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()
