def ensembleClassify(test_data, test_class, kClassifiers, kClassifiers_errors):
boosted_predicted_class = []
k_predictions = []
for i in range(0, k):
k_predictions.append(nb.predictClass(test_data, kClassifiers[i]))
for i in range(0, len(test_data)):
vote_probability = {}
for ki in range(0, k):
if k_predictions[ki][i] not in vote_probability:
vote_probability[k_predictions[ki][i]] = 0
if kClassifiers_errors[ki] == 0.0:
vote_probability[k_predictions[ki][i]] += 0
else:
vote_probability[k_predictions[ki][i]] += math.log((1.0 - kClassifiers_errors[ki]) / kClassifiers_errors[ki])
max_vote = 0
max_class = None
for class_label in vote_probability:
if vote_probability[class_label] > max_vote:
max_vote = vote_probability[class_label]
max_class = class_label
boosted_predicted_class.append(max_class)
if test_class != []:
nb.generateMeasures(test_class, boosted_predicted_class)
return boosted_predicted_class
