Chapter2-Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第2章 感知机\n",
    "感知机模型实际是最简单的模型之一，台大林轩田老师发布的《机器学习基石》教程中，称之为“知错能改验算法”，就是发现错误进行纠正，具体这个算法的过程如下。\n",
    "## 2.1感知机模型\n",
    ">定义: 输入空间到输出空间的函数为$f(x) = sign(w\\cdot x + b)$  \n",
    "其中\n",
    "$$ sign(x)=\\left\\{\n",
    "\\begin{array}{rcl}\n",
    "+1,     &      & {x \\geq 0}\\\\\n",
    "-1,     &      & {x < 0}\n",
    "\\end{array} \\right. $$\n",
    "目的: 学习一组$w$和$b$来定义一个超平面$f(x)$对数据进行线性分割.\n",
    "另外, 真实类别标签$y$的取值范围集合是$\\{ +1,-1\\}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义sign函数\n",
    "def sign(x):\n",
    "    if x>= 0:\n",
    "        return 1\n",
    "    if x<0:\n",
    "        return -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "## 2.2学习策略\n",
    "既然上面$sign\\left( x\\right)$已经定义好了，那么接下来要做的就是求解$w$和$b$了\n",
    ">先定义**损失函数**,并用它来量化模型好不好，由于损失函数是说明一个模型“出现问题”的指标，所以我们需要最小化之.\n",
    "这里以距离作为损失函数的考量指标,首先，定义输入空间任一点$(x_0,y_0)$到分类超平面的距离为\n",
    "$$ distance = \\frac{1}{\\left\\|w\\right\\|} \\left|w\\cdot x_0+b \\ \\right|$$\n",
    "对于真实数据集样本$(x_i,y_i)$,根据2.1中的式子，若分类正确,$w\\cdot x_i + b \\geq 0$时$y_i \\geq 0$,若是分错的,$w\\cdot x_i + b \\geq 0$时$y_i < 0$，\n",
    "则对于被误分类的数据$(x_i,y_i)$,会得到如下不等式关系  \n",
    "$$-y_i(w\\cdot x_i +b)>0$$\n",
    "且误分类点到超平面的距离为\n",
    "$$distance = -\\frac{1}{\\left\\|w\\right\\|}y_i(w\\cdot x_i +b)$$  \n",
    "若定义所有被误分类的点的集合为$M$,则所有被误分类的点到超平面的总距离为\n",
    "$$ALL\\_distance = -\\frac{1}{\\left\\|w\\right\\|}\\sum_{x_i\\in M} y_i(w\\cdot x_i +b)$$  \n",
    "此时,我们不关心$\\frac{1}{\\left\\|w\\right\\|}$,定义损失函数为$$L(w,b) = -\\sum_{x_i \\in M}y_i(w\\cdot x_i +b)$$  \n",
    "(注:$L$是个非负函数,可以进行即最小值的求解，且最理想的状态是使$L=0$)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 2.3设置最优化函数\n",
    ">当前,为了得到最终的$f(x)$,这里需要最小化式子$L(w,b) = -\\sum_{x_i \\in M}y_i(w\\cdot x_i +b)$来求得$w$和$b$,所以有目标\n",
    "$$\\underset{w,b}{\\min} L(w,b) = -\\sum_{x_i \\in M}\\ y_i(w\\cdot x_i +b)$$\n",
    "由于真实环境中，数据往往是高维度的数据，单纯的使用高数中学习的求导等于0的方法在这里已经很不好用了。而求解高维数据的极值问题，专业的描述方法称之为“凸优化”问题，即对凸函数求解极值的过程。为了最小化上式,这里使用SGD(随机梯度下降算法来求解),详见2.3.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.1随机梯度下降(stochastic gradient descent,SGD)\n",
    ">这里对SGD方法进行讲解:  \n",
    "首先,要求解的问题空间可以理解为求解$\\underset{w,b}{\\min} L(w,b) = -\\sum_{x_i \\in M}\\ y_i(w\\cdot x_i +b)$,在三维空间中,可以想象成求解盆地的那个时候的值.SGD的\"下降\"思路是一次随机选择一个误分类点来指导损失函数使其梯度下降（往盆地的方向走），直到下降到不能再低的时候停止。  \n",
    "假设误分类点的集合$M$固定,则$L(w,b)$的梯度为\n",
    "$$\\nabla_w L(w,b)= -\\sum_{x_i \\in M}y_i x_i$$\n",
    "$$\\nabla_b L(w,b)= -\\sum_{x_i \\in M}y_i$$\n",
    "随机选取一个误分类点$(x_i,y_i)$,对$w$和$b$进行更新:\n",
    "$$w \\leftarrow w + \\eta y_i x_i$$\n",
    "$$b \\leftarrow b + \\eta y_i$$\n",
    "其中,$\\eta$是学习率,学习率应该在(0,1]之间，学习率就相当于下台阶一样，你步子跨得大了，一次下两个台阶，下降得快，步子小了，一次跨一个，下降的就慢。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.2感知机学习算法的原始形式\n",
    ">输入:  $T=\\{(x_1,y_1),(x_2,y_2),...,(x_N,y_N)\\}$ ,学习率$\\eta(0 < \\eta \\leq 1)$  \n",
    "输出: $w$和$b$,感知机模型$f(x) = sign(w\\cdot x + b)$  \n",
    "* (1)选取$w$,$b$(可以随机选取)\n",
    "* (2)在训练集合中选择数据$(x_i,y_i)$\n",
    "* (3)如果$y_i(w\\cdot x_i +b)\\leq 0$\n",
    "$$w \\leftarrow w + \\eta y_i x_i$$\n",
    "$$b \\leftarrow b + \\eta y_i$$\n",
    "* (4)跳至(2),直至训练集中没有误分类点或者损失函数降低到给定阈值的范围之内"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里定义算法的测试样本\n",
    "import numpy as np\n",
    "x = np.array([[3,4],[1,1],[4,3],[1,0],[3,1],[4,2],[2,2],[4,0]]) # 定义三个数据向量(8行_2列,数组)\n",
    "y = np.array([1,-1,1,-1,-1,1,-1,1]).T # 定义类别标签(8行_1列,向量)\n",
    "w0 = np.array([0.5,0.5]) # 定义并初始化参数向量(1行2列,向量)\n",
    "b0 = 0.0 # 偏置项\n",
    "eta = 0.02 # 学习率\n",
    "posindex = x[y[:] == 1] # 绘图用，标记正样本id\n",
    "negindex = x[y[:] == -1] # 绘图用，标记负样本id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb0b6648d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(0)\n",
    "# c='red'定义为红色，alpha是透明度，marker是画的样式\n",
    "plt.scatter(posindex[:,0], posindex[:,1], c='red', alpha=1, marker='+', label='pickup') \n",
    "# c='red'定义为红色，alpha是透明度，marker是画的样式\n",
    "plt.scatter(negindex[:,0], negindex[:,1], c='green', alpha=1, marker='o', label='pickup') \n",
    "plt.axis([0,5,-1,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义损失函数,根据2.2-2.3的推导\n",
    "def calLoss(x,y,w,b):\n",
    "    \"\"\"\n",
    "    计算损失,包含4个参数,分别为:\n",
    "    x: 数据\n",
    "    y: 类别标签\n",
    "    w: 参数向量\n",
    "    b: 偏置项\n",
    "    返回损失值\n",
    "    \"\"\"\n",
    "    loss = 0\n",
    "    for i in range(len(x)):\n",
    "        if(y[i] * (np.dot(w,x[i].T) + b) < 0):\n",
    "            loss = loss + (-y[i] * (np.dot(w,x[i].T) + b))\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration0========================\n",
      "w = [0.48 0.48] , b = -0.02 \tLoss = 5.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb14612978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration1========================\n",
      "w = [0.46 0.46] , b = -0.04 \tLoss = 5.199999999999999\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb14612668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration2========================\n",
      "w = [0.44 0.44] , b = -0.06 \tLoss = 4.8999999999999995\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1540f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration3========================\n",
      "w = [0.42 0.42] , b = -0.08 \tLoss = 4.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb16b941d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration4========================\n",
      "w = [0.4 0.4] , b = -0.1 \tLoss = 4.299999999999999\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb160062e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration5========================\n",
      "w = [0.38 0.38] , b = -0.12000000000000001 \tLoss = 3.9999999999999987\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1461f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration6========================\n",
      "w = [0.36 0.36] , b = -0.14 \tLoss = 3.6999999999999984\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb153e6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration7========================\n",
      "w = [0.34 0.34] , b = -0.16 \tLoss = 3.399999999999998\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb145d92b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration8========================\n",
      "w = [0.32 0.32] , b = -0.18 \tLoss = 3.0999999999999988\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb177945f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration9========================\n",
      "w = [0.3 0.3] , b = -0.19999999999999998 \tLoss = 2.799999999999998\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb183ab908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration10========================\n",
      "w = [0.28 0.28] , b = -0.21999999999999997 \tLoss = 2.4999999999999982\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1ab01320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration11========================\n",
      "w = [0.26 0.26] , b = -0.23999999999999996 \tLoss = 2.199999999999998\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1b730470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration12========================\n",
      "w = [0.24 0.24] , b = -0.25999999999999995 \tLoss = 1.8999999999999977\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1c30a2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration13========================\n",
      "w = [0.22 0.22] , b = -0.27999999999999997 \tLoss = 1.6199999999999983\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1c3b6198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration14========================\n",
      "w = [0.2 0.2] , b = -0.3 \tLoss = 1.359999999999998\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1db335c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration15========================\n",
      "w = [0.18 0.18] , b = -0.32 \tLoss = 1.0999999999999983\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1cf32f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration16========================\n",
      "w = [0.16 0.16] , b = -0.34 \tLoss = 0.8399999999999983\n"
     ]
    },
    {
     "data": {
      "image/png": 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LK51zY33nKSkzq21mtYoeVwUuBD71m6r4nHP9nXN1nXP1KPzdXxBJpW5m1c3s\n0D2PgYuJnP+o4pz7FlhjZntuAtYW+GRfx/u83DGibkfgnNsFdAVeo/CNu2eccx/7TVUyZvYksBg4\n1czWmtkdvjOVQHPgZqBN0eVqHxTNHiPFscCbZvYRhZOE+c65iLtkMIIdDbxtZh8CS4E5zrm5njOV\nVDcgs+h36CxgxL4O1CdPRURCRksxIiIho2IXEQkZFbuISMio2EVEQkbFLiISMip2EZGQUbGLiISM\nil1EJGT+H9Kw4wivfkfSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1db33dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration17========================\n",
      "w = [0.1  0.14] , b = -0.36000000000000004 \tLoss = 0.5999999999999986\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1461f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration18========================\n",
      "w = [0.04 0.12] , b = -0.38000000000000006 \tLoss = 0.19999999999999862\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1db4e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration19========================\n",
      "w = [0.12 0.12] , b = -0.36000000000000004 \tLoss = 0.2200000000000007\n"
     ]
    },
    {
     "data": {
      "image/png": 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MXD2Buk3/Qtqd05h0Twa/7P/FdywppBIVu3NuvXPu80CFEZHgUa1GVca/NYxb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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1ab8eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration20========================\n",
      "w = [0.06 0.1 ] , b = -0.38000000000000006 \tLoss = 0.23999999999999866\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1ab15ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration21========================\n",
      "w = [0.14 0.1 ] , b = -0.36000000000000004 \tLoss = 0.14000000000000068\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb147aca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration22========================\n",
      "w = [0.08 0.08] , b = -0.38000000000000006 \tLoss = 0.27999999999999864\n"
     ]
    },
    {
     "data": {
      "image/png": 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MJwh5cip+P31edyyLYbETkV0q4+aKwHf8EbR4BI7sPgY/zyDs3/KN7lgWwWIn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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb18367748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration23========================\n",
      "w = [0.16 0.08] , b = -0.36000000000000004 \tLoss = 0.060000000000000664\n"
     ]
    },
    {
     "data": {
      "image/png": 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TNw3RccWDxWJRKSkpFX5eKrvf9hxBSOcX8cBT3RD+4WTdcYickoikKqUsxR3H3zylEmnc\nthH8Xx6K5KXf4tsVO3THIaLbYLFTiQ2bPhhNO/hhdnAczp+5oDsOERWBxU4lVsmtEsISQ3D+9EXE\nTFmkOw4RFYHFTqVyd4fGGDZ9MNYv3oztq1N1xyGiW2CxU6n5v/wU/No2xKygWFw8d0l3HCL6CxY7\nlZqbuxvCEifg7B/nEBv2vu44RPQXLHYqkxaWpnhm2iCsSfwaKV/t1h2HiG7AYqcyG/nq02jYsj5m\nWefj0vns4r+BiCoEi53KzL2KO8ISQ5B59DQW/P0D3XGIqBCLnWzSultzDJk8AJ/HrsOur9N0xyEi\nsNjJAKP/NQx33X0nZgbOx+WLl3XHIXJ6LHayWRXPyghLCMEfv2Ug8aUluuMQOT0WOxmi3YOtMPj5\nflgx90ukbdmnOw6RU2Oxk2HGvjkcdzaugxnjYnAlO0d3HCKnxWInw3hU9UDogmD8fvAEFv1zqe44\nRE6LxU6G6vBwWwywPoJPolbjp20HdMchckosdjJc4P89B5+GtRAZEIPcK7m64xA5HRY7Gc6ruiem\nxI3H0f2/4/3XlumOQ+R0WOxULiyPtkffMQ/jo8jPcCDlV91xiJwKi53KTdCMUbijXk1Ejp2Hq7lX\ndcchchosdio3VWt4YfJ8Kw7vOYqkf3+sOw6R02CxU7nqOqAz+ozsgaVvrcAvP/ymOw6RU2CxU7kL\nnjUa3rWrIXJsNK5dvaY7DpHDY7FTuat+RzW8EB2IX384jKVvrdAdh8jhsdipQnQf3AU9n70fSf9e\njt/S0nXHIXJoLHaqMBPeHYuqNbwQGRCDvGt5uuMQOSwWO1WYGj7eeH5OAH5O+RUfzVilOw6RwzKk\n2EUkUUQyRGSPEeOR4+rx9H14YEhXvP/aMhzZ/7vuOEQOyagZ+yIAfQ0ai8pJUloS/KL84PK6C/yi\n/JCUllThGUQEL8wbhypelTEjIBp5ec6zJGMPrz85B0OKXSm1GcAZI8ai8pGUlgTrKivSs9KhoJCe\nlQ7rKquWcqlZtwYmzB6Ln7b9jE9nf1Hh59fBnl5/cnxcY3cS4RvCkX01+6Z92VezEb4hXEueXiMe\nQLeBnbHw5SU4dvCElgwVyd5ef3JsFVbsImIVkRQRScnMzKyo01KhI1lHSrW/vIkIJsVY4VbZDTPH\nxSA/P19Ljopib68/ObYKK3alVJxSyqKUsvj4+FTUaalQI+9GpdpfEWrfdQeCZ41G2pZ9+Cx6rbYc\nFcEeX39yXFyKcRIRvSPg6eZ50z5PN09E9I7QlKjAo6N64t6+HZDwjySc+O2k1izlyV5ff3JMRl3u\nuATANgAtROSYiAQYMS4Zx7+dP+IGxsHX2xcCga+3L+IGxsG/nb/WXCKCybFBcHFxceglGXt9/ckx\niVKqwk9qsVhUSkpKhZ+X7NcX8esxKygWL0QHYuD4R3XHIbJLIpKqlLIUdxyXYsgu9BvXGx17t0P8\ni4uRcYRvrhPZgsVOdkFEMDV+PJRSmGmNhY5/SRI5ChY72Y07/epg3FvPIfWr3Vi7cKPuOESmxWIn\nuzIw+FHc81BrzA99D6d+P607DpEpsdjJrri4uCB0QTCu5V5D1Pg4LskQlQGLnezOXU3vxNiIEdi+\n+nus/2Cz7jhEpsNiJ7s0aGJftL6/BWImL8SZP87qjkNkKix2skuurq4ISwjGlexcvDthAZdkiEqB\nxU52q2GL+hj9xrP49tMd2LRsq+44RKbBYie79tSUx9Gyy92YOzEBZzOydMchMgUWO9k110quCE0I\nQfb5y5j3QoLuOESmwGInu+fXpiH8/zkUm5Ztw5ZPtuuOQ2T3WOxkCs++OAjNOjXGnAnxOH/6gu44\nRHaNxU6mUMmtEsISJ+D86YuInrxQdxwiu8ZiJ9Noco8vRrw0BBuStmDbKt72magoLHYyleEvPYnG\n7RohanwcLpy9qDsOkV1isZOpuLm7ISwxBOcyshAb+r7uOER2icVOptO8c1M8++IgrF20ETvX7NId\nh8jusNjJlJ575Wn4tm6AWdZYXDqfrTsOkV1hsZMpuVd2Q2hCCE4fP4P4aYt1xyGyKyx2Mq1WXZvh\nqSmPY3X8eny/IU13HCK7wWInUxv1xrNo0LweZgXG4PLFy7rjENkFFjuZWmWPyghNCMHJ9FNYMD1J\ndxwiu8BiJ9Nr270lBk/sh8+i1+LHzT/pjkOkHYudHMKYiOGo16QuZgRE4/KlK7rjEGnFYieH4OFV\nBaELgnH815NY9PJS3XGItGKxk8No37MNBgY/hk/f/QJ7tx7QHYdIGxY7OZRxb/mjTqPamBEQjZzL\nObrjEGlhSLGLSF8ROSAiv4jIdCPGJOMlpSXBL8oPLq+7wC/KD0lpjncViWc1D0yJC8LRA8fx/msf\n6Y7jWHr2LNjI7tlc7CLiCmAegH4AWgMYLiKtbR2XjJWUlgTrKivSs9KhoJCelQ7rKqtDlnvnR9qj\n/7jeWD7jM+zfcVB3HKIKZ8SMvQuAX5RSh5RSuQCWAhhkwLhkoPAN4ci+evM9VbKvZiN8Q7imROXL\n+s5I1LrrDswIiEFuzlXdcczt+kx906aCjTN3u2dEsdcHcPSG58cK991ERKwikiIiKZmZmQaclkrj\nSNaRUu03Oy9vL0yODcLhvUeR9K/luuMQVahKBowht9in/meHUnEA4gDAYrH8z9epfDXyboT0rPRb\n7ndUXfp1xCOjHsLSt1fggSFd0axTE92RzCk5ueDP67P068/JbhkxYz8GoOENzxsAOG7AuGSgiN4R\n8HTzvGmfp5snInpHaEpUMYJnjkaNOt6IHBuNq7lckiHnYESx7wTQTEQai4g7gGEAPjNgXDKQfzt/\nxA2Mg6+3LwQCX29fxA2Mg387f93RylW1mlUxKSYQh35Mx9L/rNAdx9ySkzlbNwlRyvZVERHpDyAK\ngCuARKXUbaeBFotFpaTww4ip4rzpH4Uty7/DvJ1vo8k9vrrjEJWJiKQqpSzFHWfIdexKqS+UUs2V\nUk2LK3UiHSbMHouqNasiMiAaedfydMchKlf8zVNyCt61q2Pi3AAcTD2EZe9wpZAcG4udnEaPoffh\nwaHdsPj1ZUj/6Wjx30BkUix2cioT546DRzUPRAbEIC+PSzLkmFjs5FRq1vHGhHfHYv/2g/hk1mrd\ncYjKBYudnM7Dw7rj/kH3YtErS3HsZ/7KBTkeFjs5HRHBC9GBqOzhjhnjYpCfn687EpGhWOzklGrV\nq4ngWWOw55v9WDHnS91xiAzFYien1WdkD3Tp3xELw5fg+K9/6I5DZBgWOzktEcHk+UFwqeSCmYHz\nuSRDDoPFTk7Np0EtBEWOwu7kvVgdu053HCJDsNjJ6fUL6IVOj9yD+L9/gJPp/KwAMj8WOzk9EcHU\nuPEAgJnW+TDixnhEOrHYiQDU9fVB4NvP4ft1P+LLhK91xyGyCYudqNCAoEfQvmcbxIa9h8xjp3XH\nISozFjtRIRcXF4QuCEb+tXzMCorlkgyZFoud6Ab1mtTFmIjh2PnlLqx7f5PuOERlwmIn+ovBE/uh\n7QMtETNlEU6fOKs7DlGpsdiJ/uL6kkzulVy8GxLPJRkyHRY70S00aH4XRr8xDFtX7sTGJd/ojkNU\nKix2oiIMmTIALbs2w7xJC3H25DndcYhKjMVOVARXV1eEJYbg8sUrmDMxQXccohJjsRPdhm+rBhj5\nytPYsvw7bF6+TXccohJhsRMV45lpT6BZ5yaY83wCsk6d1x2HqFgsdqJiuFZyxbTEEFw8exHzJiXq\njkNULBY7UQk0bueLEeFPYeOSb7F15U7dcYhui8VOVELDpg9Gk3t8MTs4DhfOXtQdh6hILHaiEnJz\nd8O0hRNwLvM8YqYu0h2HqEg2FbuIPC0ie0UkX0QsRoUisld3d2yM4dOfxLr3NmH7F9/rjkN0S7bO\n2PcAGAJgswFZiExhxMtPwa9NQ0QFxeJS1iXdcYj+h03FrpTap5Q6YFQYIjNwr+yGsMQQnDlxFrFh\ni3XHIfofXGMnKoMW996NoaFPYP+Og7h86YruOEQ3qVTcASKyHsCdt/hSuFJqZUlPJCJWANbCpzki\nsqek32uHagM4pTuEDZjfQPFVZ5b2W+wqfymZOTtg/vwtSnJQscWulOpjexZAKRUHIA4ARCRFKWXa\nN1uZXy/m18fM2QHHyF+S47gUQ0TkYGy93PFJETkG4D4Aq0VkrTGxiIiorIpdirkdpdSnAD4tw7fG\n2XJeO8D8ejG/PmbODjhJfuHHfhERORausRMRORhtxW7G2xGISF8ROSAiv4jIdN15SktEEkUkw4yX\nmopIQxHZKCL7Cn9uJunOVBoiUkVEdojI7sL8r+vOVBYi4ioiu0Tkc91ZSktEDotImoj8UNKrS+yJ\niNQQkeUisr/w78F9RR2rc8ZuqtsRiIgrgHkA+gFoDWC4iLTWm6rUFgHoqztEGV0DEKqUagWgG4AJ\nJnv9cwD0Ukq1B9ABQF8R6aY5U1lMArBPdwgbPKyU6mDSSx5nA1ijlGoJoD1u899BW7Gb8HYEXQD8\nopQ6pJTKBbAUwCDNmUpFKbUZwBndOcpCKXVCKfV94eMLKPihrq83VcmpAtfv9etWuJnqDS4RaQBg\nAIAFurM4GxGpDqAHgAQAUErlKqWK/IR1rrGXXH0AR294fgwmKhZHIiJ+ADoC2K43SekULmP8ACAD\nwDqllKnyA4gC8CKAfN1BykgB+EpEUgt/E95MmgDIBLCwcClsgYh4FXVwuRa7iKwXkT232Ew10y0k\nt9hnqhmXIxCRqgA+BjBZKWWqDyBVSuUppToAaACgi4i01Z2ppETkcQAZSqlU3Vls0F0p1QkFy6kT\nRKSH7kClUAlAJwAxSqmOAC4BKPJ9PpuuYy+OUbcjsBPHADS84XkDAMc1ZXFKIuKGglJPUkp9ojtP\nWSmlzolIMgre7zDLG9ndATwhIv0BVAFQXUQ+UEo9pzlXiSmljhf+mSEin6JgedUU7/GhoH+O3fCv\nvOW4TbFzKabkdgJoJiKNRcQdwDAAn2nO5DRERFCwvrhPKVXqu27pJiI+IlKj8LEHgD4A9utNVXJK\nqX8opRoopfxQ8LP/tZlKXUS8RKTa9ccAHoV5/qcKpdQfAI6KyPWbgPUG8FNRx+u83NFUtyNQSl0D\n8DyAtSh4426ZUmqv3lSlIyJLAGwD0EJEjolIgO5MpdAdwEgAvQovV/uhcPZoFvUAbBSRH1EwSVin\nlDLdJYMmVhfANyKyG8AOAKuVUms0ZyqtiQCSCn+GOgB4s6gD+ZunREQOhksxREQOhsVORORgWOxE\nRA6GxU5E5GBY7EREDobFTkTkYFjsREQOhsVORORg/h9XRus35FCWugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb19f50240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration24========================\n",
      "w = [0.1  0.06] , b = -0.38000000000000006 \tLoss = 0.31999999999999873\n"
     ]
    },
    {
     "data": {
      "image/png": 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EHtabqts2ApiuO0QPtQL4hVIqBsBPASz2sNf/BoCpSqlYAI8CmC4iP9WcqSfS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\nIiKTYbETEZkMi52IyGT+P5P6dF+SkQ8PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb1540c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================iteration25========================\n",
      "w = [0.1  0.06] , b = -0.38000000000000006 \tLoss = 0\n"
     ]
    }
   ],
   "source": [
    "# 测试:\n",
    "w = w0\n",
    "b = b0\n",
    "iterNum = 40\n",
    "for iter in range(iterNum):\n",
    "    print('========================iteration'+ str(iter)+'========================')\n",
    "    for i in range(len(x)):\n",
    "        if(y[i] * (np.dot(w,x[i].T) + b) < 0):# 如果wx+b小于0,则表示分错了,使用2.3.1中的SGD方法更新权值\n",
    "            w = w + eta * y[i] * x[i]\n",
    "            b = b + eta * y[i]\n",
    "            break\n",
    "        loss = calLoss(x,y,w,b)\n",
    "    print('w =',w,', b =',b,'\\tLoss = ' + str(loss))\n",
    "    if loss == 0:\n",
    "        break\n",
    "    #以下部分是画图，可以动态展示学习过程中的每次迭代过程的模型变化，如果不想看，也可以注释掉，直接输出每次迭代的参数学习结果\n",
    "    plt.figure(iter)\n",
    "    plt.scatter(posindex[:,0], posindex[:,1], c='red', alpha=1, marker='+', label='pickup')\n",
    "    plt.scatter(negindex[:,0], negindex[:,1], c='green', alpha=1, marker='o', label='pickup')\n",
    "    xrange = np.arange(-1, 6, .01)\n",
    "    yrange = np.arange(-1, 5, .01)\n",
    "    xrange, yrange = np.meshgrid(xrange, yrange)\n",
    "    f = w[0]*xrange + w[1]*yrange + b\n",
    "    plt.contour(xrange, yrange, f,0)\n",
    "#     print \"\".join([str(iter),\".jpg\"])\n",
    "#     plt.savefig(\"\".join([str(iter),\".jpg\"]))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "<b>动图演示上述计算过程</b>\n",
    ">![动图演示](img/chap11.gif)\n",
    "\n",
    "---\n",
    "\n",
    "这里我们重新定义一些散点，并试图编写自己的感知机算法来预测这些点的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pkpOT07Q/CS4r3l1DQvOJHPj1II/1f4iHuzUgPCLc61jB6eyofPFiL1NICDOzVc65yPNt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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb14587b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制最终的曲线\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(i)\n",
    "plt.scatter(posindex[:,0], posindex[:,1], c='red', alpha=1, marker='+', label='pickup')\n",
    "plt.scatter(negindex[:,0], negindex[:,1], c='green', alpha=1, marker='o', label='pickup')\n",
    "xrange = np.arange(-1, 6, .01)\n",
    "yrange = np.arange(-1, 10, .01)\n",
    "xrange, yrange = np.meshgrid(xrange, yrange)\n",
    "f = w[0]*xrange + w[1]*yrange + b\n",
    "plt.contour(xrange, yrange, f,0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vSSfUuv4uToinI3WRDTle7jQgmEJAuqcH3YR5G4AE3QOFJYVN3mlRoOuH7tRp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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb08c16160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09999999999999987 0.05999999999999983 -0.38000000000000006\n"
     ]
    }
   ],
   "source": [
    "# 重新构造一些数据\n",
    "testx = 5*np.random.rand(1000,2)\n",
    "Predicting_Lables = []\n",
    "for i in range(len(testx)):\n",
    "    Predicting_Lables.append(sign(np.dot(w,testx[i]) + b))\n",
    "Predicting_Lables = np.array(Predicting_Lables)\n",
    "\n",
    "plt.figure(0)\n",
    "t_pos_index = testx[Predicting_Lables[:] == 1]\n",
    "t_neg_index = testx[Predicting_Lables[:] == -1]\n",
    "plt.scatter(t_pos_index[:,0], t_pos_index[:,1], c='red', alpha=1, marker='+') # c='red'定义为红色，alpha是透明度，marker是画的样式\n",
    "plt.scatter(t_neg_index[:,0], t_neg_index[:,1], c='green', alpha=1, marker='o') # c='red'定义为红色，alpha是透明度，marker是画的样式\n",
    "\n",
    "xrange = np.arange(-1, 6, .01)\n",
    "yrange = np.arange(-1, 10, .01)\n",
    "xrange, yrange = np.meshgrid(xrange, yrange)\n",
    "f = w[0]*xrange + w[1]*yrange + b\n",
    "plt.contour(xrange, yrange, f,0)\n",
    "plt.show()\n",
    "print(w[0],w[1],b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 2.4定义自己的感知机函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "class myPerceptron():\n",
    "    def train(self,x,y,learningrate,iteration):\n",
    "        \"\"\"\n",
    "        x: 数据本身\n",
    "        y: 数据类别标签\n",
    "        learningrate: 学习率\n",
    "        iteration: 最大迭代次数\n",
    "        返回训练好的w和b\n",
    "        \"\"\"\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.iteration = iteration\n",
    "        self.learningrate = learningrate\n",
    "        x_dim = x.ndim\n",
    "        w_0 = np.random.rand(1,x_dim)\n",
    "        b_0 = 0\n",
    "        w = w_0\n",
    "        b = b_0\n",
    "        model = []\n",
    "        history = np.empty(iteration)\n",
    "        for iter in range(self.iteration):\n",
    "            for i in range(len(self.x)):\n",
    "                if(self.y[i] * (np.dot(w,self.x[i].T) + b) < 0):# 如果满足小于0,则表示分错了\n",
    "                    w = w + learningrate * self.y[i] * self.x[i]\n",
    "                    b = b + learningrate * self.y[i]\n",
    "                    iter_loss = calLoss(self.x,self.y,w,b)\n",
    "                    if np.mod(iter,20) == 0:\n",
    "                        print('=============================Iteration '+ str(iter)+'=============================')\n",
    "                        print('w =',w,', b =',b,'\\tLoss = ' + str(iter_loss))\n",
    "                    history[iter] = iter_loss\n",
    "                    break\n",
    "            if(iter_loss == 0):\n",
    "                break\n",
    "        model.append(w)\n",
    "        model.append(b)\n",
    "        model.append(range(iter))\n",
    "        model.append(history[:iter])\n",
    "        return model\n",
    "    \n",
    "    def predict(self,x,model):\n",
    "        \"\"\"\n",
    "        x 应该与train中的x具有相同的维度数\n",
    "        y 为预测类别标签\n",
    "        \"\"\"\n",
    "        self.x = x\n",
    "        self.model = model\n",
    "        y = []\n",
    "        for i in range(len(x)):\n",
    "            y.append(sign(np.dot(self.model[0],self.x[i]) + self.model[1]))\n",
    "        return y\n",
    "        \n",
    "    def plotTrainingHistry(self,model):\n",
    "        plt.figure(0)\n",
    "        x_ = model[2]\n",
    "        y_ = model[3]\n",
    "        plt.plot(x_,y_)\n",
    "        plt.show()\n",
    "    \n",
    "    def calLoss(self,x,y,w,b):\n",
    "        \"\"\"\n",
    "        计算损失,包含4个参数,分别为:\n",
    "        x: 数据\n",
    "        y: 类别标签\n",
    "        w: 参数向量\n",
    "        b: 偏置项\n",
    "        返回损失值\n",
    "        \"\"\"\n",
    "        loss = 0\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.w = w\n",
    "        self.b = b\n",
    "        for i in range(len(self.x)):\n",
    "            if(self.y[i] * (np.dot(self.w,self.x[i].T) + self.b) < 0):\n",
    "                loss = loss + (-self.y[i] * (np.dot(self.w,self.x[i].T) + self.b))\n",
    "        return loss\n",
    "    \n",
    "    def accuracy(self,y,realy):\n",
    "        self.y = y\n",
    "        self.realy = realy\n",
    "        return 1-sum(np.sign(np.abs(y-realy)))/len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============================Iteration 0=============================\n",
      "w = [[0.13091387 0.6357271 ]] , b = -0.0008 \tLoss = [640.77461811]\n",
      "=============================Iteration 20=============================\n",
      "w = [[0.11151412 0.61708269]] , b = -0.016800000000000002 \tLoss = [605.53772549]\n",
      "=============================Iteration 40=============================\n",
      "w = [[0.09211437 0.59843828]] , b = -0.03279999999999998 \tLoss = [570.30083287]\n",
      "=============================Iteration 60=============================\n",
      "w = [[0.07271462 0.57979387]] , b = -0.048800000000000024 \tLoss = [535.06394025]\n",
      "=============================Iteration 80=============================\n",
      "w = [[0.05331487 0.56114946]] , b = -0.06480000000000005 \tLoss = [499.87043609]\n",
      "=============================Iteration 100=============================\n",
      "w = [[0.03391512 0.54250506]] , b = -0.08079999999999996 \tLoss = [464.82010318]\n",
      "=============================Iteration 120=============================\n",
      "w = [[0.01451537 0.52386065]] , b = -0.09679999999999986 \tLoss = [430.27693857]\n",
      "=============================Iteration 140=============================\n",
      "w = [[-0.00488438  0.50521624]] , b = -0.11279999999999976 \tLoss = [397.67527702]\n",
      "=============================Iteration 160=============================\n",
      "w = [[-0.02428413  0.48657183]] , b = -0.12879999999999966 \tLoss = [366.96693311]\n",
      "=============================Iteration 180=============================\n",
      "w = [[-0.04368388  0.46792742]] , b = -0.14479999999999957 \tLoss = [339.16897462]\n",
      "=============================Iteration 200=============================\n",
      "w = [[-0.06308363  0.44928301]] , b = -0.16079999999999947 \tLoss = [314.5119384]\n",
      "=============================Iteration 220=============================\n",
      "w = [[-0.08248338  0.43063861]] , b = -0.17679999999999937 \tLoss = [293.23377286]\n",
      "=============================Iteration 240=============================\n",
      "w = [[-0.10188313  0.4119942 ]] , b = -0.19279999999999928 \tLoss = [275.31247769]\n",
      "=============================Iteration 260=============================\n",
      "w = [[-0.12128288  0.39334979]] , b = -0.20879999999999918 \tLoss = [261.39228261]\n",
      "=============================Iteration 280=============================\n",
      "w = [[-0.14068263  0.37470538]] , b = -0.22479999999999908 \tLoss = [252.65715158]\n",
      "=============================Iteration 300=============================\n",
      "w = [[-0.16045975  0.34660699]] , b = -0.240799999999999 \tLoss = [245.15202123]\n",
      "=============================Iteration 320=============================\n",
      "w = [[-0.1811174   0.29644931]] , b = -0.25679999999999914 \tLoss = [240.96373799]\n",
      "=============================Iteration 340=============================\n",
      "w = [[-0.20177506  0.24629162]] , b = -0.2727999999999996 \tLoss = [262.69232693]\n",
      "=============================Iteration 360=============================\n",
      "w = [[-0.20246576  0.22710027]] , b = -0.2807999999999998 \tLoss = [271.40239906]\n",
      "=============================Iteration 380=============================\n",
      "w = [[-0.19516969  0.22029545]] , b = -0.28559999999999997 \tLoss = [264.51047337]\n",
      "=============================Iteration 400=============================\n",
      "w = [[-0.18787362  0.21349063]] , b = -0.2904000000000001 \tLoss = [257.70308113]\n",
      "=============================Iteration 420=============================\n",
      "w = [[-0.18057754  0.20668582]] , b = -0.29520000000000024 \tLoss = [250.98246537]\n",
      "=============================Iteration 440=============================\n",
      "w = [[-0.16928808  0.20607426]] , b = -0.29840000000000033 \tLoss = [235.06568472]\n",
      "=============================Iteration 460=============================\n",
      "w = [[-0.161992    0.19926944]] , b = -0.30320000000000047 \tLoss = [228.26338766]\n",
      "=============================Iteration 480=============================\n",
      "w = [[-0.15469593  0.19246463]] , b = -0.3080000000000006 \tLoss = [221.5513127]\n",
      "=============================Iteration 500=============================\n",
      "w = [[-0.14739986  0.18565981]] , b = -0.31280000000000074 \tLoss = [214.97766522]\n",
      "=============================Iteration 520=============================\n",
      "w = [[-0.14010378  0.17885499]] , b = -0.3176000000000009 \tLoss = [208.55068861]\n",
      "=============================Iteration 540=============================\n",
      "w = [[-0.12881432  0.17824344]] , b = -0.320800000000001 \tLoss = [192.46004251]\n",
      "=============================Iteration 560=============================\n",
      "w = [[-0.12151825  0.17143862]] , b = -0.3256000000000011 \tLoss = [185.94749298]\n",
      "=============================Iteration 580=============================\n",
      "w = [[-0.11422217  0.1646338 ]] , b = -0.33040000000000125 \tLoss = [179.54313152]\n",
      "=============================Iteration 600=============================\n",
      "w = [[-0.1069261   0.15782898]] , b = -0.3352000000000014 \tLoss = [173.31452389]\n",
      "=============================Iteration 620=============================\n",
      "w = [[-0.09963002  0.15102416]] , b = -0.3400000000000015 \tLoss = [167.38759424]\n",
      "=============================Iteration 640=============================\n",
      "w = [[-0.08834056  0.15041261]] , b = -0.3432000000000016 \tLoss = [151.04333795]\n",
      "=============================Iteration 660=============================\n",
      "w = [[-0.07753218  0.13876601]] , b = -0.34800000000000175 \tLoss = [142.54882193]\n",
      "=============================Iteration 680=============================\n",
      "w = [[-0.05570579  0.12362911]] , b = -0.35120000000000184 \tLoss = [119.09748809]\n",
      "=============================Iteration 700=============================\n",
      "w = [[-0.0338794   0.10849221]] , b = -0.35440000000000194 \tLoss = [96.25618318]\n",
      "=============================Iteration 720=============================\n",
      "w = [[-0.01205301  0.09335531]] , b = -0.357600000000002 \tLoss = [74.69876429]\n",
      "=============================Iteration 740=============================\n",
      "w = [[0.01065145 0.077075  ]] , b = -0.3608000000000021 \tLoss = [55.51063404]\n",
      "=============================Iteration 760=============================\n",
      "w = [[0.06502349 0.07322445]] , b = -0.35120000000000184 \tLoss = [3.95207609]\n",
      "=============================Iteration 780=============================\n",
      "w = [[0.09131253 0.05540467]] , b = -0.34800000000000175 \tLoss = [0.00153565]\n",
      "=============================Iteration 800=============================\n",
      "w = [[0.09221205 0.05415034]] , b = -0.34800000000000175 \tLoss = [0.00427865]\n",
      "=============================Iteration 820=============================\n",
      "w = [[0.09180445 0.05544442]] , b = -0.34800000000000175 \tLoss = [0.00601164]\n",
      "=============================Iteration 840=============================\n",
      "w = [[0.09365675 0.05336031]] , b = -0.34800000000000175 \tLoss = [0.01678475]\n",
      "=============================Iteration 860=============================\n",
      "w = [[0.09322153 0.05514825]] , b = -0.34800000000000175 \tLoss = [0.02289881]\n",
      "=============================Iteration 880=============================\n",
      "w = [[0.09328412 0.0559833 ]] , b = -0.34800000000000175 \tLoss = [0.04707]\n",
      "=============================Iteration 900=============================\n",
      "w = [[0.09314567 0.05679374]] , b = -0.34800000000000175 \tLoss = [0.07346621]\n",
      "=============================Iteration 920=============================\n",
      "w = [[0.09207497 0.0587791 ]] , b = -0.34800000000000175 \tLoss = [0.1463541]\n",
      "=============================Iteration 940=============================\n",
      "w = [[0.09033444 0.05193001]] , b = -0.3496000000000018 \tLoss = [0.13662343]\n",
      "=============================Iteration 960=============================\n",
      "w = [[0.09291844 0.05813006]] , b = -0.34800000000000175 \tLoss = [0.13433525]\n",
      "=============================Iteration 980=============================\n",
      "w = [[0.09285109 0.05845884]] , b = -0.34800000000000175 \tLoss = [0.15339942]\n"
     ]
    }
   ],
   "source": [
    "mp = myPerceptron()\n",
    "model = mp.train(testx[:],Predicting_Lables,0.0008,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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InF6FQe+CSi/NTPR+HDAMeMNrfxEY7T0f5S3jrb/UzCxkFQsAPzwvjXuyevDu4q3c/94K\nnWMvIqdVqdMrzSweWAB0Bf4HWAcUOOdKB4nzgPbe8/bAZgDnXLGZFQItgF0nvedYYCxAenp6zbYi\nRv3yO13YfeAoz32xnqSEOCaM6IF+p4rIySoV9M65EqCfmSUDbwM9y+vmPZaXNKccbjrnpgBTADIz\nM3U4Wg1mxr1X9qSoJMCUz3MxgrclVNiLSFlVumDKOVdgZrOAC4BkM0vwjuo7AFu9bnlAGpBnZglA\nM2BP6EqWssyMP1x9DgHnglfOGozPUtiLyLcqc9ZNqnckj5k1AC4DVgKfAtd43cYAU73n73rLeOtn\nOg0g1yoz4/6re3H9+ek881kuk6av1pi9iBxXmSP6tsCL3jh9HPB359x7ZrYCeM3MJgKLgOe9/s8D\nL5tZDsEj+WtroW45SVyc8cCoXgA8/dk6Cg4VMXF0LxLiw+NSCeec/soQ8UmFQe+cWwL0L6c9FxhY\nTvsR4AchqU6qJC7OmDi6F80bJvHkpznsOlDEn6/rT4OkeF/r+nD5dn7x8gK+Gj+MdskNfK1FJBaF\nx+GehIyZ8esruvPAqHOYsWoH1z47l+2FR3yt6YH3VgBw0cMzGTF5NpOmr2Jb4WFfaxKJJQr6KPWv\nF3bimZ+cS86O/Xz3yS9YsNGf78MLDx0jb++3ob5y2z6emrWOCx+aSd7eQ/ouQaQOKOij2OXntOHt\ncYNolBTPtVPm8n/zNtV5sL7zzRYAmtQ7dZTw4kmf0nnCNLrd9wGvzN1Yp3WJxBIFfZTr1roJU8dd\nzEVnteS3by9l3P8tZO/Bojr5bOccf5u/iV7tmzL/3st49Id9Gdm7zSn9iooD3PfOMiZ/spb56/ew\n/8ixOqlPJFZYOPzpnJmZ6bKzs/0uI6qVBBzPzs7lvz9aTfOGSUy6pg9Du7eq1c/M3rCHa56ew8TR\nvfjJBR2Ptx8tLmHggzMoPHz6QJ9991DSUhrWan0ikc7MFjjnMivsp6CPLcu2FHLH69+wducBrjin\nNfddeXatBepNf/2ahZv28uX4YTRMOnXoZt+RYyzZXMis1Tt57ov1p6xvmBTP0O6tuKhrC0b3a0+j\ncoZ/RGKZgl5O62hxCc/NXs+TM3MIOMcPM9MYO6RLSAN/5bZ9jJg8mzuHd+PfL82osP8rczdy3zvL\nTru+f3oyL/3bQJrUTwxZjSKRTkEvFdpacJjHP1nD24u2EHAwolcbrhuYzoVdWhAXV/2Lm5xzXPfs\nXFZu28/nvxlKs4aVD+fikgC/eHkBM1btPG2f31zRnXFDu1a7PpFooaCXStteeIQXvlzPa/M3se9I\nMe2a1ec73VMZ1LUl53VKoVWTelW6qvWthXnc+ffFPPi9Xlx/fseKX3CSI8dKmJO7m89W5/Pxih1s\nKTj1nPtR/doxJCOV/unJpKc0DJsrgEXqkoJequzIsRI+WrGDfy7eytx1u9nv3aqwUVI8V/Zpy/2j\nelE/8cxX2ebs3M+oJ7+kR9um/P0XFxJfg78MSm0tOMxFD888Y5+V92f5fgWwSF1T0EuNFJcEWJxX\nyNK8AlZs28c/FuTRs01T/nhNH3q1b1bua5ZtKeSnf/0a5xz//NXFtG0WuukOAgHH6h37+Wrd7uNX\n2p6sc8tGDMloye2XdaN5o6SQfbZIuFLQS0jNWLmDe95cyt5DRYzs3Zbrzksjs1MKSQlxbCk4zGvz\nN/HMZ7mkNEri5ZsGktG6Sa3VMvWbLTz68Ro27j5U7vpmDRKZOm4Q7Zs3IFFDOhLFFPQScoWHjvHn\nmWv5e/Zm9h0pJjHeSIyP41BRCQAje7dh4ujepNTh0fSuA0fJnPjJGfs8ck0ffpCZVkcVidQdBb3U\nmsNFJcxavZPFeYWUBAK0S27AJd1b0bllI1/q2XXgKO8t3sqsNfnMWp1fbp+xQ7pwYZcW9GjbJKRD\nSiJ+UtBLTNq5/wgDH5xxxj5L//NynY8vUUFBLzGt4FARCzftZdbqfF6aU/6Eaf3Skhmc0ZJxQ7tW\neDaRSDiqbNDrmnKJSskNkxjWozXDerSmddP6PPLh6lP6fLO5gG82F7Akr5BJ/9KHVk3q1ehCMZFw\npSN6iRmBgCNr8ues2XHgtH3uzurOLZfoqluJDBq6ESnHwaPFLNy0l6/W7ebTVTtZtX3/KX36pSUz\nJKMl/Ts2p0cbfXkr4UtBL1IJy7YUctWfvzhjn3X/NTIkV/iKhJqCXqSSAgHHht0Hmb9+D+PfWnra\nfgPSk3l+zHm66lbCRmWDXpcNSsyLizO6pDbm2oHpPHJNH3q0Kf+q3oWbCrjw4Rl8uHw7G3YdpKg4\nUMeVilRPhUf0ZpYGvAS0AQLAFOfcZDNLAV4HOgEbgB865/ZacJrDycBI4BBwo3Nu4Zk+Q0f0Eo6K\nigN0u++DM/a5d2RPfj6kSx1VJHKikA3dmFlboK1zbqGZNQEWAKOBG4E9zrmHzWw80Nw5d4+ZjQR+\nRTDozwcmO+fOP9NnKOglXO06cJR5uXuYk7uLV+ZuKrdPxxYNGZzRkqxz2nJxRss6rlBiWa2N0ZvZ\nVOBJ7+cS59w275fBLOdcdzN7xnv+N6//6tJ+p3tPBb1EgvW7DjL0T7PO2Gf67YPp0rIxSQkaFZXa\nVysXTJlZJ6A/MA9oXRreXtiX3mm6PbC5zMvyvLYTgt7MxgJjAdLT06tShogvOrdsxIaHr+RYSYD1\nuw7y8Yodp1yIlfX47OPPe7Rpwhs3X0Rj3etWfFbp/4Fm1hh4E7jdObfvDHccKm/FKX82OOemAFMg\neERf2TpE/JYYH0e31k3o1roJSfFxvPb1JtblHzyl36rt+xk8aSZ3DO9Gz7ZN6dWumW6OIr6oVNCb\nWSLBkH/VOfeW17zDzNqWGbopvclnHlB2TtgOwNZQFSwSTn4+pMvxL2NveGE+n685cfbMvYeO8R9T\nlwNwWc9WPDfmvDqvUaQyX8Ya8CLBL15vL9P+CLC7zJexKc65u83sSuBWvv0y9gnn3MAzfYbG6CUa\nFJcE2L7vCCu27iN7416mfJ5bbj8zOL9zCjde1JmsXm3quEqJJqE86+ZiYDawlODplQC/JThO/3cg\nHdgE/MA5t8f7xfAkkEXw9MqfOufOmOIKeolGS/IKuPrJL8/Y5y8/PY9urZvQpml9XX0rVaYrY0XC\nRHFJgBXb9lUY+nMnXEqbZvXrqCqJBgp6kTCTvWEPc9bt5vO1+Xy9YW+5fc5KbUSfDsncdmkGnXy6\nY5dEDgW9SJj7wdNfnTbwAT64bTDtkhvQtH4CZzjLTWKYgl4kAuw5WMS6/AP84Ok5Z+x33cB0Hvp+\n7zqqSiKFgl4kgmwpOMzE91Ywe+0uDhwtLrfPgPRkerVvRv/0ZL7Xv0MdVyjhSEEvEqEKDx+j7x8+\nOmOfp39yLt/plqoLsGKcgl4kgh05VsKmPYdYkldI9oY9vPb15jP2f2fcIPqlJddRdRIuFPQiUeR/\nZ+Xwx+mn3uC8rF98pwsdUxoxsHMKXVs1rqPKxE8KepEo5Jzj5lcWMn359jP2W/VAFvUTNawT7RT0\nIlHqyLESFmzcy/KthSzeXMj7S087Azg3X3IWd1/RXadnRikFvUiM2HXgKJkTPznt+qnjBtFX4/dR\nSUEvEmMCAceWgsMM/uOnp6x76voBjOjd1oeqpDbp5uAiMSYuzkhLacg/b72Y31zRnUZlTr28+dWF\nhMNBnfhDQS8SZXp3aMa4oV1Zfn8WPds2Pd7eecI0Cg4V+ViZ+EVBLxLFnh+TSYMyZ99MnrGWouLA\nGV4h0UhBLxLF2iU3YNkfrji+/JcvNzDmhfk+ViR+UNCLRLn4OGPquEHHl+fk7mbqN1s0Zh9DFPQi\nMaBvWjL3ZPU4vnzba9/QecI0Cg8d87EqqSsKepEYMXZIF57+ybkntP33x6tPO1umRA8FvUiMiI8z\nsnq1oWOLhsfbXpqzkeufm+djVVIXFPQiMeaz3wzlz9f1P768eHMBT81apzH7KKagF4lB3+3bjjEX\ndjy+PGn6KhZsPP1tDSWyVRj0ZvaCme00s2Vl2lLM7GMzW+s9NvfazcyeMLMcM1tiZgNqs3gRqb4J\nI3vyn989+/jyNU/P4e1FeT5WJLWlMkf0fwWyTmobD8xwzmUAM7xlgBFAhvczFngqNGWKSKjVT4zn\nxkGdT2i74/XFBAIawok2FQa9c+5zYM9JzaOAF73nLwKjy7S/5ILmAslmppmURMLY+odGnrDc5bfT\nuOzRzzhyrMSniiTUqjtG39o5tw3Ae2zltbcHyt7zLM9rE5EwZWa8e+ugE9pydh7gnUVbNF1ClAj1\nl7Hl3d2g3L8DzWysmWWbWXZ+fn6IyxCRqujTIZk1E0ec0Db+raWaLiFKVDfod5QOyXiPO732PCCt\nTL8OwNby3sA5N8U5l+mcy0xNTa1mGSISKkkJcTz54/4ntM3J3c1jH6+hROP2Ea26Qf8uMMZ7PgaY\nWqb9Bu/smwuAwtIhHhEJf1f1aceGh68kuWHi8bbJM9byZc4uH6uSmqrM6ZV/A+YA3c0sz8xuAh4G\nhpvZWmC4twwwDcgFcoBngVtqpWoRqVVv/PIi4soMxN7wwnzufXupfwVJjehWgiJSLuccnSdMO6Ft\nwogexJlx08WdiYvTDcf9plsJikiNmBkf3Db4hLaHPljFg9NW8kXOLp1+GUEU9CJyWj3bNuW5G049\nYLzhhfn8y1Nf+VCRVIeCXkTO6LKzW7N64skXx8PyrfvIevxz3Yc2AijoRaRC9RLieexHfemS2uiE\n9lXb9/PqvE0+VSWVpaAXkUr5Xv8OzLzrEq4bmH5C+yMfrvapIqksBb2IVMnvv3s2r/7s/BPavtlc\n4FM1UhkKehGpkvqJ8Qzq2pJfDet6vG38m0t8rEgqoqAXkWq56/LuPPz93gC0alrf52rkTBT0IlJt\nPzovja6tGpc7m6GEDwW9iFSbmdExpSGfrcmn8NAxv8uR01DQi0iNzN8QvC/RfVOXVdBT/KKgF5Ea\n+fdhGQD8c/FWwmHuLDmVgl5EauTnQ7ocf/7Yx2t8rEROR0EvIjX2y++cBcATM3N8rkTKo6AXkRq7\nY3jG8eeHiop9rETKo6AXkRqrlxDPVX3aAnD3G7p4Ktwo6EUkJH58fnAOnPnr9/hciZxMQS8iIXHR\nWS35xZAuFBw6xuEi3ZQknCjoRSRkLujSgqKSAAs37fW7FClDQS8iIXNe5xSS4uOYuWqn36VIGQp6\nEQmZxvUSGJzRkunLtuviqTCioBeRkMrq1YYtBYc1R30YqZWgN7MsM1ttZjlmNr42PkNEwtMVvdrQ\nuF4CL3y5we9SxBPyoDezeOB/gBHA2cB1ZnZ2qD9HRMJT0/qJ/Pj8dN5fspVlWwr9LkeonSP6gUCO\ncy7XOVcEvAaMqoXPEZEw9fPBXWjeMImr/vwFL8/dyPKtCnw/JdTCe7YHNpdZzgPOP01fEYlCqU3q\n8fovLuDmVxbyu3eC0xc3a5BIo6R46ifGU+Ic8WaYQUnAYWbEGQQcOOeIiwveyqS4xJHgPS99jQMC\nzhHvtQcCwS9948zAwJW+h7cMgDvhgdJV3ksxr59z3nMX7FtaX+nnBvu447WUOEdCXBzOq8cR7B9X\nZntKa7Uy68xbd6wkwK8v787o/u1DvxPKqI2gL+9mM6d8/W5mY4GxAOnp6ae8QEQiW9dWTfjgtsEs\n37qP+ev3sGnPIQ4fK+HIsRLi44ySgMM5ygRkgIS44CBDiXfGTmKcUVzmF0HZoCwOOAwv4OHbXwAn\nLUMwlMy+jSbngp8d5wVwwDmc916lZwuZGQEX/EUTKJNgcRbsVxwIriutoyTgwDjePxAI/sKK82rF\nQUL8idudEGe0alKv1vZBqdoI+jwgrcxyB2DryZ2cc1OAKQCZmZk6D0skCiXEx9E3LZm+acl+lxLT\namOM/msgw8w6m1kScC3wbi18joiIVELIj+idc8VmdivwIRAPvOCcWx7qzxERkcqpjaEbnHPTgGm1\n8d4iIlI1ujJWRCTKKehFRKKcgl5EJMop6EVEopyCXkQkylk4zBltZvnAxmq+vCWwK4TlRAJtc2zQ\nNseGmmxzR+dcakWdwiLoa8LMsp1zmX7XUZe0zbFB2xwb6mKbNXQjIhLlFPQiIlEuGoJ+it8F+EDb\nHBu0zbFCVfywAAAD4klEQVSh1rc54sfoRUTkzKLhiF5ERM4gooM+Wm9CbmZpZvapma00s+VmdpvX\nnmJmH5vZWu+xudduZvaE9++wxMwG+LsF1WNm8Wa2yMze85Y7m9k8b3tf96a9xszqecs53vpOftZd\nE2aWbGZvmNkqb39fGAP7+Q7v//UyM/ubmdWPtn1tZi+Y2U4zW1amrcr71czGeP3XmtmY6tYTsUEf\n5TchLwbucs71BC4AxnnbNh6Y4ZzLAGZ4yxD8N8jwfsYCT9V9ySFxG7CyzPIk4DFve/cCN3ntNwF7\nnXNdgce8fpFqMjDdOdcD6Etw+6N2P5tZe+DfgUznXC+CU5lfS/Tt678CWSe1VWm/mlkK8HuCt2Id\nCPy+9JdDlQVvqRV5P8CFwIdllicAE/yuq5a2dSowHFgNtPXa2gKrvefPANeV6X+8X6T8ELwT2Qxg\nGPAewbu/7QISTt7fBO91cKH3PMHrZ35vQzW2uSmw/uTao3w/l95TOsXbd+8BV0TjvgY6Acuqu1+B\n64BnyrSf0K8qPxF7RE/5NyGv3Tvs+sD7U7U/MA9o7ZzbBuA9tvK6RcO/xePA3UDAW24BFDjnir3l\nstt0fHu99YVe/0jTBcgH/uINWT1nZo2I4v3snNsC/AnYBGwjuO8WEP37Gqq+X0O2vyM56Ct1E/JI\nZmaNgTeB251z+87UtZy2iPm3MLOrgJ3OuQVlm8vp6iqxLpIkAAOAp5xz/YGDfPvnfHkifru9oYdR\nQGegHdCI4NDFyaJtX5/J6bYxZNseyUFfqZuQRyozSyQY8q86597ymneYWVtvfVtgp9ce6f8Wg4Cr\nzWwD8BrB4ZvHgWQzK70LWtltOr693vpmwJ66LDhE8oA859w8b/kNgsEfrfsZ4DJgvXMu3zl3DHgL\nuIjo39dQ9f0asv0dyUEftTchNzMDngdWOuceLbPqXaD0m/cxBMfuS9tv8L69vwAoLP0TMRI45yY4\n5zo45zoR3I8znXPXA58C13jdTt7e0n+Ha7z+EXeU55zbDmw2s+5e06XACqJ0P3s2AReYWUPv/3np\nNkf1vvZUdb9+CFxuZs29v4Qu99qqzu8vLGr4ZcdIYA2wDrjX73pCuF0XE/wTbQnwjfczkuDY5Axg\nrfeY4vU3gmcgrQOWEjyjwfftqOa2XwK85z3vAswHcoB/APW89vreco63vovfdddge/sB2d6+fgdo\nHu37GfgDsApYBrwM1Iu2fQ38jeB3EMcIHpnfVJ39Cvybt+05wE+rW4+ujBURiXKRPHQjIiKVoKAX\nEYlyCnoRkSinoBcRiXIKehGRKKegFxGJcgp6EZEop6AXEYly/w//wx38OWNcUAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb16006978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.plotTrainingHistry(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确率： 0.999\n"
     ]
    }
   ],
   "source": [
    "y = mp.predict(testx,model)\n",
    "print('正确率：',mp.accuracy(y,Predicting_Lables))"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}