/*
 * 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 org.apache.mahout.math.random;

import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.MahoutTestCase;
import org.apache.mahout.math.stats.OnlineSummarizer;
import org.junit.Before;
import org.junit.Test;

import java.util.Arrays;

public final class NormalTest extends MahoutTestCase {

  @Override
  @Before
  public void setUp() {
    RandomUtils.useTestSeed();
  }

  @Test
  public void testOffset() {
    OnlineSummarizer s = new OnlineSummarizer();
    Sampler<Double> sampler = new Normal(2, 5);
    for (int i = 0; i < 10001; i++) {
      s.add(sampler.sample());
    }
    assertEquals(String.format("m = %.3f, sd = %.3f", s.getMean(), s.getSD()), 2, s.getMean(), 0.04 * s.getSD());
    assertEquals(5, s.getSD(), 0.12);
  }

  @Test
  public void testSample() throws Exception {
    double[] data = new double[10001];
    Sampler<Double> sampler = new Normal();
    for (int i = 0; i < data.length; i++) {
      data[i] = sampler.sample();
    }
    Arrays.sort(data);

    NormalDistribution reference = new NormalDistribution(RandomUtils.getRandom().getRandomGenerator(),
                                                          0, 1,
                                                          NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    assertEquals("Median", reference.inverseCumulativeProbability(0.5), data[5000], 0.04);
  }
}