采用基于遗传算法(GA)的二维主成分分析法(2DPCA)进行人脸识别。

# -*- coding:utf-8 -*- #author TCY #email tangchenyi@mail.hfut.edu.cn #date 2015/04/03 import os import math import sys class FeatureSelector: #supported algorithms TYPE_CHI="CHI" TYPE_MI="MI" TYPE_IG="IG" Type=(TYPE_CHI, TYPE_MI, TYPE_IG) def __init__(self, selectorType, srcDataPath, dstDicPath, numOfKind): if(selectorType not in self.Type): raise ValueError,"selectorType error" self.__selectorType = selectorType self.__srcDataPath = srcDataPath self.__dstDicPath = dstDicPath self.__numOfKind = numOfKind pass def select(self): if(self.__selectorType == self.TYPE_CHI): self.run_CHI() elif(self.__selectorType == self.TYPE_MI): self.run_MI() elif(self.__selectorType == self.TYPE_IG): self.run_IG() pass def run_CHI(self): self.count() self.calculate_CHI() return self.__terms_value pass def run_MI(self): self.count() self.calculate_MI() return self.__terms_value pass def run_IG(self): self.count() self.calculate_IG() return self.__terms_value pass def count(self): fp = open(self.__srcDataPath, "r") terms_kind=dict() numOfLine = 0; numOfEachKind = [None]*self.__numOfKind for i in range(self.__numOfKind): numOfEachKind[i] = 0 for line in fp: terms = line.split() label=int(terms[0]) unique_terms=set() for term in terms[1:]: unique_terms.add(term) for term in unique_terms: if term not in terms_kind: kind=[None]*self.__numOfKind for i in range(self.__numOfKind): kind[i] = 0 kind[label]=1 terms_kind[term]=kind else: kind=terms_kind[term] kind[label]=kind[label]+1 numOfLine+=1 numOfEachKind[label]+=1 self.__numOfLine = numOfLine self.__terms_kind=terms_kind self.__numOfEachKind = numOfEachKind pass def calculate_CHI(self): N = self.__numOfLine terms_CHI = dict() for term in self.__terms_kind.items(): kind = term[1] numOfThisTerm = 0 CHIs = [] for i in range(self.__numOfKind): numOfThisTerm += kind[i] for i in range(self.__numOfKind): A = kind[i] B = numOfThisTerm - A C = self.__numOfEachKind[i] - A #D=N-A-B-C D = N - numOfThisTerm - C #CHI=N*(A*D-B*C)^2/((A+C)*(A+B)*(B+D)*(C+D)) CHI = N * math.pow((A*D-B*C),2)/((A+C)*(A+B)*(B+D)*(C+D)) CHIs.append(CHI) CHIs.sort() terms_CHI[term[0]]=CHIs[self.__numOfKind-1] self.__terms_value = terms_CHI def calculate_IG(self): N = self.__numOfLine+0.0 terms_IG = dict() C = [None]*self.__numOfKind PC = [None]*self.__numOfKind tmp_PC = 0; for i in range(self.__numOfKind): C[i] = self.__numOfEachKind[i] PC[i] = C[i] / N tmp_PC += PC[i] * math.log(PC[i], 2) for term in self.__terms_kind.items(): kind = term[1] numOfThisTerm = 0 for i in range(self.__numOfKind): numOfThisTerm += kind[i] A = numOfThisTerm PA = A / N PA_ = 1 - PA tmp_PB = 0 tmp_PB_ = 0 for i in range(self.__numOfKind): B = kind[i] B_ = C[i] - B PB = B / A PB_ = B_ / (N - A) if PB != 0: tmp_PB += PB*math.log(PB,2) if PB_ != 0: tmp_PB_ += PB_*math.log(PB_,2) IG = PA * tmp_PB + PA_ * tmp_PB_ - tmp_PC terms_IG[term[0]]=IG self.__terms_value = terms_IG pass def calculate_MI(self): N = self.__numOfLine terms_MI = dict() for term in self.__terms_kind.items(): kind = term[1] numOfThisTerm = 0 MIs = [] for i in range(self.__numOfKind): numOfThisTerm += kind[i] for i in range(self.__numOfKind): A = kind[i] B = numOfThisTerm - A C = self.__numOfEachKind[i] - A #MI=lg(A*N/((A+C)(A+B)) if A != 0: MI = math.log(N*A/((A+C)*(A+B))) else : MI = 0 MIs.append(MI) MIs.sort() terms_MI[term[0]]=MIs[self.__numOfKind-1] self.__terms_value = terms_MI pass def printMessage(self): fp = open(self.__dstDicPath, "w") for item in self.__terms_value.items(): fp.write("%s\t%f\n"%item) fp.close() if __name__ == "__main__": selector= FeatureSelector("IG", "test.txt", "dic.txt", 3) selector.select() selector.printMessage()