{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/EscUpmPolit_p.gif \"UPM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Course Notes for Learning Intelligent Systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Óscar Araque"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Introduction to Machine Learning III](2_4_0_Intro_NN.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MultiLayer Perceptron (MLP) applied to Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we are going to apply a MLP to a simple regression task: learning the Fresnel functions.\n",
    "\n",
    "\n",
    "Answer directly in your copy of the exercise and submit it as a moodle task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-27T19:44:45.989818",
     "start_time": "2017-03-27T19:44:45.624698"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Show plots in the notebooks\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-27T19:44:47.582964",
     "start_time": "2017-03-27T19:44:46.844267"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "# function generator\n",
    "from scipy.special import fresnel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change this variables to change the train and test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-27T19:44:49.811655",
     "start_time": "2017-03-27T19:44:49.805379"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# variables to change\n",
    "\n",
    "n_examples = 500\n",
    "x_min = 0\n",
    "x_max = 3\n",
    "\n",
    "fresnel_func = 0 # can be 0,1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-27T19:45:21.738411",
     "start_time": "2017-03-27T19:45:21.489043"
    }
   },
   "outputs": [
    {
     "data": {
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RFVXUtWHvmXL4eqrw+Joomzu0wsNVgW2ro2EwmvDO/sswmYShf4mIxgQDmshK+gxG/Hlf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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad88a8aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate dataset (do not change)\n",
    "x = np.linspace(x_min, x_max, num=n_examples)\n",
    "y = fresnel(x)[fresnel_func]\n",
    "\n",
    "X = x.reshape(-1,1)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.title('Fresnel S function')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make the MLP learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-27T19:45:33.201217",
     "start_time": "2017-03-27T19:45:33.184488"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# do not change\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "# split the dataset\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the parameters of the MLPRegressor to generate a network that learns the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-27T19:44:57.970215",
     "start_time": "2017-03-27T19:44:57.916064"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHANGE THIS CODE TO MAKE THE MLP LEARN\n",
    "\n",
    "# train the network\n",
    "mlp = MLPRegressor()\n",
    "mlp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this next cell, we can see how the network approximates the original function. \n",
    "\n",
    "The R score (a regression metric) must be between 0.95 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-27T19:45:44.523305",
     "start_time": "2017-03-27T19:45:44.221047"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KU2mQ/fUQuLPPzDZbLHjvWAU+OF6B3j4LlL5yJEYFw9dXjsYWEwpL9Sgs1WNWZBB23JoC\nrVbtvEaSJDBxE7lA/sUGlFa3YVGSFskxoa4OZ8xkgoB/uCEBv3rzNN4+Wo6d2+a7OqQpYegxYlf+\nb2zbuwae5OXMM7MNpl78v/3ncKmqFaFqJbasisOyFB18B+w0qG404J2jV3DyUiP+/U/5+Nd7F2OW\nLtAp70/SwDluoinW22fGXw+VQS4TsPXGeFeHM24psaGYHROCovJmlFZfX1zEE+0tyb1uT7azT/Jq\nM3Tjv94owKWqVixK0uI/dizDqrmRg5I2AERrA/HQHen4we2pgAD85ysncKzomlNjIffmcOLetWsX\nsrOzcdddd+HcuXODrtXV1eHuu+/Gtm3b8POf/3yiMRJ5lIP5VdC3dSFrcTQiQv1dHY5DtqyaBQDI\n/aLcxZFMHuu+7P/K/x0uNF1/SpozT/Lq6unDc2+dQY3eiKxF0XjwjjT4+408ILo0RYd/vWsh/P18\n8Mf3L6Lwst5p8ZB7cyhx5+fno6KiAjk5OfjlL3+JZ555ZtD13bt3Y8eOHXjrrbcgl8tRV1fnlGCJ\npM5g6sUHxysQqPLB5hUzXR2Ow5JmhCA1ToMLFS24VNni6nAmxcAKaEOP4QxRBjttT7bFIuLFd4pR\n2WDAmvmRuCsrccxrHmZFBuHp7y2HQiHghXfOo6bR4JSYyL05lLjz8vKQlZUFAIiPj0d7ezuMRiOA\n/kU3p06dwtq1awEAP/vZzzBt2jQnhUskbR9+VQFTtxm3ZsRK/rStLaviAAC5X1xxcSTOM1IvW6VQ\nIUYdjYURc/H4kkedtjc794tyFJbqMWdmKO65KWncK8Vnz9Tge7fOQXevGc/vPwdTd59T4iL35VDi\n1uv10Gi+GSYKDQ2FXt8/TNPc3Ax/f38888wzuPvuu/Hss886J1IiiWszdOPTk9UICfTFjQuiXB3O\nhMVHBiN9VhhKqlpRUtXq6nCcYqRedoomET9d8ohTS5aWVLXig7wKaEP88OCWNCjkjs1eLp4dgZuX\nxaC+xYS3Pi91SmzkvhxaVT60CIAoirZviaIooqGhAffddx8iIyPxwAMP4PDhw1izZs2or+vp2xrY\nPulyRtv2H72Cnj4LvrdhNqIiQ5wQlfM42r7tG1Pw0+eP4pOCGmQunOHkqJxnpPZ1dBnwUsGbqDc2\nob6jcdC1AB8VdOoI6ALC8L1Fd0GtdN7q7c6uXvzxw4sQBOBfti/BzBmOz5lrtWo88K15uFjZisOF\ntVi7NBYLkyOcFqurefJniyMcStw6nc7WwwaAhoYGhIeHA+jvfUdFRSE6OhoAkJGRgdLS0jEl7sbG\nDkfCkQStVs32SZQz2qZvM+HDY1ehDfHD/Fkat/pvNZH2aQN9kRQdjJMX6nGqqBYxOvf7gB2tfS8X\nvW63ZCkAJIcm2rZ6dbWL6ILz7tufP7qIhuZObFoRi/BAH8fvwYD2fWdDMn756kn8NqcA/7FjGVRK\n6e/49fTPFkc4NC6TmZmJAwcOAACKi4uh0+ng79+/OlYulyM6OhqVlZUAgPPnzyMuLs6h4Ig8xTtf\nXoXZIuL2lXEOD4e6q1u/XmT3wfEK1wbioKHbugbOZU/WoSBlNW04XFiLKG0Abst03udj7DQ1Ni6P\nRXN7N947dtVpr0vuxaGvYwsWLEBqaiqys7Mhl8vx1FNPITc3F2q1GllZWXjiiSfw2GOPQRRFJCUl\n2RaqEXmj+uZOHDtXh8jwACyf43kLNdPiNIjRBSL/YgPuWNUJnca9t7gNrYAWrAzCwI50iibRaQVV\n7LFYRLx+sAQAcO/6ZKd/kbs1IxbHiupwML8Kq+dFuv39oPFzeBxl586dgx4nJyfb/j0mJgZ/+ctf\nHI+KyIO8n1cBi9jf25bJpFHadDwEQcCtGTPxv28X4cOvKnDfLSmuDmlEA0/zquyoRnrYHCyMmDuo\nZOlkOlxYg4r6DmSkTkPSDOevdfD1kePOtQn4/dtF2PtZKR75h7lOfw9yLelPgBC5MX2rCXnn6zA9\nzB+LkrWuDmfSLErSQqfxx5fn6nBbZhw0QX6uDsmmo8uAl4tet/WwG42DC5W09bTjp0semZJYTN19\nyP3iCvx85dg2iVXzFiVrkTwjBIWlely42uy257yTYzxrso3IzXzwVSXMFhGbMmZK5iARR8hkAm5Z\nFgOzRcSnp6pH/4UpYOgx4oWzr+B7f/8X2xav0w1n0d4zeKGTMyugjebAiUoYTL24ZVkMggOVk/Y+\ngiDgznUJAID9R8p5HKiHYeImmiQtHd04erYWESEqLJ3jOVtzhpORqkOQvw8OFda6vAiIoceIZ756\nFmf1xRiasnotvVgYMXfSF6AN1WboxoETVQgO8MX6JTGT/n4zpwVhUZIWZbXtOFPWNOnvR1OHQ+VE\nk+TD4xXoM4vYmBELuczzvyP7KORYuzAabx+9gqPnruGmxa7Z1209yau9d5gtRIIwqYvPhvPusavo\n7jXjzrUJUPrKR/8FJ9iyKg4FJY3IPVKOufFhHj3q4008/9OEyAXajD04fKYWYUFKrEjzvJXkw7lh\nYRR8FDJ8nF8Fi8U1w7P2TvIaKDFk6rentnR048iZWmhD/LBy7vQpe98obSCWp+pQ1WBAwaXG0X+B\nJIE9bqJJcOBEJXr7LLhleazH7dseSZC/L1akTcPhwloUlDRi8ezJnyIYur1r6OIzKxlkSAufjXtm\nb530mIayjr5sypg55X8Pm1bMxPHz9Xj/eAUWJWvHXQud3A8TN5GTGUy9+LygBsGBvlg1hb0rd7F+\nyQwcLqzFgfzKSU/chh4j/vPEs2j7esFZZUc1gn0HV6OSC3LM0SRje8pWp9UYH49WQ/fXoy9+yHDB\n6Mv0sAAsStbi5KVGFF9tQWocV5hLHRM3kZMdOl2D7l4zbl8ZBx/F1MxlupPpYQGYFx+GM2VNKK1p\nQ0JU8KS8T72xEbtOPIdecfBCuK6+nkH7sh9ecS+62l23qvqjr/pHX27NcN3oy60ZM3HyUiPez7vK\nxO0BmLiJnKi3z4JPT1XDz1eONfMjXR2Oy6xfGoMzZU04eKISCXekO/W1rUPjhQ1FsMBy3XWZIBu0\n+EytDHRqjfHxaDf24NDpGoSqlchMd93oS+w0NdLiNCi60jypX6ZoanjP5BvRFDhxoR5txh6snhfp\nEQc8OGp2TAhidIE4VdKIxlbT6L8wRtYV4wUNZ+0mbQBIcMHis+F8cqoKPX0WbFweCx+Faz9ub82I\nBQB8kCfNmvL0DSZuIicRRREH86sgCEDW4mhXh+NSgiBgw5IYiCLw8ckqp73uaCvG08PmYHvK1C8+\ns6e7x4zPC2oQqPKZ0pXkw0maEYL4qCAUlupxrcno6nBoApi4iZzkYkULqhoMWJQcgfBglavDcbkl\nKREIVSvxxZlr6Ozqdfh1DD1GvFz0Ov4r/3e40HT5uusCBIQog/HUsn/FD+bd55IFaPYcPXcNxq4+\nrF0YBaWP69c6WL9MAcDHJ92juh05hombyEkO5vf3LDcscU3hEXejkMuwblE0unvNOHLmmsOvYz0U\npLKjGibz4GH3EGUwdq98Cs9kPgldQPhEQ3Yai0XEwfxKKOQyrF3oPqMvC5O0CA/2w7Fz12AwOf5l\nilyLiZvICa41GXGmrAnxUUGI58IfmzXzI+HrI8Onp6pgttifkx7NSOdlP77kUbfpYQ9UUNKIxtYu\nZKZPQ1CAr6vDsZHJBGQtnoGePgsOna5xdTjkICZuIiewDj1umIIa1FIS4OeDzPTpaGrvxqkxVO4a\nOCz+UtHrMPQYETbkEJAUTSJ+uuQR7Ejb7pZJWxRFfHSiEkD/nnZ3s2rudPj5yvFpQTX6zI59mSLX\n8t5lr0RO0tHZg2PnriE82A8LktxnuNZd3LR4Bj4vqMHB/CosTdHZfY51i9eF5ssw9fUPh1d2VEMA\nkJ10BwRgys7LnqjSmjaU17ZjfkI4poe53xcLlVKB1fMicTC/Cicu1GNFmusXztH4MHETTdChwlr0\n9FmQtXiGVxwmMl7TNP6YnxCOwlL9sHuIrfPYQ+lNzQj0DXDJoSCOOnCif63Dzcvcd/Qla1E0Pj5Z\nhYMnqpCROo1lUCWGnzJEE9DbZ8FnXxdc8cbypmN109dDxtYFfEMNnce2msqzsp2hvrkTp0saETc9\nCInR7rvWITxEhUVJWlQ2GHCpstXV4dA4MXETTQALrozN7JgQzIgIxKlLDdDbKcgydB5bJVdN6VnZ\nzvLxySqIADYsneH2vdj1S/tHBIb7MkXui580RA5iwZWxEwQB65fMwMvvX8Anp6qRvS5x0HV789ju\nuPBsJMauXhw9dw1hQUosSta6OpxRJUQFY1ZkEM6U6lHX3IlpGn9Xh0RjxB43kYOsBVcWs+DKqAw9\nRhSLn0Lu24tPT1dAb2gfdN06j+3Oq8VHc6SwFj29FqxbJJ21DuuXzIAI51a3cyZjV6/LznV3Z9L4\n6yJyQwe+HmJcv9T9tvy4m70luSjUn4WgvQJznwz/+9mnrg7JqfrMFnxyqhpKHzlWz5POWodFyVqE\nBSnx5Vn3K8hS1WDAj//nKD46ftXVobgdJm4iB1xrMuKsteBKpPsuQnIX1sVniogqQDCjulzlUT2p\ngpJGtHR0Y2X6dPj7+bg6nDGTy2S2giyHC92rIMtHX1WizywiIpRD+EMxcRM5gAVXxse6+Ezw6YU8\nvBa9Xb44fXn0gixScTC/CgKkudZh1dxI+PnK8ckp9ynI0mroxokL9Zge5o+FyRGuDsftMHETjRML\nrgzPXuUzoH/x2cKIuYhRR2PuHCUAz1nNXPZ1wZV5CeHQSXCBl79ff0GWNkMPTlyod3U4AIDPCqph\ntoi4afEMyGTuvTrfFbiqnGicWHDlerbKZ00lMJm7APRXPjNb+vD9ufddV0Tl2apCFJU348q1dsRN\nD3JV2E5h/QJykxuWNx0rdyrI0tNrxqHTtQjwUyAjbZrL4nBn/NQhGgdrwRWVkgVXBnrj4j4UNJy1\nJW2ry61X7D7fdrykxHvdTW1dOHWpEdHaQMyOCXF1OA4LD1FhUXIEKhsMuOjigizHztfBYOrFjW5y\nHKo7YuImGgcWXLFvuASNYdafzZkZiihtAPIvNqC5vcv+kyTg04JqWEQR65e4f8GV0ViPo3XllymL\nKOLj/CrIZYJbHYfqbvjJQzRGAwuurFvk3R8q1qFxvakZYSoNLMMsakoIibP7c0EQcNPiGXjlw4v4\ntKAaW29ImMxwJ0VXTx8OF9YiyN8Hy+ZIfwFVfFQw4qOCUOjCgixF5c241tSJjNRpCAlUTvn7SwV7\n3ERjxIIr37AeClLZUY3TDWehVAw+c1opKLEwYi62p2wd9jUyUnVQ+/vgSGEtunvMkx2y0315rg6m\n7j7cuDAaPgrPGNK1TmEc/PpY0qn20VcVANzzOFR3wh430Rix4Mo3Pe1z+guDfh7kq0ZCaNy4Spb6\nKOS4cUEU3vnyKo6euyapUQyLKOKTk1VQyAXcsCDK1eE4zYKkcESEqHD03DVszoxDqHrqer1lNW24\nWNmK1DgNYqepp+x9pYiJm2gMrAVXEqKCvbLgSr2xEb8rfBFt3e0Q7UxcRwSEO3T05o0Lo/HB8Uoc\nzK/EDQsiJbNK/2xpE+pbTFiZPh3BAb6j/4JEyGUybMyIxSsfXsSHX1Xg7qykKXvv9/P6e9u3Lo+d\nsveUKmn8X0LkYtaCK942hGfdl/3Lr/agtbvtuqTtI/OZ0ClewQG+WDV3Ohpbu3DiQoMzQp4SH3rw\nkO6KtGkIC1LiSGEt2ow9U/Ke1Y0GFJbqER8VhGQJr86fKkzcRKNoM3TbCq4sTHL/U5+cxdBjxK78\n36Cg4SwssL/4LD08ZcKHgty8LAYyQcAHeRWwiO5fBrW0ug2Xq9swNz4M0RGBrg7H6RRyGW5ZHoue\nPgsO5k/NXPcHx6297ZmSX50/FZi4iUbx0fGrtoIr3lTFaW9JLlq72+xek0HmtPOytSEqLJujQ43e\niDOX9RN+vclmTTK3LPPccrer5vZPAXxWUDPph480tJpworgBUdoAzE0Im9T38hRM3EQj6O2z4P2j\nV7yy4Ir1YJCBBAgIUQbj35b9s1OP37w1IxYCgPfyKiC6ca+7xjqkGxmEpBmeO6Tro5DjlmUx6O4x\n48Ovv6j147USAAAgAElEQVRMlr9/cQUWUcSmjJmQsbc9JlycRjSCExfq0dLRjQ1LZ3h0wZWh+7Kz\nk+5AmEqDyo5q23NClMF4fMmjk3JWdmR4ABYmaXGqpBEXKlowZ6bG6e/hDB991T90vHF5rMcP6d6w\nIAoH8qvw8clqrFsUDU2Qn9Pfo7rRgOPn6xCtDcSSFOnvhZ8q7HETDcNacEUmEyS1VckRQ/dl7y3J\nHXQwyMKIuZOWtK02ZvSvJn7v2NVJe4+JaG7vwvHi/hOr5iV6/uEyvj5ybFkVhz6zBW8fHaYy3gTl\nHimHCOBba2axtz0OntuFIJoga8GVVfOjPLLgirWX3drXhmvtg1d0603N1x0MMtnipgchNU6D81ea\ncamyBckxoVP23mNxML8KZotoW0znDTLTpuPgiSp8ee4a1i+ZgWit8xbjlVa34fRlPRKigjEvnnPb\n48EeN9EwrAVXbl89y8WRON/AFePlzRUw9ZkGXQ9XuWaoesuq/hKp+4+Uu9Vcd5uxB4dO1yBUrURG\nqvecWCWTCdh6YzxEEXj9wCWn3ROLRcTrBy8BALbeGO/x0w7OxsRNZMfAgivJse453zoR9laMq+Qq\n27C4M1aLOyI+MhjzE8JxuboNRVeuXxznKh/kVaCnz4JNK2ZCIfeuj8258eFYmKRFSXUbvjxX55TX\n/Px0DSobDFiRNg2J0Z67yG+yeNdfINEYWU9I8sQCG4D9FeMpYYn46ZJHnLpa3BHu1utu6ejG56dr\nEBak9LqdBVZ3ZyVC6SPHW5+XoqNzYkVZ2ow9yD1SDpVSjq03Su9wGXfgcOLetWsXsrOzcdddd+Hc\nuXN2n7Nnzx7ce++9DgdH5AodnT34sqjOowuuhA0ZCg9RBruslz1UjE6NpSkRqKjrQEGJ6/d1f5BX\ngT6zBZsz47yut22lCfLD7SvjYDD14pUPLzr8hUoURfz5w4vo7O7DHatmeVS52Knk0OK0/Px8VFRU\nICcnB2VlZXjyySeRk5Mz6DllZWU4efIkfHx8nBIo0VQ5VFiL3j4LbpJ4wRVDjxFvXNzXf1a2CMSH\nxOHelK0I9A1AdtIdENDf844KicCW2M0u7WUPdfvKOORfbMDfDpdhXkKYyxJmc3sXDp+pgTbEDyvS\nvGdu2571S2bgbJkepy/r8empamQtHv9o1OEztSgs1WN2TAjWevhOjcnk0P8NeXl5yMrKAgDEx8ej\nvb0dRqNx0HN2796NnTt3TjxCoinU22fBZ6eqoVLKsVLiw6J7S3JxVl8MU58JJrMJRU3F2FuSCwC2\nFeM/XfIIfrzifrdK2gAwPSwAN8yPQl1zJz4vqHFZHG8fvYI+s4jNK7y3t20lkwm4f3Mq1P4+eOvz\nUpTV2q+qN5yymjb85eMSBPgpsOPWOV6zMn8yOPSXqNfrodF8M9QWGhoKvf6bIa3c3FwsW7YMkZGR\nE4+QaAqduFCPNmMPVs+LlHzBFXvz2PZ+5q62rIqDv1KBvx+9MuF5VUdU1nfgy7PXEK0N8PretlWo\nWon7N8+B2SLit/vO4lqTcfRfQv9iz//521mYLSJ+sCUNYcHOL+biTRz6ZBo6vyGKom05f1tbG/bv\n349XXnkF165dG9dciFbr2Wewsn3uTRRFfFpQA5lMwLabZkOr8bddk0LbOroMeKngTdQbmxAREAat\nOnRQ5TMAiAqJsNsWd2yfFsDdN8/GS38vwkcnq/HQt+c5/lrjbJ8oivjt385CBHD/HXOh0wU5/N5T\nYSrv341aNXpFAf/zViF+9WYhnvreMiTOGH7PfXlNG/bsLUR7Zy8e+vZc3LBk/Md2uuPfpys5lLh1\nOt2gHnZDQwPCw/srCR0/fhwtLS2455570N3djaqqKuzevRuPPfbYqK/b2NjhSDiSoNWq2T43V3y1\nGVevtWNpSgQEs9nWHim0zbov27rFq7y5AnPD52Bu+BzbHHdCSBy2xG6+ri3u3L6lSeF4P8wfH+Vd\nxfLZEZjhwGlcjrSvsFSPM5f1SJulwQyNym3/+wCuuX8LZmlw7/okvP5xCX76/FFsWRWHmxbPGDSd\n0Ge24NDpGvz1UBl6+iy4a10iFieGjztWd/77nChHv5A4lLgzMzPx/PPPY9u2bSguLoZOp4O/f3/v\nZMOGDdiwYQMAoKamBo8//viYkjaRqx20bQGTzqlP1upnF5ovX1dEpbW7HT9d8oiLInMOhVyG7HWJ\neO6tM3jlw4t48t5Fk75gsLvHjDcOlkAuE3AntysN68aF0QgN8sOfPriAfZ+X4cCJKsxPCIcmSIlW\nQw/OlOrR0tENlVKBR25Pw3wvKBM7VRxK3AsWLEBqaiqys7Mhl8vx1FNPITc3F2q12rZojUhKbAVX\nooMxK9K9h0UHstYYt8dV1c+cLX1WGJbP0eF4cT0+OVmF9Usn94vV37+8gqb2LmxcHosoJ5b49ETz\nE8LxzP3L8X7eVXxx5hqOnKm1XVMpFVi3MBqbM2ciiNu+nMrh1TdDV4wnJydf95yoqCi8+uqrjr4F\n0ZSxFVxxYIvLVBt4kldjZ5Pd57jTvmxnyM5KRNGVZuw/Uo75ieGICPUf/ZccUFnfgYMnqhAe7IfN\nmTMn5T08TaDKB3euTcS318TjWlMn2ozdCFT5IFob6PUr8SeLtJfNEjlBu0QKrtiGxZsuw2Q22X2O\nSq5CSlgi7ky6w+22eE1EkL8v7r4pES++U4wX3inG49sXOj0p9PSa8X/vFcMiivjHDclQ+sid+vqe\nTiGXYUZEIGaAoxSTjYmbvN7nBTXo7bNg/RL3Lrgy3LC4SqGCVhWGcJXG4xL2QMtSdDhX1oS88/XY\nf7gc29Y6d/553+dlqGk04saFUUibxdOqyH0xcZNX6+k147OCavgrFW5fcGW4PdgpmsQpPX7TVQRB\nwPb1ySi/1oGPTlQiMToYC5w0QlJQ0ohPC6oRFR7ABWnk9jgBQV7t2Pk6dHT24saFUfDzde/vsUPr\ni6sUKpee5OUKKqUCD96eCl8fGV549zyuXGuf8GtW1HXgxXfPw1chw/dvS4Uvh8jJzbn3JxXRJLKI\nIg6eqIJcJmDtQvepmzxw8VmYSoPsr4e/B9YX9/Rh8ZHE6NT4wW1p+J/9Z/HbfWfwxL2LHF6s1tze\nhd/89Qx6ey14+FvpiHZgnzjRVGPiJq91trQJdc2dyEyfhlC10tXh2A4FKWq6CItoAQBUdlRDAGxH\nbXrDkPhYzE8Mx91ZSXjj4xLser0AO++cP+7iLPo2E3715mm0GXpw59oEt16YSDQQh8rJax04UQkA\n2OAmBVesh4JYk7aVlOqLT6V1i6JxV1Yi2ow9+K83CnD+ytj/O5XVtuE/XzuFxtYubF4x02PPXSfP\nxB43eaUr19pxqaoVqXEalw+PWofGz+kv2L3uKYVUJsNNi2cgUOWDP75/Ac/uLcS6xdHYsjIO/n72\njxPu7TPjw+OVeC/vKswWEXeuTcCGSS7oQuRsTNzklay97Ztd/KE9tMb4UJ5WSGUyZKROwzSNP154\n5zw+OVmNvKI6rEibjrkJYZiu8YdC6YPL1a0oKm/GkTO1aDP2IFStxD9tTEFqHL8UkfQwcZPX0beZ\ncPJiI6K1gZgzc/hTjabC3pL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fadb8f2ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R score: 0.226100740276\n"
     ]
    }
   ],
   "source": [
    "# do not change\n",
    "\n",
    "# predict from test\n",
    "preds = mlp.predict(X_test)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.scatter(X_test, preds, color='g')\n",
    "plt.show()\n",
    "\n",
    "print('R score:',r2_score(y_test, preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [MLP documentation](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Licence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
    "\n",
    "© Óscar Araque, Universidad Politécnica de Madrid."
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
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  "latex_envs": {
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