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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* 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.analysis.differentiation;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
/** Class representing the Jacobian of a multivariate vector function.
*
* The rows iterate on the model functions while the columns iterate on the parameters; thus,
* the numbers of rows is equal to the dimension of the underlying function vector
* value and the number of columns is equal to the number of free parameters of
* the underlying function.
*
* @since 3.1
*/
public class JacobianFunction implements MultivariateMatrixFunction {
/** Underlying vector-valued function. */
private final MultivariateDifferentiableVectorFunction f;
/** Simple constructor.
* @param f underlying vector-valued function
*/
public JacobianFunction(final MultivariateDifferentiableVectorFunction f) {
this.f = f;
}
/** {@inheritDoc} */
public double[][] value(double[] point) {
// set up parameters
final DerivativeStructure[] dsX = new DerivativeStructure[point.length];
for (int i = 0; i < point.length; ++i) {
dsX[i] = new DerivativeStructure(point.length, 1, i, point[i]);
}
// compute the derivatives
final DerivativeStructure[] dsY = f.value(dsX);
// extract the Jacobian
final double[][] y = new double[dsY.length][point.length];
final int[] orders = new int[point.length];
for (int i = 0; i < dsY.length; ++i) {
for (int j = 0; j < point.length; ++j) {
orders[j] = 1;
y[i][j] = dsY[i].getPartialDerivative(orders);
orders[j] = 0;
}
}
return y;
}
}