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)')