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Java example source code file (StatisticalReferenceDataset.java)

This example Java source code file (StatisticalReferenceDataset.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, assertionerror, bufferedreader, illegalargumentexception, ioexception, leastsquaresproblem, modelfunctionjacobian, multivariatematrixfunction, name, starting, statisticalreferencedataset, string, sum, util, values

The StatisticalReferenceDataset.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.optim.nonlinear.vector.jacobian;

import java.io.BufferedReader;
import java.io.IOException;
import java.util.ArrayList;

import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.optim.nonlinear.vector.ModelFunction;
import org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian;
import org.apache.commons.math3.util.MathArrays;

/**
 * This class gives access to the statistical reference datasets provided by the
 * NIST (available
 * <a href="http://www.itl.nist.gov/div898/strd/general/dataarchive.html">here).
 * Instances of this class can be created by invocation of the
 * {@link StatisticalReferenceDatasetFactory}.
 */
@Deprecated
public abstract class StatisticalReferenceDataset {

    /** The name of this dataset. */
    private final String name;

    /** The total number of observations (data points). */
    private final int numObservations;

    /** The total number of parameters. */
    private final int numParameters;

    /** The total number of starting points for the optimizations. */
    private final int numStartingPoints;

    /** The values of the predictor. */
    private final double[] x;

    /** The values of the response. */
    private final double[] y;

    /**
     * The starting values. {@code startingValues[j][i]} is the value of the
     * {@code i}-th parameter in the {@code j}-th set of starting values.
     */
    private final double[][] startingValues;

    /** The certified values of the parameters. */
    private final double[] a;

    /** The certified values of the standard deviation of the parameters. */
    private final double[] sigA;

    /** The certified value of the residual sum of squares. */
    private double residualSumOfSquares;

    /** The least-squares problem. */
    private final LeastSquaresProblem problem;

    /**
     * Creates a new instance of this class from the specified data file. The
     * file must follow the StRD format.
     *
     * @param in the data file
     * @throws IOException if an I/O error occurs
     */
    public StatisticalReferenceDataset(final BufferedReader in)
        throws IOException {

        final ArrayList<String> lines = new ArrayList();
        for (String line = in.readLine(); line != null; line = in.readLine()) {
            lines.add(line);
        }
        int[] index = findLineNumbers("Data", lines);
        if (index == null) {
            throw new AssertionError("could not find line indices for data");
        }
        this.numObservations = index[1] - index[0] + 1;
        this.x = new double[this.numObservations];
        this.y = new double[this.numObservations];
        for (int i = 0; i < this.numObservations; i++) {
            final String line = lines.get(index[0] + i - 1);
            final String[] tokens = line.trim().split(" ++");
            // Data columns are in reverse order!!!
            this.y[i] = Double.parseDouble(tokens[0]);
            this.x[i] = Double.parseDouble(tokens[1]);
        }

        index = findLineNumbers("Starting Values", lines);
        if (index == null) {
            throw new AssertionError(
                                     "could not find line indices for starting values");
        }
        this.numParameters = index[1] - index[0] + 1;

        double[][] start = null;
        this.a = new double[numParameters];
        this.sigA = new double[numParameters];
        for (int i = 0; i < numParameters; i++) {
            final String line = lines.get(index[0] + i - 1);
            final String[] tokens = line.trim().split(" ++");
            if (start == null) {
                start = new double[tokens.length - 4][numParameters];
            }
            for (int j = 2; j < tokens.length - 2; j++) {
                start[j - 2][i] = Double.parseDouble(tokens[j]);
            }
            this.a[i] = Double.parseDouble(tokens[tokens.length - 2]);
            this.sigA[i] = Double.parseDouble(tokens[tokens.length - 1]);
        }
        if (start == null) {
            throw new IOException("could not find starting values");
        }
        this.numStartingPoints = start.length;
        this.startingValues = start;

        double dummyDouble = Double.NaN;
        String dummyString = null;
        for (String line : lines) {
            if (line.contains("Dataset Name:")) {
                dummyString = line
                    .substring(line.indexOf("Dataset Name:") + 13,
                               line.indexOf("(")).trim();
            }
            if (line.contains("Residual Sum of Squares")) {
                final String[] tokens = line.split(" ++");
                dummyDouble = Double.parseDouble(tokens[4].trim());
            }
        }
        if (Double.isNaN(dummyDouble)) {
            throw new IOException(
                                  "could not find certified value of residual sum of squares");
        }
        this.residualSumOfSquares = dummyDouble;

        if (dummyString == null) {
            throw new IOException("could not find dataset name");
        }
        this.name = dummyString;

        this.problem = new LeastSquaresProblem();
    }

    class LeastSquaresProblem {
        public ModelFunction getModelFunction() {
            return new ModelFunction(new MultivariateVectorFunction() {
                    public double[] value(final double[] a) {
                        final int n = getNumObservations();
                        final double[] yhat = new double[n];
                        for (int i = 0; i < n; i++) {
                            yhat[i] = getModelValue(getX(i), a);
                        }
                        return yhat;
                    }
                });
        }

        public ModelFunctionJacobian getModelFunctionJacobian() {
            return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
                    public double[][] value(final double[] a)
                        throws IllegalArgumentException {
                        final int n = getNumObservations();
                        final double[][] j = new double[n][];
                        for (int i = 0; i < n; i++) {
                            j[i] = getModelDerivatives(getX(i), a);
                        }
                        return j;
                    }
                });
        }
    }

    /**
     * Returns the name of this dataset.
     *
     * @return the name of the dataset
     */
    public String getName() {
        return name;
    }

    /**
     * Returns the total number of observations (data points).
     *
     * @return the number of observations
     */
    public int getNumObservations() {
        return numObservations;
    }

    /**
     * Returns a copy of the data arrays. The data is laid out as follows <li>
     * {@code data[0][i] = x[i]},</li> 
  • {@code data[1][i] = y[i]},
  • * * @return the array of data points. */ public double[][] getData() { return new double[][] { MathArrays.copyOf(x), MathArrays.copyOf(y) }; } /** * Returns the x-value of the {@code i}-th data point. * * @param i the index of the data point * @return the x-value */ public double getX(final int i) { return x[i]; } /** * Returns the y-value of the {@code i}-th data point. * * @param i the index of the data point * @return the y-value */ public double getY(final int i) { return y[i]; } /** * Returns the total number of parameters. * * @return the number of parameters */ public int getNumParameters() { return numParameters; } /** * Returns the certified values of the paramters. * * @return the values of the parameters */ public double[] getParameters() { return MathArrays.copyOf(a); } /** * Returns the certified value of the {@code i}-th parameter. * * @param i the index of the parameter * @return the value of the parameter */ public double getParameter(final int i) { return a[i]; } /** * Reurns the certified values of the standard deviations of the parameters. * * @return the standard deviations of the parameters */ public double[] getParametersStandardDeviations() { return MathArrays.copyOf(sigA); } /** * Returns the certified value of the standard deviation of the {@code i}-th * parameter. * * @param i the index of the parameter * @return the standard deviation of the parameter */ public double getParameterStandardDeviation(final int i) { return sigA[i]; } /** * Returns the certified value of the residual sum of squares. * * @return the residual sum of squares */ public double getResidualSumOfSquares() { return residualSumOfSquares; } /** * Returns the total number of starting points (initial guesses for the * optimization process). * * @return the number of starting points */ public int getNumStartingPoints() { return numStartingPoints; } /** * Returns the {@code i}-th set of initial values of the parameters. * * @param i the index of the starting point * @return the starting point */ public double[] getStartingPoint(final int i) { return MathArrays.copyOf(startingValues[i]); } /** * Returns the least-squares problem corresponding to fitting the model to * the specified data. * * @return the least-squares problem */ public LeastSquaresProblem getLeastSquaresProblem() { return problem; } /** * Returns the value of the model for the specified values of the predictor * variable and the parameters. * * @param x the predictor variable * @param a the parameters * @return the value of the model */ public abstract double getModelValue(final double x, final double[] a); /** * Returns the values of the partial derivatives of the model with respect * to the parameters. * * @param x the predictor variable * @param a the parameters * @return the partial derivatives */ public abstract double[] getModelDerivatives(final double x, final double[] a); /** * <p> * Parses the specified text lines, and extracts the indices of the first * and last lines of the data defined by the specified {@code key}. This key * must be one of * </p> * <ul> * <li>{@code "Starting Values"}, * <li>{@code "Certified Values"}, * <li>{@code "Data"}. * </ul> * <p> * In the NIST data files, the line indices are separated by the keywords * {@code "lines"} and {@code "to"}. * </p> * * @param lines the line of text to be parsed * @return an array of two {@code int}s. First value is the index of the * first line, second value is the index of the last line. * {@code null} if the line could not be parsed. */ private static int[] findLineNumbers(final String key, final Iterable<String> lines) { for (String text : lines) { boolean flag = text.contains(key) && text.contains("lines") && text.contains("to") && text.contains(")"); if (flag) { final int[] numbers = new int[2]; final String from = text.substring(text.indexOf("lines") + 5, text.indexOf("to")); numbers[0] = Integer.parseInt(from.trim()); final String to = text.substring(text.indexOf("to") + 2, text.indexOf(")")); numbers[1] = Integer.parseInt(to.trim()); return numbers; } } return null; } }

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