/*
* 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.optimization.general;
import java.util.ArrayList;
import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction;
import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction;
import org.apache.commons.math3.stat.regression.SimpleRegression;
/**
* 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 implements MultivariateDifferentiableVectorFunction {
/** 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 double[] value(double[] params) {
final Model line = new Model(new DerivativeStructure(0, 0, params[0]),
new DerivativeStructure(0, 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 DerivativeStructure[] value(DerivativeStructure[] params) {
final Model line = new Model(params[0], params[1]);
final DerivativeStructure[] model = new DerivativeStructure[points.size()];
for (int i = 0; i < points.size(); i++) {
final DerivativeStructure p0 = params[0].getField().getZero().add(points.get(i)[0]);
model[i] = line.value(p0);
}
return model;
}
/**
* 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;
}
/**
* Linear function.
*/
public static class Model implements UnivariateDifferentiableFunction {
final DerivativeStructure a;
final DerivativeStructure b;
public Model(DerivativeStructure a,
DerivativeStructure b) {
this.a = a;
this.b = b;
}
public double value(double x) {
return a.getValue() * x + b.getValue();
}
public DerivativeStructure value(DerivativeStructure x) {
return x.multiply(a).add(b);
}
}
}