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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.rng.distribution.impl;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.special.Beta;
import org.apache.commons.math3.util.FastMath;
import org.nd4j.linalg.api.iter.NdIndexIterator;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.rng.Random;
import org.nd4j.linalg.api.rng.distribution.BaseDistribution;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Iterator;
/**
* Base distribution derived from apache commons math
* http://commons.apache.org/proper/commons-math/
*
* (specifically the {@link org.apache.commons.math3.distribution.BinomialDistribution}
*
* @author Adam Gibson
*/
public class BinomialDistribution extends BaseDistribution {
/**
* The number of trials.
*/
private final int numberOfTrials;
/**
* The probability of success.
*/
private double probabilityOfSuccess;
private INDArray p;
/**
* Create a binomial distribution with the given number of trials and
* probability of success.
*
* @param trials Number of trials.
* @param p Probability of success.
* @throws org.apache.commons.math3.exception.NotPositiveException if {@code trials < 0}.
* @throws org.apache.commons.math3.exception.OutOfRangeException if {@code p < 0} or {@code p > 1}.
*/
public BinomialDistribution(int trials, double p) {
this(Nd4j.getRandom(), trials, p);
}
/**
* Creates a binomial distribution.
*
* @param rng Random number generator.
* @param trials Number of trials.
* @param p Probability of success.
* @throws org.apache.commons.math3.exception.NotPositiveException if {@code trials < 0}.
* @throws org.apache.commons.math3.exception.OutOfRangeException if {@code p < 0} or {@code p > 1}.
* @since 3.1
*/
public BinomialDistribution(Random rng, int trials, double p) {
super(rng);
if (trials < 0) {
throw new NotPositiveException(LocalizedFormats.NUMBER_OF_TRIALS, trials);
}
if (p < 0 || p > 1) {
throw new OutOfRangeException(p, 0, 1);
}
probabilityOfSuccess = p;
numberOfTrials = trials;
}
public BinomialDistribution(int n, INDArray p) {
this.random = Nd4j.getRandom();
this.numberOfTrials = n;
this.p = p;
}
/**
* Access the number of trials for this distribution.
*
* @return the number of trials.
*/
public int getNumberOfTrials() {
return numberOfTrials;
}
/**
* Access the probability of success for this distribution.
*
* @return the probability of success.
*/
public double getProbabilityOfSuccess() {
return probabilityOfSuccess;
}
/**
* {@inheritDoc}
*/
public double probability(int x) {
double ret;
if (x < 0 || x > numberOfTrials) {
ret = 0.0;
} else {
ret = FastMath.exp(SaddlePointExpansion.logBinomialProbability(x, numberOfTrials, probabilityOfSuccess,
1.0 - probabilityOfSuccess));
}
return ret;
}
/**
* {@inheritDoc}
*/
public double cumulativeProbability(int x) {
double ret;
if (x < 0) {
ret = 0.0;
} else if (x >= numberOfTrials) {
ret = 1.0;
} else {
ret = 1.0 - Beta.regularizedBeta(probabilityOfSuccess, x + 1.0, numberOfTrials - x);
}
return ret;
}
@Override
public double density(double x) {
return 0;
}
@Override
public double cumulativeProbability(double x) {
double ret;
if (x < 0) {
ret = 0.0D;
} else if (x >= this.numberOfTrials) {
ret = 1.0D;
} else {
ret = 1.0D - Beta.regularizedBeta(this.probabilityOfSuccess, x + 1.0D, (this.numberOfTrials - x));
}
return ret;
}
@Override
public double cumulativeProbability(double x0, double x1) throws NumberIsTooLargeException {
return 0;
}
/**
* {@inheritDoc}
*
* For {@code n} trials and probability parameter {@code p}, the mean is
* {@code n * p}.
*/
public double getNumericalMean() {
return numberOfTrials * probabilityOfSuccess;
}
/**
* {@inheritDoc}
*
* For {@code n} trials and probability parameter {@code p}, the variance is
* {@code n * p * (1 - p)}.
*/
public double getNumericalVariance() {
final double p = probabilityOfSuccess;
return numberOfTrials * p * (1 - p);
}
/**
* {@inheritDoc}
*
* The lower bound of the support is always 0 except for the probability
* parameter {@code p = 1}.
*
* @return lower bound of the support (0 or the number of trials)
*/
@Override
public double getSupportLowerBound() {
return probabilityOfSuccess < 1.0 ? 0 : numberOfTrials;
}
/**
* {@inheritDoc}
*
* The upper bound of the support is the number of trials except for the
* probability parameter {@code p = 0}.
*
* @return upper bound of the support (number of trials or 0)
*/
@Override
public double getSupportUpperBound() {
return probabilityOfSuccess > 0.0 ? numberOfTrials : 0;
}
@Override
public boolean isSupportLowerBoundInclusive() {
return false;
}
@Override
public boolean isSupportUpperBoundInclusive() {
return false;
}
/**
* {@inheritDoc}
*
* The support of this distribution is connected.
*
* @return {@code true}
*/
public boolean isSupportConnected() {
return true;
}
private void ensureConsistent(int i) {
probabilityOfSuccess = p.reshape(-1).getDouble(i);
}
@Override
public INDArray sample(int[] shape) {
INDArray ret = Nd4j.createUninitialized(shape, Nd4j.order());
return sample(ret);
}
@Override
public INDArray sample(INDArray ret) {
if (random.getStatePointer() != null) {
if (p != null) {
return Nd4j.getExecutioner()
.exec(new org.nd4j.linalg.api.ops.random.impl.BinomialDistributionEx(
ret, numberOfTrials, p),
random);
} else {
return Nd4j.getExecutioner()
.exec(new org.nd4j.linalg.api.ops.random.impl.BinomialDistributionEx(
ret, numberOfTrials,
probabilityOfSuccess), random);
}
} else {
Iterator idxIter = new NdIndexIterator(ret.shape()); //For consistent values irrespective of c vs. fortran ordering
long len = ret.length();
if (p != null) {
for (int i = 0; i < len; i++) {
long[] idx = idxIter.next();
org.apache.commons.math3.distribution.BinomialDistribution binomialDistribution =
new org.apache.commons.math3.distribution.BinomialDistribution(
(RandomGenerator) Nd4j.getRandom(), numberOfTrials,
p.getDouble(idx));
ret.putScalar(idx, binomialDistribution.sample());
}
} else {
org.apache.commons.math3.distribution.BinomialDistribution binomialDistribution =
new org.apache.commons.math3.distribution.BinomialDistribution(
(RandomGenerator) Nd4j.getRandom(), numberOfTrials,
probabilityOfSuccess);
for (int i = 0; i < len; i++) {
ret.putScalar(idxIter.next(), binomialDistribution.sample());
}
}
return ret;
}
}
}
