def main():
print "Loading previously stored HMM..."
hmm = get_stored_hmm()
#hmm = getHMM()
print hmm.A
print
print hmm.B
print hmm.pi
print "HMM Created successfully"
print "Loading previously stored RF model"
RF = joblib.load('RFModel.pk')
print "RF loaded successfully"
# Feature Extractor
FE = FeatureExtractor()
FE.set_class(1)
# Open socket and start listening for data
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((HOST, PORT))
s.listen(1)
subactivities = get_subactivities()
activities = get_activities()
conn, addr = s.accept()
while True:
data = conn.recv(20000)
#print data
if data:
#print data
data = data.split('\n')
# Remove extra newline
del data[-1]
csv_data = []
for d in data:
#print len(d)
if len(d) >= 53:
d2 = d[14:].strip('"')
d3 = np.fromstring(d2, dtype=int, sep=',')
csv_data.append(d3.tolist())
#print len(csv_data)
if len(csv_data) > 0:
csv_data = np.array(csv_data)
#row,col = csv_data.shape
#print row, col
features = []
for x in range(4):
f = FE.get_featurelist_from_nparray(csv_data, 2*x, 2*x + 1)
for y in f:
p = RF.predict(y[:25])[0]
if len(history[x]) >= HISTORY_LEN:
history[x].pop(0)
history_labels[x].pop(0)
history[x].append(p)
history_labels[x].append(subactivities[int(p)])
for sub in subactivities:
print sub, get_subactivity_class(sub)
for z in range(4):
hmm.mapB(history[z])
alpha = hmm.calculate_alpha(history[z])
#alpha_normalized = alpha
alpha_normalized = alpha.astype('float') / alpha.sum(axis=1)[:, np.newaxis]
c = most_common(history[z])
if len(history) > 12:
print z+1, history_labels[z][12:], subactivities[int(c)]
else:
print z+1, history_labels[z][5:], subactivities[int(c)]
print alpha_normalized[-1]
print
print "\n--------------\n"
def main():
# print "Loading previously stored HMM..."
# hmm = get_stored_hmm()
# #hmm = getHMM()
# print hmm.A
# print
# print hmm.B
# print hmm.pi
# print "HMM Created successfully"
tlist = []
print "Loading previously stored RF model"
RF = joblib.load('RFModel.pk')
print "RF loaded successfully"
# Feature Extractor
FE = FeatureExtractor()
FE.set_class(1)
# Open socket and start listening for data
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((HOST, PORT))
s.listen(1)
subactivities = get_subactivities()
activities = get_activities()
conn, addr = s.accept()
while True:
data = conn.recv(20000)
#print data
if data:
start = time.clock()
#print data
data = data.split('\n')
# Remove extra newline
del data[-1]
csv_data = []
for d in data:
#print len(d)
if len(d) >= 53:
d2 = d[14:].strip('"')
d3 = np.fromstring(d2, dtype=int, sep=',')
csv_data.append(d3.tolist())
#print len(csv_data)
if len(csv_data) > 0:
csv_data = np.array(csv_data)
#row,col = csv_data.shape
#print row, col
features = []
for x in range(4):
f = FE.get_featurelist_from_nparray(csv_data, 2*x+1, 2*x + 2)
for y in f:
row,col = y.shape
p = RF.predict(y[:col-2])[0]
if len(history[x]) >= HISTORY_LEN:
history[x].pop(0)
history_labels[x].pop(0)
history[x].append(p)
history_labels[x].append(subactivities[int(p)])
for sub in subactivities:
print sub, get_subactivity_class(sub)
elapsed = (time.clock() - start)
tlist.append(elapsed)
print tlist
