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A fast and easy to use dense and sparse matrix linear algebra library written in Java.

There is a newer version: 0.43.1

/*
 * Copyright (c) 2022, Peter Abeles. All Rights Reserved.
 *
 * This file is part of Efficient Java Matrix Library (EJML).
 *
 * 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.ejml.dense.row;

import javax.annotation.Generated;
import org.ejml.data.FMatrixRMaj;
import org.ejml.dense.row.decomposition.chol.CholeskyDecompositionInner_FDRM;

import java.util.Random;

/**
 * Generates random vectors based on a zero mean multivariate Gaussian distribution. The covariance
 * matrix is provided in the constructor.
 */
@Generated("org.ejml.dense.row.CovarianceRandomDraw_DDRM")
public class CovarianceRandomDraw_FDRM {
    private FMatrixRMaj A;
    private Random rand;
    private FMatrixRMaj r;

    /**
     * Creates a random distribution with the specified mean and covariance. The references
     * to the variables are not saved, their value are copied.
     *
     * @param rand Used to create the random numbers for the draw. Reference is saved.
     * @param cov The covariance of the distribution. Not modified.
     */
    public CovarianceRandomDraw_FDRM( Random rand, FMatrixRMaj cov ) {
        r = new FMatrixRMaj(cov.numRows, 1);
        CholeskyDecompositionInner_FDRM cholesky = new CholeskyDecompositionInner_FDRM(true);

        if (cholesky.inputModified())
            cov = cov.copy();
        if (!cholesky.decompose(cov))
            throw new RuntimeException("Decomposition failed!");

        A = cholesky.getT();
        this.rand = rand;
    }

    /**
     * Makes a draw on the distribution. The results are added to parameter 'x'
     */
    public void next( FMatrixRMaj x ) {
        for (int i = 0; i < r.numRows; i++) {
            r.set(i, 0, (float)rand.nextGaussian());
        }

        CommonOps_FDRM.multAdd(A, r, x);
    }

    /**
     * Computes the likelihood of the random draw
     *
     * @return The likelihood.
     */
    public float computeLikelihoodP() {
        float ret = 1.0f;

        for (int i = 0; i < r.numRows; i++) {
            float a = r.get(i, 0);

            ret *= (float)Math.exp(-a*a/2.0f);
        }

        return ret;
    }
}