if (type(alg) is RandomForestClassifier):
print 'RFC params: number of trees = %d, test count = %d' % \
(number_of_trees, test_count_for_rfc)
elif (type(alg) is SVC):
print 'SVC params: kernel = %s, C = %f, gamma = %f' % \
(kernel, C, gamma)
clf_exp = ClassifierExperiment(alg)
accuracy_list = []
precision_list = []
recall_list = []
f1score_list = []
for epoch in xrange(0, num_epoch):
print 'Epoch %d' % (epoch)
data, labels = data_reader.shuffle(data, labels)
# Divide data into two parts: training and testing
train_data = data[0:train_dataset_size]
train_labels = labels[0:train_dataset_size]
train_data = data_reader.change_data_view(train_data)
test_data = data[train_dataset_size:]
test_labels = labels[train_dataset_size:]
test_data = Interpolator.cut_data(test_data,
test_frame_start, test_frame_end, test_sparseness)
test_data = Interpolator.interpolate(
test_data, train_frame_start,
train_frame_end, train_sparseness,
interpolation_degree)
