# Copyright 2017 Google Inc.
#
# 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.

"""Uniform sampling method.

Samples in batches.
"""

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

import numpy as np

from .sampling_def import SamplingMethod


class UniformSampling(SamplingMethod):
    def __init__(self, X, y, seed):
        self.X = X
        self.y = y
        self.name = 'uniform'
        np.random.seed(seed)

    def select_batch_(self, already_selected, N, **kwargs):
        """Returns batch of randomly sampled datapoints.

        Assumes that data has already been shuffled.

        Args:
          already_selected: index of datapoints already selected
          N: batch size

        Returns:
          indices of points selected to label
        """

        # This is uniform given the remaining pool but biased wrt the entire pool.
        sample = [i for i in range(self.X.shape[0]) if i not in already_selected]
        return sample[0:N]

# Copyright 2017 Google Inc.
#
# 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.

"""Uniform sampling method.

Samples in batches.
"""

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

import numpy as np

from .sampling_def import SamplingMethod


class UniformSampling(SamplingMethod):
    def __init__(self, X, y, seed):
        self.X = X
        self.y = y
        self.name = 'uniform'
        np.random.seed(seed)

    def select_batch_(self, already_selected, N, **kwargs):
        """Returns batch of randomly sampled datapoints.

        Assumes that data has already been shuffled.

        Args:
          already_selected: index of datapoints already selected
          N: batch size

        Returns:
          indices of points selected to label
        """

        # This is uniform given the remaining pool but biased wrt the entire pool.
        sample = [i for i in range(self.X.shape[0]) if i not in already_selected]
        return sample[0:N]

# Copyright 2017 Google Inc. # # 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. Uniform sampling method.  Samples in batches. Returns batch of randomly sampled datapoints.          Assumes that data has already been shuffled.          Args:           already_selected: index of datapoints already selected           N: batch size          Returns:           indices of points selected to label # This is uniform given the remaining pool but biased wrt the entire pool.