c15_求数据的前n个主成分.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 获得前n个主成分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19b4795b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:,0], x[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\n",
    "\n",
    "x = demean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19b479a22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_demean = demean(x)\n",
    "plt.scatter(x_demean[:,0], x_demean[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(w, x):\n",
    "    '''目标函数'''\n",
    "    return np.sum((x.dot(w))**2)/len(x)\n",
    "\n",
    "def df(w, x):\n",
    "    return x.T.dot(x.dot(w)) * 2./len(x)\n",
    "\n",
    "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 first_component(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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.79119843, 0.61155952])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_w = np.random.random(x.shape[1])\n",
    "eta = 0.01\n",
    "w = first_component(x, initial_w, eta)\n",
    "w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### $X^{(i)}\\cdot w = \\left \\| X_{project}^{(i)} \\right \\|$\n",
    "\n",
    "$X_{project}^{(i)}=\\left \\| X_{project}^{(i)} \\right \\| \\cdot w$\n",
    "\n",
    "第二主成分上的投影向量 $=X^{(i)} - X_{project}^{(i)}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x2 = np.empty(x.shape)\n",
    "# for i in range(len(x)):\n",
    "#     x2[i] = x[i] - x[i].dot(w) * w\n",
    "\n",
    "# 求x2的向量化方法\n",
    "x2 = x - x.dot(w).reshape(-1,1) * w  #### 后面是(100,2) - (100,1)*(2, )\n",
    "# w.shape   ###(2, )\n",
    "# x.shape   ###(100, 2)\n",
    "# x.dot(w).reshape(-1, 1).shape  ###(100,1)\n",
    "# x2 = x - x.dot(w) * w   #### 这里报错是因为 (100,2) - (100,) *(2, )\n",
    "x2.shape   ####(100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19b48ade668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x2[:,0], x2[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.61155478,  0.79120209])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 复用第一主成分的代码, 求出第二主成分方向向量(即系数)\n",
    "w2 = first_component(x2, initial_w, eta)\n",
    "w2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.989241233572695e-06"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.dot(w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 综合第一和第二主成分分析函数,给出泛化函数\n",
    "def first_n_components(n, x, eta=0.01, n_iters=1e4, epsilon=1e-8):\n",
    "    \n",
    "    x_pca = x.copy()\n",
    "    x_pca = demean(x_pca)\n",
    "    res =[]\n",
    "    for i in range(n):\n",
    "        initial_w = np.random.random(x_pca.shape[1])\n",
    "        w = first_component(x_pca, initial_w, eta)\n",
    "        res.append(w)\n",
    "        \n",
    "        x_pca = x_pca - x_pca.dot(w).reshape(-1,1) * w\n",
    "    \n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.7911985 , 0.61155942]), array([-0.61155429,  0.79120247])]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_n_components(2, x)  # 有几个特征就有几个主成分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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