def exhSearch(self, trainFeatureName, trainData, trainFeatureList, trainLabel, totalK, creterionFunc):
myClassRules = classRules()
featureList = []
featureNoList = []
n = len(trainFeatureList)
combinationTuples = self.getCombinations(n, totalK)
# print len(combinationTuples), type(combinationTuples), type(combinationTuples[0])
combinationList = [list(i) for i in combinationTuples]
# print len(combinationList), type(combinationList), type(combinationList[0]), len(combinationList[0]), type(combinationList[0][0])
# featureNoList = [trainFeatureName[i] for i in combinationList[0]]
# print featureNoList
featureNoList = combinationList[0]
featureList = [trainFeatureList[featureNo] for featureNo in featureNoList]
maxJ = creterionFunc(myClassRules.DLDA, trainLabel, trainData, featureList)
# print maxJ
for combination in combinationList[1 : ]:
featureList = [trainFeatureList[featureNo] for featureNo in combination]
jValue = creterionFunc(myClassRules.DLDA, trainLabel, trainData, featureList)
if jValue > maxJ:
featureNoList = combination
maxJ = jValue
featureNameList = [trainFeatureName[i] for i in featureNoList]
print 'maxJ when totalK is', totalK, ':', maxJ
return featureNameList
def seqFwdSearch(self, trainFeatureName, trainData, trainFeatureList, trainLabel, totalK, creterionFunc):
# preserve the original lists
thisTrainFeatureName = trainFeatureName
thisTrainFeatureList = trainFeatureList
myClassRules = classRules()
featureList = []
featureNoList = []
k = 0
d = len(trainData)
if totalK > d:
totalK = d
roundNo = 1
while True:
# print 'this is', roundNo, 'round'
roundNo = roundNo + 1
# classRule, trainLabel, trainData, featureData
# print 'calculating maxJ for the first time'
# print len(featureList + trainFeatureList[0])
# currentFeatureList = featureList
if len(featureList) != 0:
# print featureList
newFeatureList = []
for feature in featureList:
newFeatureList.append(feature)
newFeatureList.append(thisTrainFeatureList[0])
maxJ = creterionFunc(myClassRules.DLDA, trainLabel, trainData, newFeatureList)
else:
maxJ = creterionFunc(myClassRules.DLDA, trainLabel, trainData, [thisTrainFeatureList[0]])
# print len(featureList)
# print 'maxJ value is:', maxJ
bestFeatureNo = 0
featureNo = 1
for feature in thisTrainFeatureList[1: ]:
# print 'doing the feature number:', featureNo, thisTrainFeatureName[featureNo]
# print feature
if featureNo in featureNoList:
# print 'continue'
continue
# print 'now computing feature number:', featureNo
if len(featureList) != 0:
newFeatureList = []
for features in featureList:
newFeatureList.append(features)
newFeatureList.append(feature)
jValue = creterionFunc(myClassRules.DLDA, trainLabel, trainData, newFeatureList)
else:
# print 'no feature yet'
jValue = creterionFunc(myClassRules.DLDA, trainLabel, trainData, [feature])
# print 'jValue is:', jValue
if jValue > maxJ:
maxJ = jValue
bestFeatureNo = featureNo
# print maxJc
featureNo = featureNo + 1
featureNoList.append(thisTrainFeatureName[bestFeatureNo])
featureList.append(thisTrainFeatureList[bestFeatureNo])
del thisTrainFeatureList[bestFeatureNo]
del thisTrainFeatureName[bestFeatureNo]
# print len(thisTrainFeatureList), len(thisTrainFeatureName)
# print featureNoList, featureList
if len(featureNoList) == totalK:
print 'maxJ when totalK is', totalK, ':', maxJ
break
return featureNoList
