old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  #
  #  Copyright 2019 The FATE Authors. 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
  from federatedml.nn.hetero_nn.strategy.comparision import Comparision
  
  
  class RelativeSelector(object):
      def __init__(self, max_size=None, beta=1, random_state=None, min_prob=0):
          self._comparision = Comparision(size=max_size)
          self.beta = beta
          self.min_prob = min_prob
          np.random.seed(random_state)
  
      def select_batch_sample(self, samples):
          select_ret = [False] * len(samples)
          for sample in samples:
              self._comparision.add(sample)
  
          for idx, sample in enumerate(samples):
              select_ret[idx] = np.random.uniform(0, 1) <= max(self.min_prob, np.power(self._comparision.get_rate(sample), self.beta))
  
          return select_ret
  
  
  class SelectorFactory(object):
      @staticmethod
      def get_selector(method, selective_size, beta=1, random_rate=None, min_prob=0):
          if not method:
              return None
          elif method == "relative":
              return RelativeSelector(selective_size, beta, random_state=random_rate, min_prob=min_prob)
          else:
              raise ValueError("Back Propagation Selector {} not supported yet")
  
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  #
  #  Copyright 2019 The FATE Authors. 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
  from federatedml.nn.hetero_nn.strategy.comparision import Comparision
  
  
  class RelativeSelector(object):
      def __init__(self, max_size=None, beta=1, random_state=None, min_prob=0):
          self._comparision = Comparision(size=max_size)
          self._beta = beta
          self._min_prob = min_prob
          np.random.seed(random_state)
  
      def select_batch_sample(self, samples):
          select_ret = [False] * len(samples)
          for sample in samples:
              self._comparision.add(sample)
  
          for idx, sample in enumerate(samples):
              select_ret[idx] = max(self._min_prob, np.power(np.random.uniform(0, 1), self._beta)) <= self._comparision.get_rate(sample)
  
          return select_ret
  
  
  class SelectorFactory(object):
      @staticmethod
      def get_selector(method, selective_size, beta=1, random_rate=None, min_prob=0):
          if not method:
              return None
          elif method == "relative":
              return RelativeSelector(selective_size, beta, random_state=random_rate, min_prob=min_prob)
          else:
              raise ValueError("Back Propagation Selector {} not supported yet")
  
  
subject: fix select strategy of nn