def __init__(self):

def setup():

def load_patterns(file):

patterns = None

sizes = np.zeros(len(words))

counter = 0

f = open(file, 'rb')

data = f.readlines()

stack = []

for i in range(np.shape(data)[0]):

data2 = map(float, data[i].split())

data2 = np.reshape(data2, (1, -1))

if i == 0:

stack = data2

else:

stack = np.vstack((stack, data2))

f.close()

sizes[counter] = np.shape(stack)[0]

counter += 1

if patterns is None:

patterns = stack

else:

patterns = np.vstack((patterns, stack))

return patterns

hidden = 1

self.go_model = GaussianHMM(n_components=hidden, covariance_type="diag", n_iter=10000).fit(

load_patterns('go.bin'))

self.back_model = GaussianHMM(n_components=hidden, covariance_type="diag", n_iter=10000).fit(

load_patterns('back.bin'))

self.right_model = GaussianHMM(n_components=hidden, covariance_type="diag", n_iter=10000).fit(

load_patterns('right.bin'))

self.left_model = GaussianHMM(n_components=hidden, covariance_type="diag", n_iter=10000).fit(

load_patterns('left.bin'))

self.stop_model = GaussianHMM(n_components=hidden, covariance_type="diag", n_iter=10000).fit(

load_patterns('stop.bin'))

setup()

self.number_of_components = 5

def match(self, pattern):

probabilities = np.zeros(5)

probabilities[0] = self.go_model.score(np.reshape(pattern, (1, -1)))

probabilities[1] = self.back_model.score(np.reshape(pattern, (1, -1)))

probabilities[2] = self.right_model.score(np.reshape(pattern, (1, -1)))

probabilities[3] = self.left_model.score(np.reshape(pattern, (1, -1)))

probabilities[4] = self.stop_model.score(np.reshape(pattern, (1, -1)))

probabilities = abs(probabilities)

index, error = min(enumerate(probabilities), key=lambda x: x[1])

if error < 9500:

if index == 0:

return 0

elif index == 1:

return 1

elif index == 2:

return 2

elif index == 3:

return 3

else:

return 4