《机器学习实战》学习笔记之第六章SVM_使用svm打印出的结果怎么看

作者：小蓝xlanll | 2024-02-25 12:51:27

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使用svm打印出的结果怎么看

1.原始数据来源：https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144269
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144269

原始数据：行为特征（基因），列为样本（两类，B为与癌症无关记-1类，A为癌症有关记1类）34262*140
在这里插入图片描述

2.数据预处理

import numpy as np
import random
from sklearn.decomposition import PCA
def loadData(file):  ##输入为 行为特征，列为样本
    dataMat = []
    f=open(file)
    for line in f.readlines():
        dataMat.append(line.split(',')[1::])
    dataMat=dataMat[1::]
    m=len(dataMat)   ##特征数
    n=len(dataMat[0])   ##样本数
    # print(m,n)

    list2=[]
    for i in range(n):
        list1 = []
        for j in range(m):
            # print(j)
            list1.append(float(dataMat[j][i]))
        list2.append(list1)
    labelMat = [1 for i in range(len(dataMat[0]))]
    for i in range(len(labelMat)):
        if i % 2 == 0:
            labelMat[i] = -1
    # print(labelMat)
    return list2,labelMat     ##已经变为行为样本，列为特征

def devideData(dataMat,labelMat):
    m,n=np.shape(dataMat)
    train=[]
    test=[]
    train_label=[]
    test_label=[]
    label1=[]   ##存类别为1的样本
    label2=[]   ##存类别为-1的样本
    for i in range(m):
        if labelMat[i]==1:
            label1.append(dataMat[i])
        else:
            label2.append(dataMat[i])
    # print(type(label1))
    num1=len(label1)
    num2=len(label2)
    for i in range(int(0.8*num1)):
        r=random.randint(0,len(label1)-1)
        train.append(label1[r])
        label1.pop(r)
        train_label.append(1)
    for j in range(int(0.8*num2)):
        r=random.randint(0,len(label2)-1)
        train.append(label2[r])
        label2.pop(r)
        train_label.append(-1)
    for i in range(len(label1)):
        test.append(label1[i])
        test_label.append(1)
    for j in range(len(label2)):
        test.append(label2[j])
        test_label.append(-1)
    return train,train_label,test,test_label

if __name__=='__main__':
    file = r'C:\Users\admin\Desktop\testsets_HCC.csv'
    ##输出的dataMat是一个行为样本，列为特征的列表    140*34262
    dataMat,labelMat=loadData(file)


    ##主成成分分析，调n_components测试
    pca=PCA(n_components=140)     ##ValueError: n_components='mle' is only supported if n_samples >= n_features
    newdataMat=pca.fit_transform(dataMat)
    train,train_label,test,test_label=devideData(newdataMat,labelMat)
    for i in range(1):
        print(type(train[i]))
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3.基本模型

（1）simple SVM（SMO算法的外循环简略）

import random
import numpy as np
import matplotlib.pyplot as plt




#########比赛用的时候一定要复制粘贴一份！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！

def dataloader(filename):
    dataMat=[]
    labelMat=[]
    f=open(filename)
    for line in f.readlines():
        res=line.strip().split('\t')
        dataMat.append([float(res[0]),float(res[1])])
        labelMat.append(float(res[2]))
    return dataMat,labelMat


def select_int(i,m):                      ##i是第一个a的下标，m是a的总数目
    j=i
    while j==i:
        j=int(random.uniform(0,m))
    return j

##调整a的大小
def clipalpha(aj,h,l):
    if aj>h:
        aj=h
    if aj<l:
        aj=l
    return aj

def simple_SOM(dataMat,labelMat,C,toler,maxinter):
    dataMatrix=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(dataMatrix)
    alphas=np.mat(np.zeros((m,1)))
    b=0
    iter=0
    while iter<maxinter:
        alphapairschanged=0
        for i in range(m):
            fxi=float((np.multiply(alphas,labelMatrix).transpose())*(dataMatrix*dataMatrix[i,:].transpose()))+b
            Ei=fxi-float(labelMat[i])
            if ((labelMat[i]*Ei<-toler) and (alphas[i]<C)) or ((labelMat[i]*Ei>toler) and (alphas[i]>0)):
                j=select_int(i,m)
                fxj=float((np.multiply(alphas,labelMatrix).transpose())*(dataMatrix*dataMatrix[j,:].transpose()))+b
                # print('fxj:',fxj)
                # print('b:',b)
                Ej=fxj-float(labelMat[j])
                alphaIold=alphas[i].copy()
                alphaJold = alphas[j].copy()
                if labelMat[i]!=labelMat[j]:
                    L=max(0,alphas[j]-alphas[i])
                    H=min(C,C+alphas[j]-alphas[i])
                else:
                    L=max(0,alphas[j]+alphas[i]-C)
                    H=min(C,alphas[j]+alphas[i])
                if L==H:
                    print('L==H')
                    continue
                nn=2*dataMatrix[i,:]*dataMatrix[j,:].transpose()-dataMatrix[i,:]*dataMatrix[i,:].transpose()-dataMatrix[j,:]*dataMatrix[j,:].transpose()
                if nn>=0:
                    print('nn>=0')
                    continue
                # print(Ei,Ej,nn)
                alphas[j]-=labelMat[j]*(Ei-Ej)/nn
                # print(alphas[j])
                alphas[j]=clipalpha(alphas[j],H,L)
                if abs(alphas[j]-alphaJold)<0.00001:
                    print('J not moving enough')
                alphas[i]+=labelMat[j]*labelMat[i]*(alphaJold-alphas[j])
                b1=b-Ei-labelMat[i]*dataMatrix[i,:]*dataMatrix[i,:].transpose()*(alphas[i]-alphaIold)-labelMat[j]*dataMatrix[i,:]*dataMatrix[j,:].transpose()*(alphas[j]-alphaJold)
                b2=b-Ej-labelMat[i]*dataMatrix[i,:]*dataMatrix[j,:].transpose()*(alphas[i]-alphaIold)-labelMat[j]*dataMatrix[j,:]*dataMatrix[j,:].transpose()*(alphas[j]-alphaJold)
                if alphas[i]>0 and C>alphas[i]:
                    b=b1
                elif alphas[j]>0 and C>alphas[j]:
                    b=b2
                else:
                    b=(b1+b2)/2.0
                alphapairschanged+=1
                print('inter:%d i:%d,pairs changed:%d'%(iter,i,alphapairschanged))
        if alphapairschanged==0:
            iter+=1
        else:
            iter=0
        print('interaction number:%d'%(iter))

    return b,alphas

##计算超平面的w
def calWs(alphas,dataMat,labelMat):
    X=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(X)
    w=np.zeros((n,1))
    for i in range(m):
        w+=np.multiply(alphas[i]*labelMatrix[i],X[i,:].transpose())
    return w

def Plot_final(dataMat,labelMat,b0,slope):
    dataArray=np.array(dataMat)
    n,m=np.shape(dataArray)
    x1=[]
    y1=[]
    x2=[]
    y2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            x1.append(dataArray[i,0])
            y1.append(dataArray[i,1])
        else:
            x2.append(dataArray[i,0])
            y2.append(dataArray[i,1])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(x1,y1,s=30,c='red',marker='s')
    ax.scatter(x2,y2,s=30,c='green')
    x=np.arange(-2.0, 10.0, 0.1)
    y=slope*x+b0
    ax.plot(x, y)
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.show()


if __name__=='__main__':
    filename=r'F:\机器学习实战源代码和数据\machinelearninginaction\Ch06\testSet.txt'
    dataMat,labelMat=dataloader(filename)
    b,alphas=simple_SOM(dataMat,labelMat,0.6,0.001,40)
    # m,n=np.shape(b)
    # print(b)
    label1=[]
    label2=[]
    for i in range(100):
        if alphas[i]>0 and labelMat[i]==1:
            label1.append(dataMat[i])
        if alphas[i]>0 and labelMat[i]==-1:
            label2.append(dataMat[i])
    print('支持向量1: ', label1)
    print('支持向量2：', label2)
    slope=(label2[1][1]-label2[0][1])/(label2[1][0]-label2[0][0])
    b1=label2[0][1]-slope*label2[0][0]
    b2=label1[0][1]-slope*label1[0][0]
    b0=(b1+b2)/2     ##算出来的b效果并不是很好，不如b0
    Plot_final(dataMat,labelMat,b0,slope)

    ws = calWs(alphas, dataMat, labelMat)
    print(ws)
    ##进行分类预测,用第一个测试点测试,若算出来的值小于0则预测为-1类
    guess = np.mat(dataMat)[0] * np.mat(ws) + b
    print(guess, labelMat[0])
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（2）外循环完整版本

import random
import numpy as np
import matplotlib.pyplot as plt


##########此方法比simple版本快，但是鲁棒性不太好
#########比赛用的时候一定要复制粘贴一份！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！



def dataloader(filename):
    dataMat=[]
    labelMat=[]
    f=open(filename)
    for line in f.readlines():
        res=line.strip().split('\t')
        dataMat.append([float(res[0]),float(res[1])])
        labelMat.append(float(res[2]))
    return dataMat,labelMat


def select_int(i,m):                      ##i是第一个a的下标，m是a的总数目
    j=i
    while j==i:
        j=int(random.uniform(0,m))
    return j

##调整a的大小
def clipalpha(aj,h,l):
    if aj>h:
        aj=h
    if aj<l:
        aj=l
    return aj

class altStruct:
    def __init__(self,dataMat,labelMat,C,toler):
        self.X=dataMat
        self.L=np.mat(labelMat).transpose()
        self.LL=labelMat
        self.C=C
        self.T=toler
        self.m=np.shape(self.X)[0]
        self.alphas=np.mat(np.zeros((self.m,1)))
        self.b=0
        self.echae=np.mat(np.zeros((self.m,2)))

    def getEk(self,oS,k):
        fxk=float(np.multiply(oS.alphas,oS.L).transpose()*oS.X*oS.X[k,:].transpose())+oS.b
        Ek=fxk-float(oS.L[k])
        return Ek

    ## 内循环中的启发式方法，寻找a2
    def selectJ(self,i,oS,Ei):               ##寻找步长最大的来增加查找速度
        maxK=-1
        maxdeltaE=0
        Ej=0
        ValidEcacheList=np.nonzero(self.echae[:,0].A)[0]     ##获取非零的E所在的对应的行索引
        if len(ValidEcacheList)>1:
            for k in ValidEcacheList:
                if k==i:
                    continue
                Ek=oS.getEk(oS,k)
                deltaE=abs(Ek-Ei)
                if deltaE>maxdeltaE:
                    maxdeltaE=deltaE
                    maxK=k
                    Ej=Ek
            return maxK,Ej
        else:
            j=select_int(i,oS.m)
            Ej=oS.getEk(oS,j)
            return j,Ej

    def updateEk(self,oS,k):
        Ek=oS.getEk(oS,k)
        oS.echae[k]=[1,Ek]
##内循环
def inner(i,oS):
    Ei=oS.getEk(oS,i)
    if ((oS.LL[i]*Ei<-oS.T) and (oS.alphas[i]<oS.C)) or ((oS.LL[i]*Ei>oS.T) and (oS.alphas[i]>0)):
        j,Ej=oS.selectJ(i,oS,Ei)
        alphaIold=oS.alphas[i].copy()
        alphaJold=oS.alphas[j].copy()
        if oS.LL[i] != oS.LL[j]:
            L = max(0, oS.alphas[j] - oS.alphas[i])
            H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
        else:
            L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
            H = min(oS.C, oS.alphas[j] + oS.alphas[i])
        if L==H:
            print('L==H')
            return 0
        nn=2*oS.X[i,:]*oS.X[j,:].transpose()-oS.X[i,:]*oS.X[i,:].transpose()-oS.X[j,:]*oS.X[j,:].transpose()
        if nn >= 0:
            print('nn>=0')
            return 0
        print(oS.alphas[j])
        print(oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn)
        oS.alphas[j]-=oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn
        oS.alphas[j]=clipalpha(oS.alphas[j],H,L)
        oS.updateEk(oS,j)
        if abs(oS.alphas[j] - alphaJold) < 0.00001:
            print('J not moving enough')
            return 0
        oS.alphas[i] += oS.LL[j] * oS.LL[i] * (alphaJold - oS.alphas[j])
        oS.updateEk(oS,i)
        b1 = oS.b - Ei - oS.LL[i] * oS.X[i, :] * oS.X[i, :].transpose() * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.X[i, :] * oS.X[j, :].transpose() * (oS.alphas[j] - alphaJold)
        b2 = oS.b - Ej - oS.LL[i] * oS.X[i, :] * oS.X[j, :].transpose() * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.X[j, :] * oS.X[j, :].transpose() * (oS.alphas[j] - alphaJold)
        if oS.alphas[i] > 0 and oS.C > oS.alphas[i]:
            oS.b = b1
        elif oS.alphas[j] > 0 and oS.C > oS.alphas[j]:
            oS.b = b2
        else:
            oS.b = (b1 + b2) / 2.0
        return 1
    else:
        return 0

##外循环，寻找a1
def Outcircle(dataMat,labelMat,C,toler,maxIter):
    oS=altStruct(np.mat(dataMat),labelMat,C,toler)
    iter=0
    entireSet=True
    alphapairsChanged=0
    ##main body
    while (iter<maxIter) and ((alphapairsChanged>0) or (entireSet)):   ##当超过迭代次数或没有a更新时退出
        alphapairsChanged=0

        ##遍历所有值
        if entireSet:
            for i in range(oS.m):
                alphapairsChanged+=inner(i,oS)
                print('fullset,iter:%d i:%d, pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1
        ##遍历在间隔边界上的支持向量，即满足KKT条件的0<ai<C的，     （遍历非‘边界’值）
        else:
            nonBoundIs=np.nonzero((oS.alphas.A>0)*(oS.alphas.A<C))[0]
            for i in nonBoundIs:
                alphapairsChanged+=inner(i,oS)
                print('nonbound,iter:%d i:%d,pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1

        ##使得遍历全值和遍历非‘边界’值交叉进行
        if entireSet:
            entireSet=False
        elif alphapairsChanged==0:
            entireSet=True
        print('interaction number:%d'%(iter))   ##迭代次数iter
    return oS.b,oS.alphas

##计算超平面的w
def calWs(alphas,dataMat,labelMat):
    X=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(X)
    w=np.zeros((n,1))
    for i in range(m):
        w+=np.multiply(alphas[i]*labelMatrix[i],X[i,:].transpose())
    return w






def Plot_final(dataMat,labelMat,b0,slope):
    dataArray=np.array(dataMat)
    n,m=np.shape(dataArray)
    x1=[]
    y1=[]
    x2=[]
    y2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            x1.append(dataArray[i,0])
            y1.append(dataArray[i,1])
        else:
            x2.append(dataArray[i,0])
            y2.append(dataArray[i,1])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(x1,y1,s=30,c='red',marker='s')
    ax.scatter(x2,y2,s=30,c='green')
    x=np.arange(-2.0, 10.0, 0.1)
    y=slope*x+b0
    ax.plot(x, y)
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.show()


if __name__=='__main__':
    filename=r'F:\机器学习实战源代码和数据\machinelearninginaction\Ch06\testSet.txt'
    dataMat,labelMat=dataloader(filename)
    b,alphas=Outcircle(dataMat,labelMat,0.6,0.001,40)
    # print(type(b))
    label1=[]
    label2=[]
    for i in range(100):
        if alphas[i]>0 and labelMat[i]==1:
            label1.append(dataMat[i])
        if alphas[i]>0 and labelMat[i]==-1:
            label2.append(dataMat[i])
    print('支持向量1: ', label1)
    print('支持向量2：',label2)

    slope=(label2[1][1]-label2[0][1])/(label2[1][0]-label2[0][0])
    b1=label2[0][1]-slope*label2[0][0]
    b2=label1[0][1]-slope*label1[0][0]
    b0=(b1+b2)/2
    Plot_final(dataMat,labelMat,b0,slope)

    ws=calWs(alphas,dataMat,labelMat)
    print('分隔面的法向量:',ws)
    ##进行分类预测,用第一个测试点测试,若算出来的值小于0则预测为-1类
    guess=np.mat(dataMat)[0]*np.mat(ws)+b
    print('one of the guess sample :',guess,'its real label:',labelMat[0])

    m, n = np.shape(dataMat)
    errorcount = 0
    for i in range(m):
        predict =np.mat(dataMat)[0]*np.mat(ws)+b
        if np.sign(predict) != np.sign(labelMat[i]):
            errorcount += 1
    print('training error rate is:', float(errorcount / m))
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（3）使用高斯核函数版本

import random
import numpy as np
import matplotlib.pyplot as plt


##########此方法比simple版本快，但是鲁棒性不太好
#########比赛用的时候一定要复制粘贴一份！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！



def dataloader(filename):
    dataMat=[]
    labelMat=[]
    f=open(filename)
    for line in f.readlines():
        res=line.strip().split('\t')
        dataMat.append([float(res[0]),float(res[1])])
        labelMat.append(float(res[2]))
    return dataMat,labelMat


def select_int(i,m):                      ##i是第一个a的下标，m是a的总数目
    j=i
    while j==i:
        j=int(random.uniform(0,m))
    return j

##调整a的大小
def clipalpha(aj,h,l):
    if aj>h:
        aj=h
    if aj<l:
        aj=l
    return aj

class altStruct:
    def __init__(self,dataMat,labelMat,C,toler,kTup):
        self.X=dataMat
        self.L=np.mat(labelMat).transpose()
        self.LL=labelMat
        self.C=C
        self.T=toler
        self.m=np.shape(self.X)[0]
        self.alphas=np.mat(np.zeros((self.m,1)))
        self.b=0
        self.echae=np.mat(np.zeros((self.m,2)))
        self.K=np.mat(np.zeros((self.m,self.m)))
        for i in range(self.m):
            self.K[:,i]=kernelTrans(self.X,self.X[i,:],kTup)

    def getEk(self,oS,k):
        fxk=float(np.multiply(oS.alphas,oS.L).transpose()*oS.K[:,k]+oS.b)
        Ek=fxk-float(oS.L[k])
        return Ek

    ## 内循环中的启发式方法，寻找a2
    def selectJ(self,i,oS,Ei):               ##寻找步长最大的来增加查找速度
        maxK=-1
        maxdeltaE=0
        Ej=0
        ValidEcacheList=np.nonzero(self.echae[:,0].A)[0]     ##获取非零的E所在的对应的行索引
        if len(ValidEcacheList)>1:
            for k in ValidEcacheList:
                if k==i:
                    continue
                Ek=oS.getEk(oS,k)
                deltaE=abs(Ek-Ei)
                if deltaE>maxdeltaE:
                    maxdeltaE=deltaE
                    maxK=k
                    Ej=Ek
            return maxK,Ej
        else:
            j=select_int(i,oS.m)
            Ej=oS.getEk(oS,j)
            return j,Ej

    def updateEk(self,oS,k):
        Ek=oS.getEk(oS,k)
        oS.echae[k]=[1,Ek]
##内循环
def inner(i,oS):
    Ei=oS.getEk(oS,i)
    if ((oS.LL[i]*Ei<-oS.T) and (oS.alphas[i]<oS.C)) or ((oS.LL[i]*Ei>oS.T) and (oS.alphas[i]>0)):
        j,Ej=oS.selectJ(i,oS,Ei)
        alphaIold=oS.alphas[i].copy()
        alphaJold=oS.alphas[j].copy()
        if oS.LL[i] != oS.LL[j]:
            L = max(0, oS.alphas[j] - oS.alphas[i])
            H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
        else:
            L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
            H = min(oS.C, oS.alphas[j] + oS.alphas[i])
        if L==H:
            print('L==H')
            return 0
        nn=2*oS.K[i,j]-oS.K[i,i]-oS.K[j,j]
        if nn >= 0:
            print('nn>=0')
            return 0
        print(oS.alphas[j])
        print(oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn)
        oS.alphas[j]-=oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn
        oS.alphas[j]=clipalpha(oS.alphas[j],H,L)
        oS.updateEk(oS,j)
        if abs(oS.alphas[j] - alphaJold) < 0.00001:
            print('J not moving enough')
            return 0
        oS.alphas[i] += oS.LL[j] * oS.LL[i] * (alphaJold - oS.alphas[j])
        oS.updateEk(oS,i)
        b1 = oS.b - Ei - oS.LL[i] * oS.K[i,i] * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[i,j]* (oS.alphas[j] - alphaJold)
        b2 = oS.b - Ej - oS.LL[i] * oS.K[i,j]* (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[j,j]* (oS.alphas[j] - alphaJold)
        if oS.alphas[i] > 0 and oS.C > oS.alphas[i]:
            oS.b = b1
        elif oS.alphas[j] > 0 and oS.C > oS.alphas[j]:
            oS.b = b2
        else:
            oS.b = (b1 + b2) / 2.0
        return 1
    else:
        return 0

##外循环，寻找a1
def newOutcircle(dataMat,labelMat,C,toler,maxIter,kTup):
    oS=altStruct(np.mat(dataMat),labelMat,C,toler,kTup)
    iter=0
    entireSet=True
    alphapairsChanged=0
    ##main body
    while (iter<maxIter) and ((alphapairsChanged>0) or (entireSet)):   ##当超过迭代次数或没有a更新时退出
        alphapairsChanged=0

        ##遍历所有值
        if entireSet:
            for i in range(oS.m):
                alphapairsChanged+=inner(i,oS)
                print('fullset,iter:%d i:%d, pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1
        ##遍历在间隔边界上的支持向量，即满足KKT条件的0<ai<C的，     （遍历非‘边界’值）
        else:
            nonBoundIs=np.nonzero((oS.alphas.A>0)*(oS.alphas.A<C))[0]
            for i in nonBoundIs:
                alphapairsChanged+=inner(i,oS)
                print('nonbound,iter:%d i:%d,pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1

        ##使得遍历全值和遍历非‘边界’值交叉进行
        if entireSet:
            entireSet=False
        elif alphapairsChanged==0:
            entireSet=True
        print('interaction number:%d'%(iter))   ##迭代次数iter
    return oS.b,oS.alphas

##计算超平面的w
def calWs(alphas,dataMat,labelMat):
    X=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(X)
    w=np.zeros((n,1))
    for i in range(m):
        w+=np.multiply(alphas[i]*labelMatrix[i],X[i,:].transpose())
    return w

def kernelTrans(X,A,kTup):
    m,n=np.shape(X)
    K=np.mat(np.zeros((m,1)))
    if kTup[0]=='lin':
        K=X*A.transpose()
    ##高斯核函数
    elif kTup[0]=='rbf':
        for j in range(m):
            deltaRow=X[j,:]-A
            K[j]=deltaRow*deltaRow.transpose()
        K=np.exp(K/(-1*kTup[1]**2))
    else:
        raise NameError('Houston we have a problem that kernel is not recoginized')
    return K





def Plot_final(dataMat,labelMat,b0,slope):
    dataArray=np.array(dataMat)
    n,m=np.shape(dataArray)
    x1=[]
    y1=[]
    x2=[]
    y2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            x1.append(dataArray[i,0])
            y1.append(dataArray[i,1])
        else:
            x2.append(dataArray[i,0])
            y2.append(dataArray[i,1])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(x1,y1,s=30,c='red',marker='s')
    ax.scatter(x2,y2,s=30,c='green')
    x=np.arange(-2.0, 10.0, 0.1)
    y=slope*x+b0
    ax.plot(x, y)
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.show()


if __name__=='__main__':
    filename=r'F:\机器学习实战源代码和数据\machinelearninginaction\Ch06\testSet.txt'
    dataMat,labelMat=dataloader(filename)
    b,alphas=newOutcircle(dataMat,labelMat,0.6,0.001,40,kTup=('rbf',1.3))    ##C的调参很重要
    # print(type(b))


    # label1=[]
    # label2=[]
    # for i in range(100):
    #     if alphas[i]>0 and labelMat[i]==1:
    #         label1.append(dataMat[i])
    #     if alphas[i]>0 and labelMat[i]==-1:
    #         label2.append(dataMat[i])
    # print('支持向量1: ', label1)
    # print('支持向量2：',label2)
    #
    # slope=(label2[1][1]-label2[0][1])/(label2[1][0]-label2[0][0])
    # b1=label2[0][1]-slope*label2[0][0]
    # b2=label1[0][1]-slope*label1[0][0]
    # b0=(b1+b2)/2
    # Plot_final(dataMat,labelMat,b0,slope)

    ws=calWs(alphas,dataMat,labelMat)
    print(ws)
    ##进行分类预测,用第一个测试点测试,若算出来的值小于0则预测为-1类
    # guess=np.mat(dataMat)[0]*np.mat(ws)+b
    # print(guess,labelMat[0])

##计算误差
    dataM=np.mat(dataMat)
    labelM=np.mat(labelMat).transpose()
    svInd=np.nonzero(alphas.A>0)[0]
    sVs=dataM[svInd]
    labelSV=labelM[svInd]
    m,n=np.shape(dataM)
    errorcount=0
    for i in range(m):
        eva=kernelTrans(sVs,dataM[i,:],('rbf',1.3))
        predict=eva.transpose()*np.multiply(labelSV,alphas[svInd])+b
        print('predict,real label:',predict,labelMat[i])
        if np.sign(predict)!=np.sign(labelMat[i]):
            errorcount+=1
    # print(errorcount,m)
    print('training error rate is:',float(errorcount/m))

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4.实验（实际测试）
（1）不使用核函数的实战，测试集精确度：30%左右

import random
import numpy as np
import matplotlib.pyplot as plt
import pre_processHCC
import SVM_kernel

##########此方法比simple版本快，但是鲁棒性不太好
#########比赛用的时候一定要复制粘贴一份！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
## 没有用核函数，C也没有改，精准度很低


def dataloader(filename):
    dataMat=[]
    labelMat=[]
    f=open(filename)
    for line in f.readlines():
        res=line.strip().split('\t')
        dataMat.append([float(res[0]),float(res[1])])
        labelMat.append(float(res[2]))
    return dataMat,labelMat


def select_int(i,m):                      ##i是第一个a的下标，m是a的总数目
    j=i
    while j==i:
        j=int(random.uniform(0,m))
    return j

##调整a的大小
def clipalpha(aj,h,l):
    if aj>h:
        aj=h
    if aj<l:
        aj=l
    return aj

class altStruct:
    def __init__(self,dataMat,labelMat,C,toler):
        self.X=dataMat
        self.L=np.mat(labelMat).transpose()
        self.LL=labelMat
        self.C=C
        self.T=toler
        self.m=np.shape(self.X)[0]
        self.alphas=np.mat(np.zeros((self.m,1)))
        self.b=0
        self.echae=np.mat(np.zeros((self.m,2)))

    def getEk(self,oS,k):
        fxk=float(np.multiply(oS.alphas,oS.L).transpose()*oS.X*oS.X[k,:].transpose())+oS.b
        Ek=fxk-float(oS.L[k])
        return Ek

    ## 内循环中的启发式方法，寻找a2
    def selectJ(self,i,oS,Ei):               ##寻找步长最大的来增加查找速度
        maxK=-1
        maxdeltaE=0
        Ej=0
        ValidEcacheList=np.nonzero(self.echae[:,0].A)[0]     ##获取非零的E所在的对应的行索引
        if len(ValidEcacheList)>1:
            for k in ValidEcacheList:
                if k==i:
                    continue
                Ek=oS.getEk(oS,k)
                deltaE=abs(Ek-Ei)
                if deltaE>maxdeltaE:
                    maxdeltaE=deltaE
                    maxK=k
                    Ej=Ek
            return maxK,Ej
        else:
            j=select_int(i,oS.m)
            Ej=oS.getEk(oS,j)
            return j,Ej

    def updateEk(self,oS,k):
        Ek=oS.getEk(oS,k)
        oS.echae[k]=[1,Ek]
##内循环
def inner(i,oS):
    Ei=oS.getEk(oS,i)
    if ((oS.LL[i]*Ei<-oS.T) and (oS.alphas[i]<oS.C)) or ((oS.LL[i]*Ei>oS.T) and (oS.alphas[i]>0)):
        j,Ej=oS.selectJ(i,oS,Ei)
        alphaIold=oS.alphas[i].copy()
        alphaJold=oS.alphas[j].copy()
        if oS.LL[i] != oS.LL[j]:
            L = max(0, oS.alphas[j] - oS.alphas[i])
            H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
        else:
            L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
            H = min(oS.C, oS.alphas[j] + oS.alphas[i])
        if L==H:
            print('L==H')
            return 0
        nn=2*oS.X[i,:]*oS.X[j,:].transpose()-oS.X[i,:]*oS.X[i,:].transpose()-oS.X[j,:]*oS.X[j,:].transpose()
        if nn >= 0:
            print('nn>=0')
            return 0
        print(oS.alphas[j])
        print(oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn)
        oS.alphas[j]-=oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn
        oS.alphas[j]=clipalpha(oS.alphas[j],H,L)
        oS.updateEk(oS,j)
        if abs(oS.alphas[j] - alphaJold) < 0.00001:
            print('J not moving enough')
            return 0
        oS.alphas[i] += oS.LL[j] * oS.LL[i] * (alphaJold - oS.alphas[j])
        oS.updateEk(oS,i)
        b1 = oS.b - Ei - oS.LL[i] * oS.X[i, :] * oS.X[i, :].transpose() * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.X[i, :] * oS.X[j, :].transpose() * (oS.alphas[j] - alphaJold)
        b2 = oS.b - Ej - oS.LL[i] * oS.X[i, :] * oS.X[j, :].transpose() * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.X[j, :] * oS.X[j, :].transpose() * (oS.alphas[j] - alphaJold)
        if oS.alphas[i] > 0 and oS.C > oS.alphas[i]:
            oS.b = b1
        elif oS.alphas[j] > 0 and oS.C > oS.alphas[j]:
            oS.b = b2
        else:
            oS.b = (b1 + b2) / 2.0
        return 1
    else:
        return 0

##外循环，寻找a1
def Outcircle(dataMat,labelMat,C,toler,maxIter):
    oS=altStruct(np.mat(dataMat),labelMat,C,toler)
    iter=0
    entireSet=True
    alphapairsChanged=0
    ##main body
    while (iter<maxIter) and ((alphapairsChanged>0) or (entireSet)):   ##当超过迭代次数或没有a更新时退出
        alphapairsChanged=0

        ##遍历所有值
        if entireSet:
            for i in range(oS.m):
                alphapairsChanged+=inner(i,oS)
                print('fullset,iter:%d i:%d, pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1
        ##遍历在间隔边界上的支持向量，即满足KKT条件的0<ai<C的，     （遍历非‘边界’值）
        else:
            nonBoundIs=np.nonzero((oS.alphas.A>0)*(oS.alphas.A<C))[0]
            for i in nonBoundIs:
                alphapairsChanged+=inner(i,oS)
                print('nonbound,iter:%d i:%d,pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1

        ##使得遍历全值和遍历非‘边界’值交叉进行
        if entireSet:
            entireSet=False
        elif alphapairsChanged==0:
            entireSet=True
        print('interaction number:%d'%(iter))   ##迭代次数iter
    return oS.b,oS.alphas

##计算超平面的w
def calWs(alphas,dataMat,labelMat):
    X=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(X)
    w=np.zeros((n,1))
    for i in range(m):
        w+=np.multiply(alphas[i]*labelMatrix[i],X[i,:].transpose())
    return w






def Plot_final(dataMat,labelMat,b0,slope):
    dataArray=np.array(dataMat)
    n,m=np.shape(dataArray)
    x1=[]
    y1=[]
    x2=[]
    y2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            x1.append(dataArray[i,0])
            y1.append(dataArray[i,1])
        else:
            x2.append(dataArray[i,0])
            y2.append(dataArray[i,1])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(x1,y1,s=30,c='red',marker='s')
    ax.scatter(x2,y2,s=30,c='green')
    x=np.arange(-2.0, 10.0, 0.1)
    y=slope*x+b0
    ax.plot(x, y)
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.show()


if __name__=='__main__':
    file = r'C:\Users\admin\Desktop\testsets_HCC.csv'
    dataMat, labelMat = pre_processHCC.loadData(file)
    b,alphas=Outcircle(dataMat,labelMat,0.6,0.001,40)
    # print(type(b))

    ##打印出支持向量
    # label1=[]
    # label2=[]
    # for i in range(100):
    #     if alphas[i]>0 and labelMat[i]==1:
    #         label1.append(dataMat[i])
    #     if alphas[i]>0 and labelMat[i]==-1:
    #         label2.append(dataMat[i])
    # print('支持向量1: ', label1)
    # print('支持向量2：',label2)

    # slope=(label2[1][1]-label2[0][1])/(label2[1][0]-label2[0][0])
    # b1=label2[0][1]-slope*label2[0][0]
    # b2=label1[0][1]-slope*label1[0][0]
    # b0=(b1+b2)/2
    # Plot_final(dataMat,labelMat,b0,slope)

    ws=calWs(alphas,dataMat,labelMat)
    # print(ws)
    ##进行分类预测,用第一个测试点测试,若算出来的值小于0则预测为-1类
    for sample in range(10):
        guess=np.mat(dataMat)[sample]*np.mat(ws)+b
        print(guess,labelMat[sample])
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（2）使用核函数，精确度65%~70%

import random
import numpy as np
import matplotlib.pyplot as plt
import pre_processHCC
import SVM_kernel
from sklearn.decomposition import PCA

##########此方法比simple版本快
#########比赛用的时候一定要复制粘贴一份！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
## 用了核函数同时也改了C的大小，没有用PCA,预测时候没有将数据进行核函数转变


def dataloader(filename):
    dataMat=[]
    labelMat=[]
    f=open(filename)
    for line in f.readlines():
        res=line.strip().split('\t')
        dataMat.append([float(res[0]),float(res[1])])
        labelMat.append(float(res[2]))
    return dataMat,labelMat


def select_int(i,m):                      ##i是第一个a的下标，m是a的总数目
    j=i
    while j==i:
        j=int(random.uniform(0,m))
    return j

##调整a的大小
def clipalpha(aj,h,l):
    if aj>h:
        aj=h
    if aj<l:
        aj=l
    return aj

class altStruct:
    def __init__(self,dataMat,labelMat,C,toler,kTup):
        self.X=dataMat
        self.L=np.mat(labelMat).transpose()
        self.LL=labelMat
        self.C=C
        self.T=toler
        self.m=np.shape(self.X)[0]
        self.alphas=np.mat(np.zeros((self.m,1)))
        self.b=0
        self.echae=np.mat(np.zeros((self.m,2)))
        self.K=np.mat(np.zeros((self.m,self.m)))
        for i in range(self.m):
            self.K[:,i]=kernelTrans(self.X,self.X[i,:],kTup)

    def getEk(self,oS,k):
        fxk=float(np.multiply(oS.alphas,oS.L).transpose()*oS.K[:,k]+oS.b)
        Ek=fxk-float(oS.L[k])
        return Ek

    ## 内循环中的启发式方法，寻找a2
    def selectJ(self,i,oS,Ei):               ##寻找步长最大的来增加查找速度
        maxK=-1
        maxdeltaE=0
        Ej=0
        ValidEcacheList=np.nonzero(self.echae[:,0].A)[0]     ##获取非零的E所在的对应的行索引
        if len(ValidEcacheList)>1:
            for k in ValidEcacheList:
                if k==i:
                    continue
                Ek=oS.getEk(oS,k)
                deltaE=abs(Ek-Ei)
                if deltaE>maxdeltaE:
                    maxdeltaE=deltaE
                    maxK=k
                    Ej=Ek
            return maxK,Ej
        else:
            j=select_int(i,oS.m)
            Ej=oS.getEk(oS,j)
            return j,Ej

    def updateEk(self,oS,k):
        Ek=oS.getEk(oS,k)
        oS.echae[k]=[1,Ek]
##内循环
def inner(i,oS):
    Ei=oS.getEk(oS,i)
    if ((oS.LL[i]*Ei<-oS.T) and (oS.alphas[i]<oS.C)) or ((oS.LL[i]*Ei>oS.T) and (oS.alphas[i]>0)):
        j,Ej=oS.selectJ(i,oS,Ei)
        alphaIold=oS.alphas[i].copy()
        alphaJold=oS.alphas[j].copy()
        if oS.LL[i] != oS.LL[j]:
            L = max(0, oS.alphas[j] - oS.alphas[i])
            H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
        else:
            L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
            H = min(oS.C, oS.alphas[j] + oS.alphas[i])
        if L==H:
            print('L==H')
            return 0
        nn=2*oS.K[i,j]-oS.K[i,i]-oS.K[j,j]
        if nn >= 0:
            print('nn>=0')
            return 0
        print(oS.alphas[j])
        print(oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn)
        oS.alphas[j]-=oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn
        oS.alphas[j]=clipalpha(oS.alphas[j],H,L)
        oS.updateEk(oS,j)
        if abs(oS.alphas[j] - alphaJold) < 0.00001:
            print('J not moving enough')
            return 0
        oS.alphas[i] += oS.LL[j] * oS.LL[i] * (alphaJold - oS.alphas[j])
        oS.updateEk(oS,i)
        b1 = oS.b - Ei - oS.LL[i] * oS.K[i,i] * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[i,j]* (oS.alphas[j] - alphaJold)
        b2 = oS.b - Ej - oS.LL[i] * oS.K[i,j]* (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[j,j]* (oS.alphas[j] - alphaJold)
        if oS.alphas[i] > 0 and oS.C > oS.alphas[i]:
            oS.b = b1
        elif oS.alphas[j] > 0 and oS.C > oS.alphas[j]:
            oS.b = b2
        else:
            oS.b = (b1 + b2) / 2.0
        return 1
    else:
        return 0

##外循环，寻找a1
def newOutcircle(dataMat,labelMat,C,toler,maxIter,kTup):
    oS=altStruct(np.mat(dataMat),labelMat,C,toler,kTup)
    iter=0
    entireSet=True
    alphapairsChanged=0
    ##main body
    while (iter<maxIter) and ((alphapairsChanged>0) or (entireSet)):   ##当超过迭代次数或没有a更新时退出
        alphapairsChanged=0

        ##遍历所有值
        if entireSet:
            for i in range(oS.m):
                alphapairsChanged+=inner(i,oS)
                print('fullset,iter:%d i:%d, pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1
        ##遍历在间隔边界上的支持向量，即满足KKT条件的0<ai<C的，     （遍历非‘边界’值）
        else:
            nonBoundIs=np.nonzero((oS.alphas.A>0)*(oS.alphas.A<C))[0]
            for i in nonBoundIs:
                alphapairsChanged+=inner(i,oS)
                print('nonbound,iter:%d i:%d,pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1

        ##使得遍历全值和遍历非‘边界’值交叉进行
        if entireSet:
            entireSet=False
        elif alphapairsChanged==0:
            entireSet=True
        print('interaction number:%d'%(iter))   ##迭代次数iter
    return oS.b,oS.alphas

##计算超平面的w
def calWs(alphas,dataMat,labelMat):
    X=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(X)
    w=np.zeros((n,1))
    for i in range(m):
        w+=np.multiply(alphas[i]*labelMatrix[i],X[i,:].transpose())
    return w

def kernelTrans(X,A,kTup):
    m,n=np.shape(X)
    K=np.mat(np.zeros((m,1)))
    if kTup[0]=='lin':
        K=X*A.transpose()
    ##高斯核函数
    elif kTup[0]=='rbf':
        for j in range(m):
            deltaRow=X[j,:]-A
            K[j]=deltaRow*deltaRow.transpose()
        K=np.exp(K/(-1*kTup[1]**2))
    else:
        raise NameError('Houston we have a problem that kernel is not recoginized')
    return K





def Plot_final(dataMat,labelMat,b0,slope):
    dataArray=np.array(dataMat)
    n,m=np.shape(dataArray)
    x1=[]
    y1=[]
    x2=[]
    y2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            x1.append(dataArray[i,0])
            y1.append(dataArray[i,1])
        else:
            x2.append(dataArray[i,0])
            y2.append(dataArray[i,1])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(x1,y1,s=30,c='red',marker='s')
    ax.scatter(x2,y2,s=30,c='green')
    x=np.arange(-2.0, 10.0, 0.1)
    y=slope*x+b0
    ax.plot(x, y)
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.show()

def calTrain(dataMat,labelMat,alphas,b):
    dataM = np.mat(dataMat)
    labelM = np.mat(labelMat).transpose()
    svInd = np.nonzero(alphas.A > 0)[0]
    sVs = dataM[svInd]
    labelSV = labelM[svInd]
    m, n = np.shape(dataM)
    errorcount = 0
    TP = 0
    P = 0
    for i in range(m):
        eva = kernelTrans(sVs, dataM[i, :], ('rbf', 1.3))
        predict = eva.transpose() * np.multiply(labelSV, alphas[svInd]) + b
        print('predict,real label:', predict, labelMat[i])
        if np.sign(predict) != np.sign(labelMat[i]):
            errorcount += 1
        if np.sign(predict) == 1 and labelMat[i] == 1:
            TP += 1
        if labelMat[i] == 1:
            P += 1
    # print(errorcount,m)
    print('training error rate is:', float(errorcount / m))
    ##计算查全率recall指标
    R = TP / P
    print('recall rate of class 1:', R)

def calTest(test,test_label,alphas,b,train,train_label):
    dataM = np.mat(test)
    labelM = np.mat(test_label).transpose()
    ws = calWs(alphas, train, train_label)
    errorcount = 0
    TP = 0
    P = 0
    m, n = np.shape(dataM)
    for i in range(m):
        # o,p=np.shape(dataM[i])
        # e,w=np.shape(ws)
        # print(o,p,e,w)
        predict = dataM[i] * np.mat(ws) + b
        print('predict,real label:', predict, test_label[i])
        if np.sign(predict) != np.sign(test_label[i]):
            errorcount += 1
        if np.sign(predict) == 1 and test_label[i] == 1:
            TP += 1
        if test_label[i] == 1:
            P += 1
    # print(errorcount,m)
    print('test accurate rate is:', 1 - float(errorcount / m))
    print('test error rate is:', float(errorcount / m))

    ##计算查全率recall指标
    R = TP / P
    print('recall rate of class 1:', R)
    return 1 - float(errorcount / m)







if __name__=='__main__':
    file = r'C:\Users\admin\Desktop\testsets_HCC.csv'
    dataMat1, labelMat1 = pre_processHCC.loadData(file)
    train, train_label, test, test_label = pre_processHCC.devideData(dataMat1, labelMat1)

    dataMat=train
    labelMat=train_label
    b, alphas = newOutcircle(dataMat, labelMat, 100, 0.001, 100,kTup=('rbf',1.3))   ##改C的大小会很大程度改变精确度
    # print(type(b))


    # label1=[]
    # label2=[]
    # for i in range(100):
    #     if alphas[i]>0 and labelMat[i]==1:
    #         label1.append(dataMat[i])
    #     if alphas[i]>0 and labelMat[i]==-1:
    #         label2.append(dataMat[i])
    # print('支持向量1: ', label1)
    # print('支持向量2：',label2)
    #
    # slope=(label2[1][1]-label2[0][1])/(label2[1][0]-label2[0][0])
    # b1=label2[0][1]-slope*label2[0][0]
    # b2=label1[0][1]-slope*label1[0][0]
    # b0=(b1+b2)/2
    # Plot_final(dataMat,labelMat,b0,slope)

    # ws=calWs(alphas,dataMat,labelMat)
    # print(ws)
    ##进行分类预测,用第一个测试点测试,若算出来的值小于0则预测为-1类
    # guess=np.mat(dataMat)[0]*np.mat(ws)+b
    # print(guess,labelMat[0])

##计算训练集上的误差
    calTrain(dataMat,labelMat,alphas,b)



##计算测试集上的误差
    calTest(test,test_label,alphas,b,dataMat,labelMat)
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（3）同（2），但测试时使用的是核函数版本（理论上这样才保持一致，才正确）测试集精确度为100%，很诡异

import random
import numpy as np
import matplotlib.pyplot as plt
import pre_processHCC
import SVM_kernel
from sklearn.decomposition import PCA

##########此方法比simple版本快
#########比赛用的时候一定要复制粘贴一份！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
## 用了核函数同时也改了C的大小，没有用PCA，并且求预测时也用了核函数转变


def dataloader(filename):
    dataMat=[]
    labelMat=[]
    f=open(filename)
    for line in f.readlines():
        res=line.strip().split('\t')
        dataMat.append([float(res[0]),float(res[1])])
        labelMat.append(float(res[2]))
    return dataMat,labelMat


def select_int(i,m):                      ##i是第一个a的下标，m是a的总数目
    j=i
    while j==i:
        j=int(random.uniform(0,m))
    return j

##调整a的大小
def clipalpha(aj,h,l):
    if aj>h:
        aj=h
    if aj<l:
        aj=l
    return aj

class altStruct:
    def __init__(self,dataMat,labelMat,C,toler,kTup):
        self.X=dataMat
        self.L=np.mat(labelMat).transpose()
        self.LL=labelMat
        self.C=C
        self.T=toler
        self.m=np.shape(self.X)[0]
        self.alphas=np.mat(np.zeros((self.m,1)))
        self.b=0
        self.echae=np.mat(np.zeros((self.m,2)))
        self.K=np.mat(np.zeros((self.m,self.m)))
        for i in range(self.m):
            self.K[:,i]=kernelTrans(self.X,self.X[i,:],kTup)

    def getEk(self,oS,k):
        fxk=float(np.multiply(oS.alphas,oS.L).transpose()*oS.K[:,k]+oS.b)
        Ek=fxk-float(oS.L[k])
        return Ek

    ## 内循环中的启发式方法，寻找a2
    def selectJ(self,i,oS,Ei):               ##寻找步长最大的来增加查找速度
        maxK=-1
        maxdeltaE=0
        Ej=0
        ValidEcacheList=np.nonzero(self.echae[:,0].A)[0]     ##获取非零的E所在的对应的行索引
        if len(ValidEcacheList)>1:
            for k in ValidEcacheList:
                if k==i:
                    continue
                Ek=oS.getEk(oS,k)
                deltaE=abs(Ek-Ei)
                if deltaE>maxdeltaE:
                    maxdeltaE=deltaE
                    maxK=k
                    Ej=Ek
            return maxK,Ej
        else:
            j=select_int(i,oS.m)
            Ej=oS.getEk(oS,j)
            return j,Ej

    def updateEk(self,oS,k):
        Ek=oS.getEk(oS,k)
        oS.echae[k]=[1,Ek]
##内循环
def inner(i,oS):
    Ei=oS.getEk(oS,i)
    if ((oS.LL[i]*Ei<-oS.T) and (oS.alphas[i]<oS.C)) or ((oS.LL[i]*Ei>oS.T) and (oS.alphas[i]>0)):
        j,Ej=oS.selectJ(i,oS,Ei)
        alphaIold=oS.alphas[i].copy()
        alphaJold=oS.alphas[j].copy()
        if oS.LL[i] != oS.LL[j]:
            L = max(0, oS.alphas[j] - oS.alphas[i])
            H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
        else:
            L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
            H = min(oS.C, oS.alphas[j] + oS.alphas[i])
        if L==H:
            print('L==H')
            return 0
        nn=2*oS.K[i,j]-oS.K[i,i]-oS.K[j,j]
        if nn >= 0:
            print('nn>=0')
            return 0
        print(oS.alphas[j])
        print(oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn)
        oS.alphas[j]-=oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn
        oS.alphas[j]=clipalpha(oS.alphas[j],H,L)
        oS.updateEk(oS,j)
        if abs(oS.alphas[j] - alphaJold) < 0.00001:
            print('J not moving enough')
            return 0
        oS.alphas[i] += oS.LL[j] * oS.LL[i] * (alphaJold - oS.alphas[j])
        oS.updateEk(oS,i)
        b1 = oS.b - Ei - oS.LL[i] * oS.K[i,i] * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[i,j]* (oS.alphas[j] - alphaJold)
        b2 = oS.b - Ej - oS.LL[i] * oS.K[i,j]* (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[j,j]* (oS.alphas[j] - alphaJold)
        if oS.alphas[i] > 0 and oS.C > oS.alphas[i]:
            oS.b = b1
        elif oS.alphas[j] > 0 and oS.C > oS.alphas[j]:
            oS.b = b2
        else:
            oS.b = (b1 + b2) / 2.0
        return 1
    else:
        return 0

##外循环，寻找a1
def newOutcircle(dataMat,labelMat,C,toler,maxIter,kTup):
    oS=altStruct(np.mat(dataMat),labelMat,C,toler,kTup)
    iter=0
    entireSet=True
    alphapairsChanged=0
    ##main body
    while (iter<maxIter) and ((alphapairsChanged>0) or (entireSet)):   ##当超过迭代次数或没有a更新时退出
        alphapairsChanged=0

        ##遍历所有值
        if entireSet:
            for i in range(oS.m):
                alphapairsChanged+=inner(i,oS)
                print('fullset,iter:%d i:%d, pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1
        ##遍历在间隔边界上的支持向量，即满足KKT条件的0<ai<C的，     （遍历非‘边界’值）
        else:
            nonBoundIs=np.nonzero((oS.alphas.A>0)*(oS.alphas.A<C))[0]
            for i in nonBoundIs:
                alphapairsChanged+=inner(i,oS)
                print('nonbound,iter:%d i:%d,pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1

        ##使得遍历全值和遍历非‘边界’值交叉进行
        if entireSet:
            entireSet=False
        elif alphapairsChanged==0:
            entireSet=True
        print('interaction number:%d'%(iter))   ##迭代次数iter
    return oS.b,oS.alphas

##计算超平面的w
def calWs(alphas,dataMat,labelMat):
    X=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(X)
    w=np.zeros((n,1))
    for i in range(m):
        w+=np.multiply(alphas[i]*labelMatrix[i],X[i,:].transpose())
    return w

def kernelTrans(X,A,kTup):
    m,n=np.shape(X)
    K=np.mat(np.zeros((m,1)))
    if kTup[0]=='lin':
        K=X*A.transpose()
    ##高斯核函数
    elif kTup[0]=='rbf':
        for j in range(m):
            deltaRow=X[j,:]-A
            K[j]=deltaRow*deltaRow.transpose()
        K=np.exp(K/(-1*kTup[1]**2))      ##m*1   算的是0到m-1个样本与第指定的样本的核函数值
    else:
        raise NameError('Houston we have a problem that kernel is not recoginized')
    return K





def Plot_final(dataMat,labelMat,b0,slope):
    dataArray=np.array(dataMat)
    n,m=np.shape(dataArray)
    x1=[]
    y1=[]
    x2=[]
    y2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            x1.append(dataArray[i,0])
            y1.append(dataArray[i,1])
        else:
            x2.append(dataArray[i,0])
            y2.append(dataArray[i,1])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(x1,y1,s=30,c='red',marker='s')
    ax.scatter(x2,y2,s=30,c='green')
    x=np.arange(-2.0, 10.0, 0.1)
    y=slope*x+b0
    ax.plot(x, y)
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.show()

def calTrain(dataMat,labelMat,alphas,b):
    dataM = np.mat(dataMat)
    labelM = np.mat(labelMat).transpose()
    svInd = np.nonzero(alphas.A > 0)[0]
    sVs = dataM[svInd]
    labelSV = labelM[svInd]
    m, n = np.shape(dataM)
    errorcount = 0
    TP = 0
    P = 0
    for i in range(m):
        eva = kernelTrans(sVs, dataM[i, :], ('rbf', 1.3))
        predict = eva.transpose() * np.multiply(labelSV, alphas[svInd]) + b
        print('predict,real label:', predict, labelMat[i])
        if np.sign(predict) != np.sign(labelMat[i]):
            errorcount += 1
        if np.sign(predict) == 1 and labelMat[i] == 1:
            TP += 1
        if labelMat[i] == 1:
            P += 1
    # print(errorcount,m)
    print('training error rate is:', float(errorcount / m))
    ##计算查全率recall指标
    R = TP / P
    print('recall rate of class 1:', R)

def calTest(test,test_label,alphas,b):
    dataM = np.mat(test)
    labelM = np.mat(test_label).transpose()
    errorcount = 0
    TP = 0
    P = 0
    m, n = np.shape(dataM)
    for j in range(m):
        # o,p=np.shape(dataM[i])
        # e,w=np.shape(ws)
        # print(o,p,e,w)
        evas=0
        for i in range(m):
            eva=kernelTrans(dataM[j,:],dataM[i, :], ('rbf', 1.3))
            evas+=np.multiply(labelM[i],alphas[i])*eva
        predict =evas+b
        print('predict,real label:', predict, test_label[j])
        if np.sign(predict) != np.sign(test_label[j]):
            errorcount += 1
        if np.sign(predict) == 1 and test_label[j] == 1:
            TP += 1
        if test_label[j] == 1:
            P += 1
    # print(errorcount,m)
    print('test accurate rate is:', 1 - float(errorcount / m))
    print('test error rate is:', float(errorcount / m))

    ##计算查全率recall指标
    R = TP / P
    print('recall rate of class 1:', R)
    return 1 - float(errorcount / m)







if __name__=='__main__':
    file = r'C:\Users\admin\Desktop\testsets_HCC.csv'
    dataMat1, labelMat1 = pre_processHCC.loadData(file)
    train, train_label, test, test_label = pre_processHCC.devideData(dataMat1, labelMat1)

    dataMat=train
    labelMat=train_label
    b, alphas = newOutcircle(dataMat, labelMat, 100, 0.001, 100,kTup=('rbf',1.3))   ##改C的大小会很大程度改变精确度
    # print(type(b))


    # label1=[]
    # label2=[]
    # for i in range(100):
    #     if alphas[i]>0 and labelMat[i]==1:
    #         label1.append(dataMat[i])
    #     if alphas[i]>0 and labelMat[i]==-1:
    #         label2.append(dataMat[i])
    # print('支持向量1: ', label1)
    # print('支持向量2：',label2)
    #
    # slope=(label2[1][1]-label2[0][1])/(label2[1][0]-label2[0][0])
    # b1=label2[0][1]-slope*label2[0][0]
    # b2=label1[0][1]-slope*label1[0][0]
    # b0=(b1+b2)/2
    # Plot_final(dataMat,labelMat,b0,slope)

    # ws=calWs(alphas,dataMat,labelMat)
    # print(ws)
    ##进行分类预测,用第一个测试点测试,若算出来的值小于0则预测为-1类
    # guess=np.mat(dataMat)[0]*np.mat(ws)+b
    # print(guess,labelMat[0])

##计算训练集上的误差
    calTrain(dataMat,labelMat,alphas,b)



##计算测试集上的误差
    calTest(test,test_label,alphas,b)
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（4）在（2）的基础上使用PCA‘，测试集上精确度达到85%~90%

import random
import numpy as np
import matplotlib.pyplot as plt
import pre_processHCC
import SVM_kernel
from sklearn.decomposition import PCA

##########此方法比simple版本快
#########比赛用的时候一定要复制粘贴一份！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
## 用了核函数改了C,以及PCA使用，PCA必须用在训练集上所以一下方法存在错误，用在全集的话会用到测试集的先验信息


def dataloader(filename):
    dataMat=[]
    labelMat=[]
    f=open(filename)
    for line in f.readlines():
        res=line.strip().split('\t')
        dataMat.append([float(res[0]),float(res[1])])
        labelMat.append(float(res[2]))
    return dataMat,labelMat


def select_int(i,m):                      ##i是第一个a的下标，m是a的总数目
    j=i
    while j==i:
        j=int(random.uniform(0,m))
    return j

##调整a的大小
def clipalpha(aj,h,l):
    if aj>h:
        aj=h
    if aj<l:
        aj=l
    return aj

class altStruct:
    def __init__(self,dataMat,labelMat,C,toler,kTup):
        self.X=dataMat
        self.L=np.mat(labelMat).transpose()
        self.LL=labelMat
        self.C=C
        self.T=toler
        self.m=np.shape(self.X)[0]
        self.alphas=np.mat(np.zeros((self.m,1)))
        self.b=0
        self.echae=np.mat(np.zeros((self.m,2)))
        self.K=np.mat(np.zeros((self.m,self.m)))
        for i in range(self.m):
            self.K[:,i]=kernelTrans(self.X,self.X[i,:],kTup)

    def getEk(self,oS,k):
        fxk=float(np.multiply(oS.alphas,oS.L).transpose()*oS.K[:,k]+oS.b)
        Ek=fxk-float(oS.L[k])
        return Ek

    ## 内循环中的启发式方法，寻找a2
    def selectJ(self,i,oS,Ei):               ##寻找步长最大的来增加查找速度
        maxK=-1
        maxdeltaE=0
        Ej=0
        ValidEcacheList=np.nonzero(self.echae[:,0].A)[0]     ##获取非零的E所在的对应的行索引
        if len(ValidEcacheList)>1:
            for k in ValidEcacheList:
                if k==i:
                    continue
                Ek=oS.getEk(oS,k)
                deltaE=abs(Ek-Ei)
                if deltaE>maxdeltaE:
                    maxdeltaE=deltaE
                    maxK=k
                    Ej=Ek
            return maxK,Ej
        else:
            j=select_int(i,oS.m)
            Ej=oS.getEk(oS,j)
            return j,Ej

    def updateEk(self,oS,k):
        Ek=oS.getEk(oS,k)
        oS.echae[k]=[1,Ek]
##内循环
def inner(i,oS):
    Ei=oS.getEk(oS,i)
    if ((oS.LL[i]*Ei<-oS.T) and (oS.alphas[i]<oS.C)) or ((oS.LL[i]*Ei>oS.T) and (oS.alphas[i]>0)):
        j,Ej=oS.selectJ(i,oS,Ei)
        alphaIold=oS.alphas[i].copy()
        alphaJold=oS.alphas[j].copy()
        if oS.LL[i] != oS.LL[j]:
            L = max(0, oS.alphas[j] - oS.alphas[i])
            H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
        else:
            L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
            H = min(oS.C, oS.alphas[j] + oS.alphas[i])
        if L==H:
            print('L==H')
            return 0
        nn=2*oS.K[i,j]-oS.K[i,i]-oS.K[j,j]
        if nn >= 0:
            print('nn>=0')
            return 0
        print(oS.alphas[j])
        print(oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn)
        oS.alphas[j]-=oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn
        oS.alphas[j]=clipalpha(oS.alphas[j],H,L)
        oS.updateEk(oS,j)
        if abs(oS.alphas[j] - alphaJold) < 0.00001:
            print('J not moving enough')
            return 0
        oS.alphas[i] += oS.LL[j] * oS.LL[i] * (alphaJold - oS.alphas[j])
        oS.updateEk(oS,i)
        b1 = oS.b - Ei - oS.LL[i] * oS.K[i,i] * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[i,j]* (oS.alphas[j] - alphaJold)
        b2 = oS.b - Ej - oS.LL[i] * oS.K[i,j]* (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[j,j]* (oS.alphas[j] - alphaJold)
        if oS.alphas[i] > 0 and oS.C > oS.alphas[i]:
            oS.b = b1
        elif oS.alphas[j] > 0 and oS.C > oS.alphas[j]:
            oS.b = b2
        else:
            oS.b = (b1 + b2) / 2.0
        return 1
    else:
        return 0

##外循环，寻找a1
def newOutcircle(dataMat,labelMat,C,toler,maxIter,kTup):
    oS=altStruct(np.mat(dataMat),labelMat,C,toler,kTup)
    iter=0
    entireSet=True
    alphapairsChanged=0
    ##main body
    while (iter<maxIter) and ((alphapairsChanged>0) or (entireSet)):   ##当超过迭代次数或没有a更新时退出
        alphapairsChanged=0

        ##遍历所有值
        if entireSet:
            for i in range(oS.m):
                alphapairsChanged+=inner(i,oS)
                print('fullset,iter:%d i:%d, pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1
        ##遍历在间隔边界上的支持向量，即满足KKT条件的0<ai<C的，     （遍历非‘边界’值）
        else:
            nonBoundIs=np.nonzero((oS.alphas.A>0)*(oS.alphas.A<C))[0]
            for i in nonBoundIs:
                alphapairsChanged+=inner(i,oS)
                print('nonbound,iter:%d i:%d,pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1

        ##使得遍历全值和遍历非‘边界’值交叉进行
        if entireSet:
            entireSet=False
        elif alphapairsChanged==0:
            entireSet=True
        print('interaction number:%d'%(iter))   ##迭代次数iter
    return oS.b,oS.alphas

##计算超平面的w
def calWs(alphas,dataMat,labelMat):
    X=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(X)
    w=np.zeros((n,1))
    for i in range(m):
        w+=np.multiply(alphas[i]*labelMatrix[i],X[i,:].transpose())
    return w

def kernelTrans(X,A,kTup):
    m,n=np.shape(X)
    K=np.mat(np.zeros((m,1)))
    if kTup[0]=='lin':
        K=X*A.transpose()
    ##高斯核函数
    elif kTup[0]=='rbf':
        for j in range(m):
            deltaRow=X[j,:]-A
            K[j]=deltaRow*deltaRow.transpose()
        K=np.exp(K/(-1*kTup[1]**2))
    else:
        raise NameError('Houston we have a problem that kernel is not recoginized')
    return K





def Plot_final(dataMat,labelMat,b0,slope):
    dataArray=np.array(dataMat)
    n,m=np.shape(dataArray)
    x1=[]
    y1=[]
    x2=[]
    y2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            x1.append(dataArray[i,0])
            y1.append(dataArray[i,1])
        else:
            x2.append(dataArray[i,0])
            y2.append(dataArray[i,1])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(x1,y1,s=30,c='red',marker='s')
    ax.scatter(x2,y2,s=30,c='green')
    x=np.arange(-2.0, 10.0, 0.1)
    y=slope*x+b0
    ax.plot(x, y)
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.show()

def calTrain(dataMat,labelMat,alphas,b):
    dataM = np.mat(dataMat)
    labelM = np.mat(labelMat).transpose()
    svInd = np.nonzero(alphas.A > 0)[0]
    sVs = dataM[svInd]
    labelSV = labelM[svInd]
    m, n = np.shape(dataM)
    errorcount = 0
    TP = 0
    P = 0
    for i in range(m):
        eva = kernelTrans(sVs, dataM[i, :], ('rbf', 1.3))
        predict = eva.transpose() * np.multiply(labelSV, alphas[svInd]) + b
        print('predict,real label:', predict, labelMat[i])
        if np.sign(predict) != np.sign(labelMat[i]):
            errorcount += 1
        if np.sign(predict) == 1 and labelMat[i] == 1:
            TP += 1
        if labelMat[i] == 1:
            P += 1
    # print(errorcount,m)
    print('training error rate is:', float(errorcount / m))
    ##计算查全率recall指标
    R = TP / P
    print('recall rate of class 1:', R)

def calTest(test,test_label,alphas,b,train,train_label):
    dataM = np.mat(test)
    labelM = np.mat(test_label).transpose()
    ws = calWs(alphas,train,train_label)
    errorcount = 0
    TP = 0
    P = 0
    m, n = np.shape(dataM)
    for i in range(m):
        # o,p=np.shape(dataM[i])
        # e,w=np.shape(ws)
        # print(o,p,e,w)
        predict = dataM[i] * np.mat(ws) + b
        print('predict,real label:', predict, test_label[i])
        if np.sign(predict) != np.sign(test_label[i]):
            errorcount += 1
        if np.sign(predict) == 1 and test_label[i] == 1:
            TP += 1
        if test_label[i] == 1:
            P += 1
    # print(errorcount,m)
    print('test accurate rate is:', 1 - float(errorcount / m))
    print('test error rate is:', float(errorcount / m))

    ##计算查全率recall指标
    R = TP / P
    print('recall rate of class 1:', R)







if __name__=='__main__':
    file = r'C:\Users\admin\Desktop\testsets_HCC.csv'
    dataMat1, labelMat1 = pre_processHCC.loadData(file)
    ##主成成分分析
    pca = PCA(n_components=140)  ##ValueError: n_components='mle' is only supported if n_samples >= n_features
    newdataMat = pca.fit_transform(dataMat1)

    train, train_label, test, test_label = pre_processHCC.devideData(newdataMat, labelMat1)


    dataMat=train
    labelMat=train_label
    b, alphas = newOutcircle(dataMat, labelMat, 100, 0.001, 100,kTup=('rbf',1.3))   ##改C的大小会很大程度改变精确度
    # print(type(b))


    # label1=[]
    # label2=[]
    # for i in range(100):
    #     if alphas[i]>0 and labelMat[i]==1:
    #         label1.append(dataMat[i])
    #     if alphas[i]>0 and labelMat[i]==-1:
    #         label2.append(dataMat[i])
    # print('支持向量1: ', label1)
    # print('支持向量2：',label2)
    #
    # slope=(label2[1][1]-label2[0][1])/(label2[1][0]-label2[0][0])
    # b1=label2[0][1]-slope*label2[0][0]
    # b2=label1[0][1]-slope*label1[0][0]
    # b0=(b1+b2)/2
    # Plot_final(dataMat,labelMat,b0,slope)

    # ws=calWs(alphas,dataMat,labelMat)
    # print(ws)
    ##进行分类预测,用第一个测试点测试,若算出来的值小于0则预测为-1类
    # guess=np.mat(dataMat)[0]*np.mat(ws)+b
    # print(guess,labelMat[0])

##计算训练集上的误差
    calTrain(dataMat,labelMat,alphas,b)



##计算测试集上的误差
    calTest(test,test_label,alphas,b,dataMat,labelMat)
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附加：自己写的PCA 时间复杂度太高，在三万多个特征下用不了

import numpy as np
##此方法simple版本无法用在特征很大的数据集
##输入要求：行为样本，列为特征

def PCA(dataX):
    dataX=np.array(dataX)
    ##进行规范化
    m,n=np.shape(dataX)   ##m为样本数
    avs=np.tile(np.mean(dataX,0),(m,1))      ##扩充到与原来同规模大
    # print(avs)
    stds=np.tile(np.std(dataX,0),(m,1))
    # print(stds)
    adujstX =(dataX-avs)/stds    ##这里都是array形式所以/是对每个元素对应除
    covX=np.cov(adujstX.transpose())
    # print(covX)
    # print(dataX-avs)
    # print(covX)
    featValue,featVec=np.linalg.eig(covX)
    # print(featValue[1])
    index=np.argsort(-featValue)
    allfeat=0
    for i in range(len(featValue)):
        allfeat+=featValue[i]
    percent=0
    k=0
    for i in range(len(featValue)):
        percent+=featValue[i]/allfeat
        if percent>0.75:
            k=i
            break
    # print(featVec)
    selectVec = np.matrix(featVec.T[index[:k]])    ##featVec的列向量是单位特征向量所以必须先转置
    # print(selectVec)
    # print(selectVec.T)
    finalData=adujstX*selectVec.T
    return finalData


if __name__=='__main__':
    a=[[1,91,3,4,3,3,3],[4,5,9,2,5,5,5],[2,3,3,5,7,7,7]]
    print(PCA(a))
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（5）使用SVM-RFE特征选择，这里存在瑕疵应该用与核函数下的特征函数计算，但是调用包时用的是线性SVM下的计算方法

首先附上自己写的DQ（核函数下的特征函数计算），由于时间复杂度高没用成
其实就是在（2）的基础上加了一个DQ函数

import random
import numpy as np
import matplotlib.pyplot as plt
import pre_processHCC
import SVM_kernel
from sklearn.decomposition import PCA

##########此方法比simple版本快
#########比赛用的时候一定要复制粘贴一份！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
## 用了核函数同时也改了C的大小，没有用PCA,使用自己写的SVMRFE算法对特征进行打分排序,注意这里用的不是线性核所以SVM-RFE算法算特征贡献有小改动
##由于自己写的SVMRFE算法时间复杂度在n**3次方所以电脑带不动，尤其是在三万多个特征上


def dataloader(filename):
    dataMat=[]
    labelMat=[]
    f=open(filename)
    for line in f.readlines():
        res=line.strip().split('\t')
        dataMat.append([float(res[0]),float(res[1])])
        labelMat.append(float(res[2]))
    return dataMat,labelMat


def select_int(i,m):                      ##i是第一个a的下标，m是a的总数目
    j=i
    while j==i:
        j=int(random.uniform(0,m))
    return j

##调整a的大小
def clipalpha(aj,h,l):
    if aj>h:
        aj=h
    if aj<l:
        aj=l
    return aj

class altStruct:
    def __init__(self,dataMat,labelMat,C,toler,kTup):
        self.X=dataMat
        self.L=np.mat(labelMat).transpose()
        self.LL=labelMat
        self.C=C
        self.T=toler
        self.m=np.shape(self.X)[0]
        self.alphas=np.mat(np.zeros((self.m,1)))
        self.b=0
        self.echae=np.mat(np.zeros((self.m,2)))
        self.K=np.mat(np.zeros((self.m,self.m)))
        for i in range(self.m):
            self.K[:,i]=kernelTrans(self.X,self.X[i,:],kTup)

    def getEk(self,oS,k):
        fxk=float(np.multiply(oS.alphas,oS.L).transpose()*oS.K[:,k]+oS.b)
        Ek=fxk-float(oS.L[k])
        return Ek

    ## 内循环中的启发式方法，寻找a2
    def selectJ(self,i,oS,Ei):               ##寻找步长最大的来增加查找速度
        maxK=-1
        maxdeltaE=0
        Ej=0
        ValidEcacheList=np.nonzero(self.echae[:,0].A)[0]     ##获取非零的E所在的对应的行索引
        if len(ValidEcacheList)>1:
            for k in ValidEcacheList:
                if k==i:
                    continue
                Ek=oS.getEk(oS,k)
                deltaE=abs(Ek-Ei)
                if deltaE>maxdeltaE:
                    maxdeltaE=deltaE
                    maxK=k
                    Ej=Ek
            return maxK,Ej
        else:
            j=select_int(i,oS.m)
            Ej=oS.getEk(oS,j)
            return j,Ej

    def updateEk(self,oS,k):
        Ek=oS.getEk(oS,k)
        oS.echae[k]=[1,Ek]
##内循环
def inner(i,oS):
    Ei=oS.getEk(oS,i)
    if ((oS.LL[i]*Ei<-oS.T) and (oS.alphas[i]<oS.C)) or ((oS.LL[i]*Ei>oS.T) and (oS.alphas[i]>0)):
        j,Ej=oS.selectJ(i,oS,Ei)
        alphaIold=oS.alphas[i].copy()
        alphaJold=oS.alphas[j].copy()
        if oS.LL[i] != oS.LL[j]:
            L = max(0, oS.alphas[j] - oS.alphas[i])
            H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
        else:
            L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
            H = min(oS.C, oS.alphas[j] + oS.alphas[i])
        if L==H:
            print('L==H')
            return 0
        nn=2*oS.K[i,j]-oS.K[i,i]-oS.K[j,j]
        if nn >= 0:
            print('nn>=0')
            return 0
        print(oS.alphas[j])
        print(oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn)
        oS.alphas[j]-=oS.LL[j]*(np.mat(Ei)-np.mat(Ej))/nn
        oS.alphas[j]=clipalpha(oS.alphas[j],H,L)
        oS.updateEk(oS,j)
        if abs(oS.alphas[j] - alphaJold) < 0.00001:
            print('J not moving enough')
            return 0
        oS.alphas[i] += oS.LL[j] * oS.LL[i] * (alphaJold - oS.alphas[j])
        oS.updateEk(oS,i)
        b1 = oS.b - Ei - oS.LL[i] * oS.K[i,i] * (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[i,j]* (oS.alphas[j] - alphaJold)
        b2 = oS.b - Ej - oS.LL[i] * oS.K[i,j]* (oS.alphas[i] - alphaIold) - \
             oS.LL[j] * oS.K[j,j]* (oS.alphas[j] - alphaJold)
        if oS.alphas[i] > 0 and oS.C > oS.alphas[i]:
            oS.b = b1
        elif oS.alphas[j] > 0 and oS.C > oS.alphas[j]:
            oS.b = b2
        else:
            oS.b = (b1 + b2) / 2.0
        return 1
    else:
        return 0

##外循环，寻找a1
def newOutcircle(dataMat,labelMat,C,toler,maxIter,kTup):
    oS=altStruct(np.mat(dataMat),labelMat,C,toler,kTup)
    iter=0
    entireSet=True
    alphapairsChanged=0
    ##main body
    while (iter<maxIter) and ((alphapairsChanged>0) or (entireSet)):   ##当超过迭代次数或没有a更新时退出
        alphapairsChanged=0

        ##遍历所有值
        if entireSet:
            for i in range(oS.m):
                alphapairsChanged+=inner(i,oS)
                print('fullset,iter:%d i:%d, pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1
        ##遍历在间隔边界上的支持向量，即满足KKT条件的0<ai<C的，     （遍历非‘边界’值）
        else:
            nonBoundIs=np.nonzero((oS.alphas.A>0)*(oS.alphas.A<C))[0]
            for i in nonBoundIs:
                alphapairsChanged+=inner(i,oS)
                print('nonbound,iter:%d i:%d,pairs changed %d'%(iter,i,alphapairsChanged))
            iter+=1

        ##使得遍历全值和遍历非‘边界’值交叉进行
        if entireSet:
            entireSet=False
        elif alphapairsChanged==0:
            entireSet=True
        print('interaction number:%d'%(iter))   ##迭代次数iter
    return oS.b,oS.alphas

##计算超平面的w
def calWs(alphas,dataMat,labelMat):
    X=np.mat(dataMat)
    labelMatrix=np.mat(labelMat).transpose()
    m,n=np.shape(X)
    w=np.zeros((n,1))
    for i in range(m):
        w+=np.multiply(alphas[i]*labelMatrix[i],X[i,:].transpose())
    return w

def kernelTrans(X,A,kTup):
    m,n=np.shape(X)
    K=np.mat(np.zeros((m,1)))
    if kTup[0]=='lin':
        K=X*A.transpose()
    ##高斯核函数
    elif kTup[0]=='rbf':
        for j in range(m):
            deltaRow=X[j,:]-A
            K[j]=deltaRow*deltaRow.transpose()
        K=np.exp(K/(-1*kTup[1]**2))
    else:
        raise NameError('Houston we have a problem that kernel is not recoginized')
    return K





def Plot_final(dataMat,labelMat,b0,slope):
    dataArray=np.array(dataMat)
    n,m=np.shape(dataArray)
    x1=[]
    y1=[]
    x2=[]
    y2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            x1.append(dataArray[i,0])
            y1.append(dataArray[i,1])
        else:
            x2.append(dataArray[i,0])
            y2.append(dataArray[i,1])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(x1,y1,s=30,c='red',marker='s')
    ax.scatter(x2,y2,s=30,c='green')
    x=np.arange(-2.0, 10.0, 0.1)
    y=slope*x+b0
    ax.plot(x, y)
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.show()

def calTrain(dataMat,labelMat,alphas,b):
    dataM = np.mat(dataMat)
    labelM = np.mat(labelMat).transpose()
    svInd = np.nonzero(alphas.A > 0)[0]
    sVs = dataM[svInd]
    labelSV = labelM[svInd]
    m, n = np.shape(dataM)
    errorcount = 0
    TP = 0
    P = 0
    for i in range(m):
        eva = kernelTrans(sVs, dataM[i, :], ('rbf', 1.3))
        predict = eva.transpose() * np.multiply(labelSV, alphas[svInd]) + b
        print('predict,real label:', predict, labelMat[i])
        if np.sign(predict) != np.sign(labelMat[i]):
            errorcount += 1
        if np.sign(predict) == 1 and labelMat[i] == 1:
            TP += 1
        if labelMat[i] == 1:
            P += 1
    # print(errorcount,m)
    print('training error rate is:', float(errorcount / m))
    ##计算查全率recall指标
    R = TP / P
    print('recall rate of class 1:', R)

def calTest(test,test_label,alphas,b,train,train_label):
    dataM = np.mat(test)
    labelM = np.mat(test_label).transpose()
    ws = calWs(alphas,train,train_label)
    errorcount = 0
    TP = 0
    P = 0
    m, n = np.shape(dataM)
    for i in range(m):
        # o,p=np.shape(dataM[i])
        # e,w=np.shape(ws)
        # print(o,p,e,w)
        predict = dataM[i] * np.mat(ws) + b
        print('predict,real label:', predict, test_label[i])
        if np.sign(predict) != np.sign(test_label[i]):
            errorcount += 1
        if np.sign(predict) == 1 and test_label[i] == 1:
            TP += 1
        if test_label[i] == 1:
            P += 1
    # print(errorcount,m)
    print('test accurate rate is:', 1 - float(errorcount / m))
    print('test error rate is:', float(errorcount / m))

    ##计算查全率recall指标
    R = TP / P
    print('recall rate of class 1:', R)

def DQ(k,dataMat,labelMat,alphas):
    dataM=np.mat(dataMat)
    newdataMat=dataM.copy()
    newdataMat=np.delete(newdataMat,k,axis=1)
    labelM=np.mat(labelMat).transpose()
    m, n = np.shape(dataM)
    dq=0
    for i in range(m):
        for j in range(m):
            kernel=kernelTrans(dataM[i,:],dataM[j,:],kTup=('rbf',1.3))-kernelTrans((newdataMat[i,:]),newdataMat[j,:],kTup=('rbf',1.3))
            dq+=alphas[i]*alphas[j]*labelM[i]*labelM[j]*kernel
    dqs=0.5*dq
    return dqs





if __name__=='__main__':
    file = r'C:\Users\admin\Desktop\testsets_HCC.csv'
    dataMat1, labelMat1 = pre_processHCC.loadData(file)
    train, train_label, test, test_label = pre_processHCC.devideData(dataMat1, labelMat1)

    dataMat=train
    labelMat=train_label
    b, alphas = newOutcircle(dataMat, labelMat, 100, 0.001, 100,kTup=('rbf',1.3))   ##改C的大小会很大程度改变精确度



    ##使用SVM-RFE进行特征排序与剔除
    m,n=np.shape(dataMat)
    valueDq=[]
    for k in range(n):
        valueDq.append(DQ(k,dataMat,labelMat,alphas))
    x=np.array(valueDq)
    kSort=x.np.argsort()
    print(kSort)
    print(valueDq[kSort])




    # print(type(b))


    # label1=[]
    # label2=[]
    # for i in range(100):
    #     if alphas[i]>0 and labelMat[i]==1:
    #         label1.append(dataMat[i])
    #     if alphas[i]>0 and labelMat[i]==-1:
    #         label2.append(dataMat[i])
    # print('支持向量1: ', label1)
    # print('支持向量2：',label2)
    #
    # slope=(label2[1][1]-label2[0][1])/(label2[1][0]-label2[0][0])
    # b1=label2[0][1]-slope*label2[0][0]
    # b2=label1[0][1]-slope*label1[0][0]
    # b0=(b1+b2)/2
    # Plot_final(dataMat,labelMat,b0,slope)

    # ws=calWs(alphas,dataMat,labelMat)
    # print(ws)
    ##进行分类预测,用第一个测试点测试,若算出来的值小于0则预测为-1类
    # guess=np.mat(dataMat)[0]*np.mat(ws)+b
    # print(guess,labelMat[0])

##计算训练集上的误差
    calTrain(dataMat,labelMat,alphas,b)



##计算测试集上的误差
    calTest(test,test_label,alphas,b,dataMat,labelMat)
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下面是调用RFE包的文件，后面会用到

from sklearn.svm import SVC
from sklearn.feature_selection import RFE
import matplotlib.pyplot as plt
import pre_processHCC
import numpy as np

def RFEmine(dataMat,labelMat,n):
# 1.读取训练数据集

##输出的dataMat是一个行为样本，列为特征的列表    140*34262

 X=np.array(dataMat)[:,:5000]    ##选取一万以上就带不动了
 # print(X.shape)
 y=np.array(labelMat)

# Create the RFE object and rank each pixel
 svc = SVC(kernel="linear", C=1)
 rfe = RFE(estimator=svc, n_features_to_select=n, step=1)     ##step是每次移除的特征数
 rfe.fit(X, y)
 return rfe.support_
# print(rfe.ranking_)

# # Plot pixel ranking
# plt.matshow(ranking)
# plt.colorbar()
# plt.title("Ranking of pixels with RFE")
# plt.show()
if __name__=='__main__':
    file= r'C:\Users\admin\Desktop\testsets_HCC.csv'
    n=500
    RFEmine(file,n)
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最终使用特征选择，根据保留的特征数目n为横坐标，纵坐标为精确度进行画图
程序如下：

import matplotlib.pyplot as plt
import RFE_test3
import pre_processHCC
import experiment2HCC_1
import numpy as np

##运行一次需要很长时间，时间复杂度比较大，特征选择必须在训练集上进行，在全集上进行则会用到测试集的标签信息
##严格上下面在全集上进行特征选择是错误的


file= r'C:\Users\admin\Desktop\testsets_HCC.csv'
dataMat1, labelMat1 = pre_processHCC.loadData(file)

y1=[]
for n in range(1,10):
    support=RFE_test3.RFEmine(dataMat1,labelMat1,n)
    dataMat1=np.array(dataMat1)[:,:5000]    ##电脑带不动只能5000开始
    dataMat2=np.array(dataMat1)[:,support]
    train, train_label, test, test_label = pre_processHCC.devideData(dataMat2, labelMat1)
    dataMat = train
    labelMat = train_label
    b, alphas = experiment2HCC_1.newOutcircle(dataMat, labelMat, 100, 0.001, 100, kTup=('rbf', 1.3))
    y1.append(experiment2HCC_1.calTest(test,test_label,alphas,b,dataMat,labelMat))

x1=range(1,10)

plt.plot(x1,y1,label='Frist line',linewidth=3,color='r',marker='o',
markerfacecolor='blue',markersize=12)
# plt.plot(x2,y2,label='second line')
plt.xlabel('n')
plt.ylabel('accuracy')
plt.title('Interesting Graph\nCheck it out')
plt.legend()
plt.show()
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结果图：

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