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From celes...@apache.org
Subject svn commit: r1332086 - in /commons/proper/math/trunk/src/test: java/org/apache/commons/math3/optimization/general/ resources/org/apache/commons/math3/optimization/ resources/org/apache/commons/math3/optimization/general/
Date Mon, 30 Apr 2012 07:41:58 GMT
Author: celestin
Date: Mon Apr 30 07:41:58 2012
New Revision: 1332086

URL: http://svn.apache.org/viewvc?rev=1332086&view=rev
Log:
Implemented convenience classes for easy access to NIST Statistical Reference Datasets (StRD).
These classes are used for unit testing of AbstractLeastSquaresOptimizer.

Added:
    commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java
    commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDataset.java
    commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java
    commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/
    commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/
    commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Hahn1.dat
    commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Kirby2.dat
    commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/MGH17.dat

Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java?rev=1332086&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java
(added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java
Mon Apr 30 07:41:58 2012
@@ -0,0 +1,96 @@
+/*
+ * 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.io.IOException;
+import java.util.Arrays;
+
+import junit.framework.Assert;
+
+import org.apache.commons.math3.optimization.PointVectorValuePair;
+import org.apache.commons.math3.util.FastMath;
+import org.junit.Test;
+
+public class AbstractLeastSquaresOptimizerTest {
+
+    public static AbstractLeastSquaresOptimizer createOptimizer() {
+        return new AbstractLeastSquaresOptimizer() {
+
+            @Override
+            protected PointVectorValuePair doOptimize() {
+                updateResidualsAndCost();
+                updateJacobian();
+                return null;
+            }
+        };
+    }
+
+    @Test
+    public void testGetChiSquare() throws IOException {
+        final StatisticalReferenceDataset dataset;
+        dataset = StatisticalReferenceDatasetFactory.createKirby2();
+        final AbstractLeastSquaresOptimizer optimizer;
+        optimizer = createOptimizer();
+        final double[] a = dataset.getParameters();
+        final double[] y = dataset.getData()[1];
+        final double[] w = new double[y.length];
+        Arrays.fill(w, 1.0);
+
+        optimizer.optimize(1, dataset.getLeastSquaresProblem(), y, w, a);
+        final double expected = dataset.getResidualSumOfSquares();
+        final double actual = optimizer.getChiSquare();
+        Assert.assertEquals(dataset.getName(), expected, actual,
+                            1E-11 * expected);
+    }
+
+    @Test
+    public void testGetRMS() throws IOException {
+        final StatisticalReferenceDataset dataset;
+        dataset = StatisticalReferenceDatasetFactory.createKirby2();
+        final AbstractLeastSquaresOptimizer optimizer;
+        optimizer = createOptimizer();
+        final double[] a = dataset.getParameters();
+        final double[] y = dataset.getData()[1];
+        final double[] w = new double[y.length];
+        Arrays.fill(w, 1.0);
+
+        optimizer.optimize(1, dataset.getLeastSquaresProblem(), y, w, a);
+        final double expected = FastMath
+            .sqrt(dataset.getResidualSumOfSquares() /
+                  dataset.getNumObservations());
+        final double actual = optimizer.getRMS();
+        Assert.assertEquals(dataset.getName(), expected, actual,
+                            1E-11 * expected);
+    }
+
+    @Test
+    public void testGuessParametersErrors() throws IOException {
+        final StatisticalReferenceDataset dataset;
+        dataset = StatisticalReferenceDatasetFactory.createKirby2();
+        final AbstractLeastSquaresOptimizer optimizer;
+        optimizer = createOptimizer();
+        final double[] a = dataset.getParameters();
+        final double[] y = dataset.getData()[1];
+        final double[] w = new double[y.length];
+        Arrays.fill(w, 1.0);
+
+        optimizer.optimize(1, dataset.getLeastSquaresProblem(), y, w, a);
+        final double[] actual = optimizer.guessParametersErrors();
+        final double[] expected = dataset.getParametersStandardDeviations();
+        for (int i = 0; i < actual.length; i++) {
+            Assert.assertEquals(dataset.getName() + ", parameter #" + i,
+                                actual[i], expected[i], 1E-8 * expected[i]);
+        }
+    }
+}

Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDataset.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDataset.java?rev=1332086&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDataset.java
(added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDataset.java
Mon Apr 30 07:41:58 2012
@@ -0,0 +1,376 @@
+/*
+ * 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.io.BufferedReader;
+import java.io.IOException;
+import java.util.ArrayList;
+
+import org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction;
+import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
+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</a>).
+ * Instances of this class can be created by invocation of the
+ * {@link StatisticalReferenceDatasetFactory}.
+ */
+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 DifferentiableMultivariateVectorFunction 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<String>();
+        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 DifferentiableMultivariateVectorFunction() {
+            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 MultivariateMatrixFunction jacobian() {
+                return 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> <li>{@code data[1][i] = y[i]},</li>
+     *
+     * @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 DifferentiableMultivariateVectorFunction 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>
+     * <li>{@code "Certified Values"},</li>
+     * <li>{@code "Data"}.</li>
+     * </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;
+    }
+}

Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java?rev=1332086&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java
(added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java
Mon Apr 30 07:41:58 2012
@@ -0,0 +1,167 @@
+/*
+ * 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.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+
+import org.apache.commons.math3.util.FastMath;
+
+/**
+ * A factory to create instances of {@link StatisticalReferenceDataset} from
+ * available resources.
+ */
+public class StatisticalReferenceDatasetFactory {
+
+    private StatisticalReferenceDatasetFactory() {
+        // Do nothing
+    }
+
+    /**
+     * Creates a new buffered reader from the specified resource name.
+     *
+     * @param name the name of the resource
+     * @return a buffered reader
+     * @throws IOException if an I/O error occured
+     */
+    public static BufferedReader createBufferedReaderFromResource(final String name)
+        throws IOException {
+        final InputStream resourceAsStream;
+        resourceAsStream = StatisticalReferenceDatasetFactory.class
+            .getResourceAsStream(name);
+        if (resourceAsStream == null) {
+            throw new IOException("could not find resource " + name);
+        }
+        return new BufferedReader(new InputStreamReader(resourceAsStream));
+    }
+
+    public static StatisticalReferenceDataset createKirby2()
+        throws IOException {
+        final BufferedReader in = createBufferedReaderFromResource("Kirby2.dat");
+        StatisticalReferenceDataset dataset = null;
+        try {
+            dataset = new StatisticalReferenceDataset(in) {
+
+                @Override
+                public double getModelValue(final double x, final double[] a) {
+                    final double p = a[0] + x * (a[1] + x * a[2]);
+                    final double q = 1.0 + x * (a[3] + x * a[4]);
+                    return p / q;
+                }
+
+                @Override
+                public double[] getModelDerivatives(final double x,
+                                                    final double[] a) {
+                    final double[] dy = new double[5];
+                    final double p = a[0] + x * (a[1] + x * a[2]);
+                    final double q = 1.0 + x * (a[3] + x * a[4]);
+                    dy[0] = 1.0 / q;
+                    dy[1] = x / q;
+                    dy[2] = x * dy[1];
+                    dy[3] = -x * p / (q * q);
+                    dy[4] = x * dy[3];
+                    return dy;
+                }
+            };
+        } finally {
+            in.close();
+        }
+        return dataset;
+    }
+
+    public static StatisticalReferenceDataset createHahn1()
+        throws IOException {
+        final BufferedReader in = createBufferedReaderFromResource("Hahn1.dat");
+        StatisticalReferenceDataset dataset = null;
+        try {
+            dataset = new StatisticalReferenceDataset(in) {
+
+                @Override
+                public double getModelValue(final double x, final double[] a) {
+                    final double p = a[0] + x * (a[1] + x * (a[2] + x * a[3]));
+                    final double q = 1.0 + x * (a[4] + x * (a[5] + x * a[6]));
+                    return p / q;
+                }
+
+                @Override
+                public double[] getModelDerivatives(final double x,
+                                                    final double[] a) {
+                    final double[] dy = new double[7];
+                    final double p = a[0] + x * (a[1] + x * (a[2] + x * a[3]));
+                    final double q = 1.0 + x * (a[4] + x * (a[5] + x * a[6]));
+                    dy[0] = 1.0 / q;
+                    dy[1] = x * dy[0];
+                    dy[2] = x * dy[1];
+                    dy[3] = x * dy[2];
+                    dy[4] = -x * p / (q * q);
+                    dy[5] = x * dy[4];
+                    dy[6] = x * dy[5];
+                    return dy;
+                }
+            };
+        } finally {
+            in.close();
+        }
+        return dataset;
+    }
+
+    public static StatisticalReferenceDataset createMGH17()
+        throws IOException {
+        final BufferedReader in = createBufferedReaderFromResource("MGH17.dat");
+        StatisticalReferenceDataset dataset = null;
+        try {
+            dataset = new StatisticalReferenceDataset(in) {
+
+                @Override
+                public double getModelValue(final double x, final double[] a) {
+                    return a[0] + a[1] * FastMath.exp(-a[3] * x) + a[2] *
+                           FastMath.exp(-a[4] * x);
+                }
+
+                @Override
+                public double[] getModelDerivatives(final double x,
+                                                    final double[] a) {
+                    final double[] dy = new double[5];
+                    dy[0] = 1.0;
+                    dy[1] = FastMath.exp(-x * a[3]);
+                    dy[2] = FastMath.exp(-x * a[4]);
+                    dy[3] = -x * a[1] * dy[1];
+                    dy[4] = -x * a[2] * dy[2];
+                    return dy;
+                }
+            };
+        } finally {
+            in.close();
+        }
+        return dataset;
+    }
+
+    /**
+     * Returns an array with all available reference datasets.
+     *
+     * @return the array of datasets
+     * @throws IOException if an I/O error occurs
+     */
+    public StatisticalReferenceDataset[] createAll()
+        throws IOException {
+        return new StatisticalReferenceDataset[] {
+            createKirby2(), createMGH17()
+        };
+    }
+}

Added: commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Hahn1.dat
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Hahn1.dat?rev=1332086&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Hahn1.dat
(added)
+++ commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Hahn1.dat
Mon Apr 30 07:41:58 2012
@@ -0,0 +1,296 @@
+NIST/ITL StRD
+Dataset Name:  Hahn1             (Hahn1.dat)
+
+File Format:   ASCII
+               Starting Values   (lines 41 to  47)
+               Certified Values  (lines 41 to  52)
+               Data              (lines 61 to 296)
+
+Procedure:     Nonlinear Least Squares Regression
+
+Description:   These data are the result of a NIST study involving
+               the thermal expansion of copper.  The response 
+               variable is the coefficient of thermal expansion, and
+               the predictor variable is temperature in degrees 
+               kelvin.
+
+
+Reference:     Hahn, T., NIST (197?). 
+               Copper Thermal Expansion Study.
+
+
+
+
+
+Data:          1 Response  (y = coefficient of thermal expansion)
+               1 Predictor (x = temperature, degrees kelvin)
+               236 Observations
+               Average Level of Difficulty
+               Observed Data
+
+Model:         Rational Class (cubic/cubic)
+               7 Parameters (b1 to b7)
+
+               y = (b1+b2*x+b3*x**2+b4*x**3) /
+                   (1+b5*x+b6*x**2+b7*x**3)  +  e
+
+
+          Starting values                  Certified Values
+
+        Start 1     Start 2           Parameter     Standard Deviation
+  b1 =   10           1            1.0776351733E+00  1.7070154742E-01
+  b2 =   -1          -0.1         -1.2269296921E-01  1.2000289189E-02
+  b3 =    0.05        0.005        4.0863750610E-03  2.2508314937E-04
+  b4 =   -0.00001    -0.000001    -1.4262662514E-06  2.7578037666E-07
+  b5 =   -0.05       -0.005       -5.7609940901E-03  2.4712888219E-04
+  b6 =    0.001       0.0001       2.4053735503E-04  1.0449373768E-05
+  b7 =   -0.000001   -0.0000001   -1.2314450199E-07  1.3027335327E-08
+
+Residual Sum of Squares:                    1.5324382854E+00 
+Residual Standard Deviation:                8.1803852243E-02
+Degrees of Freedom:                               229
+Number of Observations:                           236
+
+
+
+
+
+
+  
+Data:   y              x
+        .591E0         24.41E0  
+       1.547E0         34.82E0  
+       2.902E0         44.09E0  
+       2.894E0         45.07E0  
+       4.703E0         54.98E0  
+       6.307E0         65.51E0  
+       7.03E0          70.53E0  
+       7.898E0         75.70E0  
+       9.470E0         89.57E0  
+       9.484E0         91.14E0  
+      10.072E0         96.40E0  
+      10.163E0         97.19E0  
+      11.615E0        114.26E0  
+      12.005E0        120.25E0  
+      12.478E0        127.08E0  
+      12.982E0        133.55E0  
+      12.970E0        133.61E0  
+      13.926E0        158.67E0  
+      14.452E0        172.74E0  
+      14.404E0        171.31E0  
+      15.190E0        202.14E0  
+      15.550E0        220.55E0  
+      15.528E0        221.05E0  
+      15.499E0        221.39E0  
+      16.131E0        250.99E0  
+      16.438E0        268.99E0  
+      16.387E0        271.80E0  
+      16.549E0        271.97E0  
+      16.872E0        321.31E0  
+      16.830E0        321.69E0  
+      16.926E0        330.14E0  
+      16.907E0        333.03E0  
+      16.966E0        333.47E0  
+      17.060E0        340.77E0  
+      17.122E0        345.65E0  
+      17.311E0        373.11E0  
+      17.355E0        373.79E0  
+      17.668E0        411.82E0  
+      17.767E0        419.51E0  
+      17.803E0        421.59E0  
+      17.765E0        422.02E0  
+      17.768E0        422.47E0  
+      17.736E0        422.61E0  
+      17.858E0        441.75E0  
+      17.877E0        447.41E0  
+      17.912E0        448.7E0   
+      18.046E0        472.89E0  
+      18.085E0        476.69E0  
+      18.291E0        522.47E0  
+      18.357E0        522.62E0  
+      18.426E0        524.43E0  
+      18.584E0        546.75E0  
+      18.610E0        549.53E0  
+      18.870E0        575.29E0  
+      18.795E0        576.00E0  
+      19.111E0        625.55E0  
+        .367E0         20.15E0  
+        .796E0         28.78E0  
+       0.892E0         29.57E0  
+       1.903E0         37.41E0  
+       2.150E0         39.12E0  
+       3.697E0         50.24E0  
+       5.870E0         61.38E0  
+       6.421E0         66.25E0  
+       7.422E0         73.42E0  
+       9.944E0         95.52E0  
+      11.023E0        107.32E0  
+      11.87E0         122.04E0  
+      12.786E0        134.03E0  
+      14.067E0        163.19E0  
+      13.974E0        163.48E0  
+      14.462E0        175.70E0  
+      14.464E0        179.86E0  
+      15.381E0        211.27E0  
+      15.483E0        217.78E0  
+      15.59E0         219.14E0  
+      16.075E0        262.52E0  
+      16.347E0        268.01E0  
+      16.181E0        268.62E0  
+      16.915E0        336.25E0  
+      17.003E0        337.23E0  
+      16.978E0        339.33E0  
+      17.756E0        427.38E0  
+      17.808E0        428.58E0  
+      17.868E0        432.68E0  
+      18.481E0        528.99E0  
+      18.486E0        531.08E0  
+      19.090E0        628.34E0  
+      16.062E0        253.24E0  
+      16.337E0        273.13E0  
+      16.345E0        273.66E0  
+      16.388E0        282.10E0  
+      17.159E0        346.62E0  
+      17.116E0        347.19E0  
+      17.164E0        348.78E0  
+      17.123E0        351.18E0  
+      17.979E0        450.10E0  
+      17.974E0        450.35E0  
+      18.007E0        451.92E0  
+      17.993E0        455.56E0  
+      18.523E0        552.22E0  
+      18.669E0        553.56E0  
+      18.617E0        555.74E0  
+      19.371E0        652.59E0  
+      19.330E0        656.20E0  
+       0.080E0         14.13E0  
+       0.248E0         20.41E0  
+       1.089E0         31.30E0  
+       1.418E0         33.84E0  
+       2.278E0         39.70E0  
+       3.624E0         48.83E0  
+       4.574E0         54.50E0  
+       5.556E0         60.41E0  
+       7.267E0         72.77E0  
+       7.695E0         75.25E0  
+       9.136E0         86.84E0  
+       9.959E0         94.88E0  
+       9.957E0         96.40E0  
+      11.600E0        117.37E0  
+      13.138E0        139.08E0  
+      13.564E0        147.73E0  
+      13.871E0        158.63E0  
+      13.994E0        161.84E0  
+      14.947E0        192.11E0  
+      15.473E0        206.76E0  
+      15.379E0        209.07E0  
+      15.455E0        213.32E0  
+      15.908E0        226.44E0  
+      16.114E0        237.12E0  
+      17.071E0        330.90E0  
+      17.135E0        358.72E0  
+      17.282E0        370.77E0  
+      17.368E0        372.72E0  
+      17.483E0        396.24E0  
+      17.764E0        416.59E0  
+      18.185E0        484.02E0  
+      18.271E0        495.47E0  
+      18.236E0        514.78E0  
+      18.237E0        515.65E0  
+      18.523E0        519.47E0  
+      18.627E0        544.47E0  
+      18.665E0        560.11E0  
+      19.086E0        620.77E0  
+       0.214E0         18.97E0  
+       0.943E0         28.93E0  
+       1.429E0         33.91E0  
+       2.241E0         40.03E0  
+       2.951E0         44.66E0  
+       3.782E0         49.87E0  
+       4.757E0         55.16E0  
+       5.602E0         60.90E0  
+       7.169E0         72.08E0  
+       8.920E0         85.15E0  
+      10.055E0         97.06E0  
+      12.035E0        119.63E0  
+      12.861E0        133.27E0  
+      13.436E0        143.84E0  
+      14.167E0        161.91E0  
+      14.755E0        180.67E0  
+      15.168E0        198.44E0  
+      15.651E0        226.86E0  
+      15.746E0        229.65E0  
+      16.216E0        258.27E0  
+      16.445E0        273.77E0  
+      16.965E0        339.15E0  
+      17.121E0        350.13E0  
+      17.206E0        362.75E0  
+      17.250E0        371.03E0  
+      17.339E0        393.32E0  
+      17.793E0        448.53E0  
+      18.123E0        473.78E0  
+      18.49E0         511.12E0  
+      18.566E0        524.70E0  
+      18.645E0        548.75E0  
+      18.706E0        551.64E0  
+      18.924E0        574.02E0  
+      19.1E0          623.86E0  
+       0.375E0         21.46E0  
+       0.471E0         24.33E0  
+       1.504E0         33.43E0  
+       2.204E0         39.22E0  
+       2.813E0         44.18E0  
+       4.765E0         55.02E0  
+       9.835E0         94.33E0  
+      10.040E0         96.44E0  
+      11.946E0        118.82E0  
+      12.596E0        128.48E0  
+      13.303E0        141.94E0  
+      13.922E0        156.92E0  
+      14.440E0        171.65E0  
+      14.951E0        190.00E0  
+      15.627E0        223.26E0  
+      15.639E0        223.88E0  
+      15.814E0        231.50E0  
+      16.315E0        265.05E0  
+      16.334E0        269.44E0  
+      16.430E0        271.78E0  
+      16.423E0        273.46E0  
+      17.024E0        334.61E0  
+      17.009E0        339.79E0  
+      17.165E0        349.52E0  
+      17.134E0        358.18E0  
+      17.349E0        377.98E0  
+      17.576E0        394.77E0  
+      17.848E0        429.66E0  
+      18.090E0        468.22E0  
+      18.276E0        487.27E0  
+      18.404E0        519.54E0  
+      18.519E0        523.03E0  
+      19.133E0        612.99E0  
+      19.074E0        638.59E0  
+      19.239E0        641.36E0  
+      19.280E0        622.05E0  
+      19.101E0        631.50E0  
+      19.398E0        663.97E0  
+      19.252E0        646.9E0   
+      19.89E0         748.29E0  
+      20.007E0        749.21E0  
+      19.929E0        750.14E0  
+      19.268E0        647.04E0  
+      19.324E0        646.89E0  
+      20.049E0        746.9E0   
+      20.107E0        748.43E0  
+      20.062E0        747.35E0  
+      20.065E0        749.27E0  
+      19.286E0        647.61E0  
+      19.972E0        747.78E0  
+      20.088E0        750.51E0  
+      20.743E0        851.37E0  
+      20.83E0         845.97E0  
+      20.935E0        847.54E0  
+      21.035E0        849.93E0  
+      20.93E0         851.61E0  
+      21.074E0        849.75E0  
+      21.085E0        850.98E0  
+      20.935E0        848.23E0  

Added: commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Kirby2.dat
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Kirby2.dat?rev=1332086&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Kirby2.dat
(added)
+++ commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Kirby2.dat
Mon Apr 30 07:41:58 2012
@@ -0,0 +1,211 @@
+NIST/ITL StRD
+Dataset Name:  Kirby2            (Kirby2.dat)
+
+File Format:   ASCII
+               Starting Values   (lines 41 to  45)
+               Certified Values  (lines 41 to  50)
+               Data              (lines 61 to 211)
+
+Procedure:     Nonlinear Least Squares Regression
+
+Description:   These data are the result of a NIST study involving
+               scanning electron microscope line with standards.
+
+
+Reference:     Kirby, R., NIST (197?).  
+               Scanning electron microscope line width standards.
+
+
+
+
+
+
+
+
+Data:          1 Response  (y)
+               1 Predictor (x)
+               151 Observations
+               Average Level of Difficulty
+               Observed Data
+
+Model:         Rational Class (quadratic/quadratic)
+               5 Parameters (b1 to b5)
+
+               y = (b1 + b2*x + b3*x**2) /
+                   (1 + b4*x + b5*x**2)  +  e
+
+ 
+          Starting values                  Certified Values
+ 
+        Start 1     Start 2           Parameter     Standard Deviation
+  b1 =    2           1.5          1.6745063063E+00  8.7989634338E-02
+  b2 =   -0.1        -0.15        -1.3927397867E-01  4.1182041386E-03
+  b3 =    0.003       0.0025       2.5961181191E-03  4.1856520458E-05
+  b4 =   -0.001      -0.0015      -1.7241811870E-03  5.8931897355E-05
+  b5 =    0.00001     0.00002      2.1664802578E-05  2.0129761919E-07
+
+Residual Sum of Squares:                    3.9050739624E+00
+Residual Standard Deviation:                1.6354535131E-01
+Degrees of Freedom:                               146
+Number of Observations:                           151
+
+
+
+
+
+
+
+
+
+Data:   y             x
+       0.0082E0      9.65E0
+       0.0112E0     10.74E0
+       0.0149E0     11.81E0
+       0.0198E0     12.88E0
+       0.0248E0     14.06E0
+       0.0324E0     15.28E0
+       0.0420E0     16.63E0
+       0.0549E0     18.19E0
+       0.0719E0     19.88E0
+       0.0963E0     21.84E0
+       0.1291E0     24.00E0
+       0.1710E0     26.25E0
+       0.2314E0     28.86E0
+       0.3227E0     31.85E0
+       0.4809E0     35.79E0
+       0.7084E0     40.18E0
+       1.0220E0     44.74E0
+       1.4580E0     49.53E0
+       1.9520E0     53.94E0
+       2.5410E0     58.29E0
+       3.2230E0     62.63E0
+       3.9990E0     67.03E0
+       4.8520E0     71.25E0
+       5.7320E0     75.22E0
+       6.7270E0     79.33E0
+       7.8350E0     83.56E0
+       9.0250E0     87.75E0
+      10.2670E0     91.93E0
+      11.5780E0     96.10E0
+      12.9440E0    100.28E0
+      14.3770E0    104.46E0
+      15.8560E0    108.66E0
+      17.3310E0    112.71E0
+      18.8850E0    116.88E0
+      20.5750E0    121.33E0
+      22.3200E0    125.79E0
+      22.3030E0    125.79E0
+      23.4600E0    128.74E0
+      24.0600E0    130.27E0
+      25.2720E0    133.33E0
+      25.8530E0    134.79E0
+      27.1100E0    137.93E0
+      27.6580E0    139.33E0
+      28.9240E0    142.46E0
+      29.5110E0    143.90E0
+      30.7100E0    146.91E0
+      31.3500E0    148.51E0
+      32.5200E0    151.41E0
+      33.2300E0    153.17E0
+      34.3300E0    155.97E0
+      35.0600E0    157.76E0
+      36.1700E0    160.56E0
+      36.8400E0    162.30E0
+      38.0100E0    165.21E0
+      38.6700E0    166.90E0
+      39.8700E0    169.92E0
+      40.0300E0    170.32E0
+      40.5000E0    171.54E0
+      41.3700E0    173.79E0
+      41.6700E0    174.57E0
+      42.3100E0    176.25E0
+      42.7300E0    177.34E0
+      43.4600E0    179.19E0
+      44.1400E0    181.02E0
+      44.5500E0    182.08E0
+      45.2200E0    183.88E0
+      45.9200E0    185.75E0
+      46.3000E0    186.80E0
+      47.0000E0    188.63E0
+      47.6800E0    190.45E0
+      48.0600E0    191.48E0
+      48.7400E0    193.35E0
+      49.4100E0    195.22E0
+      49.7600E0    196.23E0
+      50.4300E0    198.05E0
+      51.1100E0    199.97E0
+      51.5000E0    201.06E0
+      52.1200E0    202.83E0
+      52.7600E0    204.69E0
+      53.1800E0    205.86E0
+      53.7800E0    207.58E0
+      54.4600E0    209.50E0
+      54.8300E0    210.65E0
+      55.4000E0    212.33E0
+      56.4300E0    215.43E0
+      57.0300E0    217.16E0
+      58.0000E0    220.21E0
+      58.6100E0    221.98E0
+      59.5800E0    225.06E0
+      60.1100E0    226.79E0
+      61.1000E0    229.92E0
+      61.6500E0    231.69E0
+      62.5900E0    234.77E0
+      63.1200E0    236.60E0
+      64.0300E0    239.63E0
+      64.6200E0    241.50E0
+      65.4900E0    244.48E0
+      66.0300E0    246.40E0
+      66.8900E0    249.35E0
+      67.4200E0    251.32E0
+      68.2300E0    254.22E0
+      68.7700E0    256.24E0
+      69.5900E0    259.11E0
+      70.1100E0    261.18E0
+      70.8600E0    264.02E0
+      71.4300E0    266.13E0
+      72.1600E0    268.94E0
+      72.7000E0    271.09E0
+      73.4000E0    273.87E0
+      73.9300E0    276.08E0
+      74.6000E0    278.83E0
+      75.1600E0    281.08E0
+      75.8200E0    283.81E0
+      76.3400E0    286.11E0
+      76.9800E0    288.81E0
+      77.4800E0    291.08E0
+      78.0800E0    293.75E0
+      78.6000E0    295.99E0
+      79.1700E0    298.64E0
+      79.6200E0    300.84E0
+      79.8800E0    302.02E0
+      80.1900E0    303.48E0
+      80.6600E0    305.65E0
+      81.2200E0    308.27E0
+      81.6600E0    310.41E0
+      82.1600E0    313.01E0
+      82.5900E0    315.12E0
+      83.1400E0    317.71E0
+      83.5000E0    319.79E0
+      84.0000E0    322.36E0
+      84.4000E0    324.42E0
+      84.8900E0    326.98E0
+      85.2600E0    329.01E0
+      85.7400E0    331.56E0
+      86.0700E0    333.56E0
+      86.5400E0    336.10E0
+      86.8900E0    338.08E0
+      87.3200E0    340.60E0
+      87.6500E0    342.57E0
+      88.1000E0    345.08E0
+      88.4300E0    347.02E0
+      88.8300E0    349.52E0
+      89.1200E0    351.44E0
+      89.5400E0    353.93E0
+      89.8500E0    355.83E0
+      90.2500E0    358.32E0
+      90.5500E0    360.20E0
+      90.9300E0    362.67E0
+      91.2000E0    364.53E0
+      91.5500E0    367.00E0
+      92.2000E0    371.30E0

Added: commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/MGH17.dat
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/MGH17.dat?rev=1332086&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/MGH17.dat
(added)
+++ commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/MGH17.dat
Mon Apr 30 07:41:58 2012
@@ -0,0 +1,93 @@
+NIST/ITL StRD
+Dataset Name:  MGH17             (MGH17.dat)
+
+File Format:   ASCII
+               Starting Values   (lines 41 to 45)
+               Certified Values  (lines 41 to 50)
+               Data              (lines 61 to 93)
+
+Procedure:     Nonlinear Least Squares Regression
+
+Description:   This problem was found to be difficult for some very
+               good algorithms.
+
+               See More, J. J., Garbow, B. S., and Hillstrom, K. E.
+               (1981).  Testing unconstrained optimization software.
+               ACM Transactions on Mathematical Software. 7(1):
+               pp. 17-41.
+
+Reference:     Osborne, M. R. (1972).  
+               Some aspects of nonlinear least squares 
+               calculations.  In Numerical Methods for Nonlinear 
+               Optimization, Lootsma (Ed).  
+               New York, NY:  Academic Press, pp. 171-189.
+ 
+Data:          1 Response  (y)
+               1 Predictor (x)
+               33 Observations
+               Average Level of Difficulty
+               Generated Data
+
+Model:         Exponential Class
+               5 Parameters (b1 to b5)
+
+               y = b1 + b2*exp[-x*b4] + b3*exp[-x*b5]  +  e
+
+
+
+          Starting values                  Certified Values
+
+        Start 1     Start 2           Parameter     Standard Deviation
+  b1 =     50         0.5          3.7541005211E-01  2.0723153551E-03
+  b2 =    150         1.5          1.9358469127E+00  2.2031669222E-01
+  b3 =   -100        -1           -1.4646871366E+00  2.2175707739E-01
+  b4 =      1          0.01        1.2867534640E-02  4.4861358114E-04
+  b5 =      2          0.02        2.2122699662E-02  8.9471996575E-04
+
+Residual Sum of Squares:                    5.4648946975E-05
+Residual Standard Deviation:                1.3970497866E-03
+Degrees of Freedom:                                28
+Number of Observations:                            33
+
+
+
+
+
+
+
+
+
+Data:  y               x
+      8.440000E-01    0.000000E+00
+      9.080000E-01    1.000000E+01
+      9.320000E-01    2.000000E+01
+      9.360000E-01    3.000000E+01
+      9.250000E-01    4.000000E+01
+      9.080000E-01    5.000000E+01
+      8.810000E-01    6.000000E+01
+      8.500000E-01    7.000000E+01
+      8.180000E-01    8.000000E+01
+      7.840000E-01    9.000000E+01
+      7.510000E-01    1.000000E+02
+      7.180000E-01    1.100000E+02
+      6.850000E-01    1.200000E+02
+      6.580000E-01    1.300000E+02
+      6.280000E-01    1.400000E+02
+      6.030000E-01    1.500000E+02
+      5.800000E-01    1.600000E+02
+      5.580000E-01    1.700000E+02
+      5.380000E-01    1.800000E+02
+      5.220000E-01    1.900000E+02
+      5.060000E-01    2.000000E+02
+      4.900000E-01    2.100000E+02
+      4.780000E-01    2.200000E+02
+      4.670000E-01    2.300000E+02
+      4.570000E-01    2.400000E+02
+      4.480000E-01    2.500000E+02
+      4.380000E-01    2.600000E+02
+      4.310000E-01    2.700000E+02
+      4.240000E-01    2.800000E+02
+      4.200000E-01    2.900000E+02
+      4.140000E-01    3.000000E+02
+      4.110000E-01    3.100000E+02
+      4.060000E-01    3.200000E+02



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