def generate_image(self, known_indexes=tuple(), known_pixels=tuple(), generate_mode='average'):
# testing code
known_indexes, known_pixels = list(known_indexes), list(known_pixels)
n_pixels = len(known_indexes)
if operator.eq(list(range(n_pixels)), list(known_indexes)):
if self.tensor_info['regular_center'] > n_pixels:
self.mps_regularization(n_pixels)
tmp_left_tensor = numpy.array([1])
tmp_left_tensor.shape = (1, 1)
index = 0
while index < self.tensor_info['n_length']:
if index >= n_pixels:
tmp_tensor = wf.tensor_contract(
tmp_left_tensor, self.tensor_data[index], [[1], [0]])
tmp_tensor /= numpy.linalg.norm(tmp_tensor)
probability, state = numpy.linalg.eigh(
wf.tensor_contract(tmp_tensor, tmp_tensor, [[0, -1], [0, -1]]))
pixels = self.anti_feature_map(state)
if generate_mode == 'max':
# stupid code
probability[numpy.where(probability == probability.min())] = 0
probability /= probability.max()
known_pixels.append((numpy.array(probability) * numpy.array(pixels)).sum())
tmp_left_tensor = numpy.dot(
tmp_left_tensor,
wf.tensor_contract(
self.tensor_data[index],
self.feature_map([known_pixels[index]]).flatten(),
[[1], [0]]))
tmp_left_tensor /= numpy.linalg.norm(tmp_left_tensor)
index += 1
return numpy.array(known_pixels)
else:
print('only work when i know first n pixels')
def measure_mps(self, operator=numpy.diag([1, -1])):
# testing code
measure_data = numpy.zeros(self.tensor_info['n_length'])
if self.tensor_info['regular_center'] != 0:
self.mps_regularization(0)
self.tensor_data[0] /= self.tensor_data[0].norm()
for ii in range(self.tensor_info['n_length']):
measure_data[ii] = wf.tensor_contract(
wf.tensor_contract(self.tensor_data[ii], self.tensor_data[ii], [[0, -1], [0, -1]]),
operator, [[0, 1], [1, 0]])
if ii < self.tensor_info['n_length'] - 1:
self.move_regular_center2next()
return measure_data
def calculate_single_entropy(self, dot_index='all'):
if operator.eq(dot_index, 'all'):
dot_index = list(range(self.tensor_info['n_length']))
elif isinstance(dot_index, int):
dot_index = [dot_index]
entropy = dict()
for ii in dot_index:
self.mps_regularization(ii)
tmp_tensor = copy.deepcopy(self.tensor_data[ii])
tmp_tensor = tmp_tensor / numpy.linalg.norm(tmp_tensor)
tmp_tensor = wf.tensor_contract(tmp_tensor, tmp_tensor, [[0, -1], [0, -1]])
tmp_spectrum = numpy.linalg.eigh(tmp_tensor)[0]
tmp_spectrum /= numpy.sum(tmp_spectrum)
tmp_spectrum[tmp_spectrum <= 0] = 1
entropy[ii] = abs((tmp_spectrum * numpy.log(tmp_spectrum)).sum())
return entropy
