def train(directory='./training_data'):
X, Y = load_measurements(directory)
classifier = KnockClassifier()
for x, y in izip(X, Y):
classifier.addData(max_distance(smooth_3d_measurements(x)), y)
classifier.fit()
return classifier
def plot(measurements, label='movement'):
mpl.rcParams['legend.fontsize'] = 10
smoothed = smooth_3d_measurements(measurements, 10)
X, Y, Z = [], [], []
for x, y, z in smoothed:
X += [x]
Y += [y]
Z += [z]
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(X, Y, Z, label=label)
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_zlabel('Z axis')
ax.legend()
plt.show()
