UniformDistribution.java
/*
* Java Genetic Algorithm Library (@__identifier__@).
* Copyright (c) @__year__@ Franz Wilhelmstötter
*
* 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.
*
* Author:
* Franz Wilhelmstötter (franz.wilhelmstoetter@gmx.at)
*/
package org.jenetics.stat;
import static java.lang.String.format;
import static java.util.Objects.requireNonNull;
import static org.jenetics.internal.util.object.eq;
import java.io.Serializable;
import java.util.Locale;
import org.jscience.mathematics.number.Float64;
import org.jenetics.internal.util.HashBuilder;
import org.jenetics.util.Function;
import org.jenetics.util.Range;
/**
* <a href="http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29">
* Uniform distribution</a> class.
*
* @see LinearDistribution
*
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @since 1.0
* @version 1.0 — <em>$Date: 2014-03-01 $</em>
*/
public class UniformDistribution<
N extends Number & Comparable<? super N>
>
implements Distribution<N>
{
/**
* <p>
* <img
* src="doc-files/uniform-pdf.gif"
* alt="f(x)=\left\{\begin{matrix}
* \frac{1}{max-min} & for & x \in [min, max] \\
* 0 & & otherwise \\
* \end{matrix}\right."
* />
* </p>
*
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @since 1.0
* @version 1.0 — <em>$Date: 2014-03-01 $</em>
*/
static final class PDF<N extends Number & Comparable<? super N>>
implements
Function<N, Float64>,
Serializable
{
private static final long serialVersionUID = 1L;
private final double _min;
private final double _max;
private final Float64 _probability;
public PDF(final Range<N> domain) {
_min = domain.getMin().doubleValue();
_max = domain.getMax().doubleValue();
_probability = Float64.valueOf(1.0/(_max - _min));
}
@Override
public Float64 apply(final N value) {
final double x = value.doubleValue();
Float64 result = Float64.ZERO;
if (x >= _min && x <= _max) {
result = _probability;
}
return result;
}
@Override
public String toString() {
return format(Locale.ENGLISH, "p(x) = %s", _probability);
}
}
/**
* <p>
* <img
* src="doc-files/uniform-cdf.gif"
* alt="f(x)=\left\{\begin{matrix}
* 0 & for & x < min \\
* \frac{x-min}{max-min} & for & x \in [min, max] \\
* 1 & for & x > max \\
* \end{matrix}\right."
* />
* </p>
*
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @since 1.0
* @version 1.0 — <em>$Date: 2014-03-01 $</em>
*/
static final class CDF<N extends Number & Comparable<? super N>>
implements
Function<N, Float64>,
Serializable
{
private static final long serialVersionUID = 1L;
private final double _min;
private final double _max;
private final double _divisor;
public CDF(final Range<N> domain) {
_min = domain.getMin().doubleValue();
_max = domain.getMax().doubleValue();
_divisor = _max - _min;
assert (_divisor > 0);
}
@Override
public Float64 apply(final N value) {
final double x = value.doubleValue();
Float64 result = Float64.ZERO;
if (x < _min) {
result = Float64.ZERO;
} else if (x > _max) {
result = Float64.ONE;
} else {
result = Float64.valueOf((x - _min)/_divisor);
}
return result;
}
@Override
public String toString() {
return format(
Locale.ENGLISH,
"P(x) = (x - %1$s)/(%2$s - %1$s)", _min, _max
);
}
}
private final Range<N> _domain;
private final Function<N, Float64> _cdf;
private final Function<N, Float64> _pdf;
/**
* Create a new uniform distribution with the given {@code domain}.
*
* @param domain the domain of the distribution.
* @throws NullPointerException if the {@code domain} is {@code null}.
*/
public UniformDistribution(final Range<N> domain) {
_domain = requireNonNull(domain, "Domain");
_cdf = new CDF<>(_domain);
_pdf = new PDF<>(_domain);
}
/**
* Create a new uniform distribution with the given min and max values.
*
* @param min the minimum value of the domain.
* @param max the maximum value of the domain.
* @throws IllegalArgumentException if {@code min >= max}
* @throws NullPointerException if one of the arguments is {@code null}.
*/
public UniformDistribution(final N min, final N max) {
this(new Range<>(min, max));
}
@Override
public Range<N> getDomain() {
return _domain;
}
/**
* Return a new PDF object.
*
* <p>
* <img
* src="doc-files/uniform-pdf.gif"
* alt="f(x)=\left\{\begin{matrix}
* \frac{1}{max-min} & for & x \in [min, max] \\
* 0 & & otherwise \\
* \end{matrix}\right."
* />
* </p>
*
*/
@Override
public Function<N, Float64> getPDF() {
return _pdf;
}
/**
* Return a new CDF object.
*
* <p>
* <img
* src="doc-files/uniform-cdf.gif"
* alt="f(x)=\left\{\begin{matrix}
* 0 & for & x < min \\
* \frac{x-min}{max-min} & for & x \in [min, max] \\
* 1 & for & x > max \\
* \end{matrix}\right."
* />
* </p>
*
*/
@Override
public Function<N, Float64> getCDF() {
return _cdf;
}
@Override
public int hashCode() {
return HashBuilder.of(getClass()).and(_domain).value();
}
@Override
public boolean equals(final Object obj) {
if (obj == this) {
return true;
}
if (obj == null || getClass() != obj.getClass()) {
return false;
}
final UniformDistribution<?> dist = (UniformDistribution<?>)obj;
return eq(_domain, dist._domain);
}
@Override
public String toString() {
return format("UniformDistribution[%s]", _domain);
}
}