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/*-
*
* * 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;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.solvers.UnivariateSolverUtils;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
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.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.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.AbstractRealDistribution}
*
* @author Adam Gibson
*/
public abstract class BaseDistribution implements Distribution {
protected Random random;
protected double solverAbsoluteAccuracy;
public BaseDistribution(Random rng) {
this.random = rng;
}
public BaseDistribution() {
this(Nd4j.getRandom());
}
/**
* For a random variable {@code X} whose values are distributed according
* to this distribution, this method returns {@code P(x0 < X <= x1)}.
*
* @param x0 Lower bound (excluded).
* @param x1 Upper bound (included).
* @return the probability that a random variable with this distribution
* takes a value between {@code x0} and {@code x1}, excluding the lower
* and including the upper endpoint.
* @throws org.apache.commons.math3.exception.NumberIsTooLargeException if {@code x0 > x1}.
*
* The default implementation uses the identity
* {@code P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)}
* @since 3.1
*/
public double probability(double x0, double x1) {
if (x0 > x1) {
throw new NumberIsTooLargeException(LocalizedFormats.LOWER_ENDPOINT_ABOVE_UPPER_ENDPOINT, x0, x1, true);
}
return cumulativeProbability(x1) - cumulativeProbability(x0);
}
/**
* {@inheritDoc}
*
* The default implementation returns
*
*
{@link #getSupportLowerBound()} for {@code p = 0},
*
{@link #getSupportUpperBound()} for {@code p = 1}.
*
*/
@Override
public double inverseCumulativeProbability(final double p) throws OutOfRangeException {
/*
* IMPLEMENTATION NOTES
* --------------------
* Where applicable, use is made of the one-sided Chebyshev inequality
* to bracket the root. This inequality states that
* P(X - mu >= k * sig) <= 1 / (1 + k^2),
* mu: mean, sig: standard deviation. Equivalently
* 1 - P(X < mu + k * sig) <= 1 / (1 + k^2),
* F(mu + k * sig) >= k^2 / (1 + k^2).
*
* For k = sqrt(p / (1 - p)), we find
* F(mu + k * sig) >= p,
* and (mu + k * sig) is an upper-bound for the root.
*
* Then, introducing Y = -X, mean(Y) = -mu, sd(Y) = sig, and
* P(Y >= -mu + k * sig) <= 1 / (1 + k^2),
* P(-X >= -mu + k * sig) <= 1 / (1 + k^2),
* P(X <= mu - k * sig) <= 1 / (1 + k^2),
* F(mu - k * sig) <= 1 / (1 + k^2).
*
* For k = sqrt((1 - p) / p), we find
* F(mu - k * sig) <= p,
* and (mu - k * sig) is a lower-bound for the root.
*
* In cases where the Chebyshev inequality does not apply, geometric
* progressions 1, 2, 4, ... and -1, -2, -4, ... are used to bracket
* the root.
*/
if (p < 0.0 || p > 1.0) {
throw new OutOfRangeException(p, 0, 1);
}
double lowerBound = getSupportLowerBound();
if (p == 0.0) {
return lowerBound;
}
double upperBound = getSupportUpperBound();
if (p == 1.0) {
return upperBound;
}
final double mu = getNumericalMean();
final double sig = FastMath.sqrt(getNumericalVariance());
final boolean chebyshevApplies;
chebyshevApplies = !(Double.isInfinite(mu) || Double.isNaN(mu) || Double.isInfinite(sig) || Double.isNaN(sig));
if (lowerBound == Double.NEGATIVE_INFINITY) {
if (chebyshevApplies) {
lowerBound = mu - sig * FastMath.sqrt((1. - p) / p);
} else {
lowerBound = -1.0;
while (cumulativeProbability(lowerBound) >= p) {
lowerBound *= 2.0;
}
}
}
if (upperBound == Double.POSITIVE_INFINITY) {
if (chebyshevApplies) {
upperBound = mu + sig * FastMath.sqrt(p / (1. - p));
} else {
upperBound = 1.0;
while (cumulativeProbability(upperBound) < p) {
upperBound *= 2.0;
}
}
}
final UnivariateFunction toSolve = new UnivariateFunction() {
public double value(final double x) {
return cumulativeProbability(x) - p;
}
};
double x = UnivariateSolverUtils.solve(toSolve, lowerBound, upperBound, getSolverAbsoluteAccuracy());
if (!isSupportConnected()) {
/* Test for plateau. */
final double dx = getSolverAbsoluteAccuracy();
if (x - dx >= getSupportLowerBound()) {
double px = cumulativeProbability(x);
if (cumulativeProbability(x - dx) == px) {
upperBound = x;
while (upperBound - lowerBound > dx) {
final double midPoint = 0.5 * (lowerBound + upperBound);
if (cumulativeProbability(midPoint) < px) {
lowerBound = midPoint;
} else {
upperBound = midPoint;
}
}
return upperBound;
}
}
}
return x;
}
/**
* Returns the solver absolute accuracy for inverse cumulative computation.
* You can override this method in order to use a Brent solver with an
* absolute accuracy different from the default.
*
* @return the maximum absolute error in inverse cumulative probability estimates
*/
protected double getSolverAbsoluteAccuracy() {
return solverAbsoluteAccuracy;
}
/**
* {@inheritDoc}
*/
@Override
public void reseedRandomGenerator(long seed) {
random.setSeed(seed);
}
/**
* {@inheritDoc}
*
* The default implementation uses the
*
* inversion method.
*
*/
@Override
public double sample() {
return inverseCumulativeProbability(random.nextDouble());
}
/**
* {@inheritDoc}
*
* The default implementation generates the sample by calling
* {@link #sample()} in a loop.
*/
@Override
public double[] sample(int sampleSize) {
if (sampleSize <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, sampleSize);
}
double[] out = new double[sampleSize];
for (int i = 0; i < sampleSize; i++) {
out[i] = sample();
}
return out;
}
/**
* {@inheritDoc}
*
* @return zero.
* @since 3.1
*/
@Override
public double probability(double x) {
return 0d;
}
@Override
public INDArray sample(int[] shape) {
INDArray ret = Nd4j.create(shape);
Iterator idxIter = new NdIndexIterator(shape); //For consistent values irrespective of c vs. fortran ordering
int len = ret.length();
for (int i = 0; i < len; i++) {
ret.putScalar(idxIter.next(), sample());
}
return ret;
}
}
