def sepTests():
gestures = ["circle", "l", "m", "round", "x", "z"]
count = 0
for gesture in gestures:
data = dataflow.dataflow("data", gesture)
test = data.get_test_xyz()
for i in range(test.shape[1]):
count = count + 1
Ef = open("data\\tests\\" + str(count) + ".csv", "w")
Ewriter = csv.writer(Ef, delimiter = ',', quotechar = '', quoting = csv.QUOTE_NONE, dialect = csv.unix_dialect)
Ewriter.writerows(test[:,i,:])
Ef.close()
print(test.shape)
def main():
print(math.log(2.14727368714e-64))
return
path = "data" + os.sep + "unitytrain"
name = "circle-r-cw"
dtf = dataflow(path, name)
(E, P, Pi, cent, thr) = dtf.load_model()
testFile = "2014-3-31--16-50-44.csv"
alltests = get_xyz_data(path + os.sep + "circle-r-cw" + os.sep + testFile)
joint = "Hand_R"
test = alltests[joint]
clusters = numpy.empty(len(test))
#f = open ("output.txt", "wt")
for i in range(len(test)):
clusters[i] = get_point_cluster(test[i,:], cent)
# f.write(str(clusters[i]) + "\n")
score = pr_hmm(clusters, P, E, Pi)
print(score)
def main ():
data = dataflow.dataflow("data", gesture)
training = data.get_train_xyz()
testing = data.get_test_xyz()
'''
****************************************************
* Initializing
****************************************************
'''
TRTS = classifier.classifier()
centroids = TRTS.get_point_centroids(training, N, D)
cent2 = TRTS.get_point_centroids(testing, N, D)
ATrainBinned = TRTS.get_point_clusters(training, centroids, D)
ATestBinned = TRTS.get_point_clusters(testing, centroids, D)
ATestBinned2 = TRTS.get_point_clusters(testing, cent2, D)
'''
****************************************************
* Training
****************************************************
'''
# Set priors
pP = TRTS.prior_transition_matrix(M, LR)
# Train the model:
b = [x for x in range(N)]
cyc = 5
E, P, Pi, LL = TRTS.dhmm_numeric(ATrainBinned, pP, b, M, cyc, .00001)
'''
****************************************************
* Testing
****************************************************
'''
sumLik = 0
minLik = numpy.Infinity
ATrainBinned = ATrainBinned[:,:,0]
ATestBinned = ATestBinned[:,:,0]
for j in range(len(ATrainBinned)):
lik = TRTS.pr_hmm(ATrainBinned[j], P, E.transpose(), Pi)
if lik < minLik:
minLik = lik
sumLik = sumLik + lik
gestureRecThreshold = 2.0 * sumLik / len(ATrainBinned)
#Uncomment the following to store the models.
# data.store_model(E, P, Pi, centroids, gestureRecThreshold)
# data.store_Binned(ATrainBinned[:,:,0],ATestBinned[:,:,0])
print('\n********************************************************************')
print('Testing %d sequences for a log likelihood greater than %.4f' % (len(ATestBinned), gestureRecThreshold))
print('********************************************************************\n')
recs = 0
tLL = numpy.zeros(shape=(len(ATestBinned)))
for j in range(len(ATestBinned)):
tLL[j] = TRTS.pr_hmm(ATestBinned[j], P, E.transpose(), Pi)
if tLL[j] > gestureRecThreshold:
recs = recs + 1
print("Log Likelihood: %.3f > %.3f (threshold) -- FOUND %s Gesture" % (tLL[j], gestureRecThreshold, gesture))
else:
print("Log Likelihood: %.3f < %.3f (threshold) -- NO %s Gesture" % (tLL[j], gestureRecThreshold, gesture))
print('Recognition success rate: %.2f percent\n' % (100 * recs / len(ATestBinned)))