def transfer(self,numberOfTests=1):
self.destinationEstimators,self.projMatrix,self.projVector = [],[],[]
if self.matrix != [] and self.vect != []:
proj,projMatrix,projVector = self.__generateProjectionNoRandom(np.array(self.vect),np.array(self.matrix))
Hproj = []
for i in range(self.sourceEstimators.size):
Hproj.append(lambda x,S=self.sourceEstimators[i],P=proj: S(P(x)))
temp = adaboost.adaBoost(self.destinationSet)
temp.run(np.array(Hproj))
self.destinationEstimators.append(temp)
self.projMatrix.append(projMatrix)
self.projVector.append(projVector)
elif self.vect != []:
for k in range(numberOfTests):
proj,projMatrix,projVector = self.__generateProjectionPseudoRandomVect(len(self.sourceSet[0][0]),len(self.destinationSet[0][0]),np.array(self.vect))
Hproj = []
for i in range(self.sourceEstimators.size):
Hproj.append(lambda x,S=self.sourceEstimators[i],P=proj: S(P(x)))
temp = adaboost.adaBoost(self.destinationSet)
temp.run(np.array(Hproj))
self.destinationEstimators.append(temp)
self.projMatrix.append(projMatrix)
self.projVector.append(projVector)
else:
for k in range(numberOfTests):
proj,projMatrix,projVector = self.__generateProjection(len(self.sourceSet[0][0]),len(self.destinationSet[0][0]))
Hproj = []
for i in range(self.sourceEstimators.size):
Hproj.append(lambda x,S=self.sourceEstimators[i],P=proj: S(P(x)))
temp = adaboost.adaBoost(self.destinationSet)
temp.run(np.array(Hproj))
self.destinationEstimators.append(temp)
self.projMatrix.append(projMatrix)
self.projVector.append(projVector)
def __init__(self,sourceSet,destinationSet,vect=[],matrix=[]):
self.sourceSet = sourceSet
self.destinationSet = destinationSet
self.sourceEstimators = adaboost.adaBoost(sourceSet).generateDecisionStump(10)
self.destinationEstimators = []
self.projMatrix = []
self.projVector = []
self.matrix = []
self.vect = []
def testadaboost(iterations):
adabooster = adaboost.adaBoost(EIGENVALUES)
adabooster.loadData(dataMatrix, labelMatrix)
adabooster.boost(iterations)
correct = 0
total = 0
for i in range(len(testLabels)):
if adabooster.classify(testData[i]) == testLabels[i]:
correct += 1
total += 1
else:
total += 1
print "Correct: ", str(float(correct) / float(total)) + "%"
correct = 0
total = 0
for i in range(len(labelMatrix)):
if adabooster.classify(dataMatrix[i]) == labelMatrix[i]:
correct += 1
total += 1
else:
total += 1
print "Correct: ", str(float(correct) / float(total)) + "%"