Package com.github.pmerienne.trident.ml.stats

Source Code of com.github.pmerienne.trident.ml.stats.AdaptiveStreamFeatureStatisticsTest

/**
* Copyright 2013-2015 Pierre Merienne
*
* Licensed 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 com.github.pmerienne.trident.ml.stats;

import static org.junit.Assert.assertEquals;

import java.util.Random;

import org.junit.Test;

import com.github.pmerienne.trident.ml.stats.AdaptiveStreamFeatureStatistics;

public class AdaptiveStreamFeatureStatisticsTest {

  @Test
  public void testUpdate() {
    // Given
    Random random = new Random();
    double expectedMean = 4.0;
    double stdDev = 3.0;
    int size = 10000;

    double[] features = new double[size];
    for (int i = 0; i < size; i++) {
      features[i] = expectedMean + random.nextGaussian() * stdDev;
    }

    // When
    AdaptiveStreamFeatureStatistics statistics = new AdaptiveStreamFeatureStatistics(1000);
    for (int i = 0; i < size; i++) {
      statistics.update(features[i]);
    }

    // Then
    assertEquals(expectedMean, statistics.getMean(), 0.3);
    assertEquals(stdDev, statistics.getStdDev(), 0.3);
  }

  @Test
  public void testUpdateWithMovingMean() {
    // Given
    Random random = new Random();
    int size = 100000;
    double startMean = 3.0;
    double finalMean = 6.0;
    double step = 10 * (finalMean - startMean) / size;
    double stdDev = 3.0;

    double[] features = new double[size];
    for (int i = 0; i < size; i++) {
      double currentMean = Math.min(finalMean, i * step + startMean);
      features[i] = currentMean + random.nextGaussian() * stdDev;
    }

    // When
    AdaptiveStreamFeatureStatistics statistics = new AdaptiveStreamFeatureStatistics(1000);
    for (int i = 0; i < size; i++) {
      statistics.update(features[i]);
    }

    // Then
    assertEquals(finalMean, statistics.getMean(), 0.35);
    assertEquals(stdDev, statistics.getStdDev(), 0.35);
  }

  @Test
  public void testUpdateWithMovingStdDev() {
    // Given
    Random random = new Random();
    int size = 100000;
    double expectedMean = 3.0;
    double startStdDev = 2.0;
    double finalStdDev = 4.0;
    double step = (finalStdDev - startStdDev) / size;

    double[] features = new double[size];
    for (int i = 0; i < size; i++) {
      double currentStdDev = i * step + startStdDev;
      features[i] = expectedMean + random.nextGaussian() * currentStdDev;
    }

    // When
    AdaptiveStreamFeatureStatistics statistics = new AdaptiveStreamFeatureStatistics(1000);
    for (int i = 0; i < size; i++) {
      statistics.update(features[i]);
    }

    // Then
    assertEquals(expectedMean, statistics.getMean(), 0.3);
    assertEquals(finalStdDev, statistics.getStdDev(), 0.3);
  }
}
TOP

Related Classes of com.github.pmerienne.trident.ml.stats.AdaptiveStreamFeatureStatisticsTest

TOP
Copyright © 2018 www.massapi.com. All rights reserved.
All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.