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/*
* 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.
*
* 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;
}
}