org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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 lombok.val;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.nd4j.linalg.api.iter.NdIndexIterator;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.rng.distribution.BaseDistribution;
import org.nd4j.linalg.factory.Nd4j;
public class UniformDistribution extends BaseDistribution {
private double upper, lower;
/**
* Create a uniform real distribution using the given lower and upper
* bounds.
*
* @param lower Lower bound of this distribution (inclusive).
* @param upper Upper bound of this distribution (exclusive).
* @throws NumberIsTooLargeException if {@code lower >= upper}.
*/
public UniformDistribution(double lower, double upper) throws NumberIsTooLargeException {
this(Nd4j.getRandom(), lower, upper);
}
/**
* Creates a uniform distribution.
*
* @param rng Random number generator.
* @param lower Lower bound of this distribution (inclusive).
* @param upper Upper bound of this distribution (exclusive).
* @throws NumberIsTooLargeException if {@code lower >= upper}.
* @since 3.1
*/
public UniformDistribution(org.nd4j.linalg.api.rng.Random rng, double lower, double upper)
throws NumberIsTooLargeException {
super(rng);
if (lower >= upper) {
throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, lower, upper,
false);
}
this.lower = lower;
this.upper = upper;
}
/**
* {@inheritDoc}
*/
public double density(double x) {
if (x < lower || x > upper) {
return 0.0;
}
return 1 / (upper - lower);
}
/**
* {@inheritDoc}
*/
public double cumulativeProbability(double x) {
if (x <= lower) {
return 0;
}
if (x >= upper) {
return 1;
}
return (x - lower) / (upper - lower);
}
@Override
public double cumulativeProbability(double x0, double x1) throws NumberIsTooLargeException {
return 0;
}
@Override
public double inverseCumulativeProbability(final double p) throws OutOfRangeException {
if (p < 0.0 || p > 1.0) {
throw new OutOfRangeException(p, 0, 1);
}
return p * (upper - lower) + lower;
}
/**
* {@inheritDoc}
*
* For lower bound {@code lower} and upper bound {@code upper}, the mean is
* {@code 0.5 * (lower + upper)}.
*/
public double getNumericalMean() {
return 0.5 * (lower + upper);
}
/**
* {@inheritDoc}
*
* For lower bound {@code lower} and upper bound {@code upper}, the
* variance is {@code (upper - lower)^2 / 12}.
*/
public double getNumericalVariance() {
double ul = upper - lower;
return ul * ul / 12;
}
/**
* {@inheritDoc}
*
* The lower bound of the support is equal to the lower bound parameter
* of the distribution.
*
* @return lower bound of the support
*/
public double getSupportLowerBound() {
return lower;
}
/**
* {@inheritDoc}
*
* The upper bound of the support is equal to the upper bound parameter
* of the distribution.
*
* @return upper bound of the support
*/
public double getSupportUpperBound() {
return upper;
}
/**
* {@inheritDoc}
*/
public boolean isSupportLowerBoundInclusive() {
return true;
}
/**
* {@inheritDoc}
*/
public boolean isSupportUpperBoundInclusive() {
return true;
}
/**
* {@inheritDoc}
*
* The support of this distribution is connected.
*
* @return {@code true}
*/
public boolean isSupportConnected() {
return true;
}
/**
* {@inheritDoc}
*/
@Override
public double sample() {
final double u = random.nextDouble();
return u * upper + (1 - u) * lower;
}
@Override
public INDArray sample(int[] shape) {
final INDArray ret = Nd4j.createUninitialized(shape, Nd4j.order());
return sample(ret);
}
@Override
public INDArray sample(INDArray ret) {
if (random.getStatePointer() != null) {
return Nd4j.getExecutioner().exec(new org.nd4j.linalg.api.ops.random.impl.UniformDistribution(
ret, lower, upper), random);
} else {
val idxIter = new NdIndexIterator(ret.shape()); //For consistent values irrespective of c vs. fortran ordering
long len = ret.length();
for (int i = 0; i < len; i++) {
ret.putScalar(idxIter.next(), sample());
}
return ret;
}
}
}