eFeatures = copy.deepcopy(features)
# Build dictionary of feature values
c = 0
for key in eFeatures.keys():
for line in newTrainData:
attr = line[c]
clss = line[-2]
if attr not in eFeatures[key].keys():
eFeatures[key][attr] = {clss: line[-1]}
else:
if clss not in eFeatures[key][attr].keys():
eFeatures[key][attr][clss] = line[-1]
else:
eFeatures[key][attr][clss] += line[-1]
c += 1
newTrainData, stump, stumpWeight = AdaBoost.adaBoost(
newTrainData, eFeatures, features)
stumps.append(stump)
stumpWeights.append(stumpWeight)
#Test
total = 0
correct = 0
incorrct = 0
for sample in testData:
total += 1
votes = {'yes': 0, 'no': 0}
# Run test sample through all trees and tally votes
for i in range(len(stumps)):
result = DecisionTree.test(sample, stumps[i], features)
votes[result] += stumpWeights[i]
predict = max(votes, key=votes.get)
