test_set[4].append(np.std(datax)) #std of each axis
test_set[5].append(np.mean(datay))
test_set[6].append(np.mean(dataz))
array_xy=np.asarray([datax,datay])
array_yz=np.asarray([datay,dataz])
array_zx=np.asarray([dataz,datax])
test_set[7].append(np.cov(array_xy)[0][1])
test_set[8].append(np.cov(array_xy)[0][1])
test_set[9].append(np.cov(array_xy)[0][1])"""
test_array = np.asarray(test_set)
print 'the test array is ', test_array
f3 = open('test_for_cc.txt', 'w')
np.save(f3, test_array)
test_feature = RealFeatures(test_array)
svm = LibLinear()
file_classifier = SerializableHdf5File(
r'letters_vs_cc/ZLabel1accuracy=0.964285714286 liblinear_cc_vs_Z_svm_classifier_with_C_10_and_normalized.h5',
'r')
status = svm.load_serializable(file_classifier)
output = svm.apply(test_feature).get_labels()
print 'output is ', output
if int(output[0]) == 1:
print 'You just made the mirrored letter Z '
elif int(output[0]) == -1:
print 'You just made a counterclockwise circle '
