LDA的实现

#!/usr/bin/python2 #-*-coding:UTF-8-*- from numpy import * #这里的posSet，negSet也是没有标签在末尾元素上的 def lda(posSet,negSet):    #The last element of one row is the label ! 0 or 1     posMeanRow=mean(posSet,axis=0,keepdims=False)     negMeanRow=mean(negSet,axis=0,keepdims=False)     posSet-=posMeanRow;    negSet-=negMeanRow    #The rows of posSet and negSet are vectors which have been centralized !     Sw=mat(posSet).T*mat(posSet)+mat(negSet).T*mat(negSet)     U,S,VT=linalg.svd(Sw)    # S is kind of array which consists of singular values     S=mat(diag(S))    # now S is of matrix type     V=VT.T     oppoS=S.I     UT=U.T     oppoSw=V*oppoS*UT    # Get the Sw^-1     dfRow=posMeanRow-negMeanRow     dfVecMat=mat(dfRow).T     W=oppoSw*dfVecMat     posCentVal=posMeanRow*W     negCentVal=negMeanRow*W     return W,posCentVal,negCentVal    #return the W,posMeanRow,negMeanRow #W必须是矩阵类型 def ldaClassify(W,row,posCent,negCent):     print "The shape of row : ",shape(row)     print "The shape of W : ",shape(W)     projectVal=row*W     if abs(projectVal-posCent)<abs(projectVal-negCent):         return 1    #离正样本的中心投影更近的话，认定为正样本，返回1作为测试样本的标签     else:         return 0