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data.py
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/
data.py
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# Copyright 2019 PIQuIL - All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
def load_data(tr_samples_path, tr_psi_path=None, tr_bases_path=None, bases_path=None):
r"""Load the data required for training.
:param tr_samples_path: The path to the training data.
:type tr_samples_path: str
:param tr_psi_path: The path to the target/true wavefunction.
:type tr_psi_path: str
:param tr_bases_path: The path to the basis data.
:type tr_bases_path: str
:param bases_path: The path to a file containing all possible bases used in
the tr_bases_path file.
:type bases_path: str
:returns: A list of all input parameters.
:rtype: list
"""
data = []
data.append(
torch.tensor(np.loadtxt(tr_samples_path, dtype="float32"), dtype=torch.double)
)
if tr_psi_path is not None:
target_psi_data = np.loadtxt(tr_psi_path, dtype="float32")
target_psi = torch.zeros(2, len(target_psi_data), dtype=torch.double)
target_psi[0] = torch.tensor(target_psi_data[:, 0], dtype=torch.double)
target_psi[1] = torch.tensor(target_psi_data[:, 1], dtype=torch.double)
data.append(target_psi)
if tr_bases_path is not None:
data.append(np.loadtxt(tr_bases_path, dtype=str))
if bases_path is not None:
bases_data = np.loadtxt(bases_path, dtype=str)
bases = []
for i in range(len(bases_data)):
tmp = ""
for j in range(len(bases_data[i])):
if bases_data[i][j] != " ":
tmp += bases_data[i][j]
bases.append(tmp)
data.append(bases)
return data
def extract_refbasis_samples(train_samples, train_bases):
r"""Extract the reference basis samples from the data.
:param train_samples: The training samples.
:type train_samples: numpy.array
:param train_bases: The bases of the training samples.
:type train_bases: numpy.array
:returns: The samples in the data that are only in the reference basis.
:rtype: torch.Tensor
"""
tmp = []
num_visible = train_samples.shape[-1]
for i in range(train_samples.shape[0]):
flag = 0
for j in range(num_visible):
if train_bases[i][j] != "Z":
flag = 1
break
if flag == 0:
tmp.append(train_samples[i])
z_samples = torch.zeros(len(tmp), num_visible, dtype=torch.double)
for i in range(len(tmp)):
for j in range(num_visible):
z_samples[i][j] = tmp[i][j]
return z_samples