{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"nr1fvE\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
       "       if(!window.letsPlotCallQueue) {\n",
       "           window.letsPlotCallQueue = [];\n",
       "       }; \n",
       "       window.letsPlotCall = function(f) {\n",
       "           window.letsPlotCallQueue.push(f);\n",
       "       };\n",
       "       (function() {\n",
       "           var script = document.createElement(\"script\");\n",
       "           script.type = \"text/javascript\";\n",
       "           script.src = \"https://cdnjs.cloudflare.com/ajax/libs/lets-plot/2.0.2/lets-plot.min.js\";\n",
       "           script.onload = function() {\n",
       "               window.letsPlotCall = function(f) {f();};\n",
       "               window.letsPlotCallQueue.forEach(function(f) {f();});\n",
       "               window.letsPlotCallQueue = [];\n",
       "               \n",
       "               \n",
       "           };\n",
       "           script.onerror = function(event) {\n",
       "               window.letsPlotCall = function(f) {};\n",
       "               window.letsPlotCallQueue = [];\n",
       "               var div = document.createElement(\"div\");\n",
       "               div.style.color = 'darkred';\n",
       "               div.textContent = 'Error loading Lets-Plot JS';\n",
       "               document.getElementById(\"nr1fvE\").appendChild(div);\n",
       "           };\n",
       "           var e = document.getElementById(\"nr1fvE\");\n",
       "           e.appendChild(script);\n",
       "       })();\n",
       "   </script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%use deeplearning4j\n",
    "%use krangl@2fcf74dfbbe382f1803d1ab9e4739439e1f5671b\n",
    "%use lets-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "val iris_data = \"sepal-length,sepal-width,petal-length,petal-width,species\\n5.1,3.5,1.4,0.2,Iris-setosa\\n4.9,3.0,1.4,0.2,Iris-setosa\\n4.7,3.2,1.3,0.2,Iris-setosa\\n4.6,3.1,1.5,0.2,Iris-setosa\\n5.0,3.6,1.4,0.2,Iris-setosa\\n5.4,3.9,1.7,0.4,Iris-setosa\\n4.6,3.4,1.4,0.3,Iris-setosa\\n5.0,3.4,1.5,0.2,Iris-setosa\\n4.4,2.9,1.4,0.2,Iris-setosa\\n4.9,3.1,1.5,0.1,Iris-setosa\\n5.4,3.7,1.5,0.2,Iris-setosa\\n4.8,3.4,1.6,0.2,Iris-setosa\\n4.8,3.0,1.4,0.1,Iris-setosa\\n4.3,3.0,1.1,0.1,Iris-setosa\\n5.8,4.0,1.2,0.2,Iris-setosa\\n5.7,4.4,1.5,0.4,Iris-setosa\\n5.4,3.9,1.3,0.4,Iris-setosa\\n5.1,3.5,1.4,0.3,Iris-setosa\\n5.7,3.8,1.7,0.3,Iris-setosa\\n5.1,3.8,1.5,0.3,Iris-setosa\\n5.4,3.4,1.7,0.2,Iris-setosa\\n5.1,3.7,1.5,0.4,Iris-setosa\\n4.6,3.6,1.0,0.2,Iris-setosa\\n5.1,3.3,1.7,0.5,Iris-setosa\\n4.8,3.4,1.9,0.2,Iris-setosa\\n5.0,3.0,1.6,0.2,Iris-setosa\\n5.0,3.4,1.6,0.4,Iris-setosa\\n5.2,3.5,1.5,0.2,Iris-setosa\\n5.2,3.4,1.4,0.2,Iris-setosa\\n4.7,3.2,1.6,0.2,Iris-setosa\\n4.8,3.1,1.6,0.2,Iris-setosa\\n5.4,3.4,1.5,0.4,Iris-setosa\\n5.2,4.1,1.5,0.1,Iris-setosa\\n5.5,4.2,1.4,0.2,Iris-setosa\\n4.9,3.1,1.5,0.1,Iris-setosa\\n5.0,3.2,1.2,0.2,Iris-setosa\\n5.5,3.5,1.3,0.2,Iris-setosa\\n4.9,3.1,1.5,0.1,Iris-setosa\\n4.4,3.0,1.3,0.2,Iris-setosa\\n5.1,3.4,1.5,0.2,Iris-setosa\\n5.0,3.5,1.3,0.3,Iris-setosa\\n4.5,2.3,1.3,0.3,Iris-setosa\\n4.4,3.2,1.3,0.2,Iris-setosa\\n5.0,3.5,1.6,0.6,Iris-setosa\\n5.1,3.8,1.9,0.4,Iris-setosa\\n4.8,3.0,1.4,0.3,Iris-setosa\\n5.1,3.8,1.6,0.2,Iris-setosa\\n4.6,3.2,1.4,0.2,Iris-setosa\\n5.3,3.7,1.5,0.2,Iris-setosa\\n5.0,3.3,1.4,0.2,Iris-setosa\\n7.0,3.2,4.7,1.4,Iris-versicolor\\n6.4,3.2,4.5,1.5,Iris-versicolor\\n6.9,3.1,4.9,1.5,Iris-versicolor\\n5.5,2.3,4.0,1.3,Iris-versicolor\\n6.5,2.8,4.6,1.5,Iris-versicolor\\n5.7,2.8,4.5,1.3,Iris-versicolor\\n6.3,3.3,4.7,1.6,Iris-versicolor\\n4.9,2.4,3.3,1.0,Iris-versicolor\\n6.6,2.9,4.6,1.3,Iris-versicolor\\n5.2,2.7,3.9,1.4,Iris-versicolor\\n5.0,2.0,3.5,1.0,Iris-versicolor\\n5.9,3.0,4.2,1.5,Iris-versicolor\\n6.0,2.2,4.0,1.0,Iris-versicolor\\n6.1,2.9,4.7,1.4,Iris-versicolor\\n5.6,2.9,3.6,1.3,Iris-versicolor\\n6.7,3.1,4.4,1.4,Iris-versicolor\\n5.6,3.0,4.5,1.5,Iris-versicolor\\n5.8,2.7,4.1,1.0,Iris-versicolor\\n6.2,2.2,4.5,1.5,Iris-versicolor\\n5.6,2.5,3.9,1.1,Iris-versicolor\\n5.9,3.2,4.8,1.8,Iris-versicolor\\n6.1,2.8,4.0,1.3,Iris-versicolor\\n6.3,2.5,4.9,1.5,Iris-versicolor\\n6.1,2.8,4.7,1.2,Iris-versicolor\\n6.4,2.9,4.3,1.3,Iris-versicolor\\n6.6,3.0,4.4,1.4,Iris-versicolor\\n6.8,2.8,4.8,1.4,Iris-versicolor\\n6.7,3.0,5.0,1.7,Iris-versicolor\\n6.0,2.9,4.5,1.5,Iris-versicolor\\n5.7,2.6,3.5,1.0,Iris-versicolor\\n5.5,2.4,3.8,1.1,Iris-versicolor\\n5.5,2.4,3.7,1.0,Iris-versicolor\\n5.8,2.7,3.9,1.2,Iris-versicolor\\n6.0,2.7,5.1,1.6,Iris-versicolor\\n5.4,3.0,4.5,1.5,Iris-versicolor\\n6.0,3.4,4.5,1.6,Iris-versicolor\\n6.7,3.1,4.7,1.5,Iris-versicolor\\n6.3,2.3,4.4,1.3,Iris-versicolor\\n5.6,3.0,4.1,1.3,Iris-versicolor\\n5.5,2.5,4.0,1.3,Iris-versicolor\\n5.5,2.6,4.4,1.2,Iris-versicolor\\n6.1,3.0,4.6,1.4,Iris-versicolor\\n5.8,2.6,4.0,1.2,Iris-versicolor\\n5.0,2.3,3.3,1.0,Iris-versicolor\\n5.6,2.7,4.2,1.3,Iris-versicolor\\n5.7,3.0,4.2,1.2,Iris-versicolor\\n5.7,2.9,4.2,1.3,Iris-versicolor\\n6.2,2.9,4.3,1.3,Iris-versicolor\\n5.1,2.5,3.0,1.1,Iris-versicolor\\n5.7,2.8,4.1,1.3,Iris-versicolor\\n6.3,3.3,6.0,2.5,Iris-virginica\\n5.8,2.7,5.1,1.9,Iris-virginica\\n7.1,3.0,5.9,2.1,Iris-virginica\\n6.3,2.9,5.6,1.8,Iris-virginica\\n6.5,3.0,5.8,2.2,Iris-virginica\\n7.6,3.0,6.6,2.1,Iris-virginica\\n4.9,2.5,4.5,1.7,Iris-virginica\\n7.3,2.9,6.3,1.8,Iris-virginica\\n6.7,2.5,5.8,1.8,Iris-virginica\\n7.2,3.6,6.1,2.5,Iris-virginica\\n6.5,3.2,5.1,2.0,Iris-virginica\\n6.4,2.7,5.3,1.9,Iris-virginica\\n6.8,3.0,5.5,2.1,Iris-virginica\\n5.7,2.5,5.0,2.0,Iris-virginica\\n5.8,2.8,5.1,2.4,Iris-virginica\\n6.4,3.2,5.3,2.3,Iris-virginica\\n6.5,3.0,5.5,1.8,Iris-virginica\\n7.7,3.8,6.7,2.2,Iris-virginica\\n7.7,2.6,6.9,2.3,Iris-virginica\\n6.0,2.2,5.0,1.5,Iris-virginica\\n6.9,3.2,5.7,2.3,Iris-virginica\\n5.6,2.8,4.9,2.0,Iris-virginica\\n7.7,2.8,6.7,2.0,Iris-virginica\\n6.3,2.7,4.9,1.8,Iris-virginica\\n6.7,3.3,5.7,2.1,Iris-virginica\\n7.2,3.2,6.0,1.8,Iris-virginica\\n6.2,2.8,4.8,1.8,Iris-virginica\\n6.1,3.0,4.9,1.8,Iris-virginica\\n6.4,2.8,5.6,2.1,Iris-virginica\\n7.2,3.0,5.8,1.6,Iris-virginica\\n7.4,2.8,6.1,1.9,Iris-virginica\\n7.9,3.8,6.4,2.0,Iris-virginica\\n6.4,2.8,5.6,2.2,Iris-virginica\\n6.3,2.8,5.1,1.5,Iris-virginica\\n6.1,2.6,5.6,1.4,Iris-virginica\\n7.7,3.0,6.1,2.3,Iris-virginica\\n6.3,3.4,5.6,2.4,Iris-virginica\\n6.4,3.1,5.5,1.8,Iris-virginica\\n6.0,3.0,4.8,1.8,Iris-virginica\\n6.9,3.1,5.4,2.1,Iris-virginica\\n6.7,3.1,5.6,2.4,Iris-virginica\\n6.9,3.1,5.1,2.3,Iris-virginica\\n5.8,2.7,5.1,1.9,Iris-virginica\\n6.8,3.2,5.9,2.3,Iris-virginica\\n6.7,3.3,5.7,2.5,Iris-virginica\\n6.7,3.0,5.2,2.3,Iris-virginica\\n6.3,2.5,5.0,1.9,Iris-virginica\\n6.5,3.0,5.2,2.0,Iris-virginica\\n6.2,3.4,5.4,2.3,Iris-virginica\\n5.9,3.0,5.1,1.8,Iris-virginica\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html><body><table><tr><th style=\"text-align:left\">sepal-length</th><th style=\"text-align:left\">sepal-width</th><th style=\"text-align:left\">petal-length</th><th style=\"text-align:left\">petal-width</th><th style=\"text-align:left\">species</th></tr><tr><td style=\"text-align:left\" title=\"5.1\">5.1</td><td style=\"text-align:left\" title=\"3.3\">3.3</td><td style=\"text-align:left\" title=\"1.7\">1.7</td><td style=\"text-align:left\" title=\"0.5\">0.5</td><td style=\"text-align:left\" title=\"Iris-setosa\">Iris-setosa</td></tr><tr><td style=\"text-align:left\" title=\"5.8\">5.8</td><td style=\"text-align:left\" title=\"2.7\">2.7</td><td style=\"text-align:left\" title=\"5.1\">5.1</td><td style=\"text-align:left\" title=\"1.9\">1.9</td><td style=\"text-align:left\" title=\"Iris-virginica\">Iris-virginica</td></tr><tr><td style=\"text-align:left\" title=\"5.6\">5.6</td><td style=\"text-align:left\" title=\"2.8\">2.8</td><td style=\"text-align:left\" title=\"4.9\">4.9</td><td style=\"text-align:left\" title=\"2.0\">2.0</td><td style=\"text-align:left\" title=\"Iris-virginica\">Iris-virginica</td></tr><tr><td style=\"text-align:left\" title=\"4.8\">4.8</td><td style=\"text-align:left\" title=\"3.0\">3.0</td><td style=\"text-align:left\" title=\"1.4\">1.4</td><td style=\"text-align:left\" title=\"0.3\">0.3</td><td style=\"text-align:left\" title=\"Iris-setosa\">Iris-setosa</td></tr><tr><td style=\"text-align:left\" title=\"7.7\">7.7</td><td style=\"text-align:left\" title=\"2.6\">2.6</td><td style=\"text-align:left\" title=\"6.9\">6.9</td><td style=\"text-align:left\" title=\"2.3\">2.3</td><td style=\"text-align:left\" title=\"Iris-virginica\">Iris-virginica</td></tr></table></body></html>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import java.util.*\n",
    "import java.io.StringReader\n",
    "\n",
    "val iris = DataFrame.readDelim(StringReader(iris_data)).shuffle()\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "'mapping':{\n",
       "},\n",
       "'kind':\"plot\",\n",
       "'scales':[],\n",
       "'layers':[{\n",
       "'mapping':{\n",
       "'color':\"color\",\n",
       "'x':\"x\",\n",
       "'y':\"y\"\n",
       "},\n",
       "'stat':\"identity\",\n",
       "'data':{\n",
       "'color':[\"Iris-setosa\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-virginica\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\"],\n",
       "'x':[5.1,5.8,5.6,4.8,7.7,5.6,6.9,5.9,4.9,6.8,6.0,6.0,5.4,5.7,7.2,6.0,6.4,5.7,5.7,6.2,5.0,5.7,6.3,7.7,4.8,5.8,5.1,6.4,5.3,4.6,7.6,4.5,5.6,5.7,6.7,6.5,5.0,6.1,6.5,6.2,4.4,5.2,7.2,5.5,6.4,4.9,6.3,5.5,4.7,6.3,6.3,7.1,5.0,6.8,4.8,6.7,5.8,5.0,6.7,5.1,6.4,7.0,6.1,5.4,4.9,5.2,6.5,5.4,7.3,5.2,5.4,6.2,5.1,4.8,7.7,5.6,6.3,5.8,5.5,4.9,6.0,5.0,5.9,5.4,6.9,4.9,5.2,5.1,5.1,6.7,5.9,5.8,4.3,6.7,6.3,5.6,4.4,4.6,5.5,6.9,6.0,7.2,6.1,5.7,5.8,4.8,6.9,5.5,5.0,4.6,4.9,6.0,6.3,6.6,7.9,5.6,5.7,5.0,5.7,6.7,6.7,6.7,5.5,5.0,4.4,6.6,7.4,6.5,6.3,6.4,6.1,4.6,5.4,5.5,6.1,5.8,6.8,6.4,7.7,5.0,6.2,5.1,6.5,5.1,6.4,5.1,5.0,4.7,6.3,6.1],\n",
       "'y':[3.3,2.7,2.8,3.0,2.6,2.9,3.1,3.0,3.1,2.8,2.2,3.4,3.9,3.0,3.0,2.7,3.2,2.8,2.5,2.8,3.5,4.4,2.5,3.0,3.0,2.7,2.5,2.8,3.7,3.4,3.0,2.3,2.7,2.6,3.0,3.0,2.3,3.0,3.0,3.4,2.9,3.5,3.6,4.2,2.9,3.0,2.5,2.4,3.2,2.7,2.3,3.0,3.5,3.0,3.4,3.1,2.6,3.2,3.3,3.5,2.7,3.2,2.8,3.4,2.4,3.4,2.8,3.0,2.9,2.7,3.9,2.2,3.5,3.4,3.8,3.0,3.4,2.8,2.3,2.5,2.2,2.0,3.2,3.4,3.1,3.1,4.1,3.8,3.8,3.1,3.0,4.0,3.0,2.5,3.3,2.5,3.2,3.1,2.6,3.1,2.9,3.2,2.8,2.9,2.7,3.1,3.2,2.4,3.4,3.2,3.1,3.0,2.9,3.0,3.8,3.0,3.8,3.4,2.8,3.3,3.1,3.0,2.5,3.3,3.0,2.9,2.8,3.0,2.8,3.2,2.9,3.6,3.7,3.5,3.0,2.7,3.2,3.1,2.8,3.0,2.9,3.4,3.2,3.7,2.8,3.8,3.6,3.2,3.3,2.6]\n",
       "},\n",
       "'alpha':1.0,\n",
       "'position':\"identity\",\n",
       "'geom':\"point\"\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"cmpOSS\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val points = geomPoint(\n",
    "    data = mapOf(\n",
    "        \"x\" to iris[\"sepal-length\"].values().toList(),\n",
    "        \"y\" to iris[\"sepal-width\"].values().toList(),\n",
    "        \"color\" to iris[\"species\"].values().toList()\n",
    "    ), alpha=1.0)\n",
    "{\n",
    "    x = \"x\" \n",
    "    y = \"y\"\n",
    "    color = \"color\"\n",
    "}\n",
    "\n",
    "ggplot() + points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html><body><table><tr><th style=\"text-align:left\">sepal-length</th><th style=\"text-align:left\">sepal-width</th><th style=\"text-align:left\">petal-length</th><th style=\"text-align:left\">petal-width</th></tr><tr><td style=\"text-align:left\" title=\"5.1\">5.1</td><td style=\"text-align:left\" title=\"3.3\">3.3</td><td style=\"text-align:left\" title=\"1.7\">1.7</td><td style=\"text-align:left\" title=\"0.5\">0.5</td></tr><tr><td style=\"text-align:left\" title=\"5.8\">5.8</td><td style=\"text-align:left\" title=\"2.7\">2.7</td><td style=\"text-align:left\" title=\"5.1\">5.1</td><td style=\"text-align:left\" title=\"1.9\">1.9</td></tr><tr><td style=\"text-align:left\" title=\"5.6\">5.6</td><td style=\"text-align:left\" title=\"2.8\">2.8</td><td style=\"text-align:left\" title=\"4.9\">4.9</td><td style=\"text-align:left\" title=\"2.0\">2.0</td></tr><tr><td style=\"text-align:left\" title=\"4.8\">4.8</td><td style=\"text-align:left\" title=\"3.0\">3.0</td><td style=\"text-align:left\" title=\"1.4\">1.4</td><td style=\"text-align:left\" title=\"0.3\">0.3</td></tr><tr><td style=\"text-align:left\" title=\"7.7\">7.7</td><td style=\"text-align:left\" title=\"2.6\">2.6</td><td style=\"text-align:left\" title=\"6.9\">6.9</td><td style=\"text-align:left\" title=\"2.3\">2.3</td></tr></table></body></html>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val irisWithoutLabel = iris.remove(\"species\")\n",
    "irisWithoutLabel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1, 3.3, 1.7, 0.5]\n",
      "[5.8, 2.7, 5.1, 1.9]\n",
      "[5.6, 2.8, 4.9, 2.0]\n",
      "[4.8, 3.0, 1.4, 0.3]\n",
      "[7.7, 2.6, 6.9, 2.3]\n",
      "[5.6, 2.9, 3.6, 1.3]\n",
      "[6.9, 3.1, 5.4, 2.1]\n",
      "[5.9, 3.0, 4.2, 1.5]\n",
      "[4.9, 3.1, 1.5, 0.1]\n",
      "[6.8, 2.8, 4.8, 1.4]\n",
      "[6.0, 2.2, 5.0, 1.5]\n",
      "[6.0, 3.4, 4.5, 1.6]\n",
      "[5.4, 3.9, 1.3, 0.4]\n",
      "[5.7, 3.0, 4.2, 1.2]\n",
      "[7.2, 3.0, 5.8, 1.6]\n",
      "[6.0, 2.7, 5.1, 1.6]\n",
      "[6.4, 3.2, 5.3, 2.3]\n",
      "[5.7, 2.8, 4.1, 1.3]\n",
      "[5.7, 2.5, 5.0, 2.0]\n",
      "[6.2, 2.8, 4.8, 1.8]\n",
      "[5.0, 3.5, 1.3, 0.3]\n",
      "[5.7, 4.4, 1.5, 0.4]\n",
      "[6.3, 2.5, 5.0, 1.9]\n",
      "[7.7, 3.0, 6.1, 2.3]\n",
      "[4.8, 3.0, 1.4, 0.1]\n",
      "[5.8, 2.7, 3.9, 1.2]\n",
      "[5.1, 2.5, 3.0, 1.1]\n",
      "[6.4, 2.8, 5.6, 2.1]\n",
      "[5.3, 3.7, 1.5, 0.2]\n",
      "[4.6, 3.4, 1.4, 0.3]\n",
      "[7.6, 3.0, 6.6, 2.1]\n",
      "[4.5, 2.3, 1.3, 0.3]\n",
      "[5.6, 2.7, 4.2, 1.3]\n",
      "[5.7, 2.6, 3.5, 1.0]\n",
      "[6.7, 3.0, 5.0, 1.7]\n",
      "[6.5, 3.0, 5.8, 2.2]\n",
      "[5.0, 2.3, 3.3, 1.0]\n",
      "[6.1, 3.0, 4.9, 1.8]\n",
      "[6.5, 3.0, 5.2, 2.0]\n",
      "[6.2, 3.4, 5.4, 2.3]\n",
      "[4.4, 2.9, 1.4, 0.2]\n",
      "[5.2, 3.5, 1.5, 0.2]\n",
      "[7.2, 3.6, 6.1, 2.5]\n",
      "[5.5, 4.2, 1.4, 0.2]\n",
      "[6.4, 2.9, 4.3, 1.3]\n",
      "[4.9, 3.0, 1.4, 0.2]\n",
      "[6.3, 2.5, 4.9, 1.5]\n",
      "[5.5, 2.4, 3.7, 1.0]\n",
      "[4.7, 3.2, 1.6, 0.2]\n",
      "[6.3, 2.7, 4.9, 1.8]\n",
      "[6.3, 2.3, 4.4, 1.3]\n",
      "[7.1, 3.0, 5.9, 2.1]\n",
      "[5.0, 3.5, 1.6, 0.6]\n",
      "[6.8, 3.0, 5.5, 2.1]\n",
      "[4.8, 3.4, 1.9, 0.2]\n",
      "[6.7, 3.1, 5.6, 2.4]\n",
      "[5.8, 2.6, 4.0, 1.2]\n",
      "[5.0, 3.2, 1.2, 0.2]\n",
      "[6.7, 3.3, 5.7, 2.5]\n",
      "[5.1, 3.5, 1.4, 0.2]\n",
      "[6.4, 2.7, 5.3, 1.9]\n",
      "[7.0, 3.2, 4.7, 1.4]\n",
      "[6.1, 2.8, 4.7, 1.2]\n",
      "[5.4, 3.4, 1.7, 0.2]\n",
      "[4.9, 2.4, 3.3, 1.0]\n",
      "[5.2, 3.4, 1.4, 0.2]\n",
      "[6.5, 2.8, 4.6, 1.5]\n",
      "[5.4, 3.0, 4.5, 1.5]\n",
      "[7.3, 2.9, 6.3, 1.8]\n",
      "[5.2, 2.7, 3.9, 1.4]\n",
      "[5.4, 3.9, 1.7, 0.4]\n",
      "[6.2, 2.2, 4.5, 1.5]\n",
      "[5.1, 3.5, 1.4, 0.3]\n",
      "[4.8, 3.4, 1.6, 0.2]\n",
      "[7.7, 3.8, 6.7, 2.2]\n",
      "[5.6, 3.0, 4.5, 1.5]\n",
      "[6.3, 3.4, 5.6, 2.4]\n",
      "[5.8, 2.8, 5.1, 2.4]\n",
      "[5.5, 2.3, 4.0, 1.3]\n",
      "[4.9, 2.5, 4.5, 1.7]\n",
      "[6.0, 2.2, 4.0, 1.0]\n",
      "[5.0, 2.0, 3.5, 1.0]\n",
      "[5.9, 3.2, 4.8, 1.8]\n",
      "[5.4, 3.4, 1.5, 0.4]\n",
      "[6.9, 3.1, 4.9, 1.5]\n",
      "[4.9, 3.1, 1.5, 0.1]\n",
      "[5.2, 4.1, 1.5, 0.1]\n",
      "[5.1, 3.8, 1.5, 0.3]\n",
      "[5.1, 3.8, 1.6, 0.2]\n",
      "[6.7, 3.1, 4.7, 1.5]\n",
      "[5.9, 3.0, 5.1, 1.8]\n",
      "[5.8, 4.0, 1.2, 0.2]\n",
      "[4.3, 3.0, 1.1, 0.1]\n",
      "[6.7, 2.5, 5.8, 1.8]\n",
      "[6.3, 3.3, 6.0, 2.5]\n",
      "[5.6, 2.5, 3.9, 1.1]\n",
      "[4.4, 3.2, 1.3, 0.2]\n",
      "[4.6, 3.1, 1.5, 0.2]\n",
      "[5.5, 2.6, 4.4, 1.2]\n",
      "[6.9, 3.1, 5.1, 2.3]\n",
      "[6.0, 2.9, 4.5, 1.5]\n",
      "[7.2, 3.2, 6.0, 1.8]\n",
      "[6.1, 2.8, 4.0, 1.3]\n",
      "[5.7, 2.9, 4.2, 1.3]\n",
      "[5.8, 2.7, 4.1, 1.0]\n",
      "[4.8, 3.1, 1.6, 0.2]\n",
      "[6.9, 3.2, 5.7, 2.3]\n",
      "[5.5, 2.4, 3.8, 1.1]\n",
      "[5.0, 3.4, 1.5, 0.2]\n",
      "[4.6, 3.2, 1.4, 0.2]\n",
      "[4.9, 3.1, 1.5, 0.1]\n",
      "[6.0, 3.0, 4.8, 1.8]\n",
      "[6.3, 2.9, 5.6, 1.8]\n",
      "[6.6, 3.0, 4.4, 1.4]\n",
      "[7.9, 3.8, 6.4, 2.0]\n",
      "[5.6, 3.0, 4.1, 1.3]\n",
      "[5.7, 3.8, 1.7, 0.3]\n",
      "[5.0, 3.4, 1.6, 0.4]\n",
      "[5.7, 2.8, 4.5, 1.3]\n",
      "[6.7, 3.3, 5.7, 2.1]\n",
      "[6.7, 3.1, 4.4, 1.4]\n",
      "[6.7, 3.0, 5.2, 2.3]\n",
      "[5.5, 2.5, 4.0, 1.3]\n",
      "[5.0, 3.3, 1.4, 0.2]\n",
      "[4.4, 3.0, 1.3, 0.2]\n",
      "[6.6, 2.9, 4.6, 1.3]\n",
      "[7.4, 2.8, 6.1, 1.9]\n",
      "[6.5, 3.0, 5.5, 1.8]\n",
      "[6.3, 2.8, 5.1, 1.5]\n",
      "[6.4, 3.2, 4.5, 1.5]\n",
      "[6.1, 2.9, 4.7, 1.4]\n",
      "[4.6, 3.6, 1.0, 0.2]\n",
      "[5.4, 3.7, 1.5, 0.2]\n",
      "[5.5, 3.5, 1.3, 0.2]\n",
      "[6.1, 3.0, 4.6, 1.4]\n",
      "[5.8, 2.7, 5.1, 1.9]\n",
      "[6.8, 3.2, 5.9, 2.3]\n",
      "[6.4, 3.1, 5.5, 1.8]\n",
      "[7.7, 2.8, 6.7, 2.0]\n",
      "[5.0, 3.0, 1.6, 0.2]\n",
      "[6.2, 2.9, 4.3, 1.3]\n",
      "[5.1, 3.4, 1.5, 0.2]\n",
      "[6.5, 3.2, 5.1, 2.0]\n",
      "[5.1, 3.7, 1.5, 0.4]\n",
      "[6.4, 2.8, 5.6, 2.2]\n",
      "[5.1, 3.8, 1.9, 0.4]\n",
      "[5.0, 3.6, 1.4, 0.2]\n",
      "[4.7, 3.2, 1.3, 0.2]\n",
      "[6.3, 3.3, 4.7, 1.6]\n",
      "[6.1, 2.6, 5.6, 1.4]]\n"
     ]
    }
   ],
   "source": [
    "//Convert the iris data into 150x4 matrix\n",
    "val row = 150\n",
    "val col = 4\n",
    "\n",
    "val irisMatrix = Array(row) { DoubleArray(col) }\n",
    "var i = 0\n",
    "for (r in 0 until row) {\n",
    "    for (c in 0 until col) {\n",
    "        irisMatrix[r][c] = irisWithoutLabel[c][r] as Double\n",
    "    }\n",
    "}\n",
    "println(Arrays.deepToString(irisMatrix).replace(\"], \", \"]\\n\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 0.0, 1.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[1.0, 0.0, 0.0]\n",
      "[0.0, 1.0, 0.0]\n",
      "[0.0, 0.0, 1.0]]\n"
     ]
    }
   ],
   "source": [
    "//Now do the same for the label data\n",
    "val irisLabel = iris.select(\"species\")[0]\n",
    "\n",
    "val rowLabel = 150\n",
    "val colLabel = 3\n",
    "\n",
    "val twodimLabel = Array(rowLabel) { DoubleArray(colLabel) }\n",
    "for (r in 0 until rowLabel) {\n",
    "    when (irisLabel[r]) {\n",
    "        \"Iris-setosa\" -> twodimLabel[r][0] = 1.0\n",
    "        \"Iris-versicolor\" -> twodimLabel[r][1] = 1.0\n",
    "        \"Iris-virginica\" -> twodimLabel[r][2] = 1.0\n",
    "    }\n",
    "}\n",
    "println(Arrays.deepToString(twodimLabel).replace(\"], \", \"]\\n\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "//Convert the data matrices into training INDArrays\n",
    "val dataIn = Nd4j.create(irisMatrix)\n",
    "val dataOut = Nd4j.create(twodimLabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import org.nd4j.linalg.lossfunctions.LossFunctions\n",
    "\n",
    "val seed: Long = 6\n",
    "\n",
    "val conf = NeuralNetConfiguration.Builder()\n",
    "        .seed(seed) //include a random seed for reproducibility\n",
    "        // use stochastic gradient descent as an optimization algorithm\n",
    "        .updater(Nadam()) //specify the rate of change of the learning rate.\n",
    "        .l2(1e-4)\n",
    "        .list()\n",
    "        .layer(DenseLayer.Builder()\n",
    "                .nIn(4)\n",
    "                .nOut(3)\n",
    "                .activation(Activation.TANH)\n",
    "                .weightInit(WeightInit.XAVIER)\n",
    "                .build())\n",
    "        .layer(org.deeplearning4j.nn.conf.layers.DenseLayer.Builder()\n",
    "                .nIn(3)\n",
    "                .nOut(3)\n",
    "                .build())\n",
    "        .layer(OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)\n",
    "                .nIn(3)\n",
    "                .nOut(3)\n",
    "                .activation(Activation.SOFTMAX)\n",
    "                .weightInit(WeightInit.XAVIER)\n",
    "                .build())\n",
    "        .build()\n",
    "\n",
    "val model = MultiLayerNetwork(conf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score \n",
      "\n",
      "========================Evaluation Metrics========================\n",
      " # of classes:    3\n",
      " Accuracy:        1,0000\n",
      " Precision:       1,0000\n",
      " Recall:          1,0000\n",
      " F1 Score:        1,0000\n",
      "Precision, recall & F1: macro-averaged (equally weighted avg. of 3 classes)\n",
      "\n",
      "\n",
      "=========================Confusion Matrix=========================\n",
      " 0 1 2\n",
      "-------\n",
      " 5 0 0 | 0 = 0\n",
      " 0 3 0 | 1 = 1\n",
      " 0 0 7 | 2 = 2\n",
      "\n",
      "Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times\n",
      "==================================================================\n"
     ]
    }
   ],
   "source": [
    "import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization\n",
    "import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize\n",
    "\n",
    "//Create a data set from the INDArrays and shuffle it \n",
    "val fullDataSet = DataSet(dataIn, dataOut)\n",
    "fullDataSet.shuffle(seed)\n",
    "\n",
    "val splitedSet = fullDataSet.splitTestAndTrain(0.90)\n",
    "val trainingData = splitedSet.train;\n",
    "val testData = splitedSet.test;\n",
    "\n",
    "//We need to normalize our data. We'll use NormalizeStandardize (which gives us mean 0, unit variance):\n",
    "val normalizer: DataNormalization = NormalizerStandardize()\n",
    "normalizer.fit(trainingData) //Collect the statistics (mean/stdev) from the training data. This does not modify the input data\n",
    "normalizer.transform(trainingData) //Apply normalization to the training data\n",
    "normalizer.transform(testData) //Apply normalization to the test data. This is using statistics calculated from the *training* set\n",
    "\n",
    "// train the network\n",
    "model.setListeners(ScoreIterationListener(100))\n",
    "for (l in 0..2000) {\n",
    "    model.fit(trainingData)\n",
    "}\n",
    "\n",
    "// evaluate the network\n",
    "val eval = Evaluation()\n",
    "val output: INDArray = model.output(testData.features)\n",
    "eval.eval(testData.labels, output)\n",
    "println(\"Score \" + eval.stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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