輸入向量機(Import Vector Machine).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 輸入向量機(Import Vector Machine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 先引入我們需要的packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import numpy as np\n",
    "import random\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 設定我們有60個點，其中30個是圈圈，30個是叉叉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 這裡我們建立一個60*2的矩陣x\n",
    "# 60代表我們有60個點，2代表為2維空間(平面)\n",
    "x=np.zeros((60,2))\n",
    "# 這裡我們建立一個長度60的向量y\n",
    "# 用來儲存我們的標記資料是圈圈(+1)還是叉叉(-1)\n",
    "y=np.zeros(60)\n",
    "# 因為有60筆資料，所以跑60個迴圈\n",
    "for i in range(0,60): \n",
    "    # 標記y前半段0~29為圈圈(+1)，後半段30~59為叉叉(-1)\n",
    "    # 也就是圈圈30個點，叉叉30個點\n",
    "    f=-0.05\n",
    "    if(i<30):\n",
    "        y[i]=1\n",
    "        x[i,0]=random.random()\n",
    "        x[i,1]=random.uniform(x[i,0],1)+f\n",
    "    else:\n",
    "        y[i]=0\n",
    "        x[i,0]=random.random()\n",
    "        x[i,1]=random.uniform(0,x[i,0])-f\n",
    "    # 這裡我們隨機給定我們資料的位置\n",
    "    # 而f可以決定我們圈圈和叉叉兩組數據分的越開或是越近\n",
    "    \n",
    "# 設定我們輸出圖片的大小\n",
    "plt.rcParams[\"figure.figsize\"] = (18,18)\n",
    "#畫出圈圈的點，bo：b代表藍色、o代表圈圈、marksize為大小\n",
    "plt.plot(x[:30,0],x[:30,1],\"bo\",markersize=20)\n",
    "#畫出叉叉的點，rX：r代表紅色、X代表叉叉、marksize為大小\n",
    "plt.plot(x[30:,0],x[30:,1],\"rX\",markersize=20)\n",
    "# 設定x和y軸座標的字體大小\n",
    "plt.xticks(fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "#畫出來\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sigmoid Function`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(s):\n",
    "    return(1/(1+np.exp(-s)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Polynomial_kernel(x,xx,power,gamma,zeta):\n",
    "    # 最後出來要為N*N的矩陣\n",
    "    # 這裡可以對照文章公式\n",
    "    return (zeta+gamma*np.dot(x,xx.T))**power"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Descend VS Newton Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:51: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N=60\n",
    "# c為次方數\n",
    "c=5\n",
    "gamma=1\n",
    "zeta=1\n",
    "lambda_2=1\n",
    "# r: 梯度下降learning Rate\n",
    "r=0.00015\n",
    "# s: 牛頓法learning Rate\n",
    "s=0.01\n",
    "K=Polynomial_kernel(x,x,c,gamma,zeta)\n",
    "########### 梯度下降 ###########\n",
    "a=np.zeros(N)\n",
    "# GD為第1000*i次迭代後的更新大小\n",
    "GD=np.zeros(100)\n",
    "k=0\n",
    "# 執行梯度下降\n",
    "for i in range(0,100000):\n",
    "    # 執行梯度下降演算法，可以對照文章中公式\n",
    "    A=(2*lambda_2/N)*np.dot(K,a)\n",
    "    B=np.dot(K/np.outer(np.ones(N)+np.exp(-np.dot(K,a)),np.ones(N)).T,np.ones(N)-y)/N\n",
    "    C=np.dot(K/np.outer(np.ones(N)+np.exp(np.dot(K,a)),np.ones(N)).T,-y)/N\n",
    "    a=a-r*(A+B+C)\n",
    "    # 存取更新值\n",
    "    if((i+1)%1000==0):\n",
    "        GD[k]=sum(abs(A+B+C))\n",
    "        k+=1\n",
    "########### 牛頓法 ###########\n",
    "a=np.zeros(N)\n",
    "# NM為第1000*i次迭代後的更新大小\n",
    "NM=np.zeros(100)\n",
    "k=0\n",
    "# 執行牛頓法\n",
    "for i in range(0,100000):\n",
    "    # 執行牛頓法，可以對照文章中公式\n",
    "    # 一次微分項\n",
    "    A=(2*lambda_2/N)*np.dot(K,a)\n",
    "    B=np.dot(K/np.outer(np.ones(N)+np.exp(-np.dot(K,a)),np.ones(N)).T,np.ones(N)-y)/N\n",
    "    C=np.dot(K/np.outer(np.ones(N)+np.exp(np.dot(K,a)),np.ones(N)).T,-y)/N\n",
    "    # 二次微分項\n",
    "    D=(2*lambda_2/N)*np.diagonal(K)\n",
    "    E=np.dot((K**2)/np.outer(2*np.ones(N)+np.exp(np.dot(K,a))+np.exp(-np.dot(K,a)),np.ones(N)).T,np.ones(N))/N\n",
    "    # 更新權重\n",
    "    a=a-s*(A+B+C)/(D+E)\n",
    "    # 存取更新值\n",
    "    if((i+1)%1000==0):\n",
    "        NM[k]=sum(abs((A+B+C)/(D+E)))\n",
    "        k+=1\n",
    "# 視覺化\n",
    "plt.rcParams[\"figure.figsize\"] = (18,18)\n",
    "iters=np.linspace(1000,100000,100000/1000)\n",
    "plt.plot(iters,GD,\"r-\",linewidth=9,label=\"Gradient Descent\")\n",
    "plt.plot(iters,NM,\"b-\",linewidth=9,label=\"Newton Method\")\n",
    "plt.xticks(fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "plt.legend(fontsize=30)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IVM Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  0  / Import vector:  55  / H:  0.47424276118456726\n",
      "iter:  1  / Import vector:  3  / H:  0.37748067303041993  / error:  0.09676208815414733\n",
      "iter:  2  / Import vector:  37  / H:  0.31169231220344146  / error:  0.06578836082697848\n",
      "iter:  3  / Import vector:  20  / H:  0.3006304009567792  / error:  0.011061911246662282\n",
      "iter:  4  / Import vector:  10  / H:  0.3000447261594902  / error:  0.0005856747972889931\n",
      "iter:  5  / Import vector:  23  / H:  0.30034036808897896  / error:  0.0002956419294887769\n",
      "iter:  6  / Import vector:  16  / H:  0.30033766777554083  / error:  2.70031343813093e-06\n"
     ]
    }
   ],
   "source": [
    "# s: learning rate / STEP: 子迴圈迭代數目\n",
    "s=0.05\n",
    "STEP=200\n",
    "a=np.zeros(N)\n",
    "# H:為cost function\n",
    "# a:為alpha值\n",
    "\n",
    "########## 第一次回圈 ############\n",
    "# 存入每筆資料當import vector計算出的H和a值\n",
    "H_candidate=np.ones(N)\n",
    "a_candidate=np.zeros(N)\n",
    "# 第一輪因為沒有import vector，所以要跑N次\n",
    "for i in range(0,N):\n",
    "    a_IV=a[i]\n",
    "    # 更新權重\n",
    "    for step in range(0,STEP):\n",
    "        A=(2*lambda_2/N)*np.dot(K[i,i],a[i])\n",
    "        B=np.dot(K[i,:]/(np.ones(N)+np.exp(-K[i,:]*a[i])).T,np.ones(N)-y)/N\n",
    "        C=np.dot(K[i,:]/(np.ones(N)+np.exp(K[i,:]*a[i])).T,-y)/N\n",
    "        D=(2*lambda_2/N)*K[i,i]\n",
    "        E=np.dot((K[i,:]**2)/(2*np.ones(N)+np.exp(K[i,:]*a[i])+np.exp(-K[i,:]*a[i])).T,np.ones(N))/N\n",
    "        a_IV=a_IV-s*(A+B+C)/(D+E)\n",
    "    # 計算H大小\n",
    "    H1=(K[i,i]*a_IV**2)*(lambda_2/N)\n",
    "    H2=np.dot(np.log(sigmoid(K[i,:]*a_IV)),-y)/N\n",
    "    H3=np.dot(np.log(sigmoid(np.ones(N)-K[i,:]*a_IV)),y-np.ones(N))/N\n",
    "    # 存入矩陣中\n",
    "    H_candidate[i]=H1+H2+H3\n",
    "    a_candidate[i]=a_IV\n",
    "# 求出H最小的資料，當作import vector\n",
    "IV=np.argmin(H_candidate)\n",
    "# 得出我們真正更新後的alpha值\n",
    "a[IV]=a_candidate[IV]\n",
    "H=min(H_candidate)\n",
    "# 輸出第一次迭代結果\n",
    "print(\"iter: \",0,\" / Import vector: \",IV,\" / H: \",H)\n",
    "# 接下來就是依序取import vector\n",
    "for t in range(0,60):\n",
    "    H_old=H\n",
    "    # 創建矩陣，存入每次迭代求出的H和a值\n",
    "    H_candidate=np.ones(N)*10000\n",
    "    a_candidate=np.zeros((N,2+t))\n",
    "    # 檢驗每筆資料\n",
    "    for i in range(0,N):\n",
    "        # 如果是import vector跳過，不是就執行\n",
    "        if(i in np.array(IV)):\n",
    "            continue\n",
    "        else:\n",
    "            # 把新的資料，連接到舊有的import vector後面\n",
    "            if(t>0):\n",
    "                temp_IV=np.append(IV,np.array(i))\n",
    "            else:\n",
    "                temp_IV=np.array([IV,i])\n",
    "            # 初始化參數\n",
    "            n=len(temp_IV)\n",
    "            K_a=np.zeros((n,n))\n",
    "            K_b=np.zeros((n,N))\n",
    "            a_IV=np.zeros(n)\n",
    "            k=0\n",
    "            for j in temp_IV:\n",
    "                K_a[k,:]=K[j,temp_IV]\n",
    "                K_b[k,:]=K[j,:]\n",
    "                a_IV[k]=a[j]\n",
    "                k+=1\n",
    "            # 更新權重\n",
    "            for step in range(0,STEP):\n",
    "                A=(2*lambda_2/N)*np.dot(K_a,a_IV)\n",
    "                B=np.dot(K_b/np.outer(np.ones(N)+np.exp(-np.dot(K_b.T,a_IV)),np.ones(n)).T,np.ones(N)-y)/N\n",
    "                C=np.dot(K_b/np.outer(np.ones(N)+np.exp(np.dot(K_b.T,a_IV)),np.ones(n)).T,-y)/N\n",
    "                D=(2*lambda_2/N)*np.diagonal(K_a)\n",
    "                E=np.dot((K_b)**2/np.outer(2*np.ones(N)+np.exp(np.dot(K_b.T,a_IV))+np.exp(-np.dot(K_b.T,a_IV)),np.ones(n)).T,np.ones(N))/N\n",
    "                a_IV=a_IV-s*(A+B+C)/(D+E)\n",
    "            # 計算H大小\n",
    "            H1=np.dot(np.dot(a_IV,K_a),a_IV)*(lambda_2/N)\n",
    "            H2=np.dot(np.log(sigmoid(np.dot(K_b.T,a_IV))),-y)/N\n",
    "            H3=np.dot(np.log(sigmoid(np.ones(N)-np.dot(K_b.T,a_IV))),y-np.ones(N))/N\n",
    "            H_candidate[i]=H1+H2+H3\n",
    "            a_candidate[i,:]=a_IV\n",
    "    # 一樣求出H最小的那筆資料\n",
    "    index=np.argmin(H_candidate)\n",
    "    new_a_IV=a_candidate[index,:]\n",
    "    H=min(H_candidate)\n",
    "    # 把他加入我們的import vector中\n",
    "    if(t>0):\n",
    "        IV=np.append(IV,index)\n",
    "    else:\n",
    "        IV=np.array([IV,index])\n",
    "    k=0\n",
    "    for j in IV:\n",
    "        a[j]=new_a_IV[k]\n",
    "        k+=1\n",
    "    # 計算錯誤率，並輸出結果\n",
    "    error=abs((H_old-H))\n",
    "    print(\"iter: \",t+1,\" / Import vector: \",index,\" / H: \",H,\" / error: \",error)\n",
    "    # 如果錯誤率低於10**-5停止運算\n",
    "    if(error<3*10**-5):\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 視覺化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (18,18)\n",
    "\n",
    "# 畫出所有的數據點\n",
    "plt.plot(x[IV,0],x[IV,1],\"ko\",markersize=40)\n",
    "plt.plot(x[:30,0],x[:30,1],\"bo\",markersize=20)\n",
    "plt.plot(x[30:,0],x[30:,1],\"rX\",markersize=20)\n",
    "\n",
    "#標記圖片邊界\n",
    "x1_low_limit=min(x[:,0])\n",
    "x1_high_limit=max(x[:,0])\n",
    "x2_low_limit=min(x[:,1])\n",
    "x2_high_limit=max(x[:,1])\n",
    "# 畫出分割線\n",
    "# 這裡的做法是\n",
    "# 從x1最小值到x1最大值中間取200個點\n",
    "# 從x2最小值到x2最大值中間取500個點\n",
    "# 總共執行10000次，每次計算其套入分割線的方程式，並存入SUM\n",
    "# 如果SUM從正變負或負變正，代表中間點為分割線\n",
    "\n",
    "x1=np.linspace(x1_low_limit,x1_high_limit,200)\n",
    "x2=np.linspace(x2_low_limit,x2_high_limit,500)\n",
    "\n",
    "for i in x1:\n",
    "    SUM_TEMP=0\n",
    "    count=0\n",
    "    for j in x2:\n",
    "        SUM=np.dot(a,Polynomial_kernel(x,np.array([i,j]),c,gamma,zeta))\n",
    "        if(np.sign(SUM)!=np.sign(SUM_TEMP)):\n",
    "            if(count>0):\n",
    "                plt.plot(i,(x2[count]+x2[count-1])/2,\"ko\")\n",
    "        count+=1\n",
    "        SUM_TEMP=SUM\n",
    "x1=np.linspace(x1_low_limit,x1_high_limit,500)\n",
    "x2=np.linspace(x2_low_limit,x2_high_limit,200)\n",
    "for j in x2:\n",
    "    SUM_TEMP=0\n",
    "    count=0\n",
    "    for i in x1:\n",
    "        SUM=np.dot(a,Polynomial_kernel(x,np.array([i,j]),c,gamma,zeta))\n",
    "        if(np.sign(SUM)!=np.sign(SUM_TEMP)):\n",
    "            if(count>0):\n",
    "                plt.plot((x1[count]+x1[count-1])/2,j,\"ko\")\n",
    "        count+=1\n",
    "        SUM_TEMP=SUM    \n",
    "\n",
    "plt.xticks(fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "#plt.xlim([x1_low_limit,x1_high_limit])\n",
    "#plt.ylim([x2_low_limit,x2_high_limit])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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