C = 1
gamma = 10
# Choose a classifier
alg = RandomForestClassifier(n_estimators=number_of_trees, criterion='entropy', max_features='log2')
# alg = SVC(kernel=kernel, C=C, gamma=gamma)
# alg = SVR()
# alg = LinearSVR()
visualize_xyz_example = False
visualize_interpolated = False
training_enabled = True
data_reader = DataReader(xyz)
print "Parsing data..."
data, labels = data_reader.parse(fname)
labels = np.array(labels)
labels[labels < 1] = -1
if (visualize_xyz_example):
timestamps = np.arange(train_frame_start,
train_frame_end + train_sparseness, train_sparseness)
visualize_count = 3
visualized = 0
for i in xrange(0, len(labels)):
if (labels[i] and visualized < visualize_count):
plt.figure(figsize=(20, 10))
plt.plot(timestamps, data[i][1], 'r')
plt.plot(timestamps, data[i][2], 'g')
plt.plot(timestamps, data[i][3], 'b')
plt.xlabel('frame time (msec)')
