def draw_example(): plt.figure() seqA = [0, 0, 0, 3, 6, 13, 25, 22, 7, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] seqB = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 12, 24, 23, 8 ,3, 1, 0, 0, 0, 0, 0] from featureEncoding import dynamicTimeWarp cmap = plt.cm.Blues cost = dynamicTimeWarp(seqA,seqB) plt.imshow(cost, interpolation='nearest', cmap=cmap) plt.colorbar() plt.tight_layout() plt.show()
def draw_cross_similarity(): sensor_data = data = np.genfromtxt('./ICS_slipperData/Alice0105db.csv', dtype=float, delimiter=',', names=True) from featureEncoding import dynamicTimeWarp cost = dynamicTimeWarp(sensor_data['Axis1'],sensor_data['Axis1']) plt.figure() title = 'Cross Similarity' cmap = plt.cm.Blues plt.imshow(cost, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() #plt.xticks(np.arange(len(label_names)), label_names, rotation=45) #plt.yticks(np.arange(len(label_names)), label_names) plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') plt.show()