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Java example source code file (StraightLineProblem.java)

This example Java source code file (StraightLineProblem.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

arraylist, model, multivariatematrixfunction, multivariatevectorfunction, simpleregression, straightlineproblem, univariatefunction, util

The StraightLineProblem.java Java example source code

/*
 * 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.fitting.leastsquares;

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;

/**
 * Class that models a straight line defined as {@code y = a x + b}.
 * The parameters of problem are:
 * <ul>
 *  <li>{@code a}
 *  <li>{@code b}
 * </ul>
 * The model functions are:
 * <ul>
 *  <li>for each pair (a, b), the y-coordinate of the line.
 * </ul>
 */
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 MultivariateVectorFunction getModelFunction() {
        return 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 MultivariateMatrixFunction getModelFunctionJacobian() {
        return 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;
        }
    }
}

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