def testing_4():
global clfback, clfarm, clfleg
trial_list = p.TrialList()
data = trial_list.readFile('test.txt')
test_back = (f.Features(data, 'k')).feature
test_leg = (f.Features(data, 'g')).feature
test_arm = (f.Features(data, 'm')).feature
res1 = clfback.test(test_back, 5)
res2 = clfleg.test(test_leg, 5)
res3 = clfarm.test(test_arm, 5)
s = 0
#s = 'back: ' + str(res1) + ' leg: '+ str(res2)+ ' arm: '+str(res3) + '\n'
if res1 < -0.5:
#s += "You're hunching your back.\n"
s += 4
if res2 < -0.5:
#s += "You're crossing your leg.\n"
s += 1
if res3 < -0.5:
#s += "Your arms are too close to your body.\n"
s += 2
if res1 > -0.5 and res2 > -0.5 and res3 > -0.5:
#s += "You have perfect posture!\n"
s = 0
return s
def getTrainingData():
l = p.trial_list.getTrials()
for x in l:
features = f.Features(x)
data.append(features.feature)
if x.head.target == 'good':
label.append(1)
else:
label.append(0)
def setupClassifier(path): l = p.getTrialList(path) for x in l: features = f.Features(x) data.append(features.feature) if x.head.target == 'good': label.append(1) else: label.append(0)
def getTestingData(path): l = p.getTrialList(path) for x in l: features = f.Features(x, path[-2]) data_test.append(features.feature) if x.head.target == 'good': label_test.append(1) else: label_test.append(0) print data_test[-1], label_test[-1]
def getTrainingData(path):
l = p.getTrialList(path)
for x in l:
features = f.Features(x)
data.append(features.feature)
if x.head.target == 'good' or path[-2] != 'k':
label.append(1)
else:
label.append(0)
print data[-1], label[-1]
def testing():
trial_list = p.TrialList()
data = trial_list.readFile('test.txt')
test_data = (f.Features(data)).feature
res = ''
#print test_data
for clf in classifiers:
# print clf
res += clf[0] + ' '
res += str(clf[1].predict(test_data)[0]) + '\n'
return res
def setupClassifier(path):
data = []
label = []
l = p.getTrialList(path)
for x in l:
features = f.Features(x, path[-2])
data.append(features.feature)
if x.head.target == 'good':
label.append(1)
else:
label.append(0)
return knn.knn(data, label)
def testing_3():
global clfback, clfarm, clfleg
trial_list = p.TrialList()
data = trial_list.readFile('test.txt')
test_data = (f.Features(data)).feature
res1 = clfback.test(test_data, 10)
res2 = clfleg.test(test_data, 10)
res3 = clfarm.test(test_data, 10)
print res1, res2, res3
minres = min(res1, res2, res3)
if minres < -1.0:
if minres == res1:
s = "You're hunching your back.\n"
elif minres == res2:
s = "You're crossing your leg.\n"
else:
s = "Your arms are too close to your body.\n"
else:
s = "You have perfect posture!\n"
return s
def testing_4():
global clf1, clf2, clf3, clf4
trial_list = p.TrialList()
data = trial_list.readFile('test.txt')
test_data = (f.Features(data)).feature
res1 = clf1.test(test_data, 10)
res2 = clf2.test(test_data, 10)
res3 = clf3.test(test_data, 10)
res4 = clf4.test(test_data, 10)
str1, str2, str3 = "", "", ""
if res1 == 0:
str1 = "You're hunching your back"
if res2 + res3 == 1:
str2 = "You're leaning to the left/right."
if res4 == 0:
str3 = "Your arms are too close to your body."
if res1 + res2 + res3 + res4 == 4:
return "You have perfect posture!\n-------------------------------------------\n"
return str1 + "\n" + str2 + "\n" + str3 + "\n-------------------------------------------\n"
def testing():
global classifier1
trial_list = p.TrialList()
data = trial_list.readFile('test.txt')
test_data = (f.Features(data)).feature
return classifier1.test(test_data, 10)