/*
    * 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.stat.descriptive.vector;
  
  import java.io.Serializable;
  import java.util.Arrays;
  
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.linear.MatrixUtils;
  import org.hipparchus.linear.RealMatrix;
  import org.hipparchus.util.MathArrays;
  
  /**
   * Returns the covariance matrix of the available vectors.
   */
  public class VectorialCovariance implements Serializable {
  
      /** Serializable version identifier */
      private static final long serialVersionUID = 4118372414238930270L;
  
      /** Sums for each component. */
      private final double[] sums;
  
      /** Sums of products for each component. */
      private final double[] productsSums;
  
      /** Indicator for bias correction. */
      private final boolean isBiasCorrected;
  
      /** Number of vectors in the sample. */
      private long n;
  
      /** Constructs a VectorialCovariance.
       * @param dimension vectors dimension
       * @param isBiasCorrected if true, computed the unbiased sample covariance,
       * otherwise computes the biased population covariance
       */
      public VectorialCovariance(int dimension, boolean isBiasCorrected) {
          sums         = new double[dimension];
          productsSums = new double[dimension * (dimension + 1) / 2];
          n            = 0;
          this.isBiasCorrected = isBiasCorrected;
      }
  
      /**
       * Add a new vector to the sample.
       * @param v vector to add
       * @throws MathIllegalArgumentException if the vector does not have the right dimension
       */
      public void increment(double[] v) throws MathIllegalArgumentException {
          MathArrays.checkEqualLength(v, sums);
          int k = 0;
          for (int i = 0; i < v.length; ++i) {
              sums[i] += v[i];
              for (int j = 0; j <= i; ++j) {
                  productsSums[k++] += v[i] * v[j];
              }
          }
          n++;
      }
  
      /**
       * Get the covariance matrix.
       * @return covariance matrix
       */
      public RealMatrix getResult() {
  
          int dimension = sums.length;
          RealMatrix result = MatrixUtils.createRealMatrix(dimension, dimension);
  
          if (n > 1) {
              double c = 1.0 / (n * (isBiasCorrected ? (n - 1) : n));
              int k = 0;
              for (int i = 0; i < dimension; ++i) {
                  for (int j = 0; j <= i; ++j) {
                      double e = c * (n * productsSums[k++] - sums[i] * sums[j]);
                      result.setEntry(i, j, e);
                      result.setEntry(j, i, e);
                  }
              }
         }
 
         return result;
 
     }
 
     /**
      * Get the number of vectors in the sample.
      * @return number of vectors in the sample
      */
     public long getN() {
         return n;
     }
 
     /**
      * Clears the internal state of the Statistic
      */
     public void clear() {
         n = 0;
         Arrays.fill(sums, 0.0);
         Arrays.fill(productsSums, 0.0);
     }
 
     /** {@inheritDoc} */
     @Override
     public int hashCode() {
         final int prime = 31;
         int result = 1;
         result = prime * result + (isBiasCorrected ? 1231 : 1237);
         result = prime * result + (int) (n ^ (n >>> 32));
         result = prime * result + Arrays.hashCode(productsSums);
         result = prime * result + Arrays.hashCode(sums);
         return result;
     }
 
     /** {@inheritDoc} */
     @Override
     public boolean equals(Object obj) {
         if (this == obj) {
             return true;
         }
         if (!(obj instanceof VectorialCovariance)) {
             return false;
         }
         VectorialCovariance other = (VectorialCovariance) obj;
         if (isBiasCorrected != other.isBiasCorrected) {
             return false;
         }
         if (n != other.n) {
             return false;
         }
         if (!Arrays.equals(productsSums, other.productsSums)) {
             return false;
         }
         if (!Arrays.equals(sums, other.sums)) {
             return false;
         }
         return true;
     }
 
 }