/*
    * 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;
  
  import java.util.ArrayList;
  import java.util.Collection;
  
  import org.hipparchus.UnitTestUtils;
  import org.hipparchus.distribution.RealDistribution;
  import org.hipparchus.distribution.continuous.UniformRealDistribution;
  import org.hipparchus.random.RandomDataGenerator;
  import org.junit.Assert;
  import org.junit.Test;
  
  
  /**
   * Test cases for {@link StatisticalSummary}.
   */
  public class StatisticalSummaryTest {
  
      /**
       * Test aggregate function by randomly generating a dataset of 10-100 values
       * from [-100, 100], dividing it into 2-5 partitions, computing stats for each
       * partition and comparing the result of aggregate(...) applied to the collection
       * of per-partition SummaryStatistics with a single SummaryStatistics computed
       * over the full sample.
       */
      @Test
      public void testAggregate() {
  
          // Generate a random sample and random partition
          double[] totalSample = generateSample();
          double[][] subSamples = generatePartition(totalSample);
          int nSamples = subSamples.length;
  
          // Compute combined stats directly
          StreamingStatistics totalStats = new StreamingStatistics();
          for (int i = 0; i < totalSample.length; i++) {
              totalStats.addValue(totalSample[i]);
          }
  
          // Now compute subsample stats individually and aggregate
          StreamingStatistics[] subSampleStats = new StreamingStatistics[nSamples];
          for (int i = 0; i < nSamples; i++) {
              subSampleStats[i] = new StreamingStatistics();
          }
          Collection<StreamingStatistics> aggregate = new ArrayList<StreamingStatistics>();
          for (int i = 0; i < nSamples; i++) {
              for (int j = 0; j < subSamples[i].length; j++) {
                  subSampleStats[i].addValue(subSamples[i][j]);
              }
              aggregate.add(subSampleStats[i]);
          }
  
          // Compare values
          StatisticalSummary aggregatedStats = StatisticalSummary.aggregate(aggregate);
          assertStatisticalSummaryEquals(totalStats.getSummary(), aggregatedStats, 10E-12);
      }
  
      /**
       * Similar to {@link #testAggregate()} but operating on
       * {@link StatisticalSummary} instead.
       */
      @Test
      public void testAggregateStatisticalSummary() {
  
          // Generate a random sample and random partition
          double[] totalSample = generateSample();
          double[][] subSamples = generatePartition(totalSample);
          int nSamples = subSamples.length;
  
          // Compute combined stats directly
          StreamingStatistics totalStats = new StreamingStatistics();
          for (int i = 0; i < totalSample.length; i++) {
              totalStats.addValue(totalSample[i]);
          }
  
          // Now compute subsample stats individually and aggregate
          StreamingStatistics[] subSampleStats = new StreamingStatistics[nSamples];
         for (int i = 0; i < nSamples; i++) {
             subSampleStats[i] = new StreamingStatistics();
         }
         Collection<StatisticalSummary> aggregate = new ArrayList<StatisticalSummary>();
         for (int i = 0; i < nSamples; i++) {
             for (int j = 0; j < subSamples[i].length; j++) {
                 subSampleStats[i].addValue(subSamples[i][j]);
             }
             aggregate.add(subSampleStats[i].getSummary());
         }
 
         // Compare values
         StatisticalSummary aggregatedStats = StatisticalSummary.aggregate(aggregate);
         assertStatisticalSummaryEquals(totalStats.getSummary(), aggregatedStats, 10E-12);
     }
 
     @Test
     public void testAggregateDegenerate() {
         double[] totalSample = {1, 2, 3, 4, 5};
         double[][] subSamples = {{1}, {2}, {3}, {4}, {5}};
 
         // Compute combined stats directly
         StreamingStatistics totalStats = new StreamingStatistics();
         for (int i = 0; i < totalSample.length; i++) {
             totalStats.addValue(totalSample[i]);
         }
 
         // Now compute subsample stats individually and aggregate
         StreamingStatistics[] subSampleStats = new StreamingStatistics[5];
         for (int i = 0; i < 5; i++) {
             subSampleStats[i] = new StreamingStatistics();
         }
         Collection<StreamingStatistics> aggregate = new ArrayList<StreamingStatistics>();
         for (int i = 0; i < 5; i++) {
             for (int j = 0; j < subSamples[i].length; j++) {
                 subSampleStats[i].addValue(subSamples[i][j]);
             }
             aggregate.add(subSampleStats[i]);
         }
 
         // Compare values
         StatisticalSummary aggregatedStats = StatisticalSummary.aggregate(aggregate);
         assertStatisticalSummaryEquals(totalStats.getSummary(), aggregatedStats, 10E-12);
     }
 
     @Test
     public void testAggregateSpecialValues() {
         double[] totalSample = {Double.POSITIVE_INFINITY, 2, 3, Double.NaN, 5};
         double[][] subSamples = {{Double.POSITIVE_INFINITY, 2}, {3}, {Double.NaN}, {5}};
 
         // Compute combined stats directly
         StreamingStatistics totalStats = new StreamingStatistics();
         for (int i = 0; i < totalSample.length; i++) {
             totalStats.addValue(totalSample[i]);
         }
 
         // Now compute subsample stats individually and aggregate
         StreamingStatistics[] subSampleStats = new StreamingStatistics[5];
         for (int i = 0; i < 4; i++) {
             subSampleStats[i] = new StreamingStatistics();
         }
         Collection<StreamingStatistics> aggregate = new ArrayList<StreamingStatistics>();
         for (int i = 0; i < 4; i++) {
             for (int j = 0; j < subSamples[i].length; j++) {
                 subSampleStats[i].addValue(subSamples[i][j]);
             }
             aggregate.add(subSampleStats[i]);
         }
 
         // Compare values
         StatisticalSummary aggregatedStats = StatisticalSummary.aggregate(aggregate);
         assertStatisticalSummaryEquals(totalStats.getSummary(), aggregatedStats, 10E-12);
     }
 
     /**
      * Verifies that a StatisticalSummary and a StatisticalSummaryValues are equal up
      * to delta, with NaNs, infinities returned in the same spots. For max, min, n, values
      * have to agree exactly, delta is used only for sum, mean, variance, std dev.
      */
     protected static void assertStatisticalSummaryEquals(StatisticalSummary expected,
                                                          StatisticalSummary observed,
                                                          double delta) {
         UnitTestUtils.assertEquals(expected.getMax(), observed.getMax(), 0);
         UnitTestUtils.assertEquals(expected.getMin(), observed.getMin(), 0);
         Assert.assertEquals(expected.getN(), observed.getN());
         UnitTestUtils.assertEquals(expected.getSum(), observed.getSum(), delta);
         UnitTestUtils.assertEquals(expected.getMean(), observed.getMean(), delta);
         UnitTestUtils.assertEquals(expected.getStandardDeviation(), observed.getStandardDeviation(), delta);
         UnitTestUtils.assertEquals(expected.getVariance(), observed.getVariance(), delta);
     }
 
     /**
      * Generates a random sample of double values.
      * Sample size is random, between 10 and 100 and values are
      * uniformly distributed over [-100, 100].
      *
      * @return array of random double values
      */
     private double[] generateSample() {
         final RealDistribution uniformDist = new UniformRealDistribution(-100, 100);
         final RandomDataGenerator randomDataGenerator = new RandomDataGenerator(100);
         final int sampleSize = randomDataGenerator.nextInt(10,  100);
         final double[] out = randomDataGenerator.nextDeviates(uniformDist, sampleSize);
         return out;
     }
 
     /**
      * Generates a partition of <sample> into up to 5 sequentially selected
      * subsamples with randomly selected partition points.
      *
      * @param sample array to partition
      * @return rectangular array with rows = subsamples
      */
     private double[][] generatePartition(double[] sample) {
         final RandomDataGenerator randomDataGenerator = new RandomDataGenerator(100);
         final int length = sample.length;
         final double[][] out = new double[5][];
         int cur = 0;          // beginning of current partition segment
         int offset = 0;       // end of current partition segment
         int sampleCount = 0;  // number of segments defined
         for (int i = 0; i < 5; i++) {
             if (cur == length || offset == length) {
                 break;
             }
             final int next;
             if (i == 4 || cur == length - 1) {
                 next = length - 1;
             } else {
                 next = randomDataGenerator.nextInt(cur, length - 1);
             }
             final int subLength = next - cur + 1;
             out[i] = new double[subLength];
             System.arraycopy(sample, offset, out[i], 0, subLength);
             cur = next + 1;
             sampleCount++;
             offset += subLength;
         }
         if (sampleCount < 5) {
             double[][] out2 = new double[sampleCount][];
             for (int j = 0; j < sampleCount; j++) {
                 final int curSize = out[j].length;
                 out2[j] = new double[curSize];
                 System.arraycopy(out[j], 0, out2[j], 0, curSize);
             }
             return out2;
         } else {
             return out;
         }
     }
 
 }