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 * https://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
18 /*
19 * This is not the original file distributed by the Apache Software Foundation
20 * It has been modified by the Hipparchus project
21 */
22 package org.hipparchus.analysis.differentiation;
23
24 import org.hipparchus.analysis.MultivariateVectorFunction;
25
26 /** Class representing the gradient of a multivariate function.
27 * <p>
28 * The vectorial components of the function represent the derivatives
29 * with respect to each function parameters.
30 * </p>
31 */
32 public class GradientFunction implements MultivariateVectorFunction {
33
34 /** Underlying real-valued function. */
35 private final MultivariateDifferentiableFunction f;
36
37 /** Simple constructor.
38 * @param f underlying real-valued function
39 */
40 public GradientFunction(final MultivariateDifferentiableFunction f) {
41 this.f = f;
42 }
43
44 /** {@inheritDoc} */
45 @Override
46 public double[] value(double[] point) {
47
48 // set up parameters
49 final DSFactory factory = new DSFactory(point.length, 1);
50 final DerivativeStructure[] dsX = new DerivativeStructure[point.length];
51 for (int i = 0; i < point.length; ++i) {
52 dsX[i] = factory.variable(i, point[i]);
53 }
54
55 // compute the derivatives
56 final DerivativeStructure dsY = f.value(dsX);
57
58 // extract the gradient
59 final double[] y = new double[point.length];
60 final int[] orders = new int[point.length];
61 for (int i = 0; i < point.length; ++i) {
62 orders[i] = 1;
63 y[i] = dsY.getPartialDerivative(orders);
64 orders[i] = 0;
65 }
66
67 return y;
68
69 }
70
71 }