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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* 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.commons.statistics.distribution;
/**
* Implementation of the Cauchy distribution.
*/
public class CauchyDistribution extends AbstractContinuousDistribution {
/** The median of this distribution. */
private final double median;
/** The scale of this distribution. */
private final double scale;
/**
* Creates a distribution.
*
* @param median Median for this distribution.
* @param scale Scale parameter for this distribution.
* @throws IllegalArgumentException if {@code scale <= 0}.
*/
public CauchyDistribution(double median,
double scale) {
if (scale <= 0) {
throw new DistributionException(DistributionException.NEGATIVE, scale);
}
this.scale = scale;
this.median = median;
}
/** {@inheritDoc} */
@Override
public double cumulativeProbability(double x) {
return 0.5 + (Math.atan((x - median) / scale) / Math.PI);
}
/**
* Access the median.
*
* @return the median for this distribution.
*/
public double getMedian() {
return median;
}
/**
* Access the scale parameter.
*
* @return the scale parameter for this distribution.
*/
public double getScale() {
return scale;
}
/** {@inheritDoc} */
@Override
public double density(double x) {
final double dev = x - median;
return (1 / Math.PI) * (scale / (dev * dev + scale * scale));
}
/**
* {@inheritDoc}
*
* Returns {@code Double.NEGATIVE_INFINITY} when {@code p == 0}
* and {@code Double.POSITIVE_INFINITY} when {@code p == 1}.
*/
@Override
public double inverseCumulativeProbability(double p) {
double ret;
if (p < 0 ||
p > 1) {
throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
} else if (p == 0) {
ret = Double.NEGATIVE_INFINITY;
} else if (p == 1) {
ret = Double.POSITIVE_INFINITY;
} else {
ret = median + scale * Math.tan(Math.PI * (p - .5));
}
return ret;
}
/**
* {@inheritDoc}
*
* The mean is always undefined no matter the parameters.
*
* @return mean (always Double.NaN)
*/
@Override
public double getMean() {
return Double.NaN;
}
/**
* {@inheritDoc}
*
* The variance is always undefined no matter the parameters.
*
* @return variance (always Double.NaN)
*/
@Override
public double getVariance() {
return Double.NaN;
}
/**
* {@inheritDoc}
*
* The lower bound of the support is always negative infinity no matter
* the parameters.
*
* @return lower bound of the support (always Double.NEGATIVE_INFINITY)
*/
@Override
public double getSupportLowerBound() {
return Double.NEGATIVE_INFINITY;
}
/**
* {@inheritDoc}
*
* The upper bound of the support is always positive infinity no matter
* the parameters.
*
* @return upper bound of the support (always Double.POSITIVE_INFINITY)
*/
@Override
public double getSupportUpperBound() {
return Double.POSITIVE_INFINITY;
}
/**
* {@inheritDoc}
*
* The support of this distribution is connected.
*
* @return {@code true}
*/
@Override
public boolean isSupportConnected() {
return true;
}
}