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/*
* Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License").
* You may not use this file except in compliance with the License.
* A copy of the License is located at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* or in the "license" file accompanying this file. This file 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.amazon.randomcutforest.testutils;
import java.util.Arrays;
import java.util.Random;
/**
* This class samples point from a mixture of 2 multi-variate normal
* distribution with covariance matrices of the form sigma * I. One of the
* normal distributions is considered the base distribution, the second is
* considered the anomaly distribution, and there are random transitions between
* the two.
*/
public class NormalMixtureTestData {
private final double baseMu;
private final double baseSigma;
private final double anomalyMu;
private final double anomalySigma;
private final double transitionToAnomalyProbability;
private final double transitionToBaseProbability;
public NormalMixtureTestData(double baseMu, double baseSigma, double anomalyMu, double anomalySigma,
double transitionToAnomalyProbability, double transitionToBaseProbability) {
this.baseMu = baseMu;
this.baseSigma = baseSigma;
this.anomalyMu = anomalyMu;
this.anomalySigma = anomalySigma;
this.transitionToAnomalyProbability = transitionToAnomalyProbability;
this.transitionToBaseProbability = transitionToBaseProbability;
}
public NormalMixtureTestData() {
this(0.0, 1.0, 4.0, 2.0, 0.01, 0.3);
}
public NormalMixtureTestData(double baseMu, double anomalyMu) {
this(baseMu, 1.0, anomalyMu, 2.0, 0.01, 0.3);
}
public double[][] generateTestData(int numberOfRows, int numberOfColumns) {
return generateTestData(numberOfRows, numberOfColumns, 0);
}
public double[][] generateTestData(int numberOfRows, int numberOfColumns, int seed) {
double[][] result = new double[numberOfRows][numberOfColumns];
boolean anomaly = false;
NormalDistribution dist;
if (seed != 0)
dist = new NormalDistribution(new Random(seed));
else
dist = new NormalDistribution(new Random());
for (int i = 0; i < numberOfRows; i++) {
if (!anomaly) {
fillRow(result[i], dist, baseMu, baseSigma);
if (Math.random() < transitionToAnomalyProbability) {
anomaly = true;
}
} else {
fillRow(result[i], dist, anomalyMu, anomalySigma);
if (Math.random() < transitionToBaseProbability) {
anomaly = false;
}
}
}
return result;
}
public MultiDimDataWithKey generateTestDataWithKey(int numberOfRows, int numberOfColumns, int seed) {
double[][] resultData = new double[numberOfRows][numberOfColumns];
int[] change = new int[numberOfRows];
int numberOfChanges = 0;
boolean anomaly = false;
NormalDistribution dist;
if (seed != 0)
dist = new NormalDistribution(new Random(seed));
else
dist = new NormalDistribution(new Random());
for (int i = 0; i < numberOfRows; i++) {
if (!anomaly) {
fillRow(resultData[i], dist, baseMu, baseSigma);
if (Math.random() < transitionToAnomalyProbability) {
change[numberOfChanges++] = i + 1; // next item is different
anomaly = true;
}
} else {
fillRow(resultData[i], dist, anomalyMu, anomalySigma);
if (Math.random() < transitionToBaseProbability) {
anomaly = false;
change[numberOfChanges++] = i + 1; // next item is different
}
}
}
return new MultiDimDataWithKey(resultData, Arrays.copyOf(change, numberOfChanges), null);
}
private void fillRow(double[] row, NormalDistribution dist, double mu, double sigma) {
for (int j = 0; j < row.length; j++) {
row[j] = dist.nextDouble(mu, sigma);
}
}
public static class NormalDistribution {
private final Random rng;
private final double[] buffer;
private int index;
public NormalDistribution(Random rng) {
this.rng = rng;
buffer = new double[2];
index = 0;
}
public double nextDouble() {
if (index == 0) {
// apply the Box-Muller transform to produce Normal variates
double u = rng.nextDouble();
double v = rng.nextDouble();
double r = Math.sqrt(-2 * Math.log(u));
buffer[0] = r * Math.cos(2 * Math.PI * v);
buffer[1] = r * Math.sin(2 * Math.PI * v);
}
double result = buffer[index];
index = (index + 1) % 2;
return result;
}
public double nextDouble(double mu, double sigma) {
return mu + sigma * nextDouble();
}
}
}
