 | 0.11840 | 0.27760 | 0.3001 | 0.14710 | 0.2419 | ⋯ | 25.38 | 17.33 | 184.60 | 2019.0 | 0.1622 | 0.6656 | 0.7119 | 0.2654 | 0.4601 | 0.11890 |\n",
       "| 2 | M | 20.57 | 17.77 | 132.90 | 1326.0 | 0.08474 | 0.07864 | 0.0869 | 0.07017 | 0.1812 | ⋯ | 24.99 | 23.41 | 158.80 | 1956.0 | 0.1238 | 0.1866 | 0.2416 | 0.1860 | 0.2750 | 0.08902 |\n",
       "| 3 | M | 19.69 | 21.25 | 130.00 | 1203.0 | 0.10960 | 0.15990 | 0.1974 | 0.12790 | 0.2069 | ⋯ | 23.57 | 25.53 | 152.50 | 1709.0 | 0.1444 | 0.4245 | 0.4504 | 0.2430 | 0.3613 | 0.08758 |\n",
       "| 4 | M | 11.42 | 20.38 |  77.58 |  386.1 | 0.14250 | 0.28390 | 0.2414 | 0.10520 | 0.2597 | ⋯ | 14.91 | 26.50 |  98.87 |  567.7 | 0.2098 | 0.8663 | 0.6869 | 0.2575 | 0.6638 | 0.17300 |\n",
       "| 5 | M | 20.29 | 14.34 | 135.10 | 1297.0 | 0.10030 | 0.13280 | 0.1980 | 0.10430 | 0.1809 | ⋯ | 22.54 | 16.67 | 152.20 | 1575.0 | 0.1374 | 0.2050 | 0.4000 | 0.1625 | 0.2364 | 0.07678 |\n",
       "| 6 | M | 12.45 | 15.70 |  82.57 |  477.1 | 0.12780 | 0.17000 | 0.1578 | 0.08089 | 0.2087 | ⋯ | 15.47 | 23.75 | 103.40 |  741.6 | 0.1791 | 0.5249 | 0.5355 | 0.1741 | 0.3985 | 0.12440 |\n",
       "\n"
      ],
      "text/plain": [
       "  V2 V3    V4    V5     V6     V7      V8      V9     V10     V11    ⋯ V23  \n",
       "1 M  17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 ⋯ 25.38\n",
       "2 M  20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 ⋯ 24.99\n",
       "3 M  19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 ⋯ 23.57\n",
       "4 M  11.42 20.38  77.58  386.1 0.14250 0.28390 0.2414 0.10520 0.2597 ⋯ 14.91\n",
       "5 M  20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 ⋯ 22.54\n",
       "6 M  12.45 15.70  82.57  477.1 0.12780 0.17000 0.1578 0.08089 0.2087 ⋯ 15.47\n",
       "  V24   V25    V26    V27    V28    V29    V30    V31    V32    \n",
       "1 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890\n",
       "2 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902\n",
       "3 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758\n",
       "4 26.50  98.87  567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300\n",
       "5 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678\n",
       "6 23.75 103.40  741.6 0.1791 0.5249 0.5355 0.1741 0.3985 0.12440"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wdbc <- read.table(\n",
    "    url(\"https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data\"), \n",
    "    sep=\",\",\n",
    "    header=FALSE\n",
    ")\n",
    "\n",
    "dim(wdbc)\n",
    "dataset <- wdbc[,(2:32)]\n",
    "head(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Creación del modelo\n",
    "\n",
    "Definimos un cross validation con 3 repeticiones y 10 folds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parámetros de nivel 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Metrica de calidad % instancias correctas sobre total\n",
    "metric <- \"Accuracy\"\n",
    "\n",
    "# Val. cruzada \n",
    "control <- trainControl(method=\"repeatedcv\", number=10, repeats=3, savePredictions=\"all\", classProbs=TRUE)\n",
    "\n",
    "# Algoritmos N0\n",
    "algorithmList <- c('nnet', 'rpart', 'C5.0', 'knn', 'svmRadial')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Creamos nivel 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in trControlCheck(x = trControl, y = target):\n",
      "“indexes not defined in trControl.  Attempting to set them ourselves, so each model in the ensemble will have the same resampling indexes.”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# weights:  33\n",
      "initial  value 355.115601 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 496.824286 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 386.269332 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 418.785008 \n",
      "iter  10 value 338.310032\n",
      "iter  20 value 285.341186\n",
      "iter  30 value 269.888517\n",
      "iter  40 value 159.584759\n",
      "iter  50 value 154.787382\n",
      "iter  60 value 136.468431\n",
      "iter  70 value 113.481567\n",
      "iter  80 value 105.635162\n",
      "iter  90 value 88.156159\n",
      "iter 100 value 60.072690\n",
      "final  value 60.072690 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 403.524226 \n",
      "iter  10 value 184.885490\n",
      "iter  20 value 124.955661\n",
      "iter  30 value 112.131918\n",
      "iter  40 value 79.114462\n",
      "iter  50 value 63.463446\n",
      "iter  60 value 53.292953\n",
      "iter  70 value 44.770018\n",
      "iter  80 value 43.340086\n",
      "iter  90 value 43.309443\n",
      "final  value 43.309300 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 623.043662 \n",
      "iter  10 value 146.171301\n",
      "iter  20 value 130.686866\n",
      "iter  30 value 120.970793\n",
      "iter  40 value 81.828529\n",
      "iter  50 value 57.046169\n",
      "iter  60 value 51.249729\n",
      "iter  70 value 50.867656\n",
      "final  value 50.867654 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 359.748638 \n",
      "final  value 338.206222 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 346.474930 \n",
      "final  value 338.207119 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 352.511033 \n",
      "final  value 338.208091 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 399.658678 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 370.782040 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 334.727256 \n",
      "iter  10 value 128.548600\n",
      "iter  20 value 100.602077\n",
      "iter  30 value 87.000552\n",
      "iter  40 value 77.798961\n",
      "iter  50 value 72.113931\n",
      "iter  60 value 70.056565\n",
      "iter  70 value 67.944201\n",
      "iter  80 value 62.984626\n",
      "iter  90 value 55.600152\n",
      "iter 100 value 50.032010\n",
      "final  value 50.032010 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 355.858659 \n",
      "iter  10 value 338.218974\n",
      "final  value 338.218969 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 361.781571 \n",
      "iter  10 value 337.747011\n",
      "iter  20 value 186.813118\n",
      "iter  30 value 125.289257\n",
      "iter  40 value 76.860743\n",
      "iter  50 value 57.606885\n",
      "iter  60 value 49.747517\n",
      "iter  70 value 47.191694\n",
      "iter  80 value 43.501187\n",
      "iter  90 value 42.888510\n",
      "iter 100 value 42.812656\n",
      "final  value 42.812656 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 343.492940 \n",
      "iter  10 value 333.134601\n",
      "iter  20 value 152.260162\n",
      "iter  30 value 146.062694\n",
      "iter  40 value 134.649561\n",
      "iter  50 value 125.947294\n",
      "iter  60 value 105.134499\n",
      "iter  70 value 98.958567\n",
      "iter  80 value 86.356442\n",
      "iter  90 value 61.222696\n",
      "iter 100 value 55.508019\n",
      "final  value 55.508019 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 338.421193 \n",
      "final  value 338.206028 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 354.958435 \n",
      "final  value 338.207000 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 644.271559 \n",
      "final  value 338.208115 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 373.130892 \n",
      "final  value 337.217686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 357.566061 \n",
      "final  value 337.217686 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 625.043280 \n",
      "final  value 337.217686 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 357.860676 \n",
      "iter  10 value 234.784785\n",
      "iter  20 value 167.699021\n",
      "iter  30 value 148.807752\n",
      "iter  40 value 140.027193\n",
      "iter  50 value 132.711799\n",
      "iter  60 value 115.620831\n",
      "iter  70 value 94.437373\n",
      "iter  80 value 66.088359\n",
      "iter  90 value 55.897731\n",
      "iter 100 value 51.410377\n",
      "final  value 51.410377 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 343.215635 \n",
      "iter  10 value 336.841062\n",
      "iter  20 value 208.417536\n",
      "iter  30 value 164.116726\n",
      "iter  40 value 133.579689\n",
      "iter  50 value 121.530792\n",
      "iter  60 value 102.395484\n",
      "iter  70 value 86.365596\n",
      "iter  80 value 56.341091\n",
      "iter  90 value 50.697207\n",
      "iter 100 value 48.641916\n",
      "final  value 48.641916 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 318.201460 \n",
      "iter  10 value 154.296090\n",
      "iter  20 value 104.804204\n",
      "iter  30 value 99.532756\n",
      "iter  40 value 67.654244\n",
      "iter  50 value 54.409715\n",
      "iter  60 value 47.112722\n",
      "iter  70 value 44.836946\n",
      "iter  80 value 44.315091\n",
      "iter  90 value 43.567259\n",
      "iter 100 value 43.459346\n",
      "final  value 43.459346 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 418.589372 \n",
      "final  value 337.218195 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 333.165425 \n",
      "iter  10 value 157.662917\n",
      "iter  20 value 105.767325\n",
      "iter  30 value 62.161330\n",
      "iter  40 value 31.489137\n",
      "iter  50 value 10.261540\n",
      "iter  60 value 4.364884\n",
      "iter  70 value 2.647530\n",
      "iter  80 value 2.372377\n",
      "iter  90 value 2.155991\n",
      "iter 100 value 1.981905\n",
      "final  value 1.981905 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 341.790527 \n",
      "final  value 337.220271 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 350.964011 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 395.116220 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 326.217105 \n",
      "iter  10 value 152.353444\n",
      "iter  20 value 147.269683\n",
      "iter  30 value 141.705310\n",
      "iter  40 value 129.030863\n",
      "iter  50 value 118.520246\n",
      "iter  60 value 96.778104\n",
      "iter  70 value 90.950825\n",
      "iter  80 value 88.091696\n",
      "iter  90 value 85.718277\n",
      "iter 100 value 81.526110\n",
      "final  value 81.526110 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 340.114417 \n",
      "iter  10 value 338.219032\n",
      "final  value 338.218999 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 415.501392 \n",
      "iter  10 value 204.131257\n",
      "iter  20 value 166.378318\n",
      "iter  30 value 158.074978\n",
      "iter  40 value 155.633341\n",
      "iter  50 value 105.210304\n",
      "iter  60 value 54.881677\n",
      "iter  70 value 50.046444\n",
      "iter  80 value 49.642319\n",
      "iter  90 value 48.406722\n",
      "iter 100 value 43.769887\n",
      "final  value 43.769887 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 343.135772 \n",
      "iter  10 value 187.025053\n",
      "iter  20 value 152.528200\n",
      "iter  30 value 148.449354\n",
      "iter  40 value 141.719110\n",
      "iter  50 value 122.667094\n",
      "iter  60 value 100.782647\n",
      "iter  70 value 79.762408\n",
      "iter  80 value 55.905653\n",
      "iter  90 value 52.654304\n",
      "iter 100 value 52.205197\n",
      "final  value 52.205197 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 371.585066 \n",
      "final  value 338.205936 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 453.749789 \n",
      "final  value 338.207350 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 355.168660 \n",
      "final  value 338.210084 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 339.887240 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 477.016609 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 358.678879 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 344.893012 \n",
      "iter  10 value 331.056886\n",
      "iter  20 value 251.722877\n",
      "iter  30 value 81.159556\n",
      "iter  40 value 65.854593\n",
      "iter  50 value 55.768668\n",
      "iter  60 value 51.088608\n",
      "final  value 51.087235 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 356.159187 \n",
      "iter  10 value 307.083649\n",
      "iter  20 value 220.705113\n",
      "iter  30 value 138.465552\n",
      "iter  40 value 128.684468\n",
      "iter  50 value 106.158873\n",
      "iter  60 value 57.363952\n",
      "iter  70 value 45.836851\n",
      "iter  80 value 45.334746\n",
      "iter  90 value 42.430795\n",
      "iter 100 value 39.558451\n",
      "final  value 39.558451 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 355.227967 \n",
      "iter  10 value 337.790772\n",
      "iter  20 value 229.335369\n",
      "iter  30 value 158.406172\n",
      "iter  40 value 103.153406\n",
      "iter  50 value 91.480757\n",
      "iter  60 value 69.756797\n",
      "iter  70 value 47.710263\n",
      "iter  80 value 45.109238\n",
      "iter  90 value 44.287592\n",
      "iter 100 value 44.206298\n",
      "final  value 44.206298 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 374.675519 \n",
      "final  value 338.205917 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 347.222846 \n",
      "final  value 338.209916 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 378.207721 \n",
      "final  value 338.221685 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 421.418798 \n",
      "final  value 337.217686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 341.487739 \n",
      "final  value 337.217686 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 523.967521 \n",
      "final  value 337.217686 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 345.583278 \n",
      "iter  10 value 323.432688\n",
      "iter  20 value 156.035225\n",
      "iter  30 value 148.658895\n",
      "iter  40 value 134.091199\n",
      "iter  50 value 119.238769\n",
      "iter  60 value 106.197395\n",
      "iter  70 value 71.567147\n",
      "iter  80 value 55.332410\n",
      "iter  90 value 54.835615\n",
      "final  value 54.835496 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 409.541351 \n",
      "iter  10 value 337.265929\n",
      "iter  20 value 336.692437\n",
      "iter  30 value 255.280912\n",
      "iter  40 value 140.685371\n",
      "iter  50 value 128.226945\n",
      "iter  60 value 120.274683\n",
      "iter  70 value 112.615430\n",
      "iter  80 value 87.619511\n",
      "iter  90 value 76.300403\n",
      "iter 100 value 54.191331\n",
      "final  value 54.191331 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial  value 526.694046 \n",
      "iter  10 value 309.460714\n",
      "iter  20 value 128.325407\n",
      "iter  30 value 83.601501\n",
      "iter  40 value 78.503841\n",
      "iter  50 value 73.652013\n",
      "iter  60 value 71.581686\n",
      "iter  70 value 59.286424\n",
      "iter  80 value 48.651590\n",
      "iter  90 value 44.296963\n",
      "iter 100 value 41.973685\n",
      "final  value 41.973685 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 340.171186 \n",
      "final  value 337.218302 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 342.712336 \n",
      "final  value 337.219375 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 316.884803 \n",
      "iter  10 value 137.954705\n",
      "iter  20 value 137.708527\n",
      "iter  30 value 137.065898\n",
      "iter  40 value 135.100680\n",
      "iter  50 value 135.077922\n",
      "iter  60 value 135.076179\n",
      "iter  70 value 134.056248\n",
      "iter  80 value 128.068957\n",
      "iter  90 value 124.622244\n",
      "iter 100 value 120.283319\n",
      "final  value 120.283319 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 340.269317 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 425.720514 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 356.542630 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 345.537736 \n",
      "iter  10 value 338.685478\n",
      "final  value 338.685465 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 391.456338 \n",
      "iter  10 value 338.722558\n",
      "iter  20 value 257.999372\n",
      "iter  30 value 156.288028\n",
      "iter  40 value 137.262602\n",
      "iter  50 value 132.679509\n",
      "iter  60 value 129.074530\n",
      "iter  70 value 99.498348\n",
      "iter  80 value 81.106012\n",
      "iter  90 value 49.784963\n",
      "iter 100 value 46.426264\n",
      "final  value 46.426264 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 437.225000 \n",
      "iter  10 value 338.437399\n",
      "iter  20 value 333.379419\n",
      "iter  30 value 248.439358\n",
      "iter  40 value 137.682870\n",
      "iter  50 value 128.231097\n",
      "iter  60 value 115.199768\n",
      "iter  70 value 97.712680\n",
      "iter  80 value 87.595589\n",
      "iter  90 value 62.455886\n",
      "iter 100 value 51.530644\n",
      "final  value 51.530644 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 338.691988 \n",
      "final  value 338.672279 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 419.951291 \n",
      "final  value 338.673248 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 349.032637 \n",
      "final  value 338.674452 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 338.233387 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 343.232742 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 342.686418 \n",
      "iter  10 value 154.851378\n",
      "iter  20 value 141.742462\n",
      "iter  30 value 137.895966\n",
      "iter  40 value 135.966794\n",
      "iter  50 value 135.379933\n",
      "iter  60 value 135.364804\n",
      "iter  70 value 135.363448\n",
      "iter  80 value 135.362015\n",
      "iter  90 value 132.829094\n",
      "iter 100 value 132.798508\n",
      "final  value 132.798508 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 406.964909 \n",
      "iter  10 value 338.219294\n",
      "iter  20 value 338.219002\n",
      "final  value 338.218997 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 343.564617 \n",
      "iter  10 value 324.288953\n",
      "iter  20 value 187.687984\n",
      "iter  30 value 120.699280\n",
      "iter  40 value 92.783754\n",
      "iter  50 value 78.629449\n",
      "iter  60 value 73.012547\n",
      "iter  70 value 70.250080\n",
      "iter  80 value 54.488861\n",
      "iter  90 value 48.014284\n",
      "iter 100 value 46.414933\n",
      "final  value 46.414933 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 334.725697 \n",
      "iter  10 value 212.731260\n",
      "iter  20 value 135.144163\n",
      "iter  30 value 128.751457\n",
      "iter  40 value 121.214345\n",
      "iter  50 value 106.720741\n",
      "iter  60 value 61.215833\n",
      "iter  70 value 50.643844\n",
      "iter  80 value 48.463886\n",
      "iter  90 value 45.621517\n",
      "iter 100 value 45.276743\n",
      "final  value 45.276743 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 399.520631 \n",
      "final  value 338.205904 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 440.294205 \n",
      "final  value 338.206834 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 338.667875 \n",
      "iter  10 value 192.932073\n",
      "iter  20 value 171.009174\n",
      "iter  30 value 148.458516\n",
      "iter  40 value 142.783626\n",
      "iter  50 value 140.992686\n",
      "iter  60 value 140.366375\n",
      "iter  70 value 140.350712\n",
      "iter  80 value 140.349105\n",
      "iter  90 value 140.348155\n",
      "iter 100 value 140.345777\n",
      "final  value 140.345777 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 399.751472 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 428.203267 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 362.074293 \n",
      "iter  10 value 163.193598\n",
      "iter  20 value 150.117316\n",
      "iter  30 value 150.088635\n",
      "iter  40 value 149.113844\n",
      "iter  50 value 147.130549\n",
      "iter  60 value 145.366502\n",
      "iter  70 value 144.908194\n",
      "iter  80 value 144.907357\n",
      "iter  80 value 144.907357\n",
      "final  value 144.907317 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 382.644395 \n",
      "iter  10 value 338.591660\n",
      "iter  20 value 332.861715\n",
      "iter  30 value 209.207815\n",
      "iter  40 value 159.828466\n",
      "iter  50 value 122.750796\n",
      "iter  60 value 116.234088\n",
      "iter  70 value 103.648892\n",
      "iter  80 value 88.384275\n",
      "iter  90 value 54.413404\n",
      "iter 100 value 50.631870\n",
      "final  value 50.631870 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 365.046181 \n",
      "iter  10 value 338.718935\n",
      "iter  20 value 284.046753\n",
      "iter  30 value 175.121867\n",
      "iter  40 value 158.691025\n",
      "iter  50 value 111.115477\n",
      "iter  60 value 77.758795\n",
      "iter  70 value 56.572831\n",
      "iter  80 value 54.806210\n",
      "iter  90 value 53.567019\n",
      "iter 100 value 50.586086\n",
      "final  value 50.586086 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 417.121244 \n",
      "iter  10 value 337.616665\n",
      "iter  20 value 319.513148\n",
      "iter  30 value 260.570677\n",
      "iter  40 value 214.634004\n",
      "iter  50 value 164.848149\n",
      "iter  60 value 118.014954\n",
      "iter  70 value 113.939733\n",
      "iter  80 value 97.347946\n",
      "iter  90 value 86.604379\n",
      "iter 100 value 78.733238\n",
      "final  value 78.733238 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 413.261692 \n",
      "final  value 338.672317 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 767.861798 \n",
      "final  value 338.673525 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 372.813836 \n",
      "iter  10 value 337.700349\n",
      "final  value 337.693137 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 370.911432 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 386.260008 \n",
      "iter  10 value 337.688742\n",
      "iter  20 value 337.682043\n",
      "iter  20 value 337.682040\n",
      "iter  20 value 337.682040\n",
      "final  value 337.682040 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 376.786153 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 395.371090 \n",
      "iter  10 value 317.142134\n",
      "iter  20 value 261.838232\n",
      "iter  30 value 215.212569\n",
      "iter  40 value 171.435491\n",
      "iter  50 value 149.739400\n",
      "iter  60 value 147.738433\n",
      "iter  70 value 140.340397\n",
      "iter  80 value 96.263962\n",
      "iter  90 value 62.140648\n",
      "iter 100 value 57.613444\n",
      "final  value 57.613444 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 369.402575 \n",
      "iter  10 value 337.569718\n",
      "iter  20 value 303.798297\n",
      "iter  30 value 254.235155\n",
      "iter  40 value 181.048871\n",
      "iter  50 value 141.668345\n",
      "iter  60 value 79.025446\n",
      "iter  70 value 64.333370\n",
      "iter  80 value 50.596037\n",
      "iter  90 value 46.895923\n",
      "iter 100 value 46.317205\n",
      "final  value 46.317205 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 430.673200 \n",
      "iter  10 value 317.723353\n",
      "iter  20 value 275.653438\n",
      "iter  30 value 160.473398\n",
      "iter  40 value 142.856874\n",
      "iter  50 value 136.288764\n",
      "iter  60 value 131.105022\n",
      "iter  70 value 116.952026\n",
      "iter  80 value 100.228952\n",
      "iter  90 value 85.548407\n",
      "iter 100 value 81.590038\n",
      "final  value 81.590038 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 343.135065 \n",
      "final  value 338.672204 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 340.972238 \n",
      "iter  10 value 135.137329\n",
      "iter  20 value 109.733259\n",
      "iter  30 value 103.109484\n",
      "iter  40 value 88.723178\n",
      "iter  50 value 72.201360\n",
      "iter  60 value 64.773049\n",
      "iter  70 value 51.546476\n",
      "iter  80 value 33.429204\n",
      "iter  90 value 20.968659\n",
      "iter 100 value 18.199628\n",
      "final  value 18.199628 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 334.039137 \n",
      "iter  10 value 167.355333\n",
      "iter  20 value 147.770885\n",
      "iter  30 value 147.721208\n",
      "iter  40 value 147.720302\n",
      "iter  50 value 147.717735\n",
      "iter  60 value 146.095685\n",
      "iter  70 value 134.695116\n",
      "iter  80 value 87.793071\n",
      "iter  90 value 66.275851\n",
      "iter 100 value 58.112174\n",
      "final  value 58.112174 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 338.488495 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 357.649055 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 328.699779 \n",
      "iter  10 value 188.771239\n",
      "iter  20 value 166.361183\n",
      "iter  30 value 159.894757\n",
      "iter  40 value 147.766908\n",
      "iter  50 value 145.309324\n",
      "iter  60 value 145.261373\n",
      "iter  70 value 145.253068\n",
      "iter  80 value 145.250031\n",
      "final  value 145.250025 \n",
      "converged\n",
      "# weights:  33\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial  value 382.640572 \n",
      "iter  10 value 337.114292\n",
      "iter  20 value 323.638667\n",
      "iter  30 value 318.908770\n",
      "iter  40 value 157.641914\n",
      "iter  50 value 122.747666\n",
      "iter  60 value 108.503956\n",
      "iter  70 value 71.043806\n",
      "iter  80 value 50.691573\n",
      "iter  90 value 50.197887\n",
      "final  value 50.197809 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 381.278435 \n",
      "iter  10 value 263.133568\n",
      "iter  20 value 244.853088\n",
      "iter  30 value 146.148768\n",
      "iter  40 value 103.451735\n",
      "iter  50 value 85.505836\n",
      "iter  60 value 59.089455\n",
      "iter  70 value 46.036387\n",
      "iter  80 value 44.998060\n",
      "iter  90 value 41.748608\n",
      "iter 100 value 41.470208\n",
      "final  value 41.470208 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 315.783164 \n",
      "iter  10 value 194.906563\n",
      "iter  20 value 131.659745\n",
      "iter  30 value 101.839593\n",
      "iter  40 value 83.677001\n",
      "iter  50 value 55.272793\n",
      "iter  60 value 47.982863\n",
      "iter  70 value 47.887476\n",
      "iter  80 value 47.885810\n",
      "final  value 47.885799 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 339.812581 \n",
      "iter  10 value 139.137918\n",
      "iter  20 value 108.119957\n",
      "iter  30 value 87.015686\n",
      "iter  40 value 71.377765\n",
      "iter  50 value 58.227653\n",
      "iter  60 value 49.573480\n",
      "iter  70 value 43.368903\n",
      "iter  80 value 28.300352\n",
      "iter  90 value 18.468215\n",
      "iter 100 value 15.907262\n",
      "final  value 15.907262 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 473.053758 \n",
      "final  value 338.208768 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 387.082954 \n",
      "iter  10 value 330.112041\n",
      "iter  20 value 142.513694\n",
      "iter  30 value 141.979850\n",
      "iter  40 value 141.978268\n",
      "iter  50 value 132.597931\n",
      "iter  60 value 130.239655\n",
      "iter  70 value 128.984434\n",
      "iter  80 value 124.627553\n",
      "iter  90 value 122.059483\n",
      "iter 100 value 121.596387\n",
      "final  value 121.596387 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 358.615518 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 346.912475 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 338.319671 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 366.569962 \n",
      "iter  10 value 338.254792\n",
      "iter  20 value 288.048956\n",
      "iter  30 value 203.997906\n",
      "iter  40 value 156.842452\n",
      "iter  50 value 120.537147\n",
      "iter  60 value 86.063960\n",
      "iter  70 value 71.430906\n",
      "iter  80 value 55.209694\n",
      "iter  90 value 55.137706\n",
      "final  value 55.137705 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 379.046696 \n",
      "iter  10 value 337.943938\n",
      "iter  20 value 298.641572\n",
      "iter  30 value 247.134184\n",
      "iter  40 value 224.017569\n",
      "iter  50 value 168.622978\n",
      "iter  60 value 149.264322\n",
      "iter  70 value 128.700270\n",
      "iter  80 value 74.079889\n",
      "iter  90 value 56.449975\n",
      "iter 100 value 55.073528\n",
      "final  value 55.073528 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 466.449800 \n",
      "iter  10 value 221.037590\n",
      "iter  20 value 209.692462\n",
      "iter  30 value 156.255900\n",
      "iter  40 value 152.258874\n",
      "iter  50 value 124.365919\n",
      "iter  60 value 104.571007\n",
      "iter  70 value 90.640512\n",
      "iter  80 value 76.899954\n",
      "iter  90 value 56.337353\n",
      "iter 100 value 47.527955\n",
      "final  value 47.527955 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 386.014842 \n",
      "final  value 338.205879 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 368.957242 \n",
      "final  value 338.207063 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 366.168779 \n",
      "iter  10 value 337.267319\n",
      "final  value 337.233906 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 344.568914 \n",
      "iter  10 value 150.555430\n",
      "iter  20 value 132.172461\n",
      "iter  30 value 122.601947\n",
      "iter  40 value 107.107666\n",
      "iter  50 value 94.097557\n",
      "iter  60 value 65.990791\n",
      "iter  70 value 51.947875\n",
      "iter  80 value 48.679288\n",
      "iter  90 value 48.305098\n",
      "iter 100 value 48.145100\n",
      "final  value 48.145100 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 368.023714 \n",
      "iter  10 value 166.880682\n",
      "iter  20 value 146.819806\n",
      "final  value 146.762791 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 434.941497 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 343.996043 \n",
      "iter  10 value 271.688244\n",
      "iter  20 value 154.528948\n",
      "iter  30 value 145.060280\n",
      "iter  40 value 119.532495\n",
      "iter  50 value 109.802858\n",
      "iter  60 value 87.878100\n",
      "iter  70 value 58.515667\n",
      "iter  80 value 57.530850\n",
      "final  value 57.530674 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 432.048159 \n",
      "iter  10 value 343.391780\n",
      "iter  20 value 260.958964\n",
      "iter  30 value 161.286119\n",
      "iter  40 value 134.361594\n",
      "iter  50 value 125.070164\n",
      "iter  60 value 109.982599\n",
      "iter  70 value 91.287316\n",
      "iter  80 value 66.346329\n",
      "iter  90 value 55.392598\n",
      "iter 100 value 53.959360\n",
      "final  value 53.959360 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 424.002980 \n",
      "iter  10 value 245.684160\n",
      "iter  20 value 144.937355\n",
      "iter  30 value 112.040385\n",
      "iter  40 value 59.222353\n",
      "iter  50 value 55.112454\n",
      "iter  60 value 55.083551\n",
      "iter  70 value 55.050720\n",
      "iter  80 value 55.049787\n",
      "final  value 55.049734 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 327.933231 \n",
      "iter  10 value 178.735676\n",
      "iter  20 value 164.654535\n",
      "iter  30 value 156.681012\n",
      "iter  40 value 152.260897\n",
      "iter  50 value 152.209203\n",
      "iter  60 value 152.208162\n",
      "iter  70 value 152.207558\n",
      "iter  80 value 144.625680\n",
      "iter  90 value 111.890783\n",
      "iter 100 value 108.784055\n",
      "final  value 108.784055 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 391.013899 \n",
      "final  value 338.207694 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 442.422493 \n",
      "final  value 338.208802 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 434.800969 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 612.147765 \n",
      "iter  10 value 209.287973\n",
      "iter  20 value 166.742885\n",
      "iter  30 value 161.764262\n",
      "iter  40 value 129.041594\n",
      "iter  50 value 122.850193\n",
      "iter  60 value 120.005116\n",
      "iter  70 value 112.469301\n",
      "iter  80 value 105.326177\n",
      "iter  90 value 100.974775\n",
      "iter 100 value 100.560768\n",
      "final  value 100.560768 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 426.025718 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 414.158626 \n",
      "iter  10 value 338.685496\n",
      "final  value 338.685465 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 348.985654 \n",
      "iter  10 value 189.658287\n",
      "iter  20 value 141.030782\n",
      "iter  30 value 124.274602\n",
      "iter  40 value 120.363893\n",
      "iter  50 value 116.670125\n",
      "iter  60 value 109.094795\n",
      "iter  70 value 103.536315\n",
      "iter  80 value 65.751984\n",
      "iter  90 value 45.923423\n",
      "iter 100 value 41.601426\n",
      "final  value 41.601426 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 378.684323 \n",
      "iter  10 value 338.749391\n",
      "iter  20 value 215.510481\n",
      "iter  30 value 172.695175\n",
      "iter  40 value 135.367101\n",
      "iter  50 value 133.126701\n",
      "iter  60 value 107.759658\n",
      "iter  70 value 91.005050\n",
      "iter  80 value 86.999577\n",
      "iter  90 value 83.738282\n",
      "iter 100 value 75.680858\n",
      "final  value 75.680858 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 413.486663 \n",
      "iter  10 value 337.689434\n",
      "final  value 337.689059 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 353.021787 \n",
      "final  value 338.673169 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 400.399117 \n",
      "final  value 338.674280 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 367.265689 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 386.205634 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 345.624189 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 401.681215 \n",
      "iter  10 value 338.219134\n",
      "final  value 338.218999 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 381.129316 \n",
      "iter  10 value 324.429762\n",
      "iter  20 value 262.483866\n",
      "iter  30 value 172.378201\n",
      "iter  40 value 61.729281\n",
      "iter  50 value 53.588683\n",
      "iter  60 value 49.151072\n",
      "iter  70 value 48.896488\n",
      "iter  80 value 48.763070\n",
      "iter  90 value 47.858180\n",
      "iter 100 value 43.261379\n",
      "final  value 43.261379 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 391.394494 \n",
      "iter  10 value 334.526409\n",
      "iter  20 value 159.894049\n",
      "iter  30 value 151.892423\n",
      "iter  40 value 124.288948\n",
      "iter  50 value 102.712728\n",
      "iter  60 value 88.893200\n",
      "iter  70 value 67.064925\n",
      "iter  80 value 58.869640\n",
      "iter  90 value 54.454135\n",
      "iter 100 value 43.062097\n",
      "final  value 43.062097 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 340.125277 \n",
      "final  value 338.205776 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 393.131910 \n",
      "iter  10 value 337.231839\n",
      "final  value 337.228163 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 346.825340 \n",
      "final  value 338.207843 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 404.623335 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 441.481940 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial  value 384.249869 \n",
      "iter  10 value 333.626760\n",
      "iter  20 value 252.954672\n",
      "iter  30 value 194.320784\n",
      "iter  40 value 188.647859\n",
      "iter  50 value 188.607350\n",
      "iter  60 value 188.600571\n",
      "final  value 188.600530 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 350.422768 \n",
      "iter  10 value 332.037262\n",
      "iter  20 value 210.127737\n",
      "iter  30 value 172.733723\n",
      "iter  40 value 109.176356\n",
      "iter  50 value 76.723126\n",
      "iter  60 value 65.110687\n",
      "iter  70 value 57.923303\n",
      "iter  80 value 57.221883\n",
      "iter  90 value 56.306585\n",
      "final  value 56.306548 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 346.796184 \n",
      "iter  10 value 338.219155\n",
      "iter  20 value 338.212919\n",
      "final  value 338.212519 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 407.876086 \n",
      "iter  10 value 176.666319\n",
      "iter  20 value 147.561892\n",
      "iter  30 value 134.499562\n",
      "iter  40 value 113.951318\n",
      "iter  50 value 80.776619\n",
      "iter  60 value 65.811576\n",
      "iter  70 value 64.284493\n",
      "iter  80 value 56.844385\n",
      "iter  90 value 50.743547\n",
      "iter 100 value 49.727364\n",
      "final  value 49.727364 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 405.887410 \n",
      "final  value 338.205853 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 390.556034 \n",
      "final  value 338.207072 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 361.738187 \n",
      "final  value 338.215244 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 338.132654 \n",
      "final  value 337.682037 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 347.979114 \n",
      "final  value 337.682037 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 462.597445 \n",
      "iter  10 value 335.990705\n",
      "iter  20 value 335.694179\n",
      "final  value 335.692786 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 339.225800 \n",
      "iter  10 value 337.709902\n",
      "iter  20 value 331.137516\n",
      "iter  30 value 323.990333\n",
      "iter  40 value 317.954861\n",
      "iter  50 value 274.340995\n",
      "iter  60 value 144.977508\n",
      "iter  70 value 130.025097\n",
      "iter  80 value 78.918919\n",
      "iter  90 value 58.112681\n",
      "iter 100 value 53.197850\n",
      "final  value 53.197850 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 339.388864 \n",
      "iter  10 value 323.523181\n",
      "iter  20 value 207.300110\n",
      "iter  30 value 187.969359\n",
      "iter  40 value 100.666837\n",
      "iter  50 value 51.585434\n",
      "iter  60 value 45.739677\n",
      "iter  70 value 44.797629\n",
      "iter  80 value 44.749420\n",
      "iter  90 value 44.745440\n",
      "iter 100 value 44.745384\n",
      "final  value 44.745384 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 512.807440 \n",
      "iter  10 value 165.233079\n",
      "iter  20 value 119.774648\n",
      "iter  30 value 110.095252\n",
      "iter  40 value 102.113427\n",
      "iter  50 value 63.560382\n",
      "iter  60 value 57.840016\n",
      "iter  70 value 56.286120\n",
      "iter  80 value 49.840854\n",
      "iter  90 value 48.879845\n",
      "iter 100 value 45.915848\n",
      "final  value 45.915848 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 349.381447 \n",
      "final  value 337.682655 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 388.795592 \n",
      "final  value 337.683602 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 352.399835 \n",
      "final  value 337.684591 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 401.993567 \n",
      "final  value 337.217686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 423.450836 \n",
      "final  value 337.217686 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 469.292301 \n",
      "iter  10 value 336.233043\n",
      "iter  20 value 336.226690\n",
      "iter  20 value 336.226687\n",
      "iter  20 value 336.226687\n",
      "final  value 336.226687 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 337.899469 \n",
      "final  value 337.231574 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 353.506672 \n",
      "iter  10 value 216.688487\n",
      "iter  20 value 164.779230\n",
      "iter  30 value 159.072969\n",
      "iter  40 value 141.452093\n",
      "iter  50 value 139.285760\n",
      "iter  60 value 124.669231\n",
      "iter  70 value 97.899425\n",
      "iter  80 value 59.227291\n",
      "iter  90 value 55.239967\n",
      "iter 100 value 53.052546\n",
      "final  value 53.052546 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 407.491557 \n",
      "iter  10 value 333.723210\n",
      "iter  20 value 188.700210\n",
      "iter  30 value 145.030548\n",
      "iter  40 value 116.709526\n",
      "iter  50 value 93.441703\n",
      "iter  60 value 74.528381\n",
      "iter  70 value 68.568545\n",
      "iter  80 value 51.040579\n",
      "iter  90 value 49.055130\n",
      "iter 100 value 47.906943\n",
      "final  value 47.906943 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 344.365719 \n",
      "final  value 337.218227 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 419.205925 \n",
      "final  value 337.219319 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 341.327835 \n",
      "iter  10 value 336.239001\n",
      "final  value 336.236418 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 340.502096 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 322.622214 \n",
      "iter  10 value 176.565521\n",
      "iter  20 value 136.349024\n",
      "iter  30 value 98.952051\n",
      "iter  40 value 65.556907\n",
      "iter  50 value 44.103670\n",
      "iter  60 value 29.886749\n",
      "iter  70 value 20.418219\n",
      "iter  80 value 15.410311\n",
      "iter  90 value 12.286767\n",
      "iter 100 value 12.170457\n",
      "final  value 12.170457 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 340.161264 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 429.460979 \n",
      "iter  10 value 335.915254\n",
      "iter  20 value 195.352783\n",
      "iter  30 value 104.200949\n",
      "iter  40 value 80.214057\n",
      "iter  50 value 60.788570\n",
      "iter  60 value 55.420248\n",
      "final  value 55.417390 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 354.105069 \n",
      "iter  10 value 258.792791\n",
      "iter  20 value 149.714272\n",
      "iter  30 value 107.542651\n",
      "iter  40 value 103.953432\n",
      "iter  50 value 90.394851\n",
      "iter  60 value 61.935869\n",
      "iter  70 value 49.253888\n",
      "iter  80 value 46.931191\n",
      "iter  90 value 45.948167\n",
      "iter 100 value 45.308767\n",
      "final  value 45.308767 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 407.888109 \n",
      "iter  10 value 330.941829\n",
      "iter  20 value 192.046280\n",
      "iter  30 value 147.919599\n",
      "iter  40 value 139.651091\n",
      "iter  50 value 117.034770\n",
      "iter  60 value 96.052995\n",
      "iter  70 value 84.995818\n",
      "iter  80 value 55.695210\n",
      "iter  90 value 54.678559\n",
      "iter 100 value 53.218252\n",
      "final  value 53.218252 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 381.166665 \n",
      "final  value 338.205898 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 365.055109 \n",
      "iter  10 value 170.225981\n",
      "iter  20 value 150.221735\n",
      "iter  30 value 150.204701\n",
      "iter  40 value 146.691657\n",
      "iter  50 value 142.479458\n",
      "iter  60 value 142.302735\n",
      "iter  70 value 142.301494\n",
      "iter  80 value 142.298946\n",
      "iter  90 value 142.297654\n",
      "iter 100 value 110.156894\n",
      "final  value 110.156894 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 442.081730 \n",
      "final  value 338.207891 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 343.141626 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 363.252806 \n",
      "iter  10 value 323.641992\n",
      "iter  20 value 274.004031\n",
      "iter  30 value 170.021666\n",
      "iter  40 value 140.454433\n",
      "final  value 140.454102 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 526.550804 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 518.895267 \n",
      "iter  10 value 338.698508\n",
      "final  value 338.685463 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 341.372341 \n",
      "iter  10 value 338.685137\n",
      "iter  20 value 338.678907\n",
      "iter  20 value 338.678904\n",
      "iter  20 value 338.678904\n",
      "final  value 338.678904 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 363.299778 \n",
      "iter  10 value 337.272752\n",
      "iter  20 value 164.609652\n",
      "iter  30 value 157.499599\n",
      "iter  40 value 152.250738\n",
      "iter  50 value 123.708242\n",
      "iter  60 value 89.139180\n",
      "iter  70 value 72.223021\n",
      "iter  80 value 45.743446\n",
      "iter  90 value 40.832244\n",
      "iter 100 value 40.374330\n",
      "final  value 40.374330 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 368.637227 \n",
      "final  value 338.672282 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 382.597003 \n",
      "final  value 338.673176 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 348.518876 \n",
      "final  value 338.674416 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 411.257812 \n",
      "final  value 337.682037 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 350.842292 \n",
      "final  value 337.682037 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 372.746106 \n",
      "final  value 337.682037 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 431.973277 \n",
      "final  value 337.709979 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 346.395184 \n",
      "iter  10 value 337.740193\n",
      "iter  20 value 273.208139\n",
      "iter  30 value 140.433489\n",
      "iter  40 value 126.306119\n",
      "iter  50 value 97.158125\n",
      "iter  60 value 82.466660\n",
      "iter  70 value 50.770449\n",
      "iter  80 value 42.128999\n",
      "iter  90 value 41.216659\n",
      "iter 100 value 40.410557\n",
      "final  value 40.410557 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 464.317633 \n",
      "iter  10 value 335.846383\n",
      "iter  20 value 198.954712\n",
      "iter  30 value 136.370739\n",
      "iter  40 value 119.902432\n",
      "iter  50 value 104.269911\n",
      "iter  60 value 89.364023\n",
      "iter  70 value 69.511494\n",
      "iter  80 value 59.852713\n",
      "iter  90 value 53.148843\n",
      "iter 100 value 52.429368\n",
      "final  value 52.429368 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial  value 353.012247 \n",
      "final  value 337.682695 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 355.613362 \n",
      "final  value 337.683243 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 368.416091 \n",
      "final  value 337.684552 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 357.418255 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 487.818841 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 338.726160 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 434.243671 \n",
      "iter  10 value 338.219194\n",
      "final  value 338.218998 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 340.343165 \n",
      "iter  10 value 336.975849\n",
      "iter  20 value 217.391374\n",
      "iter  30 value 164.823321\n",
      "iter  40 value 140.155282\n",
      "iter  50 value 113.423992\n",
      "iter  60 value 110.644401\n",
      "iter  70 value 93.666267\n",
      "iter  80 value 51.620531\n",
      "iter  90 value 51.449954\n",
      "final  value 51.449853 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 359.848612 \n",
      "iter  10 value 249.996794\n",
      "iter  20 value 162.254055\n",
      "iter  30 value 127.885693\n",
      "iter  40 value 117.443387\n",
      "iter  50 value 116.160174\n",
      "iter  60 value 111.918951\n",
      "iter  70 value 91.189207\n",
      "iter  80 value 54.167045\n",
      "iter  90 value 51.180719\n",
      "final  value 51.180623 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 401.281009 \n",
      "iter  10 value 140.439844\n",
      "iter  20 value 136.396512\n",
      "iter  30 value 136.350716\n",
      "iter  40 value 136.347660\n",
      "iter  50 value 136.346756\n",
      "final  value 136.346727 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 452.338912 \n",
      "final  value 338.208000 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 420.311683 \n",
      "final  value 338.207932 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 338.786026 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 353.258370 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 349.864788 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 338.818409 \n",
      "iter  10 value 289.607419\n",
      "iter  20 value 157.963516\n",
      "iter  30 value 144.023449\n",
      "iter  40 value 124.469211\n",
      "iter  50 value 110.706677\n",
      "iter  60 value 87.108261\n",
      "iter  70 value 60.492155\n",
      "iter  80 value 54.504007\n",
      "iter  90 value 53.311429\n",
      "final  value 53.258452 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 376.804115 \n",
      "iter  10 value 326.480820\n",
      "iter  20 value 217.633334\n",
      "iter  30 value 145.541337\n",
      "iter  40 value 68.452389\n",
      "iter  50 value 51.443232\n",
      "iter  60 value 46.217262\n",
      "iter  70 value 44.832663\n",
      "iter  80 value 44.435315\n",
      "iter  90 value 43.970165\n",
      "iter 100 value 43.957905\n",
      "final  value 43.957905 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 448.773497 \n",
      "iter  10 value 336.079658\n",
      "iter  20 value 295.470866\n",
      "iter  30 value 168.088654\n",
      "iter  40 value 134.647016\n",
      "iter  50 value 71.934847\n",
      "iter  60 value 54.206558\n",
      "iter  70 value 48.115871\n",
      "iter  80 value 46.232996\n",
      "iter  90 value 45.324967\n",
      "iter 100 value 42.106674\n",
      "final  value 42.106674 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 357.397933 \n",
      "final  value 338.205924 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 289.971833 \n",
      "iter  10 value 146.760133\n",
      "iter  20 value 133.994556\n",
      "iter  30 value 125.421060\n",
      "iter  40 value 124.136840\n",
      "iter  50 value 117.663184\n",
      "iter  60 value 117.270647\n",
      "iter  70 value 117.267889\n",
      "iter  80 value 117.266193\n",
      "iter  90 value 117.263970\n",
      "final  value 117.262221 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 401.650577 \n",
      "final  value 338.207862 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 338.210587 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 673.151825 \n",
      "iter  10 value 159.847906\n",
      "iter  20 value 155.280693\n",
      "iter  30 value 154.593272\n",
      "iter  40 value 137.982080\n",
      "iter  50 value 133.884472\n",
      "iter  60 value 133.878693\n",
      "final  value 133.878673 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 323.835393 \n",
      "iter  10 value 154.611193\n",
      "iter  20 value 142.293723\n",
      "iter  30 value 142.292083\n",
      "iter  40 value 142.290802\n",
      "iter  50 value 142.289916\n",
      "iter  60 value 142.289541\n",
      "iter  70 value 140.186090\n",
      "iter  80 value 140.128919\n",
      "iter  90 value 137.961258\n",
      "iter 100 value 137.945709\n",
      "final  value 137.945709 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 402.792004 \n",
      "iter  10 value 277.518950\n",
      "iter  20 value 198.479956\n",
      "iter  30 value 168.662807\n",
      "iter  40 value 139.496671\n",
      "iter  50 value 132.689909\n",
      "iter  60 value 129.528600\n",
      "iter  70 value 127.557832\n",
      "iter  80 value 124.642546\n",
      "iter  90 value 104.093300\n",
      "iter 100 value 63.420606\n",
      "final  value 63.420606 \n",
      "stopped after 100 iterations\n",
      "# weights:  97\n",
      "initial  value 379.216585 \n",
      "iter  10 value 339.983656\n",
      "iter  20 value 274.300187\n",
      "iter  30 value 154.476753\n",
      "iter  40 value 124.080374\n",
      "iter  50 value 91.356509\n",
      "iter  60 value 60.349850\n",
      "iter  70 value 56.182223\n",
      "iter  80 value 53.967106\n",
      "iter  90 value 50.571219\n",
      "iter 100 value 49.067876\n",
      "final  value 49.067876 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 321.836931 \n",
      "iter  10 value 154.208118\n",
      "iter  20 value 142.762028\n",
      "iter  30 value 135.701701\n",
      "iter  40 value 126.957468\n",
      "iter  50 value 115.094965\n",
      "iter  60 value 105.512492\n",
      "iter  70 value 85.199273\n",
      "iter  80 value 54.520887\n",
      "iter  90 value 53.185532\n",
      "iter 100 value 53.183948\n",
      "final  value 53.183948 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 403.476578 \n",
      "final  value 338.206055 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 374.748040 \n",
      "final  value 338.207480 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 445.248151 \n",
      "final  value 338.209670 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 434.456819 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 440.935102 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 388.466192 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 437.251448 \n",
      "iter  10 value 338.219004\n",
      "final  value 338.218998 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 348.282875 \n",
      "iter  10 value 331.776138\n",
      "iter  20 value 286.127571\n",
      "iter  30 value 178.171487\n",
      "iter  40 value 147.420879\n",
      "iter  50 value 124.476660\n",
      "iter  60 value 102.912281\n",
      "iter  70 value 91.352957\n",
      "iter  80 value 81.790818\n",
      "iter  90 value 56.049721\n",
      "iter 100 value 49.801715\n",
      "final  value 49.801715 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 355.714807 \n",
      "iter  10 value 242.659935\n",
      "iter  20 value 147.499819\n",
      "iter  30 value 141.277921\n",
      "iter  40 value 127.145129\n",
      "iter  50 value 105.558010\n",
      "iter  60 value 97.992078\n",
      "iter  70 value 81.432179\n",
      "iter  80 value 72.235980\n",
      "iter  90 value 65.614769\n",
      "iter 100 value 51.659367\n",
      "final  value 51.659367 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 338.915953 \n",
      "final  value 338.206021 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 410.461353 \n",
      "final  value 338.207188 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 366.003141 \n",
      "iter  10 value 153.835635\n",
      "iter  20 value 101.430084\n",
      "iter  30 value 73.325359\n",
      "iter  40 value 52.385990\n",
      "iter  50 value 43.107625\n",
      "iter  60 value 39.054583\n",
      "iter  70 value 36.129581\n",
      "iter  80 value 33.696740\n",
      "iter  90 value 31.594131\n",
      "iter 100 value 30.276101\n",
      "final  value 30.276101 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 394.052882 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 342.443789 \n",
      "final  value 338.671686 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 337.405340 \n",
      "iter  10 value 242.555451\n",
      "iter  20 value 242.354552\n",
      "iter  30 value 242.352217\n",
      "final  value 242.351871 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 340.285841 \n",
      "iter  10 value 337.263559\n",
      "iter  20 value 334.604195\n",
      "iter  30 value 167.517956\n",
      "iter  40 value 146.397730\n",
      "iter  50 value 140.100165\n",
      "iter  60 value 133.175753\n",
      "iter  70 value 115.576833\n",
      "iter  80 value 59.295288\n",
      "iter  90 value 54.270842\n",
      "final  value 54.245518 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 351.193606 \n",
      "iter  10 value 338.013866\n",
      "iter  20 value 327.800430\n",
      "iter  30 value 186.439870\n",
      "iter  40 value 131.490356\n",
      "iter  50 value 106.050605\n",
      "iter  60 value 90.671566\n",
      "iter  70 value 87.029828\n",
      "iter  80 value 79.246220\n",
      "iter  90 value 62.407788\n",
      "iter 100 value 56.737287\n",
      "final  value 56.737287 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 372.873185 \n",
      "iter  10 value 338.520534\n",
      "iter  20 value 197.755424\n",
      "iter  30 value 156.073537\n",
      "iter  40 value 148.761384\n",
      "iter  50 value 137.119137\n",
      "iter  60 value 131.727586\n",
      "iter  70 value 111.017859\n",
      "iter  80 value 102.605558\n",
      "iter  90 value 99.315478\n",
      "iter 100 value 87.307333\n",
      "final  value 87.307333 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 350.434834 \n",
      "final  value 338.672421 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 339.139663 \n",
      "final  value 338.673237 \n",
      "converged\n",
      "# weights:  161\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial  value 644.310551 \n",
      "final  value 338.674401 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 340.206312 \n",
      "final  value 337.682037 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 340.450821 \n",
      "final  value 337.682037 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 315.766292 \n",
      "iter  10 value 153.650297\n",
      "iter  20 value 149.020547\n",
      "iter  30 value 148.950805\n",
      "final  value 148.950734 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 338.769245 \n",
      "iter  10 value 337.707747\n",
      "final  value 337.696091 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 344.203813 \n",
      "iter  10 value 337.546092\n",
      "iter  20 value 246.564772\n",
      "iter  30 value 178.921733\n",
      "iter  40 value 148.987315\n",
      "iter  50 value 134.012113\n",
      "iter  60 value 117.546853\n",
      "iter  70 value 105.272273\n",
      "iter  80 value 83.672664\n",
      "iter  90 value 67.456255\n",
      "iter 100 value 53.138672\n",
      "final  value 53.138672 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 362.681689 \n",
      "iter  10 value 337.694047\n",
      "iter  20 value 298.872824\n",
      "iter  30 value 174.369633\n",
      "iter  40 value 157.991079\n",
      "iter  50 value 137.746015\n",
      "iter  60 value 130.490694\n",
      "iter  70 value 108.919332\n",
      "iter  80 value 85.014667\n",
      "iter  90 value 61.265348\n",
      "iter 100 value 52.725691\n",
      "final  value 52.725691 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 385.335704 \n",
      "final  value 337.682642 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 365.716643 \n",
      "iter  10 value 335.708460\n",
      "final  value 335.701750 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 351.962141 \n",
      "final  value 337.684992 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 373.353893 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 344.805329 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 321.405145 \n",
      "iter  10 value 139.618107\n",
      "iter  20 value 135.398996\n",
      "iter  30 value 133.958774\n",
      "iter  40 value 127.632429\n",
      "iter  50 value 127.588118\n",
      "iter  60 value 127.586123\n",
      "final  value 127.586089 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 338.608222 \n",
      "iter  10 value 211.479188\n",
      "iter  20 value 159.627540\n",
      "iter  30 value 137.222977\n",
      "iter  40 value 108.218093\n",
      "iter  50 value 93.581604\n",
      "iter  60 value 76.064496\n",
      "iter  70 value 52.022566\n",
      "iter  80 value 49.971317\n",
      "iter  90 value 48.365596\n",
      "final  value 48.365197 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 457.660477 \n",
      "iter  10 value 338.194861\n",
      "iter  20 value 206.781600\n",
      "iter  30 value 170.798489\n",
      "iter  40 value 135.704778\n",
      "iter  50 value 130.041461\n",
      "iter  60 value 127.568719\n",
      "iter  70 value 116.071693\n",
      "iter  80 value 97.881125\n",
      "iter  90 value 88.266866\n",
      "iter 100 value 80.267371\n",
      "final  value 80.267371 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 363.868373 \n",
      "iter  10 value 275.911548\n",
      "iter  20 value 251.236304\n",
      "iter  30 value 156.745056\n",
      "iter  40 value 143.411954\n",
      "iter  50 value 116.132290\n",
      "iter  60 value 111.389234\n",
      "iter  70 value 75.445984\n",
      "iter  80 value 58.608780\n",
      "iter  90 value 43.836076\n",
      "iter 100 value 40.146126\n",
      "final  value 40.146126 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 369.618848 \n",
      "final  value 338.205943 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 391.155519 \n",
      "final  value 338.207909 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 381.332020 \n",
      "final  value 338.207842 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 499.397465 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 338.610904 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 340.716214 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 372.304734 \n",
      "iter  10 value 338.233057\n",
      "final  value 338.219000 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 355.887364 \n",
      "iter  10 value 338.256973\n",
      "iter  20 value 288.338997\n",
      "iter  30 value 186.609086\n",
      "iter  40 value 124.752047\n",
      "iter  50 value 73.567463\n",
      "iter  60 value 66.200631\n",
      "iter  70 value 52.316866\n",
      "iter  80 value 42.115531\n",
      "iter  90 value 38.371828\n",
      "iter 100 value 37.698933\n",
      "final  value 37.698933 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 373.774516 \n",
      "iter  10 value 258.359293\n",
      "iter  20 value 138.761974\n",
      "iter  30 value 119.364911\n",
      "iter  40 value 115.425116\n",
      "iter  50 value 102.156897\n",
      "iter  60 value 81.957023\n",
      "iter  70 value 75.218372\n",
      "iter  80 value 63.725918\n",
      "iter  90 value 43.748742\n",
      "iter 100 value 42.228788\n",
      "final  value 42.228788 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 409.582857 \n",
      "final  value 338.205995 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 339.353786 \n",
      "final  value 338.206687 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 436.514011 \n",
      "final  value 338.208051 \n",
      "converged\n",
      "# weights:  33\n",
      "initial  value 328.300450 \n",
      "iter  10 value 219.810691\n",
      "iter  20 value 186.937842\n",
      "iter  30 value 186.112803\n",
      "iter  40 value 185.678027\n",
      "iter  50 value 183.377435\n",
      "iter  60 value 183.315866\n",
      "final  value 183.315594 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 346.760587 \n",
      "final  value 338.205383 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 332.672867 \n",
      "iter  10 value 146.855770\n",
      "iter  20 value 137.945584\n",
      "iter  30 value 123.773595\n",
      "iter  40 value 95.607110\n",
      "iter  50 value 53.032712\n",
      "iter  60 value 31.938159\n",
      "iter  70 value 16.645181\n",
      "iter  80 value 11.261907\n",
      "iter  90 value 8.135378\n",
      "iter 100 value 6.791188\n",
      "final  value 6.791188 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 338.954727 \n",
      "iter  10 value 338.038994\n",
      "iter  20 value 317.599325\n",
      "iter  30 value 195.144573\n",
      "iter  40 value 146.519458\n",
      "iter  50 value 134.789675\n",
      "iter  60 value 81.575040\n",
      "iter  70 value 52.685240\n",
      "iter  80 value 52.158648\n",
      "final  value 52.158639 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 377.637326 \n",
      "iter  10 value 325.636358\n",
      "iter  20 value 199.045797\n",
      "iter  30 value 152.384104\n",
      "iter  40 value 140.225695\n",
      "iter  50 value 117.214377\n",
      "iter  60 value 108.958437\n",
      "iter  70 value 99.570951\n",
      "iter  80 value 83.836303\n",
      "iter  90 value 70.819037\n",
      "iter 100 value 50.310754\n",
      "final  value 50.310754 \n",
      "stopped after 100 iterations\n",
      "# weights:  161\n",
      "initial  value 376.684528 \n",
      "iter  10 value 338.146267\n",
      "iter  20 value 287.365089\n",
      "iter  30 value 233.461836\n",
      "iter  40 value 132.247396\n",
      "iter  50 value 111.966590\n",
      "iter  60 value 99.726270\n",
      "iter  70 value 86.085992\n",
      "iter  80 value 79.690196\n",
      "iter  90 value 73.612391\n",
      "iter 100 value 69.470412\n",
      "final  value 69.470412 \n",
      "stopped after 100 iterations\n",
      "# weights:  33\n",
      "initial  value 356.959660 \n",
      "final  value 338.206031 \n",
      "converged\n",
      "# weights:  97\n",
      "initial  value 372.530920 \n",
      "final  value 338.207110 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 339.620160 \n",
      "final  value 338.208020 \n",
      "converged\n",
      "# weights:  161\n",
      "initial  value 409.146219 \n",
      "iter  10 value 375.791171\n",
      "iter  20 value 346.825530\n",
      "iter  30 value 181.577962\n",
      "iter  40 value 89.150025\n",
      "iter  50 value 74.069984\n",
      "iter  60 value 70.603652\n",
      "iter  70 value 59.179992\n",
      "iter  80 value 51.077712\n",
      "iter  90 value 45.518316\n",
      "iter 100 value 45.280357\n",
      "final  value 45.280357 \n",
      "stopped after 100 iterations\n"
     ]
    }
   ],
   "source": [
    "models <- caretList(V2~., data=dataset, trControl=control, metric=metric, methodList=algorithmList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Mostramos todas sus características"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$nnet\n",
       "Neural Network \n",
       "\n",
       "569 samples\n",
       " 30 predictor\n",
       "  2 classes: 'B', 'M' \n",
       "\n",
       "No pre-processing\n",
       "Resampling: Cross-Validated (10 fold, repeated 3 times) \n",
       "Summary of sample sizes: 512, 512, 511, 512, 512, 511, ... \n",
       "Resampling results across tuning parameters:\n",
       "\n",
       "  size  decay  Accuracy   Kappa     \n",
       "  1     0e+00  0.6443789  0.04901421\n",
       "  1     1e-04  0.6578501  0.08742169\n",
       "  1     1e-01  0.8130020  0.51288232\n",
       "  3     0e+00  0.6739424  0.13231714\n",
       "  3     1e-04  0.6683627  0.11426669\n",
       "  3     1e-01  0.9324644  0.84121635\n",
       "  5     0e+00  0.7305736  0.29217298\n",
       "  5     1e-04  0.6783446  0.14307304\n",
       "  5     1e-01  0.9478045  0.88798147\n",
       "\n",
       "Accuracy was used to select the optimal model using the largest value.\n",
       "The final values used for the model were size = 5 and decay = 0.1.\n",
       "\n",
       "$rpart\n",
       "CART \n",
       "\n",
       "569 samples\n",
       " 30 predictor\n",
       "  2 classes: 'B', 'M' \n",
       "\n",
       "No pre-processing\n",
       "Resampling: Cross-Validated (10 fold, repeated 3 times) \n",
       "Summary of sample sizes: 512, 512, 511, 512, 512, 511, ... \n",
       "Resampling results across tuning parameters:\n",
       "\n",
       "  cp           Accuracy   Kappa    \n",
       "  0.004716981  0.9215377  0.8316180\n",
       "  0.049528302  0.8963792  0.7769300\n",
       "  0.792452830  0.7673411  0.4162498\n",
       "\n",
       "Accuracy was used to select the optimal model using the largest value.\n",
       "The final value used for the model was cp = 0.004716981.\n",
       "\n",
       "$C5.0\n",
       "C5.0 \n",
       "\n",
       "569 samples\n",
       " 30 predictor\n",
       "  2 classes: 'B', 'M' \n",
       "\n",
       "No pre-processing\n",
       "Resampling: Cross-Validated (10 fold, repeated 3 times) \n",
       "Summary of sample sizes: 512, 512, 511, 512, 512, 511, ... \n",
       "Resampling results across tuning parameters:\n",
       "\n",
       "  model  winnow  trials  Accuracy   Kappa    \n",
       "  rules  FALSE    1      0.9350513  0.8595874\n",
       "  rules  FALSE   10      0.9614320  0.9168717\n",
       "  rules  FALSE   20      0.9636995  0.9216999\n",
       "  rules   TRUE    1      0.9385299  0.8675032\n",
       "  rules   TRUE   10      0.9449835  0.8818739\n",
       "  rules   TRUE   20      0.9490778  0.8907189\n",
       "  tree   FALSE    1      0.9315112  0.8527192\n",
       "  tree   FALSE   10      0.9549063  0.9027121\n",
       "  tree   FALSE   20      0.9602008  0.9142689\n",
       "  tree    TRUE    1      0.9362008  0.8627408\n",
       "  tree    TRUE   10      0.9472827  0.8865228\n",
       "  tree    TRUE   20      0.9484826  0.8891019\n",
       "\n",
       "Accuracy was used to select the optimal model using the largest value.\n",
       "The final values used for the model were trials = 20, model = rules and\n",
       " winnow = FALSE.\n",
       "\n",
       "$knn\n",
       "k-Nearest Neighbors \n",
       "\n",
       "569 samples\n",
       " 30 predictor\n",
       "  2 classes: 'B', 'M' \n",
       "\n",
       "No pre-processing\n",
       "Resampling: Cross-Validated (10 fold, repeated 3 times) \n",
       "Summary of sample sizes: 512, 512, 511, 512, 512, 511, ... \n",
       "Resampling results across tuning parameters:\n",
       "\n",
       "  k  Accuracy   Kappa    \n",
       "  5  0.9308757  0.8500878\n",
       "  7  0.9279928  0.8439764\n",
       "  9  0.9291415  0.8459601\n",
       "\n",
       "Accuracy was used to select the optimal model using the largest value.\n",
       "The final value used for the model was k = 5.\n",
       "\n",
       "$svmRadial\n",
       "Support Vector Machines with Radial Basis Function Kernel \n",
       "\n",
       "569 samples\n",
       " 30 predictor\n",
       "  2 classes: 'B', 'M' \n",
       "\n",
       "No pre-processing\n",
       "Resampling: Cross-Validated (10 fold, repeated 3 times) \n",
       "Summary of sample sizes: 512, 512, 511, 512, 512, 511, ... \n",
       "Resampling results across tuning parameters:\n",
       "\n",
       "  C     Accuracy   Kappa    \n",
       "  0.25  0.9560752  0.9062613\n",
       "  0.50  0.9648377  0.9251890\n",
       "  1.00  0.9695168  0.9352765\n",
       "\n",
       "Tuning parameter 'sigma' was held constant at a value of 0.04541934\n",
       "Accuracy was used to select the optimal model using the largest value.\n",
       "The final values used for the model were sigma = 0.04541934 and C = 1.\n",
       "\n",
       "attr(,\"class\")\n",
       "[1] \"caretList\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "summary.resamples(object = results.N0)\n",
       "\n",
       "Models: nnet, rpart, C5.0, knn, svmRadial \n",
       "Number of resamples: 30 \n",
       "\n",
       "Accuracy \n",
       "               Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's\n",
       "nnet      0.8571429 0.9298246 0.9562808 0.9478045 0.9649123 1.0000000    0\n",
       "rpart     0.8771930 0.9004840 0.9298246 0.9215377 0.9422776 0.9642857    0\n",
       "C5.0      0.9122807 0.9473684 0.9649123 0.9636995 0.9824561 1.0000000    0\n",
       "knn       0.8750000 0.9122807 0.9298246 0.9308757 0.9473684 0.9824561    0\n",
       "svmRadial 0.8947368 0.9473684 0.9822995 0.9695168 1.0000000 1.0000000    0\n",
       "\n",
       "Kappa \n",
       "               Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's\n",
       "nnet      0.6893204 0.8500892 0.9070954 0.8879815 0.9259150 1.0000000    0\n",
       "rpart     0.7294915 0.7922852 0.8492063 0.8316180 0.8755052 0.9238095    0\n",
       "C5.0      0.8057260 0.8863326 0.9257600 0.9216999 0.9619238 1.0000000    0\n",
       "knn       0.7280164 0.8114533 0.8492063 0.8500878 0.8854825 0.9622642    0\n",
       "svmRadial 0.7824427 0.8885551 0.9617312 0.9352765 1.0000000 1.0000000    0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.N0 <- resamples(models)\n",
    "summary(results.N0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Revisión de la correlacion entre los diferentes modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nbkYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dotplot(results.N0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 5 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>nnet</th><th scope=col>rpart</th><th scope=col>C5.0</th><th scope=col>knn</th><th scope=col>svmRadial</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>nnet</th><td>1.0000000</td><td>0.1163560</td><td>0.2015316</td><td>0.7329345</td><td>0.3463562</td></tr>\n",
       "\t<tr><th scope=row>rpart</th><td>0.1163560</td><td>1.0000000</td><td>0.4884161</td><td>0.2687946</td><td>0.2721461</td></tr>\n",
       "\t<tr><th scope=row>C5.0</th><td>0.2015316</td><td>0.4884161</td><td>1.0000000</td><td>0.2200800</td><td>0.5881069</td></tr>\n",
       "\t<tr><th scope=row>knn</th><td>0.7329345</td><td>0.2687946</td><td>0.2200800</td><td>1.0000000</td><td>0.2184746</td></tr>\n",
       "\t<tr><th scope=row>svmRadial</th><td>0.3463562</td><td>0.2721461</td><td>0.5881069</td><td>0.2184746</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 5 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & nnet & rpart & C5.0 & knn & svmRadial\\\\\n",
       "\\hline\n",
       "\tnnet & 1.0000000 & 0.1163560 & 0.2015316 & 0.7329345 & 0.3463562\\\\\n",
       "\trpart & 0.1163560 & 1.0000000 & 0.4884161 & 0.2687946 & 0.2721461\\\\\n",
       "\tC5.0 & 0.2015316 & 0.4884161 & 1.0000000 & 0.2200800 & 0.5881069\\\\\n",
       "\tknn & 0.7329345 & 0.2687946 & 0.2200800 & 1.0000000 & 0.2184746\\\\\n",
       "\tsvmRadial & 0.3463562 & 0.2721461 & 0.5881069 & 0.2184746 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 5 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | nnet | rpart | C5.0 | knn | svmRadial |\n",
       "|---|---|---|---|---|---|\n",
       "| nnet | 1.0000000 | 0.1163560 | 0.2015316 | 0.7329345 | 0.3463562 |\n",
       "| rpart | 0.1163560 | 1.0000000 | 0.4884161 | 0.2687946 | 0.2721461 |\n",
       "| C5.0 | 0.2015316 | 0.4884161 | 1.0000000 | 0.2200800 | 0.5881069 |\n",
       "| knn | 0.7329345 | 0.2687946 | 0.2200800 | 1.0000000 | 0.2184746 |\n",
       "| svmRadial | 0.3463562 | 0.2721461 | 0.5881069 | 0.2184746 | 1.0000000 |\n",
       "\n"
      ],
      "text/plain": [
       "          nnet      rpart     C5.0      knn       svmRadial\n",
       "nnet      1.0000000 0.1163560 0.2015316 0.7329345 0.3463562\n",
       "rpart     0.1163560 1.0000000 0.4884161 0.2687946 0.2721461\n",
       "C5.0      0.2015316 0.4884161 1.0000000 0.2200800 0.5881069\n",
       "knn       0.7329345 0.2687946 0.2200800 1.0000000 0.2184746\n",
       "svmRadial 0.3463562 0.2721461 0.5881069 0.2184746 1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelCor(results.N0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nt3F8hbPl9QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "splom(results.N0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Construcción del modelo final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parámetros de construcción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stackControl <- trainControl(method=\"repeatedcv\", number=10, repeats=3, savePredictions=TRUE, classProbs=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "N1.rf <- caretStack(models, method=\"rf\", metric=\"Accuracy\", trControl=stackControl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Mostramos las características del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A rf ensemble of 5 base models: nnet, rpart, C5.0, knn, svmRadial\n",
      "\n",
      "Ensemble results:\n",
      "Random Forest \n",
      "\n",
      "1707 samples\n",
      "   5 predictor\n",
      "   2 classes: 'B', 'M' \n",
      "\n",
      "No pre-processing\n",
      "Resampling: Cross-Validated (10 fold, repeated 3 times) \n",
      "Summary of sample sizes: 1536, 1536, 1537, 1537, 1536, 1537, ... \n",
      "Resampling results across tuning parameters:\n",
      "\n",
      "  mtry  Accuracy   Kappa    \n",
      "  2     0.9798955  0.9568792\n",
      "  3     0.9795034  0.9560515\n",
      "  5     0.9791135  0.9552296\n",
      "\n",
      "Accuracy was used to select the optimal model using the largest value.\n",
      "The final value used for the model was mtry = 2.\n"
     ]
    }
   ],
   "source": [
    "print(N1.rf)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}