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.analysis.differentiation;
18
19 import org.apache.commons.math4.legacy.analysis.MultivariateMatrixFunction;
20
21 /** Class representing the Jacobian of a multivariate vector function.
22 * <p>
23 * The rows iterate on the model functions while the columns iterate on the parameters; thus,
24 * the numbers of rows is equal to the dimension of the underlying function vector
25 * value and the number of columns is equal to the number of free parameters of
26 * the underlying function.
27 * </p>
28 * @since 3.1
29 */
30 public class JacobianFunction implements MultivariateMatrixFunction {
31
32 /** Underlying vector-valued function. */
33 private final MultivariateDifferentiableVectorFunction f;
34
35 /** Simple constructor.
36 * @param f underlying vector-valued function
37 */
38 public JacobianFunction(final MultivariateDifferentiableVectorFunction f) {
39 this.f = f;
40 }
41
42 /** {@inheritDoc} */
43 @Override
44 public double[][] value(double[] point) {
45
46 // set up parameters
47 final DerivativeStructure[] dsX = new DerivativeStructure[point.length];
48 for (int i = 0; i < point.length; ++i) {
49 dsX[i] = new DerivativeStructure(point.length, 1, i, point[i]);
50 }
51
52 // compute the derivatives
53 final DerivativeStructure[] dsY = f.value(dsX);
54
55 // extract the Jacobian
56 final double[][] y = new double[dsY.length][point.length];
57 final int[] orders = new int[point.length];
58 for (int i = 0; i < dsY.length; ++i) {
59 for (int j = 0; j < point.length; ++j) {
60 orders[j] = 1;
61 y[i][j] = dsY[i].getPartialDerivative(orders);
62 orders[j] = 0;
63 }
64 }
65
66 return y;
67 }
68 }