def gtnc(para_tot=None):
print('Preparing parameters')
if para_tot is None:
para_tot = pm.parameters_gcmpm()
n_class = len(para_tot['classes'])
paras = bf.empty_list(n_class)
for n in range(0, n_class):
paras[n] = copy.deepcopy(para_tot)
paras[n]['class'] = int(para_tot['classes'][n])
paras[n]['chi'] = para_tot['chi'][n]
paras[n]['theta'] = para_tot['theta']
paras[n]['save_exp'] = save_exp_gtn_one_class(paras[n])
classifiers = bf.empty_list(n_class)
for n in range(0, n_class):
print_dict(paras[n])
data = para_tot['data_path'] + paras[n]['save_exp']
if para_tot['if_load'] and os.path.isfile(data):
print('The classifier already exists. Load directly')
classifiers[n] = load_pr(data, 'a')
else:
print('Training the MPS of ' + str(para_tot['classes'][n]))
classifiers[n] = gtn_one_class(paras[n])[0]
# if para_tot['if_save']:
# save_pr('../data_tnml/gcmpm/', paras[n]['save_exp'],
# [classifiers[n]], ['classifier'])
# classifiers[n].mps.check_orthogonality_by_tensors(tol=1e-12)
# ==================== Testing accuracy ====================
print('Calculating the testing accuracy')
b = TNmachineLearning.MachineLearningFeatureMap(para_tot['d'])
b.load_data(data_path='..\\..\\..\\MNIST\\',
file_sample='t10k-images.idx3-ubyte',
file_label='t10k-labels.idx1-ubyte',
is_normalize=True)
b.select_samples(para_tot['classes'])
if classifiers[0].is_dct:
b.dct(shift=para_tot['shift'], factor=para_tot['factor'])
b.images2vecs(para_tot['theta'] * np.pi / 2)
fid = bf.empty_list(n_class)
for n in range(0, n_class):
fid[n] = b.compute_fidelities(classifiers[n].mps.mps)
max_fid = np.argmin(np.hstack(fid), axis=1)
predict = np.zeros(max_fid.shape, dtype=int)
for n in range(0, n_class):
predict += (max_fid == n) * int(para_tot['classes'][n])
# plot(predict)
# plot(b.labels)
accuracy = np.sum(predict == b.labels, dtype=float) / b.numVecSample
print(accuracy)
def gcmpm(para_tot=None):
print('Preparing parameters')
if para_tot is None:
para_tot = pm.parameters_gcmpm()
n_class = len(para_tot['classes'])
paras = bf.empty_list(n_class)
for n in range(0, n_class):
paras[n] = para_tot.copy()
paras[n]['class'] = para_tot['classes'][n]
paras[n]['chi'] = para_tot['chi'][n]
paras[n]['save_exp'] = save_exp_gcmpm_one_class(paras[n])
classifiers = bf.empty_list(n_class)
for n in range(0, n_class):
data = '../data_tnml/gcmpm/' + paras[n]['save_exp']
if para_tot['if_load'] and os.path.isfile(data):
print('The classifier already exists. Load directly')
classifiers[n] = load_pr(data, 'classifier')
else:
print('Training the MPS of ' + str(para_tot['classes'][n]))
classifiers[n] = gcmpm_one_class(paras[n])[0]
if para_tot['if_save']:
save_pr('../data_tnml/gcmpm/', paras[n]['save_exp'],
[classifiers[n]], ['classifier'])
# Testing accuracy
print('Calculating the testing accuracy')
labels = para_tot['classes']
b = TNmachineLearning.MachineLearningFeatureMap('MNIST', para_tot['d'],
file_sample='t10k-images.idx3-ubyte',
file_label='t10k-labels.idx1-ubyte')
b.images2vecs(para_tot['classes'], ['all', 'all'])
fid = np.zeros((n_class, ))
num_wrong = 0
for ni in range(0, b.numVecSample):
for n in range(0, n_class):
fid[n] = b.fidelity_mps_image(classifiers[n].mps.mps, ni)
n_max = int(np.argmax(fid))
if labels[n_max] != b.LabelNow[ni]:
num_wrong += 1
accuracy = num_wrong/b.numVecSample
print(accuracy)
