def getProb(trainD, trainC, testD , lrn):
orangeData = funkcije.listToOrangeSingleClass(trainD+testD, trainC+[0]*len(testD))
ind = [1]*len(trainD)+[0]*len(testD)
orangeTrainD = orangeData.select_ref(ind,1)
orangeTestD = orangeData.select_ref(ind,0)
cl = lrn(orangeTrainD)
return [cl(i, Orange.classification.Classifier.GetProbabilities)[True] for i in orangeTestD]
def getProb(trainD, trainC, testD, lrn):
orangeData = funkcije.listToOrangeSingleClass(trainD + testD,
trainC + [0] * len(testD))
ind = [1] * len(trainD) + [0] * len(testD)
orangeTrainD = orangeData.select_ref(ind, 1)
orangeTestD = orangeData.select_ref(ind, 0)
cl = lrn(orangeTrainD)
return [
cl(i, Orange.classification.Classifier.GetProbabilities)[True]
for i in orangeTestD
]
def reliefFilter(trainD, trainC):
orangeData = funkcije.listToOrangeSingleClass(trainD, trainC)
meas = Orange.feature.scoring.Relief()
mr = [ (a.name, meas(a, orangeData)) for a in orangeData.domain.attributes]
mr.sort(key=lambda x: -x[1]) #sort decreasingly by the score
return [i[0] for i in mr]
def reliefFilter(trainD, trainC):
orangeData = funkcije.listToOrangeSingleClass(trainD, trainC)
meas = Orange.feature.scoring.Relief()
mr = [(a.name, meas(a, orangeData)) for a in orangeData.domain.attributes]
mr.sort(key=lambda x: -x[1]) #sort decreasingly by the score
return [i[0] for i in mr]
