//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.
  
  package org.hipparchus.random;
  
  import org.hipparchus.UnitTestUtils;
  import org.hipparchus.linear.Array2DRowRealMatrix;
  import org.hipparchus.linear.RealMatrix;
  import org.junit.Assert;
  import org.junit.Test;
  
  public class UncorrelatedRandomVectorGeneratorTest {
      private double[] mean;
      private double[] standardDeviation;
      private UncorrelatedRandomVectorGenerator generator;
  
      public UncorrelatedRandomVectorGeneratorTest() {
          mean              = new double[] {0.0, 1.0, -3.0, 2.3};
          standardDeviation = new double[] {1.0, 2.0, 10.0, 0.1};
          RandomGenerator rg = new JDKRandomGenerator();
          rg.setSeed(17399225432l);
          generator =
              new UncorrelatedRandomVectorGenerator(mean, standardDeviation,
                      new GaussianRandomGenerator(rg));
      }
  
      @Test
      public void testMeanAndCorrelation() {
          final int n = generator.nextVector().length;
          final double[] estimatedMean = new double[generator.nextVector().length];
          final RealMatrix matrix = new Array2DRowRealMatrix(10000, n);
          for (int i = 0; i < 10000; ++i) {
              double[] v = generator.nextVector();
              matrix.setRow(i, v);
          }
  
          for (int i = 0; i < n; i++) {
              estimatedMean[i] = UnitTestUtils.mean(matrix.getColumn(i));
          }
  
          // double[] estimatedMean = meanStat.getResult();
          double scale;
          RealMatrix estimatedCorrelation = UnitTestUtils.covarianceMatrix(matrix);
          //RealMatrix estimatedCorrelation = covStat.getResult();
          for (int i = 0; i < estimatedMean.length; ++i) {
              Assert.assertEquals(mean[i], estimatedMean[i], 0.07);
              for (int j = 0; j < i; ++j) {
                  scale = standardDeviation[i] * standardDeviation[j];
                  Assert.assertEquals(0, estimatedCorrelation.getEntry(i, j) / scale, 0.03);
              }
              scale = standardDeviation[i] * standardDeviation[i];
              Assert.assertEquals(1, estimatedCorrelation.getEntry(i, i) / scale, 0.025);
          }
      }
  }