def start(self):
measurement = []
for i in range(self.repeat):
measure = []
splitted_dataset = cross.split_list(self.raw_data, self.k, True)
k_fold = cross.KFold(splitted_dataset)
for fold in k_fold:
stats, class_prob = NormalDist.calc_normal_stats(fold[0])
confusion_matrices = ConfusionMatrix.ConfusionMatrixStatistic(
stats)
for item in fold[1]:
confusion_matrices.add_result(
item[len(item) - 1],
NormalDist.get_class(stats, class_prob, item))
measure.append(confusion_matrices.calc_stats())
measurement.append(ConfusionMatrix.Measure.connect(measure))
return ConfusionMatrix.Measure.connect(measurement)
def start(self):
measurement = []
for i in range(self.repeat):
measure = []
splitted_dataset = cross.split_list(self.raw_data, self.k, True)
k_fold = cross.KFold(splitted_dataset)
for fold in k_fold:
stats, class_prob, buckets = edp.create_dictionary_with_buckets(
self.raw_data, fold[0], self.bins)
confusion_matrices = ConfusionMatrix.ConfusionMatrixStatistic(
stats)
for item in fold[1]:
confusion_matrices.add_result(
item[len(item) - 1],
edp.get_class(stats, class_prob, buckets, item))
measure.append(confusion_matrices.calc_stats())
measurement.append(ConfusionMatrix.Measure.connect(measure))
return ConfusionMatrix.Measure.connect(measurement)