/*
    * 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
    *
    *      https://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.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  package org.hipparchus.distribution.multivariate;
  
  import java.util.ArrayList;
  import java.util.List;
  
  import org.hipparchus.distribution.MultivariateRealDistribution;
  import org.hipparchus.exception.LocalizedCoreFormats;
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.exception.MathRuntimeException;
  import org.hipparchus.random.RandomGenerator;
  import org.hipparchus.random.Well19937c;
  import org.hipparchus.util.Pair;
  
  /**
   * Class for representing <a href="http://en.wikipedia.org/wiki/Mixture_model">
   * mixture model</a> distributions.
   *
   * @param <T> Type of the mixture components.
   */
  public class MixtureMultivariateRealDistribution<T extends MultivariateRealDistribution>
      extends AbstractMultivariateRealDistribution {
      /** Normalized weight of each mixture component. */
      private final double[] weight;
      /** Mixture components. */
      private final List<T> distribution;
  
      /**
       * Creates a mixture model from a list of distributions and their
       * associated weights.
       * <p>
       * <b>Note:</b> this constructor will implicitly create an instance of
       * {@link Well19937c} as random generator to be used for sampling only (see
       * {@link #sample()} and {@link #sample(int)}). In case no sampling is
       * needed for the created distribution, it is advised to pass {@code null}
       * as random generator via the appropriate constructors to avoid the
       * additional initialisation overhead.
       *
       * @param components List of (weight, distribution) pairs from which to sample.
       */
      public MixtureMultivariateRealDistribution(List<Pair<Double, T>> components) {
          this(new Well19937c(), components);
      }
  
      /**
       * Creates a mixture model from a list of distributions and their
       * associated weights.
       *
       * @param rng Random number generator.
       * @param components Distributions from which to sample.
       * @throws MathIllegalArgumentException if any of the weights is negative.
       * @throws MathIllegalArgumentException if not all components have the same
       * number of variables.
       */
      public MixtureMultivariateRealDistribution(RandomGenerator rng,
                                                 List<Pair<Double, T>> components) {
          super(rng, components.get(0).getSecond().getDimension());
  
          final int numComp = components.size();
          final int dim = getDimension();
          double weightSum = 0;
          for (final Pair<Double, T> comp : components) {
              if (comp.getSecond().getDimension() != dim) {
                  throw new MathIllegalArgumentException(LocalizedCoreFormats.DIMENSIONS_MISMATCH,
                          comp.getSecond().getDimension(), dim);
              }
              if (comp.getFirst() < 0) {
                  throw new MathIllegalArgumentException(LocalizedCoreFormats.NUMBER_TOO_SMALL, comp.getFirst(), 0);
              }
              weightSum += comp.getFirst();
          }
  
          // Check for overflow.
          if (Double.isInfinite(weightSum)) {
              throw new MathRuntimeException(LocalizedCoreFormats.OVERFLOW);
          }
  
          // Store each distribution and its normalized weight.
          distribution = new ArrayList<>();
         weight = new double[numComp];
         for (int i = 0; i < numComp; i++) {
             final Pair<Double, T> comp = components.get(i);
             weight[i] = comp.getFirst() / weightSum;
             distribution.add(comp.getSecond());
         }
     }
 
     /** {@inheritDoc} */
     @Override
     public double density(final double[] values) {
         double p = 0;
         for (int i = 0; i < weight.length; i++) {
             p += weight[i] * distribution.get(i).density(values);
         }
         return p;
     }
 
     /** {@inheritDoc} */
     @Override
     public double[] sample() {
         // Sampled values.
         double[] vals = null;
 
         // Determine which component to sample from.
         final double randomValue = random.nextDouble();
         double sum = 0;
 
         for (int i = 0; i < weight.length; i++) {
             sum += weight[i];
             if (randomValue <= sum) {
                 // pick model i
                 vals = distribution.get(i).sample();
                 break;
             }
         }
 
         if (vals == null) {
             // This should never happen, but it ensures we won't return a null in
             // case the loop above has some floating point inequality problem on
             // the final iteration.
             vals = distribution.get(weight.length - 1).sample();
         }
 
         return vals;
     }
 
     /** {@inheritDoc} */
     @Override
     public void reseedRandomGenerator(long seed) {
         // Seed needs to be propagated to underlying components
         // in order to maintain consistency between runs.
         super.reseedRandomGenerator(seed);
 
         for (int i = 0; i < distribution.size(); i++) {
             // Make each component's seed different in order to avoid
             // using the same sequence of random numbers.
             distribution.get(i).reseedRandomGenerator(i + 1 + seed);
         }
     }
 
     /**
      * Gets the distributions that make up the mixture model.
      *
      * @return the component distributions and associated weights.
      */
     public List<Pair<Double, T>> getComponents() {
         final List<Pair<Double, T>> list = new ArrayList<>(weight.length);
 
         for (int i = 0; i < weight.length; i++) {
             list.add(new Pair<>(weight[i], distribution.get(i)));
         }
 
         return list;
     }
 }