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Java example source code file (DefaultProcessModel.java)

This example Java source code file (DefaultProcessModel.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

array2drowrealmatrix, arrayrealvector, defaultprocessmodel, dimensionmismatchexception, nodataexception, nullargumentexception, realmatrix, realvector

The DefaultProcessModel.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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.apache.commons.math3.filter;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NoDataException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;

/**
 * Default implementation of a {@link ProcessModel} for the use with a {@link KalmanFilter}.
 *
 * @since 3.0
 */
public class DefaultProcessModel implements ProcessModel {
    /**
     * The state transition matrix, used to advance the internal state estimation each time-step.
     */
    private RealMatrix stateTransitionMatrix;

    /**
     * The control matrix, used to integrate a control input into the state estimation.
     */
    private RealMatrix controlMatrix;

    /** The process noise covariance matrix. */
    private RealMatrix processNoiseCovMatrix;

    /** The initial state estimation of the observed process. */
    private RealVector initialStateEstimateVector;

    /** The initial error covariance matrix of the observed process. */
    private RealMatrix initialErrorCovMatrix;

    /**
     * Create a new {@link ProcessModel}, taking double arrays as input parameters.
     *
     * @param stateTransition
     *            the state transition matrix
     * @param control
     *            the control matrix
     * @param processNoise
     *            the process noise matrix
     * @param initialStateEstimate
     *            the initial state estimate vector
     * @param initialErrorCovariance
     *            the initial error covariance matrix
     * @throws NullArgumentException
     *             if any of the input arrays is {@code null}
     * @throws NoDataException
     *             if any row / column dimension of the input matrices is zero
     * @throws DimensionMismatchException
     *             if any of the input matrices is non-rectangular
     */
    public DefaultProcessModel(final double[][] stateTransition,
                               final double[][] control,
                               final double[][] processNoise,
                               final double[] initialStateEstimate,
                               final double[][] initialErrorCovariance)
            throws NullArgumentException, NoDataException, DimensionMismatchException {

        this(new Array2DRowRealMatrix(stateTransition),
                new Array2DRowRealMatrix(control),
                new Array2DRowRealMatrix(processNoise),
                new ArrayRealVector(initialStateEstimate),
                new Array2DRowRealMatrix(initialErrorCovariance));
    }

    /**
     * Create a new {@link ProcessModel}, taking double arrays as input parameters.
     * <p>
     * The initial state estimate and error covariance are omitted and will be initialized by the
     * {@link KalmanFilter} to default values.
     *
     * @param stateTransition
     *            the state transition matrix
     * @param control
     *            the control matrix
     * @param processNoise
     *            the process noise matrix
     * @throws NullArgumentException
     *             if any of the input arrays is {@code null}
     * @throws NoDataException
     *             if any row / column dimension of the input matrices is zero
     * @throws DimensionMismatchException
     *             if any of the input matrices is non-rectangular
     */
    public DefaultProcessModel(final double[][] stateTransition,
                               final double[][] control,
                               final double[][] processNoise)
            throws NullArgumentException, NoDataException, DimensionMismatchException {

        this(new Array2DRowRealMatrix(stateTransition),
                new Array2DRowRealMatrix(control),
                new Array2DRowRealMatrix(processNoise), null, null);
    }

    /**
     * Create a new {@link ProcessModel}, taking double arrays as input parameters.
     *
     * @param stateTransition
     *            the state transition matrix
     * @param control
     *            the control matrix
     * @param processNoise
     *            the process noise matrix
     * @param initialStateEstimate
     *            the initial state estimate vector
     * @param initialErrorCovariance
     *            the initial error covariance matrix
     */
    public DefaultProcessModel(final RealMatrix stateTransition,
                               final RealMatrix control,
                               final RealMatrix processNoise,
                               final RealVector initialStateEstimate,
                               final RealMatrix initialErrorCovariance) {
        this.stateTransitionMatrix = stateTransition;
        this.controlMatrix = control;
        this.processNoiseCovMatrix = processNoise;
        this.initialStateEstimateVector = initialStateEstimate;
        this.initialErrorCovMatrix = initialErrorCovariance;
    }

    /** {@inheritDoc} */
    public RealMatrix getStateTransitionMatrix() {
        return stateTransitionMatrix;
    }

    /** {@inheritDoc} */
    public RealMatrix getControlMatrix() {
        return controlMatrix;
    }

    /** {@inheritDoc} */
    public RealMatrix getProcessNoise() {
        return processNoiseCovMatrix;
    }

    /** {@inheritDoc} */
    public RealVector getInitialStateEstimate() {
        return initialStateEstimateVector;
    }

    /** {@inheritDoc} */
    public RealMatrix getInitialErrorCovariance() {
        return initialErrorCovMatrix;
    }
}

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