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   "source": [
    "# Problema 4 - Treinando para a função seno\n",
    "\n",
    "* Minicurso Machine Learning -- Hands on com Python\n",
    "* Samsung Ocean Manaus\n",
    "* Facilitadora: Elloá B. Guedes\n",
    "* Repositório: http://bit.ly/mlpython\n",
    "* Nome:\n",
    "* Email:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0,2*np.pi,361)\n",
    "y = np.around(np.sin(x),3)\n",
    "\n",
    "for z,w in zip(x,y):\n",
    "    print(z,w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.title(\"Função seno\")\n",
    "plt.plot(x, y, '--')\n",
    "plt.xlim([0, 2*np.pi])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rede = MLPRegressor(hidden_layer_sizes=, activation=, solver='lbfgs', alpha=0.001)\n",
    "x = x.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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