gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sknn.mlp import Regressor, Classifier, Layer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# on génère 2 distributions gaussiennes, avec labels respectifs 0 et 1\n",
    "nsamples = 100\n",
    "X1 = np.random.randn(nsamples, 1)\n",
    "X2 = 2*np.random.randn(nsamples, 1)+3\n",
    "y1 = np.zeros((nsamples,1))\n",
    "y2 = np.ones((nsamples,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([  0.,   3.,  22.,  45.,  25.,   5.,   0.,   0.,   0.,   0.]),\n",
       "  array([  1.,   1.,   2.,  10.,  18.,  18.,  19.,  13.,  11.,   7.])],\n",
       " array([-4.40053734, -3.16480556, -1.92907377, -0.69334199,  0.54238979,\n",
       "         1.77812157,  3.01385336,  4.24958514,  5.48531692,  6.7210487 ,\n",
       "         7.95678049]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Aj7YfkiRpAfJeAJIkFaib0wAlSV2ampqi1Wp11cfw8DDLly/vUUUqhQFAkgZkamqKFS9b\nwcyjM131s2yXZaxds9YQoI4YACRpQFqtVvXh/z5guG4nMPPtGVqtlgFAHTEASNKgDVPdN1WaR04C\nlCSpQAYASZIK5CEASbVNTk7WXnc+Zq53O8Pe2fVaygwAkmq4BwJWrVpVu4d+z1zvxQx7Z9drKTMA\nSKrhweqi4HVnr8/DzPWuZ9g7u15LnAFAUn2LYfb6YqhRGgAnAUqSVCADgCRJBTIASJJUIAOAJEkF\nMgBIklQgA4AkSQUyAEiSVCADgCRJBTIASJJUIAOAJEkFMgBIklQgA4AkSQUyAEiSVKCOA0BEHBoR\n342IuyLi6Yh495zXv9Zun/24pHclS5KkbtUZAdgNuBE4keqO4FvyPWAvYO/2o1GrOkmS1Bc7dbpC\nZl4KXAoQEbGVxR7LzPXdFCZJkvqnX3MA3hwR90XEmog4OyL+TZ+2I0mSauh4BGA7fA/4FrAO+E3g\n88AlEXFIZm7tkIEkSZpHPQ8AmXnBrKc3R8TPgF8Abwb+aWvrjY2NMTQ0tFlbo9Gg0XD6gCRJzWaT\nZrO5Wdv09HTt/voxArCZzFwXES3gQLYRAMbHxxkZGel3OZIkLUpb+lI8MTHB6Ohorf76fh2AiNgP\neBFwT7+3JUmStk/HIwARsRvVt/mNZwAcEBEHAw+0H5+imgNwb3u5vwT+BbisFwVLkqTu1TkE8Fqq\nofxsP77Ybv868J+AVwEfBPYA7qb64P+vmflE19VKkqSeqHMdgKvZ9qGDd9QvR5IkzQfvBSBJUoEM\nAJIkFcgAIElSgQwAkiQVyAAgSVKBDACSJBXIACBJUoH6fi8ASdLiNjU1RavV6qqP4eFhli9f3qOK\n1AsGAEnSVk1NTbHiZSuYeXSmq36W7bKMtWvWGgIWEAOAJGmrWq1W9eH/PmC4bicw8+0ZWq2WAWAB\nMQBIkp7bMLDvoItQLzkJUJKkAhkAJEkqkAFAkqQCGQAkSSqQAUCSpAIZACRJKpABQJKkAhkAJEkq\nkAFAkqQCGQAkSSqQlwKWJC163rGwcwYASdKi5h0L6+k4AETEocCfAKPAPsDvZ+Z35yzz58DxwB7A\ntcBHMvPW7suVJGlz3rGwnjojALsBNwJ/B3xr7osR8QngJOCPgHXAXwCXRcTKzHy8i1olSdo671jY\nkY4DQGZeClwKEBGxhUVOBj6TmRe3l/kgcB/w+8AF9UuVJEm90tOzACJif2Bv4IqNbZn5EHAdcEgv\ntyVJkurr9WmAewNJ9Y1/tvvar0mSpAVgvs4CCKpgIGk7TU5O1l63tNOZJHWu1wHgXqoP+73YfBRg\nT+An21pxbGyMoaGhzdoajQaNRqPHJUoL3T0QsGrVqto9lHY6k1SCZrNJs9ncrG16erp2fz0NAJm5\nLiLuBY4AfgoQES8E3gCcta11x8fHGRkZ6WU50iL1YDVeVveUpgJPZ5JKsKUvxRMTE4yOjtbqr851\nAHYDDqT6pg9wQEQcDDyQmXcAZwCfjIhbgV8CnwHuBL5Tq0KpVJ7SJKmP6owAvBb4J6rvKAl8sd3+\ndeC4zDwtInYF/pbqQkD/F/g9rwEgSdLCUec6AFfzHGcPZOangU/XK0mSJPWbdwOUJKlABgBJkgpk\nAJAkqUAGAEmSCmQAkCSpQAYASZIKZACQJKlABgBJkgpkAJAkqUAGAEmSCmQAkCSpQAYASZIKZACQ\nJKlABgBJkgrU8e2AJUlS56ampmi1Wl31MTw8zPLly3tSjwFAkqQ+m5qaYsXLVjDz6ExX/SzbZRlr\n16ztSQgwAEiS1GetVqv68H8fMFy3E5j59gytVssAIEnSojIM7DvoIipOApQkqUAGAEmSCmQAkCSp\nQAYASZIKZACQJKlABgBJkgrU8wAQEZ+KiKfnPG7p9XYkSVJ9/boOwM+BI4BoP3+yT9uRJEk19CsA\nPJmZ6/vUtyRJ6lK/5gC8NCLuiohfRMTqiPh3fdqOJEmqoR8jAD8CPgSsBfYBPg38n4h4RWY+0oft\nSVqiurl72uTkZI+rkZaWngeAzLxs1tOfR8T1wO3AHwBf29p6Y2NjDA0NbdbWaDRoNBq9LlHSIjA1\nNcWKFSuZmdkw6FKkheHW6j+zPy+np6drd9f3mwFl5nRE/Atw4LaWGx8fZ2RkpN/lSFokWq1W+8N/\nNbCyRg+XAKf2tihpkA4Ertz883JiYoLR0dFa3fU9AETE7sBvAuf1e1uSlqKVQJ0vBx4CkLalH9cB\n+KuIOCwifiMifgu4iOo0wGavtyVJkurpxwjAfsA3gBcB64FrgDdm5v192JYkSaqhH5MAnbUnSdIC\n570AJEkqkAFAkqQCGQAkSSqQAUCSpAIZACRJKpABQJKkAhkAJEkqUN8vBazFo5s7r200PDzM8uXL\ne1SRJKlfDAACenfntWXLdmXt2klDgCQtcAYAAb248xrAJDMzq2i1WgYASVrgDACao+6d1yRJi4mT\nACVJKpABQJKkAhkAJEkqkHMAVKTJycna63qqo6SlwACgwtwDAatWrardw7JdlrF2zVpDgKRFzQCg\nwjwICbwPGK6xegtmvj3jqY6SFj0DgMo0DOw76CIkaXCcBChJUoEMAJIkFchDAIvEo48+yl133dVV\nH3vvvTe77757jyqSJC1mBoBF4l3veRdX/O8ruurj4JGDufGGG3tU0dZ5ip0kLXwGgAWm2WzSaDSe\n1b5m7Rp4JfUv038z3Lr21q5qe269O8Xu2muv3eJ+KM7PqH7uApqA7wnfE5ts7e+ltk/fAkBEnAh8\nDNgbuAn4aGb+c7+2t1Rs8w09BOxfs+N76lbUid6dYucvdpt/7GcxAAC+J2bx70R3+hIAIuIPgS8C\n/xG4HhgDLouIgzKz1Y9tagHxFDtJWvD6dRbAGPC3mXleZq4BTgA2AMf1aXuSJKkDPQ8AEfE8YBR4\nZsZaZiZwOXBIr7cnSZI6149DAMPAjsB9c9rvA1ZsYfll0N3M8cXg9ttvZ/369du13DnnnPOs9g0b\nNsC9wI9rFnAHPPXUU0xMTGzx5U37/xKg7s/i2uo//wrUOdDz6021TE9PP6vWhVbj1nRf56waHwJ+\n2uHq813jvO3HO4HzO9hI/2sEWL9+Pa1WvSOb69atq/7RSY1z3xPbUee81zhXn2q88847Of/88xd0\njRv1q8ZZ/17WaXdRfTnvnYjYB7gLOCQzr5vVfhrw25n5W3OW/wCd/VZLkqTNHZOZ3+hkhX6MALSA\np4C95rTvybNHBQAuA44BfgnM9KEeSZKWqmXAS6g+SzvS8xEAgIj4EXBdZp7cfh7AFHBmZv5Vzzco\nSZI60q/rAJwOfD0ibmDTaYC7Auf2aXuSJKkDfQkAmXlBRAwDf051KOBG4O2Z+dyz4CRJUt/15RCA\nJEla2LwdsCRJBTIASJJUoAUXACLinRHxo4jYEBEPRMS3B13TIEXE8yPixoh4OiJeNeh65lNE/EZE\nfCUibmu/H/41Ij7dvtrkkhcRJ0bEuoh4tP078bpB1zTfIuKUiLg+Ih6KiPsi4qKIOGjQdQ1ae788\nHRGnD7qW+RYR+0bE30dEq/134aaIqHuf1EUrInaIiM/M+vt4a0R8spM+FtTtgCPiKOAc4E+BK4Hn\nAa8YaFGDdxrVJdBKvP/Xy4AAPgz8guq98BWqM0o+PsC6+s4baj3jUOBLVNfA3An4PPD9iFiZmY8O\ntLIBaQfBD1PdZbUoEbEH1SUerwDeTnXdmZfyzDXyivKnwB8DHwRuAV4LnBsRD2bml7engwUzCTAi\ndqS6GNCpmXnuYKtZGCLi94C/Bo6i+gG/OjM7vTDskhIRHwNOyMwDB11LP23lWhp3UF1L47SBFjdA\n7bOLfgUclpnXDLqe+RYRuwM3AB8BTgV+kpn/ZbBVzZ+I+ALVVWYPH3QtgxYRFwP3ZuaHZ7VdCGzI\nzA9uTx8L6RDACO2byEbERETcHRGXRMTLB1zXQETEXlSjIauAIr/pbMUewAODLqKfvKHWNu0BJEv8\nPbANZwEXZ+aVgy5kQN4F/DgiLmgfEpqIiOMHXdSA/AA4IiJeChARBwNvorp5xnZZSAHgAKrh3k9R\nXT/gnVTDOle3h31K8zXg7Mz8yaALWSgi4kDgJOBvBl1Ln23rhlp7z385C0N7FOQM4JrMvGXQ9cy3\niDgaeDVwyqBrGaADqEY/1gJvo/pbcGZErBpoVYPxBeAfgDUR8TjVyNAZmfk/t7eDvgeAiPh8e7LK\n1h5PtSf1bKzlLzLzf7U/+I6lSvv/vt91zoft3RcR8Z+BFwB/uXHVAZbdcx28J2av82+B7wH/kJl/\nN5jKBy6ofh9KdTbwcuDoQRcy3yJiP6rwsyoznxh0PQO0A3BDZp6amTdl5jnA/6AKBaX5Q+ADVL8P\nrwH+CPiTiPgP29vBfEwC/Guqb7Pbchvt4X9m3Zs0Mx+PiNuA5X2qbb5tz75YB/wO8EbgsepLzzN+\nHBHnZ+axfapvvmzvewKoZv1STQq9JjP/uJ+FLRCd3lBryYuILwNHAodm5j2DrmcARoEXAzfEpj8K\nOwKHRcRJwM65UCZ09dc9PPv+1ZPA+wZQy6CdBnwuM7/Zfn5zRLyEaoTo77eng74HgMy8H7j/uZZr\n3zfgMWAF1bGNjcdCXwLc3scS500H++KjwJ/NatqX6k5Pf0A1I3xR2979AM98878S+GfguH7WtVBk\n5hPt34cjgO/CM8PfRwBnDrK2QWh/+L8HODwzpwZdz4BczrPPBDqX6sPvC4V8+EN1BsCKOW0rWCKf\nER3alWePCD5NByP7C+Y0wMx8OCL+BvhvEXEn1Q/041T/g9/c5spLTGbeOft5RDxCNfx7W2bePZiq\n5l9E7ANcRXV2yMeBPTd++cnMpf5N2BtqARFxNtAA3g080p4cCzCdmcXcPjwzH6E6E+gZ7b8L92fm\n3G/ES9k4cG1EnAJcALwBOJ7qtMjSXAz8WUTcAdxMNZF+jOpU6e2yYAJA28eAJ4DzgF2A64C3ZOb0\nQKtaGEpJ+LO9jWrSzwFUp8DBpuPgOw6qqPngDbWecQLVz/uqOe3HUv2dKFlxfxMy88cR8V6qCXCn\nUh0yPbmTiW9LyEnAZ6jODNkTuBv47+227bJgrgMgSZLmz0I6DVCSJM0TA4AkSQUyAEiSVCADgCRJ\nBTIASJJUIAOAJEkFMgBIklQgA4AkSQUyAEiSVCADgCRJBTIASJJUoP8PtTjSyFVOEvUAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fce2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist((X1, X2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# on construit le dataset en incluant tous les exemples\n",
    "Xt = np.vstack((X1,X2))\n",
    "yt = np.vstack((y1,y2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# création de l'architecture\n",
    "nn = Classifier(\n",
    "    layers=[\n",
    "        Layer(\"Sigmoid\", units=100),\n",
    "        Layer(\"Softmax\")],\n",
    "    learning_rate=0.001,\n",
    "    n_iter=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# training\n",
    "nn.fit(Xt, yt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(50, 2)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1113ba3c8>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Aa8ATgYeKz4oPu1fIuJTKaVzS6ovR9bN1CJiBXA7xSfe88ssrIVqFMcBj5my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\nJElSNgOEJEnKZoCQJEnZDBCSJCnb/wcEVNsJTcLiFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111210d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# on feed des données jamais vues au \n",
    "ntest = 50\n",
    "Xtest = np.linspace(-5, 10, num=ntest).reshape((ntest,1))\n",
    "ytest = nn.predict(Xtest)\n",
    "\n",
    "result = np.hstack((Xtest, ytest))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.hist((X1, X2))\n",
    "ax.scatter(Xtest, np.multiply(50*np.ones((ntest,1)),ytest)-5, c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Régression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x111520588>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MdXG+IxAlyPvJsoy9HNYb7fixHxs772n0oosuWfZ1cU/P59r0BkcgSlLHNkl1\nMYgjEDsj4u8j4mRETEbEZVU3aNjl/WTZzfbRW7XeaEdErHoafSqnT38T+JX23yzm6XllXsZLgQkg\n/625i9z2u87fez11HRWRBk6vEUivL2Ac+Fng3wLXAJ8E7gMeu875jkCUoO5Pcffcc8+6IxrLRzvO\n/xzLvy72M56fl7GQoJh5+Sp3y6zbTp11/9mVqpTnCESkrFOujYjYBvwtcDSl9O413h8FZvft28e2\nbdtWvNdoNGg0GuU0dAgcOHAdMzN3srj4FlbuVbH34SJQZeu21sPtt9/OxMQE2cjDZcDtZCMBS19f\nB9wJ5P8ZW60Wu3fvZmUdDIDfBX6FD3/4w1xzzTU9fY/VqqzqWaeKonX82ZXK1mw2aTabK46dPXuW\n48ePA+xJKc319A16jUCKeAF3Ab+9znuOQJSkbk+WKS3PKZhsP9lPbriqY+MRiGJHBVa2t9hVKFqp\njj+7Uh3kOQJRebBwXoPgXwGngRev874BRMnKSIDsxFaHps/vxJ+aYNuKTv2CC7al0dGn5/4Z7ciq\nVZefXaku8gwgKi9lHRFvAD5ENm3xb4BXA98Bmhv9PZWnLltTd5Ict1Y719oI7KKLLuH06XNfX3NN\nMSWv3TCsWnX52ZUGUeUBBPC9wPuAi4AvA58A9qaUTlfaKtXOyloPy3MKNq5suV4nXmanbkcmadBU\nHkCklMx6VEeWlgzOzBxhcTGxMjlu8yWDqztxO3VJ2rq61IGQOlJWZUtJ0sYqH4GQ1rPW9tDmFEhS\nPRhAqHY6qfXg9IMkVcspDNWOGyFJUv05AqFaWdo/YmX1xoMsLiampw8zPz/vyIMk1YAjECrE6q2z\nO+VGSJLUHwwglKuFhQUOHLiO3bt3MzExwa5duzhw4DrOnDnT0d9fWethuY1rPUiSymUAoVz1mr9Q\nx+2hJUnnM4BQbpbyFxYX30qWv3AZWf7CW5ienup4OmOYaz1sdepHkspmEqVys9W9KlYbxloP3W5T\nLklVcwRCuck7f2Hnzp1ce+21Ax88gEtXJfUfAwjlxvyFrclr6keSymQAoVwNc/7CVrl0VVI/MgdC\nuapb/sJa+2nUzVa3KZekKhlAqBBV71WRZ1Ji0UFIr9uUS1IVnMLQQMojKbHXoljdcOpHUr9xBEJA\nfwz1d6rX/TSW7sVrX/s7fOpTn29fZx9wnJmZIzQahzh27LZc21y3qR9J2owBxBBrtVp89rOf5e1v\nfwcf//jcJxu4AAAPlElEQVQdDx8vuv5A0cHKVutRrDXtUfamXlVP/UhSp5zCGELLh+af+9wGH//4\nZymj/kBZUwJbrUexctrjPe2jroyQpLUYQAyhcx3lG4CHgN+jjPoDZRVL2ko9ivNrMextv+OmXpK0\nFgOIIbOyo/yh9tHin7LLLpbUbVLi+dMeu4AJwKJYkrQWcyCGzMqO8p/bfy6+/kBe+2R0qtukxLVr\nMUwCP04WhGTGxiZcGSFJGEAMnfM7yqWn7GLrD1RVLKnTpMT1azGc4sor9/Orv/pyV0ZI0jIGEEPm\n/I7ydcDPUfRTdj8US2o2J2k0DjE9ff69cEdMSVrJAGIIrdVRXnXVfl7ykhfytKc9rbDOfKMOug6s\nxSBJnTOAGEJFdZSb1Xfolw7aWgyStDkDiCGWV0fZ7b4TdtCS1P9cxqmelVXfQZJUH7UIICLiRRFx\nX0T8c0TcGRE/UnWbBkWr1eL222/Pvc7C8uuXWd9BklQPlQcQEfFc4I3AK4GnAZ8DpiPiCZU2rM+V\nVTa6k/oOkqTBU3kAARwF/mtK6b0ppS8ALwC+Afxitc3qb2VNK2x134n1FD1iIknKR6UBREQ8EtgD\n/OnSsZRSAmaAK6tqV78rc1phK/tOrKWsERNJUj6qHoF4AjACPLjq+IPApeU3ZzCUPa3Q7b4TazER\nU5L6S12XcQZZbeV1HT16lG3btq041mg0aDQaRbarL5RdNrrX+g5LIyZZ8LDU3oMsLiampw8zPz/v\nsk9J6lKz2aTZbK44dvbs2dyuX3UA8RVgEbhk1fGLOX9UYoWbbrqJ0dHRotrV16oqG73V+g5lb7Ql\nScNgrYfqubk59uzZk8v1K53CSCl9G5gFnr10LCKi/fWnqmrXIOh1WqHMZMa8EzElScWregQC4E3A\neyJiFriLbFXGY4A/rLJR/WyppPTb3vZm4M1dTSt0W1UyD52OmGxWKluSVKKUUuUv4IXAF4F/Bv4M\nePoG544CaXZ2Nmml06dPp/HxiUSWP5KAND4+kRYWFjq+xvj4RBoZuTDBZIJTCSbTyMiFaXx8osCW\np7SwsLBu2/P4XJKklGZnZ5d+j46mHvvuSGnDXMXaiYhRYHZ2dtYciFUOHLiOmZk728s39wHHGRk5\nwtjYXo4du23Tv99qtdi9ezcrkxlpf32YVqtV+JP/WomYvX4uSVJmWQ7EnpTSXC/XqsMUhnKQx0qG\nOiQzrk7EdIWGJNVT1XUglJM8aj/UMZnRUtmSVE8GEAMij84/r6qSeapjUCNJMoAYGHl1/nlUlcxT\nHYMaSZI5EAOl2Zyk0TjE9PThh4+NjU101fl3W1WyjKWVeXwuSVK+DCAGSK8lpZfbrKpkmfUiNvtc\n1oeQpPIZQAygrZaU7sbKza+ypZUzM0doNA4VtrRy9eeqouiVJCljDoS6VuZ24RtxB09Jqo4BhLpW\nh6WVdQliJGlYGUCoa3VYWlmHIEaShpkBhLpWh6WVdQhiJGmYGUBoS6quF1GHIEaShpmrMLQleS4Z\n3SrrQ0hSdQwg1JMyloyupw5BjCQNKwMI9b0qgxhJGlbmQEiSpK4ZQEiSpK4ZQEiSpK4ZQEiSpK4Z\nQEiSpK4ZQEiSpK4ZQEiSpK4ZQEiSpK4ZQEiSpK4ZQEiSpK4ZQEiSpK4ZQEiSpK5VGkBExBcj4qFl\nr8WIeFmVbZIkSZurejfOBPw68E4g2sf+qbrmSJKkTlQdQAB8LaX05aobIUmSOleHHIj/HBFfiYi5\niHhpRIxU3SBJkrSxqkcg3gLMAQvAvwdeB1wKvLTKRkmSpI3lHkBExGuBl29wSgKuSCm1UkpvXnb8\nryLi28D/FRGvSCl9O++2SZKkfBQxAvG7wLs3OefedY7/OVmbvh+Y3+gCR48eZdu2bSuONRoNGo1G\nZ62UJGmANZtNms3mimNnz57N7fqRUsrtYr2KiIPAHwJPSCmt+SkjYhSYnZ2dZXR0tMzmSZLU1+bm\n5tizZw/AnpTSXC/XqiwHIiL2As8E/gfZ0s1/D7wJuHm94EGSJNVDlUmU3wKeB7wSeBRwH/BG4KYK\n29Q3Wq0WJ0+eZMeOHezcubPq5kiShkxlAURK6TPAlVV9/361sLDADTccZnp66uFj4+MTNJuTbN++\nvcKWSZKGSR3qQKgLN9xwmJmZO4FJ4BQwyczMnTQahypumSRpmFRdB0JdaLVa7ZGHSeBg++hBFhcT\n09OHmZ+fdzpDklQKRyD6yMmTJ9t/2rfqnf0AnDhxotT2SJKGlwFEH7n88svbfzq+6p07ANixY0ep\n7ZEkDS8DiD6ya9cuxscnGBk5QjaNcT8wycjIjYyPTzh9IUkqjQFEn2k2Jxkb2wscBp4EHGZsbC+v\nec2ruP3225mf37CApyRJuTCA6DPbt2/n2LHbaLVaTE1NcddddwHwjGc8g4mJCXbt2sWBA9dx5syZ\nilsqSRpkBhB9aufOnVx77bX8xm+8ymWdkqTSuYyzj7msU5JUFUcg+pjLOiVJVTGA6GMu65QkVcUA\noo+5rFOSVBUDiD633rLOZnNyzfNbrZbLPSVJPTOJss8tLeucn5/nxIkT627v7S6ekqQ8OQIxIJaW\nda43beEunpKkPDkCMQRc7ilJypsjEEPA5Z6SpLwZQAwBl3tKkvJmADEEXO4pScqbAcSQ6Ha5pyRJ\nGzGJckh0utxTkqROGEAMmZ07dxo4SJJ65hSGJEnqmgGEJEnqmgGEJEnqmgGEJEnqmgGEJEnqmgFE\nH2s2m1U3oTa8Fxnvwznei4z3IeN9yF9hAURE/GpEfDIivh4RC+ucc1lE3NY+54GIeH1EGNR0yP8h\nzvFeZLwP53gvMt6HjPchf0V21o8EbgH+z7XebAcKU2S1KPYCzwd+HvjNAtskSZJyUFgAkVJ6dUrp\nLcDn1zllHPifgYMppc+nlKaB3wBeFBEWuJIkqcaqnC7YC3w+pfSVZcemgW3AD1XTJEmS1Ikqn/Qv\nBR5cdezBZe99bp2/92iAu+++u6Bm9Y+zZ88yNzdXdTNqwXuR8T6c473IeB8y3ofMsr7z0b1eK1JK\nnZ8c8Vrg5RuckoArUkqtZX/n+cBNKaULV13rvwJPSildu+zYdwNfBw6klD68ThtuAP6o40ZLkqTV\nDqaU3tfLBbodgfhd4N2bnHNvh9d6APiRVccuaf939cjEctPAQeCLwDc7/F6SJCkbefh+sr60J10F\nECml08DpXr9p258BvxoRT1iWB/ETwFngbzZpQ09RkyRJQ+xTeVyksByIiLgMuBD4PmAkIp7SfutE\nSunrwIfJAoWbI+LlwPcArwHenlL6dlHtkiRJvesqB6KrC0e8G/i5Nd76sZTS8fY5l5HViXgWWe7D\nHwKvSCk9VEijJElSLgoLICRJ0uCybLQkSeqaAYQkSepa3wYQEfF9EfH7EXFvRHwjIuYj4lUR8ciq\n21aGiHhRRNwXEf8cEXdGxOolsQMtIl4REXdFxFcj4sGI+GBE7Kq6XVVr35eHIuJNVbelChHxxIi4\nOSK+0v698LmIGK26XWWLiAsi4jXLfj+eiIhfr7pdRYuIqyPi1oj4+/b/B89Z45zfjIgvte/LRyJi\nRxVtLdpG9yIiHhERvxMRfxkRX2uf856I+J5uvkffBhBk+2gE8MvADwJHgRcAv11lo8oQEc8F3gi8\nEngaWdXO6Yh4QqUNK9fVwNuAZwJjZJu3fbhdjGwotYPIX2b9Kq4DLSIeD3wS+BbZXjtXAP8HcKbK\ndlXkPwP/EXgh2e/KlwEvi4gXV9qq4j0W+CzwIrLChiu0V/y9mOzePIMseX86Ir6rzEaWZKN78Rjg\nqcCryfqQnwZ2A3/SzTcYqCTKiHgp8IKU0kBGlEsi4k7gz1NKN7a/DuB+4K0ppddX2riKtIOnfwT2\npZQ+UXV7yhYR/wqYBf4T2aZ0n0kp/e/VtqpcEfE64MqU0v6q21K1iPgQ8EBK6ZeXHXs/8I2U0lqr\n4wZORDwEXJ9SunXZsS8Bb0gp3dT++nFkhQufn1K6pZqWFm+te7HGOU8H/hz4vpTS33Vy3X4egVjL\n44GFqhtRpPYUzR7gT5eOpSwKnAGurKpdNfB4sih7oP/9N/B7wIdSSh+tuiEV+kngLyLilva01lxE\n/FLVjarIp4BnR8ROgHYdnh8FpiptVYUi4gfI9lla/rvzq2Sd5jD/7lyy9Dv0/+v0LwzMttnteawX\nA4P+1PUEYIS1NyLbXX5zqtcegXkz8ImU0rpVTAdVRDyPbDjy6VW3pWJPJhuBeSPZVOYzgbdGxDdT\nSpOVtqx8rwMeB3whIhbJHhZ/LaX0/1TbrEpdStZBrvW789Lym1MfEfEosp+Z96WUvtbp36tdALHF\nDbv+DXA78P+mlP6g4CbWVbDGnN+QeAdZHsyPVt2QskXE95IFT9dYwZULgLtSSr/R/vpzEfFDZEHF\nsAUQzwVuAJ5HVvH3qcBbIuJLKaWbK21Z/Qzz704i4hHAH5Pdgxd283drF0DQ5YZdEfFE4KNkT5//\nsciG1cRXgEXObTy25GI23oRsIEXE24EJ4OqU0j9U3Z4K7AH+NTDbHomBbIRqXzth7lFpkBKdNvYP\nwN2rjt0N/EwFbana64H/klL64/bXfx0R3w+8AhjWAOIBsmDhElb+rrwY+EwlLarYsuDhMuDHuxl9\ngBoGEN1s2NUeefgo8GngF4tsV12klL4dEbPAs4Fb4eEh/GcDb62ybWVrBw8/BexPKZ2quj0VmQH+\n3apjf0jWcb5uiIIHyFZgrJ7G2w38bQVtqdpjOP+p+iEGL++tYyml+yLiAbLflX8JDydRPpMsh2io\nLAsenky2xUTXq5VqF0B0qr1e9WNk23q/DLh46QEspTToT+JvAt7TDiTuIlvC+hiyjmMoRMQ7gAbw\nHODrEbE0InM2pTQ027y3N6ZbkfcREV8HTqeUVj+ND7qbgE9GxCuAW8g6hl8iW9o6bD4E/FpE3A/8\nNTBK9nvi9yttVcEi4rHADrKRBoAntxNIF1JK95NN9/16RJwg6zteA/wdXS5f7Acb3QvgS8AHyKa2\n/hfgkct+hy50Oh3at8s4I+L5wOp8hyBblDBSQZNKFREvJAucLiFb6/uSlNJfVNuq8rSXJa31w/sL\nKaX3lt2eOomIjwKfHbZlnAARMUGWDLYDuA944zDmRbU7j9eQre+/mKzDeB/wmpTSd6psW5EiYj/w\nPzj/d8N7Ukq/2D7nVcB/IFt18HHgRSmlE2W2swwb3Quy+g/3rXpvKRfk4Q0vN/0e/RpASJKk6gzt\nfJgkSdo6AwhJktQ1AwhJktQ1AwhJktQ1AwhJktQ1AwhJktQ1AwhJktQ1AwhJktQ1AwhJktQ1AwhJ\nktQ1AwhJktS1/x8Kk58BGFo+HQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11136cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nsamples = 100\n",
    "Xt = np.linspace(0,10, num=nsamples).reshape(nsamples,1)\n",
    "# la fonction à retrouver est y = x * 2\n",
    "yt = Xt * 2 \n",
    "# on rajoute du bruit\n",
    "yt = yt +np.random.randn(100,1) * 3\n",
    "plt.scatter(Xt, yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nn = Regressor(\n",
    "    layers=[\n",
    "        Layer(\"Sigmoid\", units=100),\n",
    "        Layer(\"Linear\")],\n",
    "    learning_rate=0.02,\n",
    "    n_iter=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nn.fit(Xt, yt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtest = np.linspace(0,10, num=nsamples).reshape(nsamples,1)\n",
    "ytest = nn.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10fb29cf8>]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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szHacxra8wmjaeaODvP9ulSsFECISmUymFfL5Rz6qTjeM1R/FtAlV1u+/sxNeeQWeew6e\nfz747wsvwPPPc1BbGw8CO7In2/EGw+no8aMdf/oT7LgjH2yyCa8ByziEl9mNl9mODioI63erHCmA\nEJHI9JxW6L6aYGjTCoN9wo2q0w1zB8/Kysq8vocwVjb0nEo4kO34f6y9/UwuO+RQzjr+OPj3v+HZ\nZ4P/dgUN77+/4QLDhsEOO8COO7LVTjvx0sfGMO+5V3jOj+NFPssL/JtXhl3Apw6dwN9vWwDAq6kU\nR1dXA4fR83frL0BxTVnFlrsX1QOoAby1tdVFpPgsW7bMm5qaPJVKubt7XV2DV1SMdrjOYZXDdV5R\nMdrr6hoyvubq1au9rq7BgfWPuroGb29vD+ttZGzZsmXpNjU6eLfHdQ6svw9Ry9s9fO899xUr3O+4\nw/2qq/zVk0/2q8HvYA9vY6y/y6bdb4J3bLaZe2Wl+yGHuB93nPsZZ7hfdpn7X//q/sAD7s895/7B\nBz1eor29PaO25uN3q9S0trZ23bMaz7U/zvUCA14cDgJuBZ4DOoHP93r+6vTx7o+mQa6pAEKkCPXX\nQf3zn//MuePa0FE0pjuKxth0FE1NTen3tapXALHKAW9qaoq6ie6exT3s7HR/8UX3e+91v+4695/8\nxP34490nT3bfeWf3YcN6BAjvjB7t94EnOcJ/yXf9ZC7xI/ib702TbwPeNG/ekNucSqV6BKO9ZRpo\nlJN8BhBhT2FsCTwMXAX8qZ9z5gPHA5b+fl3IbRKRCPQ3jP/tb5+S07RCIRMxhyLf0zRh2PgeOjtx\nMJUdJ/Kx5l/Q/s1vMrq9HZYvhxUr4M03N/zwttvCbrvBxz8OBxwAu+4afL3rrjBmDKtWreKA6mrg\nGDYuegXjcvi3GWz6ppjyRIpRqAGEuy8AFgCYmfVz2jp3fyXMdohItDLt5Ifyx73QiZjZisPqj369\n8QYsW8Y7N93ET4AqbqSa86mkjS15G4AOYN2tt8KnPw0TJsCMGTB2bBA07LYbfPjDA75EHN5/vvNE\nJBCHJMqDzewlYA1wJ3CWu7dH3CYRyaMwO/li+IQf6ZJLd3j5ZXjySXjqqQ2Pp58OEhaBvYCPAClW\ncD8HcR0zSVFFG0+xkrk8vmhRTh1wMS45lcFFHUDMJ5jaWAmMBc4BmsxsonuRbdIhIv0Ks5PPxyfc\nsPdUKNhQ+urV8PjjGx5PPBEEDqtXB88PHw6VlbD77nD88cF/P/EJqKriP79ybHqfkIPYcA9/mZdR\nAk0llKhckygyfdBHEmUf53w8fd4hA5yjJEqRIhRmRvxQk+XivHpjQOvWuT/yiPu117qfdpr74Ye7\n77jjhuTFTTZx/9Sn3I85Jkhy/NOf3J98Mlgh0Q8lHJaHfCZRFmw3TjPrBI5y91sHOe9l4PvufkU/\nz9cArZMmTWLEiBE9nkskEiQSiXw1WUTyaM2aNelh7PC20s72E25R7M752mvw8MM9H08+uaFOwq67\nwp57BjkKe+4Jn/pUMMqwySZDejmNEpSOZDJJMpnscWzt2rUsXLgQimk770wCCDP7GPAMcKS7/79+\nztF23iJFLC4dVCy3xG5vh9bWDY+lS+Gf/wye23zzIEjYe2/Ya6/g8elPQ68PUiIDyed23qHmQJjZ\nlsA4NizR3M3M9gLa04+zCXIgXkyf90sgBTSH2S4RyVx/+QFDzRuIS0Z85Ks33nwzCBKWLIEHH4QH\nHoCVK4PnPvxhqKmBI4+Emhr+NWoUT7sztro6FvdOBMJPotwX+D82zKn9On38GuAkYE/gOGAk8DxB\n4PBDd39/40uJSCH1t3/B5ZdfwkknnRrqVEQhFHT1RkdHMO2weHHwWLIk+L6zE7bcMggWvvAF2Hdf\nGD8exo0jtXw5Dz/8MJdeejmLFt29/lIHHjiJU089mX322UfBhEQr1ySKQj9QEqVIQfRXmXCbbbaP\nbdXHbIWW2Nne7t7U5H7WWe6f+5z7VlsFyY3DhrnvtZf7N7/pfuWV7o8+ulGZ5p6JncMcRqTv9aMO\ne5d9kmPvUuiSnaIpZR3GQwGESPj637/hvKLY1yFTeVl50NnpvnJlsCLiW99y/+QnN9yYbbd1P/JI\n93POcb/rLvc33hj0chuCmvN73esGh9II3IaiaFfMxEwxlbIWkSLUf37A9v0cj0fVx2xlW58glUqx\nYvly9jBjl2eegYULYdGiYBdJgD32gM9+Fr773eC/Y8dCv0V4+77+hoqdo9NHJxGkhsW3XHchhLmj\nqQyNAggR2Uj/+QEv9XM8PlUfh2LAxE53XvvHP2j8xjfZ/qknmEQQRnWY4fvsw/Bjj4WDDgoChm22\nyakdPQO3d9JfL6RnMNFdcQZu2Yr7fiflaljUDRCR+Omq7lhRMYvgj/azQCMVFeeyzTbb93F8NnV1\nEe/rkE8rV8KVV/L6f/wH67bZhpGf/SzfeuoJdqKSKzmFw5nLaBvJf2y7A5x/Pnz+80MKHlKpFPPn\nz6etrQ3oHbhVAQ3ALODxbse7K+7ALVOZrJiRCOQ6B1LoB8qBECmI/vID8rH9duysXu1+881BDsNu\nu7mDfwC+GPwX4IeBb8GVecv7GGg+v2diZ/fEya6EyvxX8oy7/nNyijP3JkpKolQAIVIwqVSqz6z3\n/o4Xhfffd7/3Xvcf/tB9//2D1RHgXl3tfsopfvY+4330sJHpDuua9B/cVb06r1UOeFNTU9Yv398K\nl+5BRPfg4sADJ/v//u//+kEHTS6twC0LYZZCLycKIBRAiEi2XnjB/eqr3Y8+2n3kyODP36hR7l/5\nSrCk8pln3L2vT7v5/fSb6afpkgzccqC9OvJDqzBERAbT2RlUepw3L3g8+GCwImLffWHWLJgyBfbb\nDyoqevzY3Xd3FW3qmm/vnovgDGW3z+4yrYDZX2JnXCp5Fpp29IwfBRAikndhbo894LXfeQdaWuDv\nfw8eL74II0dCXV0QNNTXw7bb9nndjStvdl9p0gh8Dpi5/vza2qD6ZrYKWgGzBJVrABVHCiBEilCY\nHXQu+it/nY8y1/1d+8bLLmbkPffAX/8Kt90Gb78d7EY5fToccUSwvHL44H/qetYZuAo4lZ4jDquY\nOHEyZ545N6f73rXCpaVlFh0duY9oiEQm1zmQQj9QDoSUsbhX4xsoOTCf196JJX4SX/U7GO4fmLmb\nuU+c6H7uue5PPZX1tTfOS2hPV34M5z6HNZ+vMs8yGCVRKoCQMhVWB52PjifMpXbLli3zj4H/F9P9\nHg5wB3+P4T6fT/sJ4CvuvXfI13Z3b2pq6melxd0O+BVXXJHT9fuTr4TIuAeWEh/5DCBUSEqkSHRV\n4+vouJhg7nwMQTW+i2hublpfjCgb7e3t1NdPpbq6moaGBqqqqqivn8qaNWuyvlYoxX6eew4uvJBt\njzySZ4FzuZnVbMNxXMN2vMwU5nEFsGzt2uyv3U3PvITuVgEwefLknK7fn8rKSqZMmZLztEXP6ZdV\nQCMtLYtJJGbko5kifVIAIVIkwuig89nx9N8JZ5kc+Oqr8NvfwqRJMGYMzJ3LJjvtxHRgWy7lSG7l\nOo7jNUZlf+1+9F95M/4VNsMILEUyoQBCpEjkrYNOy3fHM5ROuKuU8/JHHoEbboCpU2HHHeGUU3jL\nnUfnzGHFffex1R13sLqugbcrvpfxtbOVTDZSWzuBYKXFzsBMamsnDGmlRSFFVea5dxluKUO5zoEU\n+oFyIKSM5bMaX//z/kOvsJhpcuDq1at9yuFTvBb8GvA30i/+/mc+42+ed54ffcihG12jUCW0i61Q\nU6HLPCvforgpiVIBhJSpfGbvh9nxDNgJP/mk3/jx3fxZzB38Kcb69/myjxs2wuvqGgZNFC22Dj5T\nuSSyFrLMc5grbSR8CiAUQEiZy1cnWrCO57XX3H/3u2DfCfDV4JdS6/txv0Nnj8ClkJ+m4yAfn+gL\nVeZZm1oVP63CEClz+creD3Xe3x0WLYLjjgvyGr79bdhmGx4680x2BE7hKh7gM4Clf6D7Sofy2bY5\nH4msXWWeU6kUTU1NpFIpFiyYl3Pxrt60rbZ0p0qUImUslP0FXnkFrrkGrrgCUikYOxZ+8IMgkPjo\nR9kyleK9X/yC/ko5B8qjzHNXImsQPHS93+l0dDjNzTNpa2vL6t8j7DLPKsMt3SmAEJHcOx53WLgw\nWH755z8Hm1Z96Uvwu98FyzGHbRjsHKyUM1A2ZZ4z3VgrLlSGW7rTFIZImQhl2d3atXDppfDJT8LB\nB8PSpXDOOUEBqOuvD44N2/jPzEBTJ8W6nLK7TO91vpfmFkIp/PtInuSaRFHoB0qiFMlKKMvuHn/c\n/cQT3bfc0r2iwv3LX3a/4w73zs6sLjNQMmgxrrYYyr0u5AqKfCrGfx/RKgwFECJZyNuyuw8+cP/L\nX9w/97ngT8cOO7iffbb7c8+F0u5iNJR7XagVFCLu+Q0glAMhkidx3GJ7KEl6G72P11+Hq66Ciy+G\nlSth4sSgauSXvgSbblrotxRbQ02IDCWRNU/i+Dst8aEAQiRH7e3tTJs2M915BOrqGkgmG/O+jC5b\n2STp9X4fuwC/2WVXvrD6Vezdd+GYY+Cmm2C//QrT+CKTa0Jk2CsoshHn32mJDyVRiuQglUpx2GH1\nsd0JMZskva56BPvyY27kP1iBcfAzz3DTttvDM89AY6OChwEUY0Jkf7S7p2Qk1zmQQj9QDoTEwMbJ\ncvGtzJdJkt6yp5/2KeB3srs7eBtj/SQu9S24IjbvoxgUa0Jkd6o2WdqKphKlmR1kZrea2XNm1mlm\nn+/jnJ+Y2fNm9raZ3W5mxROmS9na8Antu+kj8a3MN+Cyuw8+gOuvZ4f6epqALdiML/InqlnG5ZzM\n29QB8Xgfcda1bPNnP/tx0S9xVLVJyVTYORBbAg8DVwF/6v2kmc0FTgG+CqwEfgY0m9nu7v5eyG0T\nGZKeyXL7AecT58p8fSbpjRkDV18N558PK1dSMWkSk//1LxbyHeCL3X46Pu8jjvrLFXjggQd45ZVX\nijL5UNUmJVOhBhDuvgBYAGBm1scps4Gfuvvf0+ccB7wEHAX8Mcy2iQxVz09oY4AGYBbBqGC8KvP1\nzqKv3HHHoDrkr34FL78MX/kK/PnPbLn33nyofioVLbPp6CB272MgUa4U6JkrMAlYSEvLLOBsFiyY\nV9C25IuqTUrGcp0DyfQBdAKf7/b9x9PH9ux13l3ABQNcRzkQEqmN54jbHeK1jr93jsaHwa+qrPKO\n0aPdhw93//rX3XvNZRdbPYJQCmRloZRzBYrtd0EyVyp1IHZIv4mXeh1/Kf2cSCz1/QktwbBh97L3\n3pXceOMNkX9K6/pkvDW/51RS/Df/w5ZtKeaN2Zkjli6FXXbZ6GfiXI+gL/19+k8kZhTk03+x7WOR\njWL7XZBoxLEOhBEEFgOaM2cOI0aM6HEskUiQSCTCapfIeslkI4nEDJqbZ64/dthh8Vgnn0qluLe5\nibl8he8wly14mys4gV9SyXPPzib13nsM1BXEqR5Bf/K9i+VQlEOuQKa/Cyo4FU/JZJJkMtnj2Nq1\na/P3ArkOYWT6QFMYUoJitx/Am2/601/7mr8K/i6b+kWc6jvx7/TQ+ioHvKmpqaBNWrZsWd7vUVNT\nU3oYdlWv6YPCvsdSWLaZi6inkSR7RbOMcyDuvhJ4ETi065iZbQ3sD9wXVbtEBtJ7l8XKykqmTJkS\n/aeud9+Fiy6C3XajqrGRm4CxnM9sLuZ5Ppo+qbCfjNvb26mvn0p1dTUNDQ1UVVVRXz+VNWvW5Hzt\nuBRtKvedKVVwqszlGoEM9CBYxrkXsDfBaMN/pb8fk37+dGA1cATwaeCvQBuw6QDX1AiEFFxsP2m9\n/777FVe4f+xj7sOGuX/ta+4rV8bik3HeNvEa9PrRf/qP3UhUAZRyEmkpK5rdOAmyiTqBjl6Pq7qd\n8yPgeeBtoBkYN8g1FUBIwYXdGWats9P95pvdq6qC/42POcb96afXPx11Fn0hOpeo32O5i8s0kmSn\naFZhuPvdDLLfhrv/iCCIEImlOCTs9XDnnTB3Ljz4INTXw403wj779Dgl6iz6QqxQiPo9lrtySCKV\ngcVxFYZIrES1XG+jzPZHHoHvfQ8WLID994e77oLJkwe8RlQrKgrZuRTDqpFSpIJTot04RQZR6IS9\n3smHh1R05iZxAAAd/0lEQVRVcftHP4bvsw+sWAG33AL/+MegwUOUujqXiopZBCM3zwKNVFTMpq5O\nnUupKPck0nKnEQiRQRT6k1ZXZvuH+T2n8xjf4be88fzzXLb7HpzyyEOwySZ5fb2w9FUro7a2YUid\nSz7qDKhWQf5pGqnM5ZpEUegHSqKUCBQqYW/ZsmU+DPwEvu4vsp2/zeb+M870D/P7os1sz2WFQj5W\nv8R2BY1IBEqiDoRIMen6pJVKpWhqaiKVSrFgwby8V5187ZZbeAj4PVdxG4dTRYqz+DlvUA/0vZVy\n79oUcZNLrYx81BlQrQKRkOQagRT6gUYgpBSlUu5HHOEOvgh8X3486PLHUv9knY+loKpVINKTRiBE\nSsXatfDd78InPxmssrjpJn52+BQeqriIwZIPS/2TdSarXwpxDRHpmwIIkSh0dsLVV0NVFVx+Ofzw\nh/D003D00SRvvH7QzPau2hQdHRcTLJMcQ1Cb4iKam5sGnc6I+7QH5Gf1S1xKXouUIgUQIoV2//0w\nYQJ8/etQWwvLlsFZZ8GHPgRklm8x1E/WYe5PkW/5WAqq5aQi4VEAIVIoL78M//mfQfDw/vuwaBGp\ns89m/mOP9TkSMFDy4VA/WRfbtEc+6gyoVoFISHJNoij0AyVRSrF5/333Sy5xHznSfdQo9//5H1/9\n8stZJUD2tSV2tptJFXNCYT42qyrHDa9EeiuazbTCeCiAkKLyj3+47723u5n7CSe4v/KKu2e+OddA\nKy2yrU2Rj82P+gpkRKR4aBWGSMwtv/9+nq2vh4kToaICFi+G3/8ePvKRrBIgB5pyyLY2RS4JhcWU\nOyEihaEAQiSP2l99lQs+tScjJ0xgq+Zmvg00bLMda7rlMWSaAJlpoJFpoaZcEgqLLXdCRMKnAEIk\nXx57jBcqq5jzxGM0cSCfoJXf0shtd9zfo6PNdCQgjBoGQ0kozHXJqIiUJgUQErliqEkwoLfegrlz\n8Zoahr22hoM5k6+yiJepoa+ONtORgDBqGAylJLeKMYlIXxRASFby2dmXxLx6U1NQRfKii2ibNo29\ngLs5sddJG3e0mYwEhFnDIJv9KVSMSUT6lGsWZqEfaBVGJMLYdyHTlQix9MIL7kcfHSxjOOww97a2\nIS2THGxpYaF2AR1MtktGRSSetIxTAUTB5buzL9qaBB0d7r/7nfuIEe7bbut+/fXunZ3rnw6ro426\nhkFcAhkRyU0+A4jhBRzskCLVlUQXDKNPTx+dTkeH09w8k7a2tqyH0zOZV49dmeGnn4ZvfhMWLQrK\nUJ9/Powe3eOUZLKRRGIGzc0z1x+rrW3IuephZWVlpPejK3eira2N5cuXM27cuPj9+4hIQSmAkEGF\n0dn3nFef3u2ZGM6rv/cenHsu/PznsPPOcOedcMghfZ5a6h1t1IGMiMSHAggZVBidfVeCYEvLLDo6\nnCAYuZuKitnU1sZok6PFi+Eb3whGH04/HX7wg/WbXg1EHa2IlDqtwpBB5WM1QF+rN2K9ydFbb8Gc\nOXDAAbD55tDaCr/4RUbBg4hIOdAIhGRkqHP77e3tTJs2M51DEairC34utsP9LS1BrsOLLwZ5DrNn\nw3D9ryIi0p3+KkpGhtrZ9yyBPAlYSEvLLBKJGSxYMA/I/3B/KpVixYoV2Qckr70G3/kOXHVVkONw\n++2wfvpGRES6UwAhWcmmsw9j9cZABhvtGNDf/gbf/nYwdfH73wd5D2Z5a1t3Qw5wIr52nF9bRApP\nORASmkKXQB7Shk+vvgqJBBx1FNTUwBNPwAknhBI8hFl5M8qqniVRUVREsqYAQgY11PLVhSyBPKQN\nn26+GfbYA5qbeeG885h/0km0vfNO3trUW5g7Wka5W2acd+os+n1WROIs10pUuT6As4HOXo8nBzhf\nlSgLJB/lqwtVArmpqSndxlW9KluucsCbmpo2nPzSS+5f/rI7+LqpU/3Ygw8NvcLiggULQqu8GWVV\nz7hWFA2j9LpIKchnJcq4jEA8DmwP7JB+HBhtcwTy88myUEs1MxntSC1bxkPf+x4ffOITcNddPH/B\nBUx8/iVuXvQQYX167hrer6+vTx/J/3ROlLtlxnWnzjiPioiUjFwjkFwfBCMQS7M4XyMQBZDvT5aF\n2Muhv9GOQw6p9aMPPtRvTr+Jm8CrR23b7dNpeJ+eN7TpfI1AFEgc2yQSF6U4AlFpZs+Z2QozazSz\nMVE3qNzl+5NlNttHD1V/ox0Hv/wSl951J5P5MF/hco5hb5ateQ/4bvonw/n03DMv4zSgAcj/1txh\nbvsd59fuT1xHRURKTRwCiMXA8UAdcCLwcWChmW0ZZaPKXSETIIeir+S4rloVqVSKpqYmVixezM3W\nyQ+feIy72I9PspxbOBR4GLgM+Eb6J8N5jxt3ZI1AONM5UVb1jFtF0bj/7oqUCvNgWiA2zGwE8Aww\nx92v7uP5GqB10qRJjBgxosdziUSCRCJRmIaWgfr6qbS0LKaj4yJ67lUxYX0RqELLuNbDn/8MJ57I\ne+vWMfP11/kjqwhWZswnGAno+n4qQQyb//eYSqWorq6mZx0MgF8B3+W2227jsMMOy+k1eouyqmec\nKorG8XdXpNCSySTJZLLHsbVr17Jw4UKA8e6+NKcXyHUOJIwHsAT4eT/PKQeiQNrb22OXyb4hp6Ax\nnefQ2HNVx+rV7tOmBZPeRx3ly++9t9d8eO/58XaH8N5joVahSE9x/N0ViYN85kBEHixs1CDYClgN\nnNLP8wogCqwQCZCZGCw57t+/+537jju6jxzpft117p2d7t5XJ763w4genfqwYSO8pmbfvL9HdWTR\nisvvrkhc5DOAiLyUtZmdD/ydYNrio8CPgQ+A5EA/J4UTl62p+0uO+zA1XAB89FvfgilT4Ior4KMf\nXf98XxuBbbPN9qxeveH7ww7LsOR1lmK7YViZiMvvrkgpijyAAD4G3ABsA7wC3ANMcPfVkbZKYqdn\nclyQU3AId3I1RzMaeOnnP2f7M87YqAx1f514ITt1dWQiUmoiDyDcXVmPkpGuJYMtLbPYtONdzuUe\nZvEH7mI4P5x0MNeceeaAP9+7E1enLiIydJEHECLZSCYb+fGUqZx0/zcYQ1BVoe3ww7jhxuujbpqI\nSFlRACGxtdH20O+9x6hf/YoLH7ifd/fckyWnnMKpBx+sUQQRkQgogJDY6avWw7cOOJBL31jL8Kee\ngp/8hM3nzmXScP36iohERX+BJXa6b4Q0jM/y35zBz+67kX9vtRW7LlkC++wTdRNFRMpeHEpZi6zX\nff+IjzORu5jJL7mJi2ngE2++SdtWW0XdRBERQQGEhKSvvSoy0VXr4T95jkfYi4/xbw7mLk7nt6xD\nGyGJiMSFAgjJq/b2durrp1JdXU1DQwNVVVXU109lzZo1Gf181dZbcytwJXO5iWPYk0dZxCS0EZKI\nSLwogJC86p6/EGxY1UhLy2ISiRmD//Cf/8zYI4/kwE035ahhW3ECB/MmrxH19tAiIrIxBRCSN93z\nF4JKkWOA6XR0XERzc1P/0xlr18Lxx8OXvgSTJmGPP867h00iLttDF9JQp35ERApNqzAkb/rbqyLY\nTjnIX9hoBOHuu+GrX4X2dvjDH+C44xhpVnb7R2S8TbmISExoBELypudeFd31kb+wbh1897twyCGw\nyy7w6KNBINFtH4vKykqmTJlS8sED5Dj1IyISAY1ASN5036uio8MJRh7upqJiNrW13fIXHn0UZsyA\nZcvgvPNgzhyoqIiy6ZHqmvoJgofp6aPT6ehwmptn0tbWVhZBlIgUF41ASF4lk43U1k6gz/yFjg44\n/3zYbz9whwcegNNOK+vgATKb+hERiRuNQEhe9bd1Ns88A0cdBYsWwXe+Az/9KWy+eejt2Wg/jRjq\na5vygJauikh8KYCQUKzfKtsdrr0WTjkFRo2CO++Egw8O/fXzmZQYdhCS8dSPiEiMaApDwrN6NRx9\ndJAcedRRQe5DAYIHyE9SYq5FsbIx4NSPiEgMaQRCgBA+Zd92W1DbYd06+OMf4Stfyf2aGco1KbHr\nXpxzzi+5777H0teZBCykpWUWicQMFiyYl9c29zv1IyISUwogylgqleLhhx/m0ksvZ9Giu9cfz6n+\nwDvvwNy5cMklcPjhcPXVsNNOG71umFMCQ6pHQd/THoVeGbF+6kdEJOY0hVGGug/NH3NMgkWLHiYv\n9QceegjGj4crroCLL4b583sED4WaEsiqHkU3Pac9rkkf1coIEZG+KIAoQxs6yvOBTuAysio93VtH\nB5xzDuy/P2y2GbS2wqmnwrCev16FKpbUlZRYUTEr/VrPMth+GhuX4Z6Qfia7IEREpFwogCgzPTvK\nT6aP5vApe+XKIDHy+98Plmfefz/ssccgr5tDsJKhbJMSN572qAIagMyDEBGRcqIciDLTs6N8J/31\nEOoPdC3PPPVUGD062NPioIMyfN3uBs5LGKpskxL7rsXQCHyOIAgJ1NY2aGWEiAgKIMrOxh1l16fs\nLOoPrF4NJ54It9wSLNG8+GLYeussX7dLuFMCmSYl9l+LYRUTJ07mzDPnamWEiEg3CiDKzMYd5bnA\ncWT8Kfv224Plme+8AzffDF/+8hBfN37FkpLJRhKJGTQ3b3wvtCOmiEhPyoEoQz3zA/YEHubAAydz\n0003kUqlWLBg3sYd5jvvwH/9V7A0c4894LHHMg4e+n7d+BVL6pr2SKVSNDU19X8vREREIxDlKOui\nRY88AtOnw/LlcOGFfa6wgMHrOxRLsSTVYhARGZwCiDI2aEfZ2Qm/+U2wwuITn4AHH4RPfWqj07Ld\nd0IdtIhI8dMUhvTt2Wfh0EPh9NNh9mxYsqTP4AEKV99BRETiIxYBhJmdbGYrzewdM1tsZvtF3aZS\nkUqlmD9/fnZ1Fm68EfbcM5iyuOMOOO+8oEBUP9cvZH0HERGJh8gDCDM7Bvg1cDawD/AI0GxmH4m0\nYUVuSGWjX3sNZsyARALq6oLdMw85ZMDXyaS+g4iIlJ7IAwhgDvA7d7/W3Z8GTgTeBr4ebbOKW9bT\nCgsXwl57wd//Do2NkExCBqsPhrrvRH+GNGIiIiIFF2kAYWabAOOBO7qOubsDLcDEqNpV7LKaVnjv\nPTjjjKAc9S67bFhxYZbRaw1l34m+FGqjLRERyY+oRyA+AlQAL/U6/hKwQ+GbUxoynlZ4+mmYOBF+\n9Sv4xS/g//4Pdt0169fLR30HJWKKiBSXuC7jNILayv2aM2cOI0aM6HEskUiQSCTCbFdRGLRs9Nix\ncPnlcNppwajD/fdDTc2QXy/X+g5dIyZB8NDV3ul0dDjNzTNpa2vTsk8RkSwlk0mSyWSPY2vXrs3b\n9S2YMYhGegrjbeBL7n5rt+N/AEa4+xf6+JkaoLW1tZWaHDq9UldfP5WWlsV0dFxE97LRXz5oH27c\nYjNoaoKTToLzz4cttoi0rfPnz6ehoYFg5GFMt2eeBXamqamJKVOmRNM4EZESsnTpUsaPHw8w3t2X\n5nKtSKcw3P19oBU4tOuYmVn6+/uialcp6Gta4aw9P84Njz8SFISaNw8uu6zf4KGQyYz5TsQUEZHw\nxWEK4zfANWbWCiwhWJWxBfCHKBtVzLpKSl9yyYXAhax8/HH2v+kmRtx0ExxxBFx5JWy3XZ8/m21V\nyXzIdKOtwUpli4hIAbl75A/gJOBfwDvAP4B9Bzi3BvDW1laXnlavXu11dQ1OkD/igJ864QD/YOxY\n9w99yP23v3Xv7BzwGnV1DV5RMdqh0WGVQ6NXVIz2urqGUNve3t6+Udvr6hq8vb29z/fV9ZyIiGSu\ntbW16+9ojefYd0eaAzEUyoHo34a8h4sZxmf5HqfzI25m5dYjqFpyP1RXD/jzqVSK6upqeiYzkv5+\nJqlUKvRP/n0lYnZ/X8HKkoVUVMyitnYCCxbMC7U9IiKlJJ85EHGYwpA86L6SYVcO4DpmMJF/cA6f\n58ev38qTw4YxWNefyfLPsAOI3httaYWGiEg8RV0HQvKkq/Ofycs8wl58lOeYzN38gEv5gMxKSscx\nmVGlskVE4kkBRImo3GYbbgSu5b/5C19gLx7hXg4km84/X1Ul8ymOQY2IiCiAKA133MG4L36RhuHD\nOda25HgO4w3WMpTOPx9VJfMpjkGNiIgogChu69YF1SRra6Gqio6HHuK1wyeTS+ffVVUylUrR1NRE\nKpViwYJ5/S7hLES9iLgFNSIioiTK4vX448GmV08/HexlMWcOI4cNy6mkdHe9kxl7K2S9iMFKZas+\nhIhI4SmAKFb33AMdHbBkSbANdzeDdf750HPzq2BpZUvLLBKJGaEtrez9vqIoeiUiIgFNYRSrb30L\nWls3Ch4KIavtwkOkHTxFRKKjAKJYmcFmm0Xy0nFYWhmXIEZEpFwpgJCsxWFpZRyCGBGRcqYAQrIW\nh6WVcQhiRETKmQIIGZKol1bGIYgRESlnWoUhQzLY0spCSCYbSSRm0Nw8c/2x2toG1YcQESkABRCS\nk0IsGe1PHIIYEZFypQBCil6UQYyISLlSDoSIiIhkTQGEiIiIZE0BhIiIiGRNAYSIiIhkTQGEiIiI\nZE0BhIiIiGRNAYSIiIhkTQGEiIiIZE0BhIiIiGRNAYSIiIhkTQGEiIiIZE0BhIiIiGQt0gDCzP5l\nZp3dHh1mdnqUbRIREZHBRb0bpwNnAVcAlj72RnTNERERkUxEHUAAvOnur0TdCBEREclcHHIgvmdm\nr5rZUjM7zcwqom6QiIiIDCzqEYiLgKVAO3AAcC6wA3BalI0SERGRgeU9gDCzc4C5A5ziwO7unnL3\nC7sdf9zM3gd+a2ZnuPv7+W6biIiI5EcYIxC/Aq4e5Jx/9nP8foI27Qq0DXSBOXPmMGLEiB7HEokE\niUQis1aKiIiUsGQySTKZ7HFs7dq1ebu+uXveLpYrM5sO/AH4iLv3+S7NrAZobW1tpaamppDNExER\nKWpLly5l/PjxAOPdfWku14osB8LMJgD7A/9HsHTzAOA3wHX9BQ8iIiISD1EmUa4DjgXOBjYDVgK/\nBi6IsE1FI5VKsWLFCsaNG0dlZWXUzRERkTITWQDh7g8BE6N6/WLV3t7OtGkzaW5uWn+srq6BZLKR\nUaNGRdgyEREpJ3GoAyFZmDZtJi0ti4FGYBXQSEvLYhKJGRG3TEREyknUdSAkC6lUKj3y0AhMTx+d\nTkeH09w8k7a2Nk1niIhIQWgEooisWLEi/dWkXs9MBmD58uUFbY+IiJQvBRBFZOzYsemvFvZ65m4A\nxo0bV9D2iIhI+VIAUUSqqqqoq2ugomIWwTTGs0AjFRWzqatr0PSFiIgUjAKIIpNMNlJbOwGYCewM\nzKS2dgI//emPmD9/Pm1tAxbwFBERyQsFEEVm1KhRLFgwj1QqRVNTE0uWLAHgM5/5DA0NDVRVVVFf\nP5U1a9ZE3FIRESllCiCKVGVlJVOmTOEHP/iRlnWKiEjBaRlnEdOyThERiYpGIIqYlnWKiEhUFEAU\nMS3rFBGRqCiAKGJa1ikiIlFRAFHk+lvWmUw29nl+KpXSck8REcmZkiiLXNeyzra2NpYvX97v9t7a\nxVNERPJJIxAlomtZZ3/TFtrFU0RE8kkjEGVAyz1FRCTfNAJRBrTcU0RE8k0BRBnQck8REck3BRBl\nQMs9RUQk3xRAlIlsl3uKiIgMREmUZSLT5Z4iIiKZUABRZiorKxU4iIhIzjSFISIiIllTACEiIiJZ\nUwAhIiIiWVMAISIiIllTACEiIiJZUwBRxJLJZNRNiA3di4Duwwa6FwHdh4DuQ/6FFkCY2Zlmdq+Z\nvWVm7f2cM8bM5qXPedHMzjMzBTUZ0v8QG+heBHQfNtC9COg+BHQf8i/MznoT4I/A//T1ZDpQaCKo\nRTEB+CpwPPCTENskIiIieRBaAOHuP3b3i4DH+jmlDvgEMN3dH3P3ZuAHwMlmpgJXIiIiMRbldMEE\n4DF3f7XbsWZgBPDJaJokIiIimYjyk/4OwEu9jr3U7blH+vm5zQGeeuqpkJpVPNauXcvSpUujbkYs\n6F4EdB820L0I6D4EdB8C3frOzXO9lrl75iebnQPMHeAUB3Z391S3n/kqcIG7j+51rd8BO7v7lG7H\nPgS8BdS7+239tGEacH3GjRYREZHeprv7DblcINsRiF8BVw9yzj8zvNaLwH69jm2f/m/vkYnumoHp\nwL+AdzN8LREREQlGHnYl6EtzklUA4e6rgdW5vmjaP4Azzewj3fIgDgfWAk8O0oacoiYREZEydl8+\nLhJaDoSZjQFGA7sAFWa2V/qp5e7+FnAbQaBwnZnNBXYEfgpc6u7vh9UuERERyV1WORBZXdjsauC4\nPp46xN0Xps8ZQ1An4mCC3Ic/AGe4e2cojRIREZG8CC2AEBERkdKlstEiIiKSNQUQIiIikrWiDSDM\nbBczu9LM/mlmb5tZm5n9yMw2ibpthWBmJ5vZSjN7x8wWm1nvJbElzczOMLMlZva6mb1kZn8xs6qo\n2xW19H3pNLPfRN2WKJjZTmZ2nZm9mv678IiZ1UTdrkIzs2Fm9tNufx+Xm9lZUbcrbGZ2kJndambP\npf8/+Hwf5/zEzJ5P35fbzWxcFG0N20D3wsyGm9kvzexRM3szfc41ZrZjNq9RtAEEwT4aBpwA7AHM\nAU4Efh5lowrBzI4Bfg2cDexDULWz2cw+EmnDCusg4BJgf6CWYPO229LFyMpSOog8gf6ruJY0MxsJ\n3AusI9hrZ3fgO8CaKNsVke8B3wJOIvhbeTpwupmdEmmrwrcl8DBwMkFhwx7SK/5OIbg3nyFI3m82\ns00L2cgCGehebAHsDfyYoA/5AlAN/C2bFyipJEozOw040d1LMqLsYmaLgfvdfXb6ewOeBS529/Mi\nbVxE0sHTy8Akd78n6vYUmpltBbQC3ybYlO4hd//vaFtVWGZ2LjDR3SdH3ZaomdnfgRfd/YRux24B\n3nb3vlbHlRwz6wSOcvdbux17Hjjf3S9If781QeHCr7r7H6Npafj6uhd9nLMvcD+wi7v/O5PrFvMI\nRF9GAu1RNyJM6Sma8cAdXcc8iAJbgIlRtSsGRhJE2SX97z+Ay4C/u/udUTckQkcAD5rZH9PTWkvN\n7BtRNyoi9wGHmlklQLoOz2eBpkhbFSEz+zjBPkvd/3a+TtBplvPfzi5df0Nfy/QHSmbb7PQ81ilA\nqX/q+ghQQd8bkVUXvjnRS4/AXAjc4+79VjEtVWZ2LMFw5L5RtyViuxGMwPyaYCpzf+BiM3vX3Rsj\nbVnhnQtsDTxtZh0EHxa/7+43RtusSO1A0EH29bdzh8I3Jz7MbDOC35kb3P3NTH8udgHEEDfs+igw\nH7jJ3a8KuYlxZfQx51cmLifIg/ls1A0pNDP7GEHwdJgquDIMWOLuP0h//4iZfZIgqCi3AOIYYBpw\nLEHF372Bi8zseXe/LtKWxU85/+3EzIYDNxPcg5Oy+dnYBRBkuWGXme0E3Enw6fNbYTYsJl4FOtiw\n8ViX7Rh4E7KSZGaXAg3AQe7+QtTticB4YFugNT0SA8EI1aR0wtxmXkqJTgN7AXiq17GngC9G0Jao\nnQf8wt1vTn//hJntCpwBlGsA8SJBsLA9Pf9Wbgc8FEmLItYteBgDfC6b0QeIYQCRzYZd6ZGHO4EH\ngK+H2a64cPf3zawVOBS4FdYP4R8KXBxl2wotHTwcCUx291VRtyciLcCnex37A0HHeW4ZBQ8QrMDo\nPY1XDTwTQVuitgUbf6rupPTy3jLm7ivN7EWCv5WPwvokyv0JcojKSrfgYTeCLSayXq0UuwAiU+n1\nqncRbOt9OrBd1wcwdy/1T+K/Aa5JBxJLCJawbkHQcZQFM7scSACfB94ys64RmbXuXjbbvKc3puuR\n92FmbwGr3b33p/FSdwFwr5mdAfyRoGP4BsHS1nLzd+D7ZvYs8ARQQ/B34spIWxUyM9sSGEcw0gCw\nWzqBtN3dnyWY7jvLzJYT9B0/Bf5NlssXi8FA9wJ4HvgTwdTWfwCbdPsb2p7pdGjRLuM0s68CvfMd\njGBRQkUETSooMzuJIHDanmCt76nu/mC0rSqc9LKkvn55v+bu1xa6PXFiZncCD5fbMk4AM2sgSAYb\nB6wEfl2OeVHpzuOnBOv7tyPoMG4AfuruH0TZtjCZ2WTg/9j4b8M17v719Dk/Ar5JsOpgEXCyuy8v\nZDsLYaB7QVD/YWWv57pyQdZveDnoaxRrACEiIiLRKdv5MBERERk6BRAiIiKSNQUQIiIikjUFECIi\nIpI1BRAiIiKSNQUQIiIikjUFECIiIpI1BRAiIiKSNQUQIiIikjUFECIiIpI1BRAiIiKStf8PHNmL\nkWwW4HwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f38add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.scatter(Xt, yt)\n",
    "ax.plot(Xtest, ytest, c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# succès!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

NeuroWednesday.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sknn.mlp import Regressor, Classifier, Layer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# on génère 2 distributions gaussiennes, avec labels respectifs 0 et 1\n",
    "nsamples = 100\n",
    "X1 = np.random.randn(nsamples, 1)\n",
    "X2 = 2*np.random.randn(nsamples, 1)+3\n",
    "y1 = np.zeros((nsamples,1))\n",
    "y2 = np.ones((nsamples,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([  0.,   3.,  22.,  45.,  25.,   5.,   0.,   0.,   0.,   0.]),\n",
       "  array([  1.,   1.,   2.,  10.,  18.,  18.,  19.,  13.,  11.,   7.])],\n",
       " array([-4.40053734, -3.16480556, -1.92907377, -0.69334199,  0.54238979,\n",
       "         1.77812157,  3.01385336,  4.24958514,  5.48531692,  6.7210487 ,\n",
       "         7.95678049]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Aj7YfkiRpAfJeAJIkFaib0wAlSV2ampqi1Wp11cfw8DDLly/vUUUqhQFAkgZkamqKFS9b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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fce2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist((X1, X2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# on construit le dataset en incluant tous les exemples\n",
    "Xt = np.vstack((X1,X2))\n",
    "yt = np.vstack((y1,y2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# création de l'architecture\n",
    "nn = Classifier(\n",
    "    layers=[\n",
    "        Layer(\"Sigmoid\", units=100),\n",
    "        Layer(\"Softmax\")],\n",
    "    learning_rate=0.001,\n",
    "    n_iter=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# training\n",
    "nn.fit(Xt, yt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(50, 2)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1113ba3c8>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Aa8ATgYeKz4oPu1fIuJTKaVzS6ovR9bN1CJiBXA7xSfe88ssrIVqFMcBj5my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\nJElSNgOEJEnKZoCQJEnZDBCSJCnb/wcEVNsJTcLiFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111210d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# on feed des données jamais vues au \n",
    "ntest = 50\n",
    "Xtest = np.linspace(-5, 10, num=ntest).reshape((ntest,1))\n",
    "ytest = nn.predict(Xtest)\n",
    "\n",
    "result = np.hstack((Xtest, ytest))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.hist((X1, X2))\n",
    "ax.scatter(Xtest, np.multiply(50*np.ones((ntest,1)),ytest)-5, c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Régression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x111520588>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MdXG+IxAlyPvJsoy9HNYb7fixHxs772n0oosuWfZ1cU/P59r0BkcgSlLHNkl1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\nktQ1AwhJktS1/x8Kk58BGFo+HQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11136cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nsamples = 100\n",
    "Xt = np.linspace(0,10, num=nsamples).reshape(nsamples,1)\n",
    "# la fonction à retrouver est y = x * 2\n",
    "yt = Xt * 2 \n",
    "# on rajoute du bruit\n",
    "yt = yt +np.random.randn(100,1) * 3\n",
    "plt.scatter(Xt, yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nn = Regressor(\n",
    "    layers=[\n",
    "        Layer(\"Sigmoid\", units=100),\n",
    "        Layer(\"Linear\")],\n",
    "    learning_rate=0.02,\n",
    "    n_iter=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nn.fit(Xt, yt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtest = np.linspace(0,10, num=nsamples).reshape(nsamples,1)\n",
    "ytest = nn.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10fb29cf8>]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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szHacxra8wmjaeaODvP9ulSsFECISmUymFfL5Rz6qTjeM1R/FtAlV1u+/sxNeeQWeew6e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\nkWwW4HwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f38add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.scatter(Xt, yt)\n",
    "ax.plot(Xtest, ytest, c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# succès!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}