Added:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StatisticalReferenceDataset.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StatisticalReferenceDataset.java?rev=1508481&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StatisticalReferenceDataset.java
(added)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StatisticalReferenceDataset.java
Tue Jul 30 15:04:22 2013
@@ -0,0 +1,370 @@
+/*
+ * 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.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.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 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<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 LeastSquaresProblem();
+ }
+
+ class LeastSquaresProblem {
+ public MultivariateVectorFunction getModelFunction() {
+ return 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 MultivariateMatrixFunction getModelFunctionJacobian() {
+ 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 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>
+ * <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;
+ }
+}
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Added:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StatisticalReferenceDatasetFactory.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StatisticalReferenceDatasetFactory.java?rev=1508481&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StatisticalReferenceDatasetFactory.java
(added)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StatisticalReferenceDatasetFactory.java
Tue Jul 30 15:04:22 2013
@@ -0,0 +1,201 @@
+/*
+ * 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.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;
+ }
+
+ public static StatisticalReferenceDataset createLanczos1()
+ throws IOException {
+ final BufferedReader in =
+ createBufferedReaderFromResource("Lanczos1.dat");
+ StatisticalReferenceDataset dataset = null;
+ try {
+ dataset = new StatisticalReferenceDataset(in) {
+
+ @Override
+ public double getModelValue(final double x, final double[] a) {
+ System.out.println(a[0]+", "+a[1]+", "+a[2]+", "+a[3]+",
"+a[4]+", "+a[5]);
+ return a[0] * FastMath.exp(-a[3] * x) +
+ a[1] * FastMath.exp(-a[4] * x) +
+ a[2] * FastMath.exp(-a[5] * x);
+ }
+
+ @Override
+ public double[] getModelDerivatives(final double x,
+ final double[] a) {
+ final double[] dy = new double[6];
+ dy[0] = FastMath.exp(-x * a[3]);
+ dy[1] = FastMath.exp(-x * a[4]);
+ dy[2] = FastMath.exp(-x * a[5]);
+ dy[3] = -x * a[0] * dy[0];
+ dy[4] = -x * a[1] * dy[1];
+ dy[5] = -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()
+ };
+ }
+}
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svn:keywords = Id Revision
Added:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StraightLineProblem.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StraightLineProblem.java?rev=1508481&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StraightLineProblem.java
(added)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StraightLineProblem.java
Tue Jul 30 15:04:22 2013
@@ -0,0 +1,165 @@
+/*
+ * 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>
+ * <li>{@code b}</li>
+ * </ul>
+ * The model functions are:
+ * <ul>
+ * <li>for each pair (a, b), the y-coordinate of the line.</li>
+ * </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;
+ }
+ }
+}
Propchange:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StraightLineProblem.java
------------------------------------------------------------------------------
svn:eol-style = native
Propchange:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/fitting/leastsquares/StraightLineProblem.java
------------------------------------------------------------------------------
svn:keywords = Id Revision
Added:
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Hahn1.dat
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Hahn1.dat?rev=1508481&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Hahn1.dat
(added)
+++
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Hahn1.dat
Tue Jul 30 15:04:22 2013
@@ -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
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+ 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
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+ 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
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+ 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
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+ 10.040E0 96.44E0
+ 11.946E0 118.82E0
+ 12.596E0 128.48E0
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+ 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
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+ 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/fitting/leastsquares/Kirby2.dat
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Kirby2.dat?rev=1508481&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Kirby2.dat
(added)
+++
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Kirby2.dat
Tue Jul 30 15:04:22 2013
@@ -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
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+ 43.4600E0 179.19E0
+ 44.1400E0 181.02E0
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+ 45.2200E0 183.88E0
+ 45.9200E0 185.75E0
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+ 87.6500E0 342.57E0
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+ 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/fitting/leastsquares/Lanczos1.dat
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Lanczos1.dat?rev=1508481&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Lanczos1.dat
(added)
+++
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/Lanczos1.dat
Tue Jul 30 15:04:22 2013
@@ -0,0 +1,84 @@
+NIST/ITL StRD
+Dataset Name: Lanczos1 (Lanczos1.dat)
+
+File Format: ASCII
+ Starting Values (lines 41 to 46)
+ Certified Values (lines 41 to 51)
+ Data (lines 61 to 84)
+
+Procedure: Nonlinear Least Squares Regression
+
+Description: These data are taken from an example discussed in
+ Lanczos (1956). The data were generated to 14-digits
+ of accuracy using
+ f(x) = 0.0951*exp(-x) + 0.8607*exp(-3*x)
+ + 1.5576*exp(-5*x).
+
+
+Reference: Lanczos, C. (1956).
+ Applied Analysis.
+ Englewood Cliffs, NJ: Prentice Hall, pp. 272-280.
+
+
+
+
+Data: 1 Response (y)
+ 1 Predictor (x)
+ 24 Observations
+ Average Level of Difficulty
+ Generated Data
+
+Model: Exponential Class
+ 6 Parameters (b1 to b6)
+
+ y = b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x) + e
+
+
+
+ Starting values Certified Values
+
+ Start 1 Start 2 Parameter Standard Deviation
+ b1 = 1.2 0.5 9.5100000027E-02 5.3347304234E-11
+ b2 = 0.3 0.7 1.0000000001E+00 2.7473038179E-10
+ b3 = 5.6 3.6 8.6070000013E-01 1.3576062225E-10
+ b4 = 5.5 4.2 3.0000000002E+00 3.3308253069E-10
+ b5 = 6.5 4 1.5575999998E+00 1.8815731448E-10
+ b6 = 7.6 6.3 5.0000000001E+00 1.1057500538E-10
+
+Residual Sum of Squares: 1.4307867721E-25
+Residual Standard Deviation: 8.9156129349E-14
+Degrees of Freedom: 18
+Number of Observations: 24
+
+
+
+
+
+
+
+
+Data: y x
+ 2.513400000000E+00 0.000000000000E+00
+ 2.044333373291E+00 5.000000000000E-02
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+ 8.833209084540E-02 1.000000000000E+00
+ 7.833544019350E-02 1.050000000000E+00
+ 6.976693743449E-02 1.100000000000E+00
+ 6.239312536719E-02 1.150000000000E+00
Added:
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/MGH17.dat
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/MGH17.dat?rev=1508481&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/MGH17.dat
(added)
+++
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/fitting/leastsquares/MGH17.dat
Tue Jul 30 15:04:22 2013
@@ -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
