1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17 package org.apache.commons.math4.legacy.filter;
18
19 import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
20 import org.apache.commons.math4.legacy.exception.NoDataException;
21 import org.apache.commons.math4.legacy.exception.NullArgumentException;
22 import org.apache.commons.math4.legacy.linear.Array2DRowRealMatrix;
23 import org.apache.commons.math4.legacy.linear.ArrayRealVector;
24 import org.apache.commons.math4.legacy.linear.RealMatrix;
25 import org.apache.commons.math4.legacy.linear.RealVector;
26
27 /**
28 * Default implementation of a {@link ProcessModel} for the use with a {@link KalmanFilter}.
29 *
30 * @since 3.0
31 */
32 public class DefaultProcessModel implements ProcessModel {
33 /**
34 * The state transition matrix, used to advance the internal state estimation each time-step.
35 */
36 private final RealMatrix stateTransitionMatrix;
37
38 /**
39 * The control matrix, used to integrate a control input into the state estimation.
40 */
41 private final RealMatrix controlMatrix;
42
43 /** The process noise covariance matrix. */
44 private final RealMatrix processNoiseCovMatrix;
45
46 /** The initial state estimation of the observed process. */
47 private final RealVector initialStateEstimateVector;
48
49 /** The initial error covariance matrix of the observed process. */
50 private final RealMatrix initialErrorCovMatrix;
51
52 /**
53 * Create a new {@link ProcessModel}, taking double arrays as input parameters.
54 *
55 * @param stateTransition
56 * the state transition matrix
57 * @param control
58 * the control matrix
59 * @param processNoise
60 * the process noise matrix
61 * @param initialStateEstimate
62 * the initial state estimate vector
63 * @param initialErrorCovariance
64 * the initial error covariance matrix
65 * @throws NullArgumentException
66 * if any of the input arrays is {@code null}
67 * @throws NoDataException
68 * if any row / column dimension of the input matrices is zero
69 * @throws DimensionMismatchException
70 * if any of the input matrices is non-rectangular
71 */
72 public DefaultProcessModel(final double[][] stateTransition,
73 final double[][] control,
74 final double[][] processNoise,
75 final double[] initialStateEstimate,
76 final double[][] initialErrorCovariance)
77 throws NullArgumentException, NoDataException, DimensionMismatchException {
78
79 this(new Array2DRowRealMatrix(stateTransition),
80 new Array2DRowRealMatrix(control),
81 new Array2DRowRealMatrix(processNoise),
82 new ArrayRealVector(initialStateEstimate),
83 new Array2DRowRealMatrix(initialErrorCovariance));
84 }
85
86 /**
87 * Create a new {@link ProcessModel}, taking double arrays as input parameters.
88 * <p>
89 * The initial state estimate and error covariance are omitted and will be initialized by the
90 * {@link KalmanFilter} to default values.
91 *
92 * @param stateTransition
93 * the state transition matrix
94 * @param control
95 * the control matrix
96 * @param processNoise
97 * the process noise matrix
98 * @throws NullArgumentException
99 * if any of the input arrays is {@code null}
100 * @throws NoDataException
101 * if any row / column dimension of the input matrices is zero
102 * @throws DimensionMismatchException
103 * if any of the input matrices is non-rectangular
104 */
105 public DefaultProcessModel(final double[][] stateTransition,
106 final double[][] control,
107 final double[][] processNoise)
108 throws NullArgumentException, NoDataException, DimensionMismatchException {
109
110 this(new Array2DRowRealMatrix(stateTransition),
111 new Array2DRowRealMatrix(control),
112 new Array2DRowRealMatrix(processNoise), null, null);
113 }
114
115 /**
116 * Create a new {@link ProcessModel}, taking double arrays as input parameters.
117 *
118 * @param stateTransition
119 * the state transition matrix
120 * @param control
121 * the control matrix
122 * @param processNoise
123 * the process noise matrix
124 * @param initialStateEstimate
125 * the initial state estimate vector
126 * @param initialErrorCovariance
127 * the initial error covariance matrix
128 */
129 public DefaultProcessModel(final RealMatrix stateTransition,
130 final RealMatrix control,
131 final RealMatrix processNoise,
132 final RealVector initialStateEstimate,
133 final RealMatrix initialErrorCovariance) {
134 this.stateTransitionMatrix = stateTransition;
135 this.controlMatrix = control;
136 this.processNoiseCovMatrix = processNoise;
137 this.initialStateEstimateVector = initialStateEstimate;
138 this.initialErrorCovMatrix = initialErrorCovariance;
139 }
140
141 /** {@inheritDoc} */
142 @Override
143 public RealMatrix getStateTransitionMatrix() {
144 return stateTransitionMatrix;
145 }
146
147 /** {@inheritDoc} */
148 @Override
149 public RealMatrix getControlMatrix() {
150 return controlMatrix;
151 }
152
153 /** {@inheritDoc} */
154 @Override
155 public RealMatrix getProcessNoise() {
156 return processNoiseCovMatrix;
157 }
158
159 /** {@inheritDoc} */
160 @Override
161 public RealVector getInitialStateEstimate() {
162 return initialStateEstimateVector;
163 }
164
165 /** {@inheritDoc} */
166 @Override
167 public RealMatrix getInitialErrorCovariance() {
168 return initialErrorCovMatrix;
169 }
170 }