Java tutorial
/* * * * Copyright 2015 Skymind,Inc. * * * * 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 org.nd4j.linalg.api.rng.distribution.impl; import java.util.Iterator; 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; /** * Base distribution derived from apache commons math * http://commons.apache.org/proper/commons-math/ * <p/> * (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} * <p/> * For {@code n} trials and probability parameter {@code p}, the mean is * {@code n * p}. */ public double getNumericalMean() { return numberOfTrials * probabilityOfSuccess; } /** * {@inheritDoc} * <p/> * 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} * <p/> * 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} * <p/> * 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} * <p/> * The support of this distribution is connected. * * @return {@code true} */ public boolean isSupportConnected() { return true; } private void ensureConsistent(int i) { probabilityOfSuccess = p.linearView().getDouble(i); } @Override public INDArray sample(int[] shape) { if (random.getStatePointer() != null) { if (p != null) { return Nd4j.getExecutioner().exec(new org.nd4j.linalg.api.ops.random.impl.BinomialDistribution( Nd4j.createUninitialized(shape, Nd4j.order()), numberOfTrials, p), random); } else { return Nd4j.getExecutioner() .exec(new org.nd4j.linalg.api.ops.random.impl.BinomialDistribution( Nd4j.createUninitialized(shape, Nd4j.order()), numberOfTrials, probabilityOfSuccess), random); } } else { INDArray ret = Nd4j.createUninitialized(shape, Nd4j.order()); Iterator<int[]> idxIter = new NdIndexIterator(shape); //For consistent values irrespective of c vs. fortran ordering int len = ret.length(); if (p != null) { for (int i = 0; i < len; i++) { int[] 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; } } }