Compare_BW_BW_training_size_10000.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pysindy as ps\n",
    "from scipy.integrate import odeint\n",
    "from math import pi\n",
    "from pysindy.differentiation import FiniteDifference\n",
    "fd = FiniteDifference(order=2, d=1)\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import glob\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "np.random.seed(29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.25, -1.  , -0.75, -0.5 , -0.25,  0.  ,  0.25,  0.5 ,  0.75,\n",
       "         1.  ,  1.25]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid(linestyle='dotted')\n",
    "t = np.linspace(0,20*pi,10000)\n",
    "x = np.sin(t)\n",
    "plt.plot(t, x)\n",
    "plt.xlabel('Time', fontsize = 20)\n",
    "plt.ylabel('Voltage', fontsize = 20)\n",
    "#plt.legend(['Actual function' , 'Learned function', 'Training Data'], prop={'size': 16})\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "#plt.savefig(\"Results/test2_input.png\", dpi = 300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f82d52a1a10>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dx = fd._differentiate(x, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f82d5239ad0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,dx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdx = np.abs(dx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f82d514e450>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, mdx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(y, t):\n",
    "    dydt = np.cos(t) - 0.5*np.abs(np.cos(t))*y - 2*np.cos(t)*np.abs(y)\n",
    "    return dydt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y0 = 0\n",
    "y = odeint(model, y0, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f82d50d7790>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f82d5061e90>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f82d4f80210>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dy = fd._differentiate(y, t)\n",
    "plt.plot(t, dy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "my = np.abs(y)\n",
    "my = my.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f82d4f0b610>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = y.reshape(-1,)\n",
    "dy = dy.reshape(-1,)\n",
    "t1 = dx\n",
    "t2 = mdx*y\n",
    "t3 = dx*my\n",
    "terms = t1-0.5*t2-2*t3\n",
    "plt.plot(t,terms)\n",
    "plt.plot(t, dy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.stack((y, x, dx, mdx, my), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(x0)' = 1.000 x2 + -0.500 x0 x3 + -2.000 x2 x4\n",
      "(x1)' = 1.000 x2\n",
      "(x2)' = -1.000 x1\n",
      "(x3)' = -7070.300 1 + -16.165 x0 + 7.126 x1 + 11.898 x3 + 25.239 x4 + -40.465 x0^2 + 73.788 x0 x1 + 28.846 x0 x2 + 11.611 x0 x3 + -9.691 x0 x4 + 7043.867 x1^2 + -15.419 x1 x2 + -3.991 x1 x3 + 1.533 x1 x4 + 3528.113 x2^2 + 1.811 x2 x3 + 3528.113 x3^2 + -24.664 x3 x4 + -40.465 x4^2\n",
      "(x4)' = 464.245 1 + -10.294 x0 + 4.730 x1 + 0.728 x2 + 11.811 x3 + 25.428 x4 + -6.491 x0^2 + 7.726 x0 x1 + 6.401 x0 x2 + 7.089 x0 x3 + -475.325 x1^2 + -2.848 x1 x2 + -2.524 x1 x3 + -1.898 x1 x4 + -238.166 x2^2 + 0.459 x2 x3 + -1.325 x2 x4 + -238.166 x3^2 + -25.645 x3 x4 + -6.491 x4^2\n"
     ]
    }
   ],
   "source": [
    "model = ps.SINDy()\n",
    "model.fit(X,t)\n",
    "model.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kkk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = 1\n",
    "c2 = -0.5\n",
    "c3 = -2\n",
    "def test_model(y, t):\n",
    "    dydt = c1*np.cos(t) + c2*np.abs(np.cos(t))*y + c3*np.cos(t)*np.abs(y)\n",
    "    return dydt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytest_0 = 0\n",
    "y_test = odeint(test_model, ytest_0, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.5, -0.4, -0.3, -0.2, -0.1,  0. ,  0.1,  0.2,  0.3,  0.4,  0.5]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid(linestyle='dotted')\n",
    "plt.plot(x, y, 'r')\n",
    "plt.plot(x, y_test, linewidth=2, linestyle=':')\n",
    "plt.xlabel('Voltage', fontsize = 20)\n",
    "plt.ylabel('Displacement', fontsize = 20)\n",
    "plt.legend(['Ground truth' , 'Learned relation'], loc='upper left', prop={'size': 12})\n",
    "plt.xticks(fontsize=20, rotation='45')\n",
    "plt.yticks(fontsize=20)\n",
    "#plt.savefig(\"Results/test2_inputvsOutput.png\", dpi = 300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.25, -1.  , -0.75, -0.5 , -0.25,  0.  ,  0.25,  0.5 ,  0.75,\n",
       "         1.  ,  1.25]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = c1*t1+c2*t2+c3*t3\n",
    "plt.plot(t,dy, 'b')\n",
    "plt.plot(t,test, '--r')\n",
    "plt.grid(linestyle='dotted')\n",
    "plt.xlabel('Time', fontsize = 20)\n",
    "plt.ylabel('Time derivative of disp.', fontsize = 18)\n",
    "plt.legend(['Ground truth' , 'Learned dynamics'], prop={'size': 12})\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "#plt.savefig(\"Results/test2_Output.jpg\", dpi = 300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test SINDy:  0.0 %\n"
     ]
    }
   ],
   "source": [
    "y = y.reshape(-1,1)\n",
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = np.mean((y_test - y)**2)/np.mean(y**2)\n",
    "print(\"Relative Error Test SINDy: \", relative_error_test*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1=t1.reshape(-1,1)\n",
    "t2=t2.reshape(-1,1)\n",
    "t3=t3.reshape(-1,1)\n",
    "Y = np.concatenate((t1, t2, t3), axis=1)\n",
    "Y.shape\n",
    "dy = dy.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(Y, dy, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.98643951 -0.5115245  -1.94002538]]\n",
      "[8.58801572e-05]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "clf = Ridge(alpha=1.0)\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.coef_)\n",
    "print(clf.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kkk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = 0.986\n",
    "c2 = -0.511\n",
    "c3 = -1.94\n",
    "def test_model(y, t):\n",
    "    dydt = c1*np.cos(t) + c2*np.abs(np.cos(t))*y + c3*np.cos(t)*np.abs(y)\n",
    "    return dydt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytest_0 = 0\n",
    "y_test = odeint(test_model, ytest_0, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test Ridge:  0.0010571522795876237 %\n"
     ]
    }
   ],
   "source": [
    "y = y.reshape(-1,1)\n",
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = np.mean((y_test - y)**2)/np.mean(y**2)\n",
    "print(\"Relative Error Test Ridge: \", relative_error_test*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linreg = LinearRegression().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.99993107, -0.50004947, -1.99970164]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.49262494e-06])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kkk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = 0.99\n",
    "c2 = -0.5\n",
    "c3 = -1.999\n",
    "def test_model(y, t):\n",
    "    dydt = c1*np.cos(t) + c2*np.abs(np.cos(t))*y + c3*np.cos(t)*np.abs(y)\n",
    "    return dydt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytest_0 = 0\n",
    "y_test = odeint(test_model, ytest_0, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test Lin reg:  0.009419503761515211 %\n"
     ]
    }
   ],
   "source": [
    "y = y.reshape(-1,1)\n",
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = np.mean((y_test - y)**2)/np.mean(y**2)\n",
    "print(\"Relative Error Test Lin reg: \", relative_error_test*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(Y, dy, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils\n",
    "import torch.utils.data\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Type of optimizer (ADAM or LBFGS)\n",
    "opt_type = \"LBFGS\"\n",
    "# Random Seed for dataset generation\n",
    "sampling_seed = 78\n",
    "torch.manual_seed(sampling_seed)\n",
    "\n",
    "# Number of training samples\n",
    "n_samples = 10000\n",
    "\n",
    "x = X_train\n",
    "y = y_train\n",
    "y = y.reshape(-1,1)\n",
    "\n",
    "x = x.astype(np.float32)\n",
    "y = y.astype(np.float32)\n",
    "\n",
    "x = torch.from_numpy(x)\n",
    "y = torch.from_numpy(y)\n",
    "\n",
    "batch_size = n_samples\n",
    "training_set = DataLoader(torch.utils.data.TensorDataset(x, y), batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNet(nn.Module):\n",
    "\n",
    "    def __init__(self, input_dimension, output_dimension, n_hidden_layers, neurons):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        # Number of input dimensions n\n",
    "        self.input_dimension = input_dimension\n",
    "        # Number of output dimensions m\n",
    "        self.output_dimension = output_dimension\n",
    "        # Number of neurons per layer \n",
    "        self.neurons = neurons\n",
    "        # Number of hidden layers \n",
    "        self.n_hidden_layers = n_hidden_layers\n",
    "        # Activation function \n",
    "        self.activation = nn.Tanh()\n",
    "        \n",
    "        self.input_layer = nn.Linear(self.input_dimension, self.neurons)\n",
    "        self.hidden_layers = nn.ModuleList([nn.Linear(self.neurons, self.neurons) for _ in range(n_hidden_layers)])\n",
    "        self.output_layer = nn.Linear(self.neurons, self.output_dimension)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # The forward function performs the set of affine and non-linear transformations defining the network \n",
    "        # (see equation above)\n",
    "        x = self.activation(self.input_layer(x))\n",
    "        for k, l in enumerate(self.hidden_layers):\n",
    "            x = self.activation(l(x))\n",
    "        return self.output_layer(x)\n",
    "\n",
    "def NeuralNet_Seq(input_dimension, output_dimension, n_hidden_layers, neurons):\n",
    "    modules = list()\n",
    "    modules.append(nn.Linear(input_dimension, neurons))\n",
    "    modules.append(nn.Tanh())\n",
    "    for _ in range(n_hidden_layers):\n",
    "        modules.append(nn.Linear(neurons, neurons))\n",
    "        modules.append(nn.Tanh())\n",
    "    modules.append(nn.Linear(neurons, output_dimension))\n",
    "    model =  nn.Sequential(*modules)\n",
    "    return model\n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model definition\n",
    "my_network = NeuralNet(input_dimension=x.shape[1], output_dimension=y.shape[1], n_hidden_layers=4, neurons=20)\n",
    "# my_network = NeuralNet_Seq(input_dimension=x.shape[1], output_dimension=y.shape[1], n_hidden_layers=4, neurons=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.8615],\n",
      "        [ 0.6235],\n",
      "        [ 0.8096],\n",
      "        ...,\n",
      "        [ 0.7619],\n",
      "        [-0.4852],\n",
      "        [ 0.1229]], grad_fn=<AddmmBackward0>)\n"
     ]
    }
   ],
   "source": [
    "def init_xavier(model, retrain_seed):\n",
    "    torch.manual_seed(retrain_seed)\n",
    "    def init_weights(m):\n",
    "        if type(m) == nn.Linear and m.weight.requires_grad and m.bias.requires_grad:\n",
    "            g = nn.init.calculate_gain('tanh')\n",
    "            torch.nn.init.xavier_uniform_(m.weight, gain=g)\n",
    "            #torch.nn.init.xavier_normal_(m.weight, gain=g)\n",
    "            m.bias.data.fill_(0)\n",
    "    model.apply(init_weights)\n",
    "\n",
    "# Random Seed for weight initialization\n",
    "retrain = 128\n",
    "# Xavier weight initialization\n",
    "init_xavier(my_network, retrain)\n",
    "# Model definition\n",
    "\n",
    "# Predict network value of x\n",
    "print(my_network(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "if opt_type == \"ADAM\":\n",
    "    optimizer_ = optim.Adam(my_network.parameters(), lr=0.001)\n",
    "elif opt_type == \"LBFGS\":\n",
    "    optimizer_ = optim.LBFGS(my_network.parameters(), lr=0.1, max_iter=1, max_eval=50000, tolerance_change=1.0 * np.finfo(float).eps)\n",
    "else:\n",
    "    raise ValueError(\"Optimizer not recognized\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(model, training_set, num_epochs, optimizer, p, verbose=True):\n",
    "    history = list()\n",
    "    \n",
    "    # Loop over epochs\n",
    "    for epoch in range(num_epochs):\n",
    "        if verbose: print(\"################################ \", epoch, \" ################################\")\n",
    "\n",
    "        running_loss = list([0])\n",
    "        \n",
    "        # Loop over batches\n",
    "        for j, (x_train_, u_train_) in enumerate(training_set):\n",
    "            \n",
    "            def closure():\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "                # forward + backward + optimize\n",
    "                u_pred_ = model(x_train_)\n",
    "                # Item 1. below\n",
    "                loss = torch.mean((u_pred_.reshape(-1, ) - u_train_.reshape(-1, ))**p)\n",
    "                # Item 2. below\n",
    "                loss.backward()\n",
    "                # Compute average training loss over batches for the current epoch\n",
    "                running_loss[0] += loss.item()\n",
    "                return loss\n",
    "            \n",
    "            # Item 3. below\n",
    "            optimizer.step(closure=closure)\n",
    "            \n",
    "        print('Loss: ', (running_loss[0] / len(training_set)))\n",
    "        history.append(running_loss[0])\n",
    "\n",
    "    return history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################  0  ################################\n",
      "Loss:  0.9666993618011475\n",
      "################################  1  ################################\n",
      "Loss:  0.9202477335929871\n",
      "################################  2  ################################\n",
      "Loss:  0.7406691312789917\n",
      "################################  3  ################################\n",
      "Loss:  0.5712133049964905\n",
      "################################  4  ################################\n",
      "Loss:  0.4558830261230469\n",
      "################################  5  ################################\n",
      "Loss:  0.38598042726516724\n",
      "################################  6  ################################\n",
      "Loss:  0.33923614025115967\n",
      "################################  7  ################################\n",
      "Loss:  0.3046376407146454\n",
      "################################  8  ################################\n",
      "Loss:  0.27740341424942017\n",
      "################################  9  ################################\n",
      "Loss:  0.2550617456436157\n",
      "################################  10  ################################\n",
      "Loss:  0.2361314296722412\n",
      "################################  11  ################################\n",
      "Loss:  0.21963852643966675\n",
      "################################  12  ################################\n",
      "Loss:  0.2048988938331604\n",
      "################################  13  ################################\n",
      "Loss:  0.19140630960464478\n",
      "################################  14  ################################\n",
      "Loss:  0.1787673383951187\n",
      "################################  15  ################################\n",
      "Loss:  0.16666239500045776\n",
      "################################  16  ################################\n",
      "Loss:  0.15482135117053986\n",
      "################################  17  ################################\n",
      "Loss:  0.1430092453956604\n",
      "################################  18  ################################\n",
      "Loss:  0.13102765381336212\n",
      "################################  19  ################################\n",
      "Loss:  0.1187424585223198\n",
      "################################  20  ################################\n",
      "Loss:  0.1061551570892334\n",
      "################################  21  ################################\n",
      "Loss:  0.09337761998176575\n",
      "################################  22  ################################\n",
      "Loss:  0.08034077286720276\n",
      "################################  23  ################################\n",
      "Loss:  0.06831175088882446\n",
      "################################  24  ################################\n",
      "Loss:  0.058675236999988556\n",
      "################################  25  ################################\n",
      "Loss:  0.04986920580267906\n",
      "################################  26  ################################\n",
      "Loss:  0.04482263699173927\n",
      "################################  27  ################################\n",
      "Loss:  0.04083149880170822\n",
      "################################  28  ################################\n",
      "Loss:  0.03654029965400696\n",
      "################################  29  ################################\n",
      "Loss:  0.03227255493402481\n",
      "################################  30  ################################\n",
      "Loss:  0.027671540156006813\n",
      "################################  31  ################################\n",
      "Loss:  0.022924337536096573\n",
      "################################  32  ################################\n",
      "Loss:  0.0181678868830204\n",
      "################################  33  ################################\n",
      "Loss:  0.014068854041397572\n",
      "################################  34  ################################\n",
      "Loss:  0.01074786577373743\n",
      "################################  35  ################################\n",
      "Loss:  0.008346804417669773\n",
      "################################  36  ################################\n",
      "Loss:  0.006619214080274105\n",
      "################################  37  ################################\n",
      "Loss:  0.005175961181521416\n",
      "################################  38  ################################\n",
      "Loss:  0.004171861335635185\n",
      "################################  39  ################################\n",
      "Loss:  0.0034654103219509125\n",
      "################################  40  ################################\n",
      "Loss:  0.002918558195233345\n",
      "################################  41  ################################\n",
      "Loss:  0.0024991491809487343\n",
      "################################  42  ################################\n",
      "Loss:  0.0021711778827011585\n",
      "################################  43  ################################\n",
      "Loss:  0.0018941649468615651\n",
      "################################  44  ################################\n",
      "Loss:  0.0016747312620282173\n",
      "################################  45  ################################\n",
      "Loss:  0.0015002427389845252\n",
      "################################  46  ################################\n",
      "Loss:  0.0013596710050478578\n",
      "################################  47  ################################\n",
      "Loss:  0.001242320635356009\n",
      "################################  48  ################################\n",
      "Loss:  0.0011418652720749378\n",
      "################################  49  ################################\n",
      "Loss:  0.0010541134979575872\n",
      "################################  50  ################################\n",
      "Loss:  0.0009722948307171464\n",
      "################################  51  ################################\n",
      "Loss:  0.0008944286382757127\n",
      "################################  52  ################################\n",
      "Loss:  0.0008277360466308892\n",
      "################################  53  ################################\n",
      "Loss:  0.000774916319642216\n",
      "################################  54  ################################\n",
      "Loss:  0.0007269605994224548\n",
      "################################  55  ################################\n",
      "Loss:  0.0006820475100539625\n",
      "################################  56  ################################\n",
      "Loss:  0.0006412376533262432\n",
      "################################  57  ################################\n",
      "Loss:  0.0006034174002707005\n",
      "################################  58  ################################\n",
      "Loss:  0.0005675624706782401\n",
      "################################  59  ################################\n",
      "Loss:  0.0005332002183422446\n",
      "################################  60  ################################\n",
      "Loss:  0.0004995769122615457\n",
      "################################  61  ################################\n",
      "Loss:  0.00046679313527420163\n",
      "################################  62  ################################\n",
      "Loss:  0.00043415671098046005\n",
      "################################  63  ################################\n",
      "Loss:  0.00040217311470769346\n",
      "################################  64  ################################\n",
      "Loss:  0.00036987176281400025\n",
      "################################  65  ################################\n",
      "Loss:  0.0003384505689609796\n",
      "################################  66  ################################\n",
      "Loss:  0.00030631115077994764\n",
      "################################  67  ################################\n",
      "Loss:  0.000275644677458331\n",
      "################################  68  ################################\n",
      "Loss:  0.000246440147748217\n",
      "################################  69  ################################\n",
      "Loss:  0.00021796906366944313\n",
      "################################  70  ################################\n",
      "Loss:  0.00019262150453869253\n",
      "################################  71  ################################\n",
      "Loss:  0.0001694515231065452\n",
      "################################  72  ################################\n",
      "Loss:  0.00014901391114108264\n",
      "################################  73  ################################\n",
      "Loss:  0.00013105034304317087\n",
      "################################  74  ################################\n",
      "Loss:  0.00011524940782692283\n",
      "################################  75  ################################\n",
      "Loss:  0.00010165399726247415\n",
      "################################  76  ################################\n",
      "Loss:  8.987956971395761e-05\n",
      "################################  77  ################################\n",
      "Loss:  7.996783824637532e-05\n",
      "################################  78  ################################\n",
      "Loss:  7.152574835345149e-05\n",
      "################################  79  ################################\n",
      "Loss:  6.446798943215981e-05\n",
      "################################  80  ################################\n",
      "Loss:  5.8560730394674465e-05\n",
      "################################  81  ################################\n",
      "Loss:  5.3642386774299666e-05\n",
      "################################  82  ################################\n",
      "Loss:  4.955053373123519e-05\n",
      "################################  83  ################################\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:  4.614230419974774e-05\n",
      "################################  84  ################################\n",
      "Loss:  4.3299489334458485e-05\n",
      "################################  85  ################################\n",
      "Loss:  4.091882874490693e-05\n",
      "################################  86  ################################\n",
      "Loss:  3.891906817443669e-05\n",
      "################################  87  ################################\n",
      "Loss:  3.722693872987293e-05\n",
      "################################  88  ################################\n",
      "Loss:  3.578681935323402e-05\n",
      "################################  89  ################################\n",
      "Loss:  3.454859324847348e-05\n",
      "################################  90  ################################\n",
      "Loss:  3.347365418449044e-05\n",
      "################################  91  ################################\n",
      "Loss:  3.252853639423847e-05\n",
      "################################  92  ################################\n",
      "Loss:  3.168667899444699e-05\n",
      "################################  93  ################################\n",
      "Loss:  3.0925202736398205e-05\n",
      "################################  94  ################################\n",
      "Loss:  3.022524469997734e-05\n",
      "################################  95  ################################\n",
      "Loss:  2.957107062684372e-05\n",
      "################################  96  ################################\n",
      "Loss:  2.894694080168847e-05\n",
      "################################  97  ################################\n",
      "Loss:  2.8341839424683712e-05\n",
      "################################  98  ################################\n",
      "Loss:  2.7741292797145434e-05\n",
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      "Loss:  2.71345616056351e-05\n",
      "################################  100  ################################\n",
      "Loss:  2.6508332666708156e-05\n",
      "################################  101  ################################\n",
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      "################################  102  ################################\n",
      "Loss:  2.5306035240646452e-05\n",
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      "################################  105  ################################\n",
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      "################################  121  ################################\n",
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      "################################  122  ################################\n",
      "Loss:  1.2036566658935044e-05\n",
      "################################  123  ################################\n",
      "Loss:  1.1513173376442865e-05\n",
      "################################  124  ################################\n",
      "Loss:  1.1033406735805329e-05\n",
      "################################  125  ################################\n",
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    {
     "name": "stdout",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "################################  999  ################################\n",
      "Loss:  5.220979346631793e-07\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 1000\n",
    "history = fit(my_network, training_set, n_epochs, optimizer_, p=2, verbose=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f826d195a90>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid(True, which=\"both\", ls=\":\")\n",
    "plt.plot(np.arange(1,n_epochs+1), np.log10(history), label=\"Train Loss\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = X_test.astype(np.float32)\n",
    "X_test = torch.from_numpy(X_test)\n",
    "\n",
    "y_test = y_test.astype(np.float32)\n",
    "y_test = torch.from_numpy(y_test)\n",
    "\n",
    "y_test_pred = my_network(X_test).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test DNN:  0.00039245969674084336 %\n"
     ]
    }
   ],
   "source": [
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = torch.mean((y_test_pred - y_test)**2)/torch.mean(y_test**2)\n",
    "print(\"Relative Error Test DNN: \", relative_error_test.detach().numpy()*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2500, 1])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2500, 1])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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