pythonでのサポートベクトル実装

コードは主に机械を参考にして実戦のあの本を勉强して、ベクトル机を支持して过程の个人がすべてのアルゴリズムの中で使う数学の知识の最も多い1种のアルゴリズムだと思って、理解して一定の难易度があって、下のコードの中でただ核の関数の実现がないことを実现して、核の関数のがあるのは少し修正するだけでいいです
具体的なコードは以下の通りです.

#encoding:utf-8
'''
Created on 2015 9 16 
@author: ZHOUMEIXU204
'''
path=u'D:\\Users\\zhoumeixu204\\Desktop\\python      \\           python\\        \\machinelearninginaction\\Ch06\\'
import numpy  as np
import matplotlib.pyplot  as plt
def    loadDataSet(fileName):
    dataMat=[];labelMat=[]
    fr=open(fileName)
    for line in fr.readlines():
        lineArr=line.strip().split('\t')
        dataMat.append([float(lineArr[0]),float(lineArr[1])])
        labelMat.append(float(lineArr[2]))
    return  dataMat,labelMat
def   selectJrand(i,m):
    j=i
    while(j==i):
        j=int(np.random.uniform(0,m))
    return j
def  clipAlpha(aj,H,L):
    if aj>H:
        aj=H
    if L>aj:
        aj=L
    return aj
#multiply    a*b   multiply(a,b)
#ultiply numpy ufunc  ，           ，               ，
#   matlab    ，          ，                     ，     a    5 5    

def smoSimple(dataMatIn, classLabels, C, toler, maxIter):
#           、   、   C、   、        
    dataMatrix =np.mat(dataMatIn); labelMat = np.mat(classLabels).transpose()
    b = 0; m,n =np.shape(dataMatrix)
    alphas = np.mat(np.zeros((m,1)))
    iter = 0
    while (iter < maxIter):
        alphaPairsChanged = 0
        for i in range(m):
            fXi = float(np.multiply(alphas,labelMat).T*(dataMatrix*dataMatrix[i,:].T)) + b
            Ei = fXi - float(labelMat[i])#if checks if an example violates KKT conditions
            if ((labelMat[i]*Ei < -toler) and (alphas[i] < C)) or ((labelMat[i]*Ei > toler) and (alphas[i] > 0)):
                j = selectJrand(i,m)
                fXj = float(np.multiply(alphas,labelMat).T*(dataMatrix*dataMatrix[j,:].T)) + 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
                eta = 2.0 * dataMatrix[i,:]*dataMatrix[j,:].T - dataMatrix[i,:]*dataMatrix[i,:].T - dataMatrix[j,:]*dataMatrix[j,:].T
                if eta >= 0: print "eta>=0"; continue
                alphas[j] -= labelMat[j]*(Ei - Ej)/eta
                alphas[j] = clipAlpha(alphas[j],H,L)
                if (abs(alphas[j] - alphaJold) < 0.00001): print "j not moving enough"; continue
                alphas[i] += labelMat[j]*labelMat[i]*(alphaJold - alphas[j])#update i by the same amount as j
                                                                        #the update is in the oppostie direction
                b1 = b - Ei- labelMat[i]*(alphas[i]-alphaIold)*dataMatrix[i,:]*dataMatrix[i,:].T - labelMat[j]*(alphas[j]-alphaJold)*dataMatrix[i,:]*dataMatrix[j,:].T
                b2 = b - Ej- labelMat[i]*(alphas[i]-alphaIold)*dataMatrix[i,:]*dataMatrix[j,:].T - labelMat[j]*(alphas[j]-alphaJold)*dataMatrix[j,:]*dataMatrix[j,:].T
                if (0 < alphas[i]) and (C > alphas[i]): b = b1
                elif (0 < alphas[j]) and (C > alphas[j]): b = b2
                else: b = (b1 + b2)/2.0
                alphaPairsChanged += 1
                print "iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged)
        if (alphaPairsChanged == 0): iter += 1
        else: iter = 0
        print "iteration number: %d" % iter
    return b,alphas

dataArr,labelArr=loadDataSet(path+'testSet.txt')
print(labelArr)
#           、   、   C、   、        
b,alphas=smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
# print(b)
# print(alphas)
print(alphas[alphas>0])
for i in range(100):
    if alphas[i]>0.0:
        print(dataArr[i],labelArr[i])



def  kernelTrans(X,A,kTup):
    m,n=np.shape(X)
    K=np.mat(np.zeros((m,1)))
    if kTup[0]=='lin':K=X*A.T
    elif kTup[0]=='rbf':
        for j in range(m):
            deltaRow=X[j,:]-A
            K[j]=deltaRow*deltaRow.T
        K=np.exp(K/(-1*kTup[1]**2))
    else:
        raise NameError('Houston we hava a problem the kernel is not recognized')
    return K


#   PlattSMO  
class optStruct:
    def __init__(self,dataMatIn, classLabels, C, toler, kTup):  # Initialize the structure with the parameters 
        self.X = dataMatIn
        self.labelMat = classLabels
        self.C = C
        self.tol = toler
        self.m =np.shape(dataMatIn)[0]
        self.alphas =np.mat(np.zeros((self.m,1)))
        self.b = 0
        self.eCache =np.mat(np.zeros((self.m,2))) #first column is valid flag
        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  calcEk(oS,k):
      fXk=float(np.multiply(oS.alphas,oS.labelMat).T*(oS.X*oS.X[k,:].T))+oS.b
      Ek=fXk-float(oS.labelMat[k])
      return Ek
def  selectJ(i,oS,Ei):
    maxK=-1;maxDeltaE=0;Ej=0
    oS.eCache[i]=[1,Ei]
    validEcacheList=np.nonzero(oS.eCache[:,0].A)[0]
    if (len(validEcacheList))>1:
        for  k in validEcacheList:
            if k==i:continue
            Ek=calcEk(oS, k)
            deltaE=np.abs(Ei-Ek)
            if (deltaE>maxDeltaE):
                maxK=k;maxDeltaE=deltaE;Ej=Ek
        return maxK,Ej
    else:
        j=selectJrand(i, oS.m)
        Ej=calcEk(oS, j)
    return j,Ej
def  updateEk(oS,k):
    Ek=calcEk(oS, k)
    oS.eCache[k]=[1,Ek]

def innerL(i, oS):
    Ei = calcEk(oS, i)
    if ((oS.labelMat[i]*Ei < -oS.tol) and (oS.alphas[i] < oS.C)) or ((oS.labelMat[i]*Ei > oS.tol) and (oS.alphas[i] > 0)):
        j,Ej = selectJ(i, oS, Ei) #this has been changed from selectJrand
        alphaIold = oS.alphas[i].copy(); alphaJold = oS.alphas[j].copy();
        if (oS.labelMat[i] != oS.labelMat[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
        eta = 2.0 * oS.K[i,j] - oS.K[i,i] - oS.K[j,j] #changed for kernel
        if eta >= 0: print "eta>=0"; return 0
        oS.alphas[j] -= oS.labelMat[j]*(Ei - Ej)/eta
        oS.alphas[j] = clipAlpha(oS.alphas[j],H,L)
        updateEk(oS, j) #added this for the Ecache
        if (abs(oS.alphas[j] - alphaJold) < 0.00001): print "j not moving enough"; return 0
        oS.alphas[i] += oS.labelMat[j]*oS.labelMat[i]*(alphaJold - oS.alphas[j])#update i by the same amount as j
        updateEk(oS, i) #added this for the Ecache                    #the update is in the oppostie direction
        b1 = oS.b - Ei- oS.labelMat[i]*(oS.alphas[i]-alphaIold)*oS.K[i,i] - oS.labelMat[j]*(oS.alphas[j]-alphaJold)*oS.K[i,j]
        b2 = oS.b - Ej- oS.labelMat[i]*(oS.alphas[i]-alphaIold)*oS.K[i,j]- oS.labelMat[j]*(oS.alphas[j]-alphaJold)*oS.K[j,j]
        if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]): oS.b = b1
        elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]): oS.b = b2
        else: oS.b = (b1 + b2)/2.0
        return 1
    else: return 0
        
        
def smoP(dataMatIn, classLabels, C, toler, maxIter,kTup=('lin', 0)):    #full Platt SMO
    oS = optStruct(np.mat(dataMatIn),np.mat(classLabels).transpose(),C,toler, kTup)
    iter = 0
    entireSet = True; alphaPairsChanged = 0
    while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
        alphaPairsChanged = 0
        if entireSet:   #go over all
            for i in range(oS.m):        
                alphaPairsChanged += innerL(i,oS)
                print "fullSet, iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged)
            iter += 1
        else:#go over non-bound (railed) alphas
            nonBoundIs =np.nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
            for i in nonBoundIs:
                alphaPairsChanged += innerL(i,oS)
                print "non-bound, iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged)
            iter += 1
        if entireSet: entireSet = False #toggle entire set loop
        elif (alphaPairsChanged == 0): entireSet = True  
        print "iteration number: %d" % iter
    return oS.b,oS.alphas
b,alphas=smoP(dataArr,labelArr,0.6,0.01,40)
print(b)
print(alphas)
        
        
        
#