# Path: utils/sent_utils.py
# def inf_batch_iterator(n, batch_size, shuffle=True, include_last=False):
#   while True:
#     for indices in iterate_minibatches_indices(n, batch_size,
#                                                shuffle, include_last):
#       yield indices
# 
# def iterate_minibatches_indices(n, batch_size, shuffle=False,
#                                 include_last=True):
#   indices = np.arange(n)
# 
#   num_batches = n // batch_size
#   if include_last:
#     num_batches += (n % batch_size != 0)
# 
#   if num_batches == 0:
#     num_batches = 1
# 
#   if shuffle:
#     np.random.shuffle(indices)
# 
#   for batch_idx in range(num_batches):
#     batch_indices = indices[batch_idx * batch_size:
#                             (batch_idx + 1) * batch_size]
#     yield batch_indices
# 
# Path: utils/common_utils.py
# def log(msg):
#   if msg[-1] != '\n':
#     msg += '\n'
#   sys.stderr.write(msg)
#   sys.stderr.flush()


from utils.sent_utils import inf_batch_iterator, iterate_minibatches_indices
from utils.common_utils import log
import sklearn.linear_model as linear_model
import tensorflow as tf
import numpy as np
# Copyright 2019 Google LLC
#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function




def linear_mapping(x, y, n_jobs=8):
  # train least square
  projection = linear_model.LinearRegression(n_jobs=n_jobs)

# Path: utils/sent_utils.py
# def inf_batch_iterator(n, batch_size, shuffle=True, include_last=False):
#   while True:
#     for indices in iterate_minibatches_indices(n, batch_size,
#                                                shuffle, include_last):
#       yield indices
# 
# def iterate_minibatches_indices(n, batch_size, shuffle=False,
#                                 include_last=True):
#   indices = np.arange(n)
# 
#   num_batches = n // batch_size
#   if include_last:
#     num_batches += (n % batch_size != 0)
# 
#   if num_batches == 0:
#     num_batches = 1
# 
#   if shuffle:
#     np.random.shuffle(indices)
# 
#   for batch_idx in range(num_batches):
#     batch_indices = indices[batch_idx * batch_size:
#                             (batch_idx + 1) * batch_size]
#     yield batch_indices
# 
# Path: utils/common_utils.py
# def log(msg):
#   if msg[-1] != '\n':
#     msg += '\n'
#   sys.stderr.write(msg)
#   sys.stderr.flush()


# Path: train_feature_mapper.py
from utils.sent_utils import inf_batch_iterator, iterate_minibatches_indices
from utils.common_utils import log
import sklearn.linear_model as linear_model
import tensorflow as tf
import numpy as np

# Copyright 2019 Google LLC
#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function




def linear_mapping(x, y, n_jobs=8):
  # train least square
  projection = linear_model.LinearRegression(n_jobs=n_jobs)

  projection.fit(x, y)