machinelearning/gradientDescent/01-GradientDescentSimulations/01-GradientDescentSimulations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模拟梯度下降法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.  , -0.95, -0.9 , -0.85, -0.8 , -0.75, -0.7 , -0.65, -0.6 ,\n",
       "       -0.55, -0.5 , -0.45, -0.4 , -0.35, -0.3 , -0.25, -0.2 , -0.15,\n",
       "       -0.1 , -0.05,  0.  ,  0.05,  0.1 ,  0.15,  0.2 ,  0.25,  0.3 ,\n",
       "        0.35,  0.4 ,  0.45,  0.5 ,  0.55,  0.6 ,  0.65,  0.7 ,  0.75,\n",
       "        0.8 ,  0.85,  0.9 ,  0.95,  1.  ,  1.05,  1.1 ,  1.15,  1.2 ,\n",
       "        1.25,  1.3 ,  1.35,  1.4 ,  1.45,  1.5 ,  1.55,  1.6 ,  1.65,\n",
       "        1.7 ,  1.75,  1.8 ,  1.85,  1.9 ,  1.95,  2.  ,  2.05,  2.1 ,\n",
       "        2.15,  2.2 ,  2.25,  2.3 ,  2.35,  2.4 ,  2.45,  2.5 ,  2.55,\n",
       "        2.6 ,  2.65,  2.7 ,  2.75,  2.8 ,  2.85,  2.9 ,  2.95,  3.  ,\n",
       "        3.05,  3.1 ,  3.15,  3.2 ,  3.25,  3.3 ,  3.35,  3.4 ,  3.45,\n",
       "        3.5 ,  3.55,  3.6 ,  3.65,  3.7 ,  3.75,  3.8 ,  3.85,  3.9 ,\n",
       "        3.95,  4.  ,  4.05,  4.1 ,  4.15,  4.2 ,  4.25,  4.3 ,  4.35,\n",
       "        4.4 ,  4.45,  4.5 ,  4.55,  4.6 ,  4.65,  4.7 ,  4.75,  4.8 ,\n",
       "        4.85,  4.9 ,  4.95,  5.  ,  5.05,  5.1 ,  5.15,  5.2 ,  5.25,\n",
       "        5.3 ,  5.35,  5.4 ,  5.45,  5.5 ,  5.55,  5.6 ,  5.65,  5.7 ,\n",
       "        5.75,  5.8 ,  5.85,  5.9 ,  5.95,  6.  ])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linspace线性增大 \n",
    "plot_x = np.linspace(-1., 6., 141)\n",
    "plot_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 抛物线函数。\n",
    "plot_y = (plot_x-2.5)**2 - 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vWIDVkZSTaKGrb7huTDy/nzWK1dllWupuKLfsBHPmbcBhDIvunUBKVLDVkZQT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c734b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(plot_x, plot_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 接受误差\n",
    "epsilon = 1e-8\n",
    "# 学习率\n",
    "eta = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.499891109642585\n",
      "-0.99999998814289\n"
     ]
    }
   ],
   "source": [
    "# theta对应的 值\n",
    "def J(theta):\n",
    "    return (theta-2.5)**2 - 1.\n",
    "\n",
    "# 函数求导 \n",
    "def dJ(theta):\n",
    "    return 2*(theta-2.5)\n",
    "\n",
    "theta = 0.0\n",
    "while True:\n",
    "    gradient = dJ(theta)\n",
    "    last_theta = theta\n",
    "    theta = theta - eta * gradient\n",
    "    \n",
    "    if(abs(J(theta) - J(last_theta)) < epsilon):\n",
    "        break\n",
    "    \n",
    "print(theta)\n",
    "print(J(theta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QJNSf0X08a3QOtRuhJwCZwAwR2SAi00TEfU5brUZifAQD20TyzpIUCk/qKF0pV7FiXzZr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\nPqhgwi92AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c9c6e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta = 0.0\n",
    "theta_history = [theta]\n",
    "while True:\n",
    "    gradient = dJ(theta)\n",
    "    last_theta = theta\n",
    "    theta = theta - eta * gradient\n",
    "    theta_history.append(theta)\n",
    "    \n",
    "    if(abs(J(theta) - J(last_theta)) < epsilon):\n",
    "        break\n",
    "\n",
    "plt.plot(plot_x, J(plot_x))\n",
    "# 下降中，导数的变换曲线。\n",
    "plt.plot(np.array(theta_history), J(np.array(theta_history)), color=\"r\", marker='+')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(theta_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "theta_history = []\n",
    "\n",
    "# \n",
    "def gradient_descent(initial_theta, eta, epsilon=1e-8):\n",
    "    theta = initial_theta\n",
    "    theta_history.append(initial_theta)\n",
    "\n",
    "    while True:\n",
    "        gradient = dJ(theta)\n",
    "        last_theta = theta\n",
    "        theta = theta - eta * gradient\n",
    "        theta_history.append(theta)\n",
    "    \n",
    "        if(abs(J(theta) - J(last_theta)) < epsilon):\n",
    "            break\n",
    "            \n",
    "def plot_theta_history():\n",
    "    plt.plot(plot_x, J(plot_x))\n",
    "    plt.plot(np.array(theta_history), J(np.array(theta_history)), color=\"r\", marker='+')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z3FcmqszjjApdWqelUtcKGE9U1dZxx7MFPL9oG1+9YBCP3Tqe9BRttBVvNOUi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\nQhcRiREqdBGRGPH/AQvhHj7AkBH7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb767b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta = 0.01\n",
    "theta_history = []\n",
    "gradient_descent(0, eta)\n",
    "plot_theta_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "424"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(theta_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G5xDaCD0bKAFeNrM1ZvaimcXOaatB5GR1YvzALvx+QR5HqzRKF4kWS3aUsiL/IA9PHBB3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb1ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta = 0.001 # 学习率变小\n",
    "theta_history = []\n",
    "gradient_descent(0, eta)\n",
    "plot_theta_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3682"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(theta_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m+PlnaRR2Fi98l8rOvGJeuq4nrX09zY7jlKSg27G/j+5G5xA/Hly4ifxiufnH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd7dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta = 0.8\n",
    "theta_history = []\n",
    "gradient_descent(0, eta)\n",
    "plot_theta_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "OverflowError",
     "evalue": "(34, 'Result too large')",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mOverflowError\u001B[0m                             Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-41-81ad09e9d3e0>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0meta\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;36m1.1\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      2\u001B[0m \u001B[0mtheta_history\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 3\u001B[0;31m \u001B[0mgradient_descent\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0meta\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[0;32m<ipython-input-35-37822183f4df>\u001B[0m in \u001B[0;36mgradient_descent\u001B[0;34m(initial_theta, eta, epsilon)\u001B[0m\n\u001B[1;32m     11\u001B[0m         \u001B[0mtheta_history\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtheta\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     12\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 13\u001B[0;31m         \u001B[0;32mif\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mabs\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mJ\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtheta\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mJ\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlast_theta\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m<\u001B[0m \u001B[0mepsilon\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     14\u001B[0m             \u001B[0;32mbreak\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     15\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m<ipython-input-32-e8b6a6f56c24>\u001B[0m in \u001B[0;36mJ\u001B[0;34m(theta)\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;32mdef\u001B[0m \u001B[0mJ\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtheta\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mtheta\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0;36m2.5\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m**\u001B[0m\u001B[0;36m2\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0;36m1.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0;32mdef\u001B[0m \u001B[0mdJ\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtheta\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m     \u001B[0;32mreturn\u001B[0m \u001B[0;36m2\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtheta\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0;36m2.5\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mOverflowError\u001B[0m: (34, 'Result too large')"
     ]
    }
   ],
   "source": [
    "eta = 1.1\n",
    "theta_history = []\n",
    "gradient_descent(0, eta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def J(theta):\n",
    "    # 越来越大的情况，防止溢出，给个最大浮点数值\n",
    "    try:\n",
    "        return (theta-2.5)**2 - 1.\n",
    "    except:\n",
    "        return float('inf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gradient_descent(initial_theta, eta, n_iters = 1e4, epsilon=1e-8):\n",
    "    \n",
    "    theta = initial_theta\n",
    "    i_iter = 0\n",
    "    theta_history.append(initial_theta)\n",
    "    # 控制迭代次数。 \n",
    "    while i_iter < n_iters:\n",
    "        gradient = dJ(theta)\n",
    "        last_theta = theta\n",
    "        theta = theta - eta * gradient\n",
    "        theta_history.append(theta)\n",
    "    \n",
    "        if(abs(J(theta) - J(last_theta)) < epsilon):\n",
    "            break\n",
    "            \n",
    "        i_iter += 1\n",
    "        \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eta = 1.1\n",
    "theta_history = []\n",
    "gradient_descent(0, eta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10001"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(theta_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IyJS4dAn27EkM9Lffji7EDdDW5kP81ltjA72yMmNFn+7GGvRfB/4ScOHHvwXuG803cM49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\n8pyCXkQkz/1/CxE6FcrtP+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb310b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta = 1.1 # 越来越大，\n",
    "theta_history = []\n",
    "gradient_descent(0, eta, n_iters=10)\n",
    "plot_theta_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}