artificial_intelligence/svm_classical.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  _*SVM with a classical RBF kernel*_\n",
    "\n",
    "We have shown here a QSVM_Kernel notebook with the classification problem solved using a quantum algorithm. By comparison this shows the problem solved classically.\n",
    "\n",
    "**This notebook shows the SVM implementation based on the classical RBF kernel.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import *\n",
    "from qiskit_aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name\n",
    "from qiskit_aqua.input import get_input_instance\n",
    "from qiskit_aqua import run_algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we prepare the dataset, which is used for training, testing and the finally prediction.\n",
    "\n",
    "*Note: You can easily switch to a different dataset, such as the Breast Cancer dataset, by replacing 'ad_hoc_data' to 'Breast_cancer' below.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66b9efb400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66b7deb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': 0, 'B': 1}\n"
     ]
    }
   ],
   "source": [
    "n = 2 # dimension of each data point\n",
    "training_dataset_size = 20\n",
    "testing_dataset_size = 10\n",
    "\n",
    "sample_Total, training_input, test_input, class_labels = ad_hoc_data(training_size=training_dataset_size, \n",
    "                                                                     test_size=testing_dataset_size, \n",
    "                                                                     n=n, gap=0.3, PLOT_DATA=True)\n",
    "\n",
    "datapoints, class_to_label = split_dataset_to_data_and_labels(test_input)\n",
    "print(class_to_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the dataset ready we initialize the necessary inputs for the algorithm:\n",
    "- the input dictionary (params) \n",
    "- the input object containing the dataset info (algo_input)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'problem': {'name': 'svm_classification'},\n",
    "    'algorithm': {\n",
    "        'name': 'SVM'\n",
    "    }\n",
    "}\n",
    "\n",
    "algo_input = get_input_instance('SVMInput')\n",
    "algo_input.training_dataset  = training_input\n",
    "algo_input.test_dataset = test_input\n",
    "algo_input.datapoints = datapoints[0] # 0 is data, 1 is labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With everything setup, we can now run the algorithm.\n",
    "\n",
    "For the testing, the result includes the details and the success ratio.\n",
    "\n",
    "For the prediction, the result includes the predicted labels.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kernel matrix during the training:\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJztnXl8VPXVxp/DvoggEJBNQKD4imIMEVARFbSCRREUNxQQreDWan2rKCiCiqhFqK0bIoJ1AatocUFBFltAkBDCrrLvS0AQBAkCv/ePDH2TnHPh3tzJJOE+38+HD5kn59753cmcTObM+T1HnHMghESPEoW9AEJI4cDkJySiMPkJiShMfkIiCpOfkIjC5CckojD5CYkoTH5CIkqo5BeRDiLyvYisFJF+8VoUIaTgkfx2+IlISQA/ALgcwEYA8wDc5Jxbdoxj1J2dm5JixpYQyde6jvLLwYOmXr5MGd/nyNz9k9KSqlRW2hGPx9C6hi2ZP5qxtZKq+l5XEDZuyVRa3VpJZqz1XNhvPI5ej2HYn5kXew8cUFqlcuXM2E1b9fVWq1ZFaeVKl/Z9/9Y5AaDOqfbj6JcgzxuvLD185Eiu2xvWr8fOHTt8/SBK+QnyoCWAlc651QAgIuMAdAbgmfwWM2fPNvWyxg+nZAn7DxXrQVyycaMZ27xePaXlfQCPMvKTL5XW5+oOSjvg8YumQtmySnv61bfN2Ef6dFdakGTyeiI98syrSnvmkb5m7K+HDiktfd06pTWvV9c8vmJZOyHDMn2ZfkpdeuaZZmw/43p79bxaab+pVcs83nrMHxn6mhn7TL8+pu6XIC9Qvx4+bMbu/eWXXLfbtW3r+/7D/NlfB8CGHLc3xjRCSDEgzCu/L0TkTgB3FvT9EEKCESb5NwHI+Td03ZiWC+fcSAAjAfs9PyGkcAhT8CuF7IJfe2Qn/TwANzvnlnodk9Kihcv7Hr+iR+Hmr+P/pbR7u11lxj72/CilPf3Q783YtNWrlbZu504ztktqqtKs+kDpkiXN46334Zt37TJj5xnr6tyihRnb574hSnv97/3N2CAM/vtbSnv83h5Ke/Klf5jH9+t7s9KeGzXOjL2/17VKKyH2u1DrPfC6HTvM2PrVqyvN+jkMf2eCefz0cV8p7dNPXzFjx06bobSb2rYxY3vd9rjS3v2H/jkCdoGzgs9CdauWLZGWllawBT/n3CERuRfAlwBKAhh9rMQnhBQtQr3nd859DuDzOK2FEJJA2OFHSERh8hMSUZj8hESUfFf780Nqaqqb++23ubSXPvjUjP3jDZ2V5rXWihV1y+2+fbo1FwAaNmyutFWrF5qxVmV/Ynq60q497zzz+O+3bFFak1NPNWMtZv3wg6lv+0lf23UtW/o+rxdZv/6qNKvTsk6dJubx/UYMU9qcT+aYse+8ZVe6/dK+/a2mPnWq/UlEXg4a3YwAsN745KdxzZpmrNV15/XJz9pM3SLcIMluD35n5iylndPgNDO2UrnyuW5fddllWJSR4avaz1d+QiIKk5+QiMLkJySiMPkJiSgJL/ilpaXl0oLsaRaPLa5hryHD2LYKAMn164c6bxC+XbVKaS0bNTJjrccsHnvptxqFxFMr62JqPOh5x0ClVatdzYx9YfAf4n7/89esMXWrIHty+fJGJPDpggVKe6z3/5qx02d9orQqFSoca4n5IjU11Xd7L1/5CYkoTH5CIgqTn5CIwuQnJKIw+QmJKAVu45WXvJVqy4gDAEYM0lVTr6q+9SmAV2xycjulzZs/xYz1awxqmYICwIqtW5XWyKNV9NwGDZRmtYQCwPhPpint4d43mLFBqGgYjlpYjyEADBg5VGlD+g4wY9PTJ/tfmEHXrn8y9QkTXvB1/Bm1bQPP9LX6k5+LmjY1Y9s3a6a0TgummrFzV65UWqvGjc3YkZ9q49hr2rY2Y8uUyt1+7WVGa8FXfkIiCpOfkIjC5CckojD5CYkoodp7RWQtgL0ADgM45JzTdrc5aNa8uXvvs89yaV7FsrAEaQW29rED9l52i/POu9LU583T9oYDR7xpxva/V+9PL1MqfD22x+3aNfatNwabsdbjkJJ8qdKWLp0Zel0FxeTFi5XWvZ3++Wzbvt483mqT9iqieU2Q8stTL9vTmwbcfUu+zxmkvTce1f5LnXO2jzIhpMjCP/sJiShhk98BmCwi82NjuRQicqeIpIlI2q4f7Qm1hJDEEzb52zjnUgB0BHCPiKgRoc65kc65VOdc6ilVC2YMNSEkOKGS3zm3Kfb/dgAfIXtsNyGkGBBmVl9FACWcc3tjX08BMNg594XXMZaZhzU7DwC6tb9GaWvWLDJjrXbTjAzdAhtbt9K8HgOrvfefc+cq7YbWduvlG1/qVs/br2hvxlpMWmi7CidVqqS01NNP931eL/yahHTvac8FLGFUv0+qUtGMfWV4v4Cry025cvZ5DxzY5+t4LxOZn405eV5mHkGw5u9V8phT+dCTLyut9+06HwCgep7nwmUXX4yM9PQCr/bXBPBRLJlKAXj3WIlPCClahBnUuRrAOXFcCyEkgfCjPkIiCpOfkIhS6OO6Pp4/34ztkqo7hRett1sym9Wtq7QjHi2ZVstuWFfg/VlZpl7B2B//jbGvGwAuaKJHYHnd/5rM7UprmFTDjA3i9HvKKdq5dtcu7UlgjakC7FFVXmOxet/xhNIeerKPGRu2BTxzzx6lpTRrZcb2HaD9B/r36W7GWiPDHn3RLmRmLPhOaQ/ecq0ZGwa69xJCjguTn5CIwuQnJKIw+QmJKEx+QiJKQqv99Rs3cf2GvZhL63N1BzPWMlCwqslAsIp2kFjrUwDLkbexhyOvRRDjEGt2HmDPz5uyZIkZ29pwiLXmAgK2G631eP20f795fGVj9lw8Zgg2apSstFWrMsxYy/G4QVJSqPu3qvoAMHXqP0KdNwiWSQkATJ7wda7bb7/2HLZuXs9qPyHEGyY/IRGFyU9IRGHyExJREt7e++28ebk0L2fUienpSrv2vPPM2EUbNijNqyV0/Jw5SvPaj79y2zalNTlVt8B6PYa/HDyotPJlypixFmXL2vvIs7J+UdquffY+9lMq6n3v1t5ywHt/eV7GTpth6gtnaL+Fpwf0NWODPA57ftHXO2P5cjP26pQU3+f1S5B25nhgFYXr1m5kxrZt2y3X7WnT3sGuXVtZ8COEeMPkJySiMPkJiShMfkIiynELfiIyGkAnANudc2fFtKoAxgNoAGAtgOudc7uOd2cpLVq4mbNn59KsPe/xIMgIrSD78S2C+AF8kMfP4Ci/S9ZdbEGKYl58sUgX4To0b27GWoWmIS/pkVKD7r8t9LqsQm/Y8VeAXRxslaINXpcum600ID4diX55d5a9hpsvvCDf54z3fv4xAPL24PYDMNU51wTA1NhtQkgx4rjJ75z7N4C8o3Y6Axgb+3osANtXmBBSZMnv31k1nXNbYl9vRbaNt0nOcV07jE0XhJDCIfSbLJf9xtazcJBzXFf1kLurCCHxI7/Jv01EagFA7H/tKEkIKdL4au8VkQYAPs1R7X8ewE7n3FAR6QegqnPuoeOdxxrX5TU2ydo337RWLd+xVhsuEH6EVpCW3SCjwSy8PoUoZbSVlikVZvhSMLzag9/76mulXZisPQIA23E5CI8PH23qgx/oHeq8RYGOHfXA688+f82MzfvpRFyr/SLyHoBvADQVkY0icjuAoQAuF5EVAC6L3SaEFCOO+3LhnLvJ41v+Xy4JIUUOdvgRElGY/IRElITu569T/3R39yODc2k9r7fbcGufcorSgphyDvrrGDPWak31GqGVUr++0iyjTa+W3etatlSaVyvw/DVr9P03aGDGBjEhfX7MP5X2517djEjg8wxtimm1y3b18FUIUnS0WomtxxYIdr0Z69Yp7cWhY5X21JD7zOOrGCakhz1ypKSxBq+WcOt6szxGmZ1c3vZx8APHdRFCjguTn5CIwuQnJKIw+QmJKEx+QiJKobv3/mv+fDO2c4sWSktbvdqMPdeointdl1WRDmLGYWG1/AJ222/62rVmbIuGDX3fv+UqHGRkmBfVqtVW2s6dm5Xm5bhsmXEc9Kho33prf6X9+ek+Zmzq6aebul9+/PlnpZ31G22eAgB39R+otMfuscd1tWlzrdL6/W2AGbssY4XSHrrtejM2DKz2E0KOC5OfkIjC5CckojD5CYkoCS/4+d3PP+uHH5R2UdOmZmyQmeyTFi5UWsdzzjFjt/70k9Lq19A+Adb4LMDejx/ErThIITKR47qmLFli6h+PnaS04c88YMYGaQW2HseZxvMDAH579tm+z+sXr+doQTn9WkXSxqfbjssdu+QuRk4Y9xIyt21kwY8Q4g2Tn5CIwuQnJKIw+QmJKH48/EaLyHYRWZJDe0JENolIRuyfvSmfEFJk8VNyHQPg7wDeyqMPd879JewC+tw3xNSv6HG50ryqruM/maa0h3vfYMYmVaqktDWZtvN4w6QaSvOq7FtYLrte17B6u16D1ycxYV2Bvar6s1foFtQLmjRRWgUPt+IRQ/+ktCs72G66n34+Smnb9+wxY+tVq6a0hiFnQIyfM8fUX354hNK+/nqcGTtm6gyldb2glRnbof3NSps9+yMzdvGGDUpbv36ZGZuX+bM+9BUH5H9cFyGkmBPmPf+9IrIo9rZAe24RQoo0+U3+VwA0ApAMYAuAYV6BOWf1ZXJWHyFFhnwlv3Num3PusHPuCIDXAWinyv+P/e+sviTO6iOkyJDfcV21jk7pFZEHALRyzt14vPNY+/kLqkUyHlhtrKnGvnurhTYeBGnZDdIKXKKELkQCwJEjhwOsLnG8NOEzpd3T9XcJu/8NO3eaulWILCgWGUVAAFi/Y0eu2w90744Vy5b5SqrjVvtj47ouAVBdRDYCGAjgEhFJRvZ03rUAbBcGQkiRJb/jut4ogLUQQhIIO/wIiShMfkIiCpOfkIiSUDOPU+vUdz3v6ZdLe/bRuwrkvnrc/ripv/XGYKV5tdzuM0wkrNbYLxYtMo/v0FwbMFiz8wDv+XlhCdIKbD0OKefqSewZGbqduiCx3JEtZ2QAaN36KqVd1Uu3evfve4vv+1+4fr2pn3Paab7PYfHyx5+b+t3X5H+rDN17CSHHhclPSERh8hMSUZj8hESUQh/XVZTbe6cuXaq0lo0aKc2v621QgrjsBmnZDTueLNG8+P5Epf3h+qsTdv+WOzTg7RBdEKwy/B4AYGOe1uM7u3XD90uWsOBHCPGGyU9IRGHyExJRmPyERBQmPyERxf/AtDjgnMOveeaQPfvae2bsw330TuJd+/ebsRWN+XdlDOdcAChtzIirVrWWGbtr11ZTz0vWr7+aetnSpZX2eUaGGXtre93SuXPnZjPWctn1MuKwWnaDuAIfPnJEaVbbM2B/CrHnF9vt+PUPdWtr1ysuMmPDVvZ37N2rtHPOSDFjO12n3YZf++sjZmyHDncobdioJ8zY0aO0U++wJ+4zYy0a1dBO0pYe5JMnvvITElGY/IREFCY/IRHFz7iueiIyXUSWichSEfljTK8qIlNEZEXsf3r3E1KMOG57r4jUAlDLOZcuIpUAzAdwDYBeAH50zg0VkX4ATnHOPXysc/3P2We70R/lLnyc37hxiOV706xZG1NfunSm73NYxTKrHXngiDfN4wfdf5vSxn3zjRl74/nn+15XEJKT2ynNaz++db0lS+jXh6LaBgzYP4uNP2xU2hsvP+b7nPs9CpwVjEJzEDp2vNPUJ00ame9zxnU/v3Nui3MuPfb1XgDLAdQB0BnA2FjYWGT/QiCEFBMCveeP+fefC2AugJpHvfsBbAVQM64rI4QUKL6TX0ROAvAhgPudc7nGqbrsvwPNvwVzjuva9SPnfRJSVPCV/CJSGtmJ/45zbkJM3harBxytC5h7DnOO6zqlatV4rJkQEgf8FPwE2e/pf3TO3Z9Dfx7AzhwFv6rOuYeOda6UFi3cf2bPyqW9MMo2tHx1iDba3LRJd7YBwYpa3Xv2V9qY0fq+AOBnYz/9xLnzlNaz3SXm8dZ+/LJGhyFgF9amLbNnslcwzCsv/M1vzNggWOu1Osa8/AA+SktT2orFq83YIIalVpfh4BfHGpF2kdXCMgUFgM27dyutQfXqZqxVIC3t0VlqjfzyGve1cts2peUdy3WUvF2kt3ftiu987uf30957IYBbASwWkaO9qY8CGArgfRG5HcA6ANf7uUNCSNHAz7iumQC8fpNoX2dCSLGAHX6ERBQmPyERhclPSERJ6H7+EiKoWDZ39bhf35vN2Co1Kvs+74CRQ/2vwaiqe1VoK1eooLSFM4zRXO0uMY9/76uvlXZnpyuOub6cfDx2kqmPGPon3+cIgt+94FZVHwC6pKYqzapcB8X6JGTe5Dl2sM9qv9e4L6998+a6fEd6V/YtVmzVPhI/7v3ZjG3VJHd7vNdz2YKv/IREFCY/IRGFyU9IRGHyExJRElrws3hu1DhTXzZLj8q6r5tt5Dik7wClXZc+2Yw9qUpFpR3MYyp6lDJGK+7TA/qasRYXJjfzHWutYfgzD5ixV3bQJpNTptjtrkGwzDZPLl9eaV4tu1Zxr3FNe7On1VZutfECdsHv3Et0cTEIlqknAHwyVxczb7vsUt/nqF6pkhn75lfTfZ93xmezlTakXx8zNm+rtWUa6wVf+QmJKEx+QiIKk5+QiMLkJySiMPkJiSjHNfOIJ6mpqS4tT2vovixtIAFAtQEDQM87BpqxY0cNCrWuW3rpTwsA4O0xT/k6PkiV2mu0V48eeg3j33vWjLXOEaTK68Wwtz9U2oO3XBv6vBaWIcjYGbodGgB6XNw21H39eliPMus/5DUz9pZeVymteb16ZuyDT/xNae6InU+9ft/F93nDEFf3XkLIiQmTn5CIwuQnJKKEGdf1hIhsEpGM2D89Y5oQUmTx0957CMCDOcd1iciU2PeGO+f+EmYBJcT/Hx/VavvfEx2Eh560Wyf9YhX2vPAqzP35af9r2L5nj9KC7Bf3ousVF4U+R168iqFWca/nJRebsT1CFqWtPe7n/baFGbvDeGy9OLnayUobOdQuEl/aRY+PK4iCXxD8GHhuAbAl9vVeETk6rosQUowJM64LAO4VkUUiMppTegkpXoQZ1/UKgEYAkpH9l8Ewj+P+O64rMzMzDksmhMSDfI/rcs5tc84dds4dAfA6gJbWsTnHdSUlJcVr3YSQkPip9guANwAsd869kEOvlSOsC4Al8V8eIaSg8DOrrw2A/wBYDOBo6fZRADch+09+B2AtgD45RnabND3rLPfq++/n0i4988z8rDshNGqUrLQFS75RmmV44YU13w3IdjbOy/6sLDO2QtmySntpwmdmbO9Olyvt9Y+/MGP/cL1tlpKXIO3M8cBqBfZ63q4zZtrV95i155fWrXXLLwDMmfNJqPMGwXL0BYB/L8xtevPkfXdj7Q/fx2dW3zHGdX3u5w4IIUUTdvgRElGY/IREFCY/IRGl0PfzWwUaAOh9g3aunTr1H2Zs1656fNWECS8YkUC5ctq998CBfWasxcT0dKVdnZJixj4+fLTSBj+gnXe9mLx4sak3ND4ybXLqqb7PG5aBI940dWuElpfL7tMP/T7UGqwiIOBdCPRLQXklBDlv7z7an6L/k3eZsUl53IIvvvBCLEhP535+Qog3TH5CIgqTn5CIwuQnJKIw+QmJKAmt9teqW9/1vOeRXNrQR/zPvguCV6X8t2ef7fsca41diA2MSrs14w6w234z1q0zY5Pr1/e9riBYralB2lKtyv6g+28LtaagBGnZtT4F+G7zZqU1rVVLaV68+P5EU/fbDu3FtKV6HiUAtGvmf8ZjXujeSwg5Lkx+QiIKk5+QiMLkJySi+HHvjRvVqlVBr565iyRB9rcHoXs720k8M3OD1jwcW63inkWrlHamvny53vv/4tCxZuxfntctylVPOsnX/R+Lq3rd4Dt2x969Stv4w8bQa7CwRmhZLrtAsP34VnHvjNq1leY1Nq1MKZ0S93az9/Nbz90gz9v9Bw/6ji0I+MpPSERh8hMSUZj8hEQUPwae5UTkWxFZGBvXNSimNxSRuSKyUkTGi0iZgl8uISRe+DHwFAAVnXM/xyy8ZwL4I4A/AZjgnBsnIq8CWOice+VY50pNTXXfzpuXSxv+zgQz9r4bOytt8QZdrAOAM2rrbq3yZbTJpRf1T7NNRDdsWO7r+CBFy827dpmxqc3O07GbV5qx4+foffM3tG59rCX6ok6dJkrbtGmF0n7xKFSVL6N//1tFRAB4boT2ZvAaodWtVStT98vBQ4eU5rWX/rLLeihtyhS7SFu5si4I/23ih2bs+OfeVdpnn71qxoYhrh1+LpufYzdLx/45AO0AfBDTxwK4Jh9rJYQUEn6HdpQUkQwA2wFMAbAKwG7n3NFfqRvB+X2EFCt8JX9sMk8ygLrInsxzht874LguQoomgar9zrndAKYDOB9AFRE52hFRF8Amj2M4rouQIoifan+SiFSJfV0ewOUAliP7l8B1sbCeAP5VUIskhMQfP+29tQCMFZGSyP5l8b5z7lMRWQZgnIg8BWABsuf5HZe8FfDp474y4zpf3kZpXg616Wv1HvmLmjY1Y629930HDDBj/RKkpbNKhQqmflf/gb7P8fLDI5R2w9fjfB/vRafr/DkLb96929Qb1aihtE/mphmRwC29dMvsDo82awuv9lyrim+17FpVfQD46qu3DNWu9qek/FZpzRvYvgy9Jr1uqHa1v6AchPPiZ1zXIgDnGvpqeEzmJYQUfdjhR0hEYfITElGY/IRElIQbePa699Fc2jP9+hTIfcVjhnz79rcq7YvJY5TmtQ/dYu+BA6ZeqVw5pQVpG96wc6cZ++M+PYqssmEsCtj+BfuzspTmVXwK8tgGIYgJqWW2ae3HD1KkLajRYEF+vvuy7OfN2szc5qbXX3klli5aRANPQog3TH5CIgqTn5CIwuQnJKIw+QmJKAl1761zapKq7o+dNsOMvfnii5T25aJFZmx7Y7yRZSzhhVXVB4CpU7XhRFhKelSO27S5VmkzZ9rGEGOmzlBar/aXmLH1qlXzvbYOHe5Q2hdfjFKa5bwLANZnHl5mHs8MG6O0k6udbMYGGS9mjdCyquqWEQdgt+x6VfWtTwEeHz7ajJ3x0ZdK+zpAS3bFsvrTIABoVrdurttBnvd85SckojD5CYkoTH5CIgqTn5CIktCCn8VNbfW+fcBumX2s9/+asZ0WTA21hkdf7Bfq+CBUKGu7Cvf7m39Pga4XhHOz9WLYqCd8xQVpZ65eqZKpuyO6iDZy6FNm7MD7evq+PwurXdbLZddrP76FVdwb/IDtiTBnhXZBLmz4yk9IRGHyExJRmPyERBQmPyERJcysvjEiskZEMmL/kgt+uYSQeOGn2p8FoF3OWX0iMin2vT875z44xrHHpddtj5v6kBceUNr0WXab59yVeqZdq8aNzVjLTCNjwXdmrNU2HBYv19llGboa3Olc5ZsKAOjQ/malzZ79UbiFARg9Sp9j2BP3Kc3LOMRqJX7zq+lmbK/fd1HapV3sT34sgrj3Wliz8wDbZffIEbud2WrZ9arqt26i5yB6tQ1bsxCDtO36xY97rwNgzeojhBRj8jWrzzk3N/atp0VkkYgMFxHzA2yO6yKkaJKvWX0ichaAR5A9s+88AFUBPOxxLMd1EVIEye+svg7OuS2x8d1ZAN4EB3gQUqw4rnuviCQB+NU5tzs2q28ygGcBzHfObZHsTc3DARxwzh2zTzalRQs365tvcmkFUcgAgKdeftvUB9x9S9zv691Zs0395gsvUJo1LgwATvZw1A3Lyx9/rrS7r7nS9/EdO96ptEmTRoZaU0EybelSpe03CmhexVSLIC67QSgIV+DU1FSkpaX5WliYWX3TYr8YBEAGgL75XjEhJOGEmdXXrkBWRAhJCOzwIySiMPkJiShMfkIiSkJn9aWmprq0tLRcmtfsuolp85XWvc2FZuzIT3Wb5Z2drjBjH3ryZaU999jdZqyF1Vbq1VIatlJ+8NAhU1+8YYPSWjRs6Pu8YVm5bZupr9i6VWkzPrM/CXn20btCraF3n0GmPvq1gaHOG+TnG4QgLbvWpwBesyfzfuIQpNrPV35CIgqTn5CIwuQnJKIw+QmJKAkt+LVITXVz5s7NpXm1SC7fvElpZ9WtZ8Zu3/OT0mqcXNmM/W7zZqWdUbu2GTt58WKldW/3O6VlZq43j7faQoO0hJ522pmmvn79MqUtMoqAANC8nn7MVm3fbsY2qlHD17qsFloA2LJrt9JuvOB8M7ZkCf+vO1YhsYTH8X6vIQj7suyitNcIrbBYzxuvx+v008/JdXvjxh+QlbWfBT9CiDdMfkIiCpOfkIjC5CckojD5CYkoCZ3Vd/jIEezNY2ZRuUIFM7ZSOf/mFmVK+W+/9JodZzF5wtdKa9u2m+/jw5o9dOxyq+/Y9Tt2mLpV7d/o4b7rt1Lu1e7aqol2TPYyrAjCvxfqTxe6tdVGKQXF2kz7sW1Wt26B3J/1vMlb1T/K6tUL838/+T6SEFKsYfITElGY/IREFCY/IREloe29IpIJYF3sZnUAdiWleMPrKn6cSNdW3znna0BGQpM/1x2LpDnnUgvlzgsQXlfx40S+tmPBP/sJiShMfkIiSmEmf9Ed+xIOXlfx40S+Nk8K7T0/IaRw4Z/9hESUhCe/iHQQke9FZKWIHHOwZ1FHREaLyHYRWZJDqyoiU0RkRez/UwpzjflBROqJyHQRWSYiS0XkjzG9WF+biJQTkW9FZGHsugbF9IYiMjf2nBwvIgUzPbaIkdDkjw37fAlARwBnArhJRGyvquLBGAAd8mj9AEx1zjUBMDV2u7hxCMCDzrkzAbQGcE/s51Tcry0LQDvn3DkAkgF0EJHWyJ46Pdw51xjALgC3F+IaE0aiX/lbAljpnFvtnDsIYByAzgleQ9xwzv0bwI955M4Axsa+HgvgmoQuKg4457Y459JjX+8FsBxAHRTza3PZ/By7WTr2zwFoB+CDmF7sriu/JDr56wDI6TS5MaadSNR0zm2Jfb0VQM3CXExYRKQBsqc0z8UJcG0iUlJEMgBsBzAFwCoAu51zR8cjnYjPSRMoUh03AAABTElEQVQW/AoQl/1RSrH9OEVETgLwIYD7nXN7cn6vuF6bc+6wcy4ZQF1k/yV6RiEvqdBIdPJvApDTXaJuTDuR2CYitQAg9r/tk13EEZHSyE78d5xzE2LyCXFtAOCc2w1gOoDzAVQRkaPGNific9Ik0ck/D0CTWHW1DIAbAUxM8BoKmokAesa+7gngX4W4lnwh2fY7bwBY7px7Ice3ivW1iUiSiFSJfV0ewOXIrmdMB3BdLKzYXVd+SXiTj4hcCWAEgJIARjvnnk7oAuKIiLwH4BJk7wrbBmAggI8BvA/gNGTvYLzeOZe3KFikEZE2AP4DYDGAo+NhH0X2+/5ie20i0hzZBb2SyH7he985N1hETkd28bkqgAUAbnHOZRXeShMDO/wIiSgs+BESUZj8hEQUJj8hEYXJT0hEYfITElGY/IREFCY/IRGFyU9IRPk/c2Mci4fx7ygAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66b7d43b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing success ratio:  0.7\n",
      "predicted classes: ['A', 'B', 'A', 'B', 'B', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'A', 'B', 'B', 'B', 'B', 'B', 'A', 'B']\n"
     ]
    }
   ],
   "source": [
    "result = run_algorithm(params, algo_input)\n",
    "print(\"kernel matrix during the training:\")\n",
    "kernel_matrix = result['kernel_matrix_training']\n",
    "img = plt.imshow(np.asmatrix(kernel_matrix),interpolation='nearest',origin='upper',cmap='bone_r')\n",
    "plt.show()\n",
    "\n",
    "print(\"testing success ratio: \", result['testing_accuracy'])\n",
    "print(\"predicted classes:\", result['predicted_classes'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The breast cancer dataset\n",
    "Now we run our algorithm with the real-world dataset: the breast cancer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66b7d43588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': 0, 'B': 1} {0: 'A', 1: 'B'}\n"
     ]
    }
   ],
   "source": [
    "sample_Total, training_input, test_input, class_labels = Breast_cancer(training_size=20, test_size=10, n=2, PLOT_DATA=True)\n",
    "# n =2 is the dimension of each data point\n",
    "\n",
    "datapoints, class_to_label = split_dataset_to_data_and_labels(test_input)\n",
    "label_to_class = {label:class_name for class_name, label in class_to_label.items()}\n",
    "print(class_to_label, label_to_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kernel matrix during the training:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66b3641ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing success ratio:  0.85\n",
      "ground truth: ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B']\n",
      "predicted:    ['A', 'A', 'A', 'A', 'B', 'A', 'B', 'B', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B']\n"
     ]
    }
   ],
   "source": [
    "algo_input = get_input_instance('SVMInput')\n",
    "algo_input.training_dataset  = training_input\n",
    "algo_input.test_dataset = test_input\n",
    "algo_input.datapoints = datapoints[0]\n",
    "result = run_algorithm(params, algo_input)\n",
    "# print(result)\n",
    "print(\"kernel matrix during the training:\")\n",
    "kernel_matrix = result['kernel_matrix_training']\n",
    "img = plt.imshow(np.asmatrix(kernel_matrix),interpolation='nearest',origin='upper',cmap='bone_r')\n",
    "plt.show()\n",
    "\n",
    "print(\"testing success ratio: \", result['testing_accuracy'])\n",
    "\n",
    "print(\"ground truth: {}\".format(map_label_to_class_name(datapoints[1], label_to_class)))\n",
    "print(\"predicted:    {}\".format(result['predicted_classes']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}