content: /*
   * Licensed to the Apache Software Foundation (ASF) under one
   * or more contributor license agreements.  See the NOTICE file
   * distributed with this work for additional information
   * regarding copyright ownership.  The ASF licenses this file
   * to you 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.
   */
  
  package accord.utils.random;
  
  import java.util.Arrays;
  import java.util.function.Supplier;
  
  import accord.utils.Invariants;
  import accord.utils.RandomSource;
  
  public class Picker
  {
      public static float[] randomWeights(RandomSource random, int length)
      {
          float[] weights = new float[length - 1];
          float sum = 0;
          for (int i = 0 ; i < weights.length ; ++i)
              weights[i] = sum += random.nextFloat();
          sum += random.nextFloat();
          for (int i = 0 ; i < weights.length ; ++i)
              weights[i] /= sum;
          return weights;
      }
  
      static abstract class Weighted
      {
          final RandomSource random;
          final float[] weights;
  
          public Weighted(RandomSource random, float[] weights)
          {
              this.random = random;
              this.weights = weights;
          }
  
  
          static float[] randomWeights(RandomSource random, float[] bias)
          {
              float[] weights = new float[bias.length - 1];
              float sum = 0;
              for (int i = 0 ; i < weights.length ; ++i)
                  weights[i] = sum += random.nextFloat() * bias[i];
              sum += random.nextFloat() * bias[weights.length];
              for (int i = 0 ; i < weights.length ; ++i)
                  weights[i] /= sum;
              return weights;
          }
  
          static float[] normaliseWeights(float[] input)
          {
              float[] output = new float[input.length - 1];
              float sum = 0;
              for (int i = 0 ; i < output.length ; ++i)
                  output[i] = sum += input[i];
              sum += input[output.length];
              for (int i = 0 ; i < output.length ; ++i)
                  output[i] /= sum;
              return output;
          }
  
          int pickIndex()
          {
              int i = Arrays.binarySearch(weights, random.nextFloat());
              if (i < 0) i = -1 - i;
              return i;
          }
      }
  
      public static class WeightedObjectPicker<T> extends Weighted implements Supplier<T>
      {
          final T[] values;
  
          private WeightedObjectPicker(RandomSource random, T[] values, float[] weights)
          {
              super(random, weights);
              this.values = values;
          }
  
          @Override
          public T get()
          {
              return values[pickIndex()];
          }
  
          public static <T> WeightedObjectPicker<T> randomWeighted(RandomSource random, T[] values)
          {
              return new WeightedObjectPicker<>(random, values, Picker.randomWeights(random, values.length));
          }
  
          public static <T> WeightedObjectPicker<T> randomWeighted(RandomSource random, T[] values, float[] bias)
          {
              Invariants.requireArgument(values.length == bias.length);
              return new WeightedObjectPicker<>(random, values, randomWeights(random, bias));
          }
  
          public static <T> WeightedObjectPicker<T> weighted(RandomSource random, T[] values, float[] proportionalWeights)
          {
              Invariants.requireArgument(values.length == proportionalWeights.length);
              return new WeightedObjectPicker<>(random, values, normaliseWeights(proportionalWeights));
          }
      }
  }
  