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* 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,
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* See the License for the specific language governing permissions and
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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 ProcessModel
for the use with a KalmanFilter
. Since: 3.0
/**
* 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.
/**
* 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.
/**
* The control matrix, used to integrate a control input into the state estimation.
*/
private RealMatrix controlMatrix;
The process noise covariance matrix. /** The process noise covariance matrix. */
private RealMatrix processNoiseCovMatrix;
The initial state estimation of the observed process. /** The initial state estimation of the observed process. */
private RealVector initialStateEstimateVector;
The initial error covariance matrix of the observed process. /** The initial error covariance matrix of the observed process. */
private RealMatrix initialErrorCovMatrix;
Create a new ProcessModel
, taking double arrays as input parameters. Params: - stateTransition –
the state transition matrix
- control –
the control matrix
- processNoise –
the process noise matrix
- initialStateEstimate –
the initial state estimate vector
- initialErrorCovariance –
the initial error covariance matrix
Throws: - NullArgumentException – if any of the input arrays is
null
- NoDataException –
if any row / column dimension of the input matrices is zero
- DimensionMismatchException –
if any of the input matrices is non-rectangular
/**
* 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 ProcessModel
, taking double arrays as input parameters. The initial state estimate and error covariance are omitted and will be initialized by the KalmanFilter
to default values.
Params: - stateTransition –
the state transition matrix
- control –
the control matrix
- processNoise –
the process noise matrix
Throws: - NullArgumentException – if any of the input arrays is
null
- NoDataException –
if any row / column dimension of the input matrices is zero
- DimensionMismatchException –
if any of the input matrices is non-rectangular
/**
* 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 ProcessModel
, taking double arrays as input parameters. Params: - stateTransition –
the state transition matrix
- control –
the control matrix
- processNoise –
the process noise matrix
- initialStateEstimate –
the initial state estimate vector
- initialErrorCovariance –
the initial error covariance matrix
/**
* 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} /** {@inheritDoc} */
public RealMatrix getStateTransitionMatrix() {
return stateTransitionMatrix;
}
{@inheritDoc} /** {@inheritDoc} */
public RealMatrix getControlMatrix() {
return controlMatrix;
}
{@inheritDoc} /** {@inheritDoc} */
public RealMatrix getProcessNoise() {
return processNoiseCovMatrix;
}
{@inheritDoc} /** {@inheritDoc} */
public RealVector getInitialStateEstimate() {
return initialStateEstimateVector;
}
{@inheritDoc} /** {@inheritDoc} */
public RealMatrix getInitialErrorCovariance() {
return initialErrorCovMatrix;
}
}