c14_主成分分析PCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 原理部分\n",
    "### 目标: 求w, 使得 $Var(X_{project})=f(X)=\\frac{1}{m}\\sum_{i=1}^{m}(X_1^{(i)}w_1 + X_2^{(i)}w_2+\\dots+ X_n^{(i)}w_n)^2$ 最大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\bigtriangledown f=\n",
    "\\begin{pmatrix}\n",
    "\\frac{\\partial f}{\\partial w_1}\\\\\n",
    "\\frac{\\partial f}{\\partial w_2}\\\\\n",
    "\\cdots\\\\\n",
    "\\frac{\\partial f}{\\partial w_n}\n",
    "\\end{pmatrix}=\\frac{2}{m}\n",
    "\\begin{pmatrix}\n",
    "\\sum_{i=1}^m (X_1^{(i)}w_1 + X_2^{(i)}w_2 + \\ldots + X_n^{(i)}w_n)X_1^{(i)} \\\\\n",
    "\\sum_{i=1}^m (X_1^{(i)}w_1 + X_2^{(i)}w_2 + \\ldots + X_n^{(i)}w_n)X_2^{(i)} \\\\\n",
    "\\cdots\\\\\n",
    "\\sum_{i=1}^m (X_1^{(i)}w_1 + X_2^{(i)}w_2 + \\ldots + X_n^{(i)}w_n)X_n^{(i)} \\\\\n",
    "\\end{pmatrix}\n",
    "=\\frac{2}{m}\n",
    "\\begin{pmatrix}\n",
    "\\sum_{i=1}^m (X^{(i)}w)X_1^{(i)} \\\\\n",
    "\\sum_{i=1}^m (X^{(i)}w)X_2^{(i)} \\\\\n",
    "\\cdots \\\\\n",
    "\\sum_{i=1}^m (X^{(i)}w)X_n^{(i)} \\\\\n",
    "\\end{pmatrix}\n",
    "$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $-----> \\frac{2}{m} \\cdot (X^{(1)}w , \\ \\ X^{(2)}w, \\ \\ X^{(3)}w, \\ \\ldots, \\ \\ X^{(m)}w)\n",
    "\\cdot \\begin{pmatrix}\n",
    "X_1^{(1)} \\ \\ X_2^{(1)} \\ \\ X_3^{(1)}\\ \\ \\ldots \\ \\ X_n^{(1)}\\\\\n",
    "X_1^{(2)} \\ \\ X_2^{(2)} \\ \\ X_3^{(2)}\\ \\ \\ldots \\ \\ X_n^{(2)}\\\\\n",
    "X_1^{(3)} \\ \\ X_2^{(3)} \\ \\ X_3^{(3)}\\ \\ \\ldots \\ \\ X_n^{(3)}\\\\\n",
    "\\cdots \\ \\ \\ \\cdots \\ \\ \\ \\cdots \\ \\ \\ \\cdots \\ \\ \\ \\cdots \\\\\n",
    "X_1^{(m)} \\ \\ X_2^{(m)} \\ \\ X_3^{(m)}\\ \\ \\ldots \\ \\ X_n^{(m)}\\\\\n",
    "\\end{pmatrix}$\n",
    "\n",
    "### $=\\frac{2}{m} \\cdot (Xw)^T \\cdot X$ -----> $\\frac{2}{m}\\cdot X^T(Xw) = \\bigtriangledown f$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.使用梯度上升法求解主成分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.empty((100,2))\n",
    "x[:, 0] = np.random.uniform(0., 100., size=100)\n",
    "x[:, 1] = 0.75 * x[:, 0] + 3. + np.random.normal(0, 10., size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e6025d1d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:,0], x[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### demean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def demean(x):\n",
    "    return x - np.mean(x, axis=0)  # 在列上求mean,即 样本 - 各个特征的均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1.5, 2. ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = np.array([[1,2,3],\n",
    "                 [1,1,1]])\n",
    "np.mean(test, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e60229ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_demean = demean(x)\n",
    "plt.scatter(x_demean[:,0], x_demean[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 梯度上升法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(w, x):\n",
    "    '''目标函数'''\n",
    "    return np.sum((x.dot(w))**2)/len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_math(w, x):\n",
    "    return x.T.dot(x.dot(w)) * 2./len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用debug函数测试梯度求导的公式是否正确\n",
    "def df_debug(w, x, epsilon=0.0001):\n",
    "    res = np.empty(len(w))\n",
    "    for i in range(len(w)):\n",
    "        w_1 = w.copy()\n",
    "        w_1[i] += epsilon\n",
    "        w_2 = w.copy()\n",
    "        w_2[i] -= epsilon\n",
    "        res[i] = (f(w_1, x) - f(w_2, x)) / (2 * epsilon)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def direction(w):\n",
    "    '''将一个向量变成单位向量'''\n",
    "    return w / np.linalg.norm(w)  # norm 求向量的模(实际上是根据ord返回范数)\n",
    "\n",
    "def gradient_ascent(df, x, initial_w, eta, n_iters=1e4, epsilon=1e-8):\n",
    "    \n",
    "    w = direction(initial_w)\n",
    "    cur_iter = 0\n",
    "    \n",
    "    while cur_iter < n_iters:\n",
    "        gradient = df(w, x)\n",
    "        last_w = w  # 我们投影的时候是投影在方向向量w上面的,即需要将w变成单位方向向量\n",
    "        w = w + eta * gradient\n",
    "        w = direction(w)   # 注意归一化为单位方向向量 [1]\n",
    "        if (abs(f(w, x) - f(last_w, x)) < epsilon):\n",
    "            break\n",
    "        cur_iter += 1\n",
    "    \n",
    "    return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.86944443, 0.34837115])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化的w向量不能为0 , 这点与梯度下降法求解线性回归不同, 注意[2]!\n",
    "initial_w = np.random.random(x.shape[1])\n",
    "eta = 0.001\n",
    "initial_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77106583, 0.63675544])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 注意![3]不能进行StandardScaler标准化数据\n",
    "# 因为我们的目的就是要投影在一个向量上, 然后投影的向量的方差最大\n",
    "# 如果标准化了数据, 那么方差就为1了, 固定了\n",
    "gradient_ascent(df_debug, x_demean, initial_w, eta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77106583, 0.63675544])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = gradient_ascent(df_math, x_demean, initial_w, eta)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e6026ca710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_demean[:,0], x_demean[:,1])\n",
    "plt.plot([0, w[0]*60], [0, w[1]*60], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 如果不加入噪音"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = np.empty((100,2))\n",
    "x2[:, 0] = np.random.uniform(0., 100., size=100)\n",
    "x2[:, 1] = 0.75 * x2[:, 0] + 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e6026cab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x2[:,0], x2[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2_demean = demean(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.80000018, 0.59999976])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2 = gradient_ascent(df_math, x2_demean, initial_w, eta)\n",
    "w2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7499999999999999"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.6/0.8  # 正好是系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e602800e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x2_demean[:,0], x2_demean[:,1])\n",
    "plt.plot([0, w2[0]*60], [0, w2[1]*60], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}