| /* |
| * 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.optim.nonlinear.vector.jacobian; |
| |
| import java.util.ArrayList; |
| |
| import org.apache.commons.math3.analysis.MultivariateVectorFunction; |
| import org.apache.commons.math3.analysis.MultivariateMatrixFunction; |
| import org.apache.commons.math3.analysis.UnivariateFunction; |
| import org.apache.commons.math3.stat.regression.SimpleRegression; |
| import org.apache.commons.math3.optim.nonlinear.vector.ModelFunction; |
| import org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian; |
| |
| /** |
| * Class that models a straight line defined as {@code y = a x + b}. |
| * The parameters of problem are: |
| * <ul> |
| * <li>{@code a}</li> |
| * <li>{@code b}</li> |
| * </ul> |
| * The model functions are: |
| * <ul> |
| * <li>for each pair (a, b), the y-coordinate of the line.</li> |
| * </ul> |
| */ |
| @Deprecated |
| class StraightLineProblem { |
| /** Cloud of points assumed to be fitted by a straight line. */ |
| private final ArrayList<double[]> points; |
| /** Error (on the y-coordinate of the points). */ |
| private final double sigma; |
| |
| /** |
| * @param error Assumed error for the y-coordinate. |
| */ |
| public StraightLineProblem(double error) { |
| points = new ArrayList<double[]>(); |
| sigma = error; |
| } |
| |
| public void addPoint(double px, double py) { |
| points.add(new double[] { px, py }); |
| } |
| |
| /** |
| * @return the list of x-coordinates. |
| */ |
| public double[] x() { |
| final double[] v = new double[points.size()]; |
| for (int i = 0; i < points.size(); i++) { |
| final double[] p = points.get(i); |
| v[i] = p[0]; // x-coordinate. |
| } |
| |
| return v; |
| } |
| |
| /** |
| * @return the list of y-coordinates. |
| */ |
| public double[] y() { |
| final double[] v = new double[points.size()]; |
| for (int i = 0; i < points.size(); i++) { |
| final double[] p = points.get(i); |
| v[i] = p[1]; // y-coordinate. |
| } |
| |
| return v; |
| } |
| |
| public double[] target() { |
| return y(); |
| } |
| |
| public double[] weight() { |
| final double weight = 1 / (sigma * sigma); |
| final double[] w = new double[points.size()]; |
| for (int i = 0; i < points.size(); i++) { |
| w[i] = weight; |
| } |
| |
| return w; |
| } |
| |
| public ModelFunction getModelFunction() { |
| return new ModelFunction(new MultivariateVectorFunction() { |
| public double[] value(double[] params) { |
| final Model line = new Model(params[0], params[1]); |
| |
| final double[] model = new double[points.size()]; |
| for (int i = 0; i < points.size(); i++) { |
| final double[] p = points.get(i); |
| model[i] = line.value(p[0]); |
| } |
| |
| return model; |
| } |
| }); |
| } |
| |
| public ModelFunctionJacobian getModelFunctionJacobian() { |
| return new ModelFunctionJacobian(new MultivariateMatrixFunction() { |
| public double[][] value(double[] point) { |
| return jacobian(point); |
| } |
| }); |
| } |
| |
| /** |
| * Directly solve the linear problem, using the {@link SimpleRegression} |
| * class. |
| */ |
| public double[] solve() { |
| final SimpleRegression regress = new SimpleRegression(true); |
| for (double[] d : points) { |
| regress.addData(d[0], d[1]); |
| } |
| |
| final double[] result = { regress.getSlope(), regress.getIntercept() }; |
| return result; |
| } |
| |
| private double[][] jacobian(double[] params) { |
| final double[][] jacobian = new double[points.size()][2]; |
| |
| for (int i = 0; i < points.size(); i++) { |
| final double[] p = points.get(i); |
| // Partial derivative wrt "a". |
| jacobian[i][0] = p[0]; |
| // Partial derivative wrt "b". |
| jacobian[i][1] = 1; |
| } |
| |
| return jacobian; |
| } |
| |
| /** |
| * Linear function. |
| */ |
| public static class Model implements UnivariateFunction { |
| final double a; |
| final double b; |
| |
| public Model(double a, |
| double b) { |
| this.a = a; |
| this.b = b; |
| } |
| |
| public double value(double x) { |
| return a * x + b; |
| } |
| } |
| } |