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Posted to commits@commons.apache.org by tn...@apache.org on 2015/02/25 22:49:40 UTC
[12/18] [math] Remove deprecated optimization package.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/direct/NelderMeadSimplex.java
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diff --git a/src/main/java/org/apache/commons/math4/optimization/direct/NelderMeadSimplex.java b/src/main/java/org/apache/commons/math4/optimization/direct/NelderMeadSimplex.java
deleted file mode 100644
index f193ccf..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/direct/NelderMeadSimplex.java
+++ /dev/null
@@ -1,283 +0,0 @@
-/*
- * 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.math4.optimization.direct;
-
-import java.util.Comparator;
-
-import org.apache.commons.math4.analysis.MultivariateFunction;
-import org.apache.commons.math4.optimization.PointValuePair;
-
-/**
- * This class implements the Nelder-Mead simplex algorithm.
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 3.0
- */
-@Deprecated
-public class NelderMeadSimplex extends AbstractSimplex {
- /** Default value for {@link #rho}: {@value}. */
- private static final double DEFAULT_RHO = 1;
- /** Default value for {@link #khi}: {@value}. */
- private static final double DEFAULT_KHI = 2;
- /** Default value for {@link #gamma}: {@value}. */
- private static final double DEFAULT_GAMMA = 0.5;
- /** Default value for {@link #sigma}: {@value}. */
- private static final double DEFAULT_SIGMA = 0.5;
- /** Reflection coefficient. */
- private final double rho;
- /** Expansion coefficient. */
- private final double khi;
- /** Contraction coefficient. */
- private final double gamma;
- /** Shrinkage coefficient. */
- private final double sigma;
-
- /**
- * Build a Nelder-Mead simplex with default coefficients.
- * The default coefficients are 1.0 for rho, 2.0 for khi and 0.5
- * for both gamma and sigma.
- *
- * @param n Dimension of the simplex.
- */
- public NelderMeadSimplex(final int n) {
- this(n, 1d);
- }
-
- /**
- * Build a Nelder-Mead simplex with default coefficients.
- * The default coefficients are 1.0 for rho, 2.0 for khi and 0.5
- * for both gamma and sigma.
- *
- * @param n Dimension of the simplex.
- * @param sideLength Length of the sides of the default (hypercube)
- * simplex. See {@link AbstractSimplex#AbstractSimplex(int,double)}.
- */
- public NelderMeadSimplex(final int n, double sideLength) {
- this(n, sideLength,
- DEFAULT_RHO, DEFAULT_KHI, DEFAULT_GAMMA, DEFAULT_SIGMA);
- }
-
- /**
- * Build a Nelder-Mead simplex with specified coefficients.
- *
- * @param n Dimension of the simplex. See
- * {@link AbstractSimplex#AbstractSimplex(int,double)}.
- * @param sideLength Length of the sides of the default (hypercube)
- * simplex. See {@link AbstractSimplex#AbstractSimplex(int,double)}.
- * @param rho Reflection coefficient.
- * @param khi Expansion coefficient.
- * @param gamma Contraction coefficient.
- * @param sigma Shrinkage coefficient.
- */
- public NelderMeadSimplex(final int n, double sideLength,
- final double rho, final double khi,
- final double gamma, final double sigma) {
- super(n, sideLength);
-
- this.rho = rho;
- this.khi = khi;
- this.gamma = gamma;
- this.sigma = sigma;
- }
-
- /**
- * Build a Nelder-Mead simplex with specified coefficients.
- *
- * @param n Dimension of the simplex. See
- * {@link AbstractSimplex#AbstractSimplex(int)}.
- * @param rho Reflection coefficient.
- * @param khi Expansion coefficient.
- * @param gamma Contraction coefficient.
- * @param sigma Shrinkage coefficient.
- */
- public NelderMeadSimplex(final int n,
- final double rho, final double khi,
- final double gamma, final double sigma) {
- this(n, 1d, rho, khi, gamma, sigma);
- }
-
- /**
- * Build a Nelder-Mead simplex with default coefficients.
- * The default coefficients are 1.0 for rho, 2.0 for khi and 0.5
- * for both gamma and sigma.
- *
- * @param steps Steps along the canonical axes representing box edges.
- * They may be negative but not zero. See
- */
- public NelderMeadSimplex(final double[] steps) {
- this(steps, DEFAULT_RHO, DEFAULT_KHI, DEFAULT_GAMMA, DEFAULT_SIGMA);
- }
-
- /**
- * Build a Nelder-Mead simplex with specified coefficients.
- *
- * @param steps Steps along the canonical axes representing box edges.
- * They may be negative but not zero. See
- * {@link AbstractSimplex#AbstractSimplex(double[])}.
- * @param rho Reflection coefficient.
- * @param khi Expansion coefficient.
- * @param gamma Contraction coefficient.
- * @param sigma Shrinkage coefficient.
- * @throws IllegalArgumentException if one of the steps is zero.
- */
- public NelderMeadSimplex(final double[] steps,
- final double rho, final double khi,
- final double gamma, final double sigma) {
- super(steps);
-
- this.rho = rho;
- this.khi = khi;
- this.gamma = gamma;
- this.sigma = sigma;
- }
-
- /**
- * Build a Nelder-Mead simplex with default coefficients.
- * The default coefficients are 1.0 for rho, 2.0 for khi and 0.5
- * for both gamma and sigma.
- *
- * @param referenceSimplex Reference simplex. See
- * {@link AbstractSimplex#AbstractSimplex(double[][])}.
- */
- public NelderMeadSimplex(final double[][] referenceSimplex) {
- this(referenceSimplex, DEFAULT_RHO, DEFAULT_KHI, DEFAULT_GAMMA, DEFAULT_SIGMA);
- }
-
- /**
- * Build a Nelder-Mead simplex with specified coefficients.
- *
- * @param referenceSimplex Reference simplex. See
- * {@link AbstractSimplex#AbstractSimplex(double[][])}.
- * @param rho Reflection coefficient.
- * @param khi Expansion coefficient.
- * @param gamma Contraction coefficient.
- * @param sigma Shrinkage coefficient.
- * @throws org.apache.commons.math4.exception.NotStrictlyPositiveException
- * if the reference simplex does not contain at least one point.
- * @throws org.apache.commons.math4.exception.DimensionMismatchException
- * if there is a dimension mismatch in the reference simplex.
- */
- public NelderMeadSimplex(final double[][] referenceSimplex,
- final double rho, final double khi,
- final double gamma, final double sigma) {
- super(referenceSimplex);
-
- this.rho = rho;
- this.khi = khi;
- this.gamma = gamma;
- this.sigma = sigma;
- }
-
- /** {@inheritDoc} */
- @Override
- public void iterate(final MultivariateFunction evaluationFunction,
- final Comparator<PointValuePair> comparator) {
- // The simplex has n + 1 points if dimension is n.
- final int n = getDimension();
-
- // Interesting values.
- final PointValuePair best = getPoint(0);
- final PointValuePair secondBest = getPoint(n - 1);
- final PointValuePair worst = getPoint(n);
- final double[] xWorst = worst.getPointRef();
-
- // Compute the centroid of the best vertices (dismissing the worst
- // point at index n).
- final double[] centroid = new double[n];
- for (int i = 0; i < n; i++) {
- final double[] x = getPoint(i).getPointRef();
- for (int j = 0; j < n; j++) {
- centroid[j] += x[j];
- }
- }
- final double scaling = 1.0 / n;
- for (int j = 0; j < n; j++) {
- centroid[j] *= scaling;
- }
-
- // compute the reflection point
- final double[] xR = new double[n];
- for (int j = 0; j < n; j++) {
- xR[j] = centroid[j] + rho * (centroid[j] - xWorst[j]);
- }
- final PointValuePair reflected
- = new PointValuePair(xR, evaluationFunction.value(xR), false);
-
- if (comparator.compare(best, reflected) <= 0 &&
- comparator.compare(reflected, secondBest) < 0) {
- // Accept the reflected point.
- replaceWorstPoint(reflected, comparator);
- } else if (comparator.compare(reflected, best) < 0) {
- // Compute the expansion point.
- final double[] xE = new double[n];
- for (int j = 0; j < n; j++) {
- xE[j] = centroid[j] + khi * (xR[j] - centroid[j]);
- }
- final PointValuePair expanded
- = new PointValuePair(xE, evaluationFunction.value(xE), false);
-
- if (comparator.compare(expanded, reflected) < 0) {
- // Accept the expansion point.
- replaceWorstPoint(expanded, comparator);
- } else {
- // Accept the reflected point.
- replaceWorstPoint(reflected, comparator);
- }
- } else {
- if (comparator.compare(reflected, worst) < 0) {
- // Perform an outside contraction.
- final double[] xC = new double[n];
- for (int j = 0; j < n; j++) {
- xC[j] = centroid[j] + gamma * (xR[j] - centroid[j]);
- }
- final PointValuePair outContracted
- = new PointValuePair(xC, evaluationFunction.value(xC), false);
- if (comparator.compare(outContracted, reflected) <= 0) {
- // Accept the contraction point.
- replaceWorstPoint(outContracted, comparator);
- return;
- }
- } else {
- // Perform an inside contraction.
- final double[] xC = new double[n];
- for (int j = 0; j < n; j++) {
- xC[j] = centroid[j] - gamma * (centroid[j] - xWorst[j]);
- }
- final PointValuePair inContracted
- = new PointValuePair(xC, evaluationFunction.value(xC), false);
-
- if (comparator.compare(inContracted, worst) < 0) {
- // Accept the contraction point.
- replaceWorstPoint(inContracted, comparator);
- return;
- }
- }
-
- // Perform a shrink.
- final double[] xSmallest = getPoint(0).getPointRef();
- for (int i = 1; i <= n; i++) {
- final double[] x = getPoint(i).getPoint();
- for (int j = 0; j < n; j++) {
- x[j] = xSmallest[j] + sigma * (x[j] - xSmallest[j]);
- }
- setPoint(i, new PointValuePair(x, Double.NaN, false));
- }
- evaluate(evaluationFunction, comparator);
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/direct/PowellOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optimization/direct/PowellOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/direct/PowellOptimizer.java
deleted file mode 100644
index a0a396e..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/direct/PowellOptimizer.java
+++ /dev/null
@@ -1,352 +0,0 @@
-/*
- * 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.math4.optimization.direct;
-
-import org.apache.commons.math4.analysis.MultivariateFunction;
-import org.apache.commons.math4.analysis.UnivariateFunction;
-import org.apache.commons.math4.exception.NotStrictlyPositiveException;
-import org.apache.commons.math4.exception.NumberIsTooSmallException;
-import org.apache.commons.math4.optimization.ConvergenceChecker;
-import org.apache.commons.math4.optimization.GoalType;
-import org.apache.commons.math4.optimization.MultivariateOptimizer;
-import org.apache.commons.math4.optimization.PointValuePair;
-import org.apache.commons.math4.optimization.univariate.BracketFinder;
-import org.apache.commons.math4.optimization.univariate.BrentOptimizer;
-import org.apache.commons.math4.optimization.univariate.SimpleUnivariateValueChecker;
-import org.apache.commons.math4.optimization.univariate.UnivariatePointValuePair;
-import org.apache.commons.math4.util.FastMath;
-import org.apache.commons.math4.util.MathArrays;
-
-/**
- * Powell algorithm.
- * This code is translated and adapted from the Python version of this
- * algorithm (as implemented in module {@code optimize.py} v0.5 of
- * <em>SciPy</em>).
- * <br/>
- * The default stopping criterion is based on the differences of the
- * function value between two successive iterations. It is however possible
- * to define a custom convergence checker that might terminate the algorithm
- * earlier.
- * <br/>
- * The internal line search optimizer is a {@link BrentOptimizer} with a
- * convergence checker set to {@link SimpleUnivariateValueChecker}.
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.2
- */
-@Deprecated
-public class PowellOptimizer
- extends BaseAbstractMultivariateOptimizer<MultivariateFunction>
- implements MultivariateOptimizer {
- /**
- * Minimum relative tolerance.
- */
- private static final double MIN_RELATIVE_TOLERANCE = 2 * FastMath.ulp(1d);
- /**
- * Relative threshold.
- */
- private final double relativeThreshold;
- /**
- * Absolute threshold.
- */
- private final double absoluteThreshold;
- /**
- * Line search.
- */
- private final LineSearch line;
-
- /**
- * This constructor allows to specify a user-defined convergence checker,
- * in addition to the parameters that control the default convergence
- * checking procedure.
- * <br/>
- * The internal line search tolerances are set to the square-root of their
- * corresponding value in the multivariate optimizer.
- *
- * @param rel Relative threshold.
- * @param abs Absolute threshold.
- * @param checker Convergence checker.
- * @throws NotStrictlyPositiveException if {@code abs <= 0}.
- * @throws NumberIsTooSmallException if {@code rel < 2 * Math.ulp(1d)}.
- */
- public PowellOptimizer(double rel,
- double abs,
- ConvergenceChecker<PointValuePair> checker) {
- this(rel, abs, FastMath.sqrt(rel), FastMath.sqrt(abs), checker);
- }
-
- /**
- * This constructor allows to specify a user-defined convergence checker,
- * in addition to the parameters that control the default convergence
- * checking procedure and the line search tolerances.
- *
- * @param rel Relative threshold for this optimizer.
- * @param abs Absolute threshold for this optimizer.
- * @param lineRel Relative threshold for the internal line search optimizer.
- * @param lineAbs Absolute threshold for the internal line search optimizer.
- * @param checker Convergence checker.
- * @throws NotStrictlyPositiveException if {@code abs <= 0}.
- * @throws NumberIsTooSmallException if {@code rel < 2 * Math.ulp(1d)}.
- */
- public PowellOptimizer(double rel,
- double abs,
- double lineRel,
- double lineAbs,
- ConvergenceChecker<PointValuePair> checker) {
- super(checker);
-
- if (rel < MIN_RELATIVE_TOLERANCE) {
- throw new NumberIsTooSmallException(rel, MIN_RELATIVE_TOLERANCE, true);
- }
- if (abs <= 0) {
- throw new NotStrictlyPositiveException(abs);
- }
- relativeThreshold = rel;
- absoluteThreshold = abs;
-
- // Create the line search optimizer.
- line = new LineSearch(lineRel,
- lineAbs);
- }
-
- /**
- * The parameters control the default convergence checking procedure.
- * <br/>
- * The internal line search tolerances are set to the square-root of their
- * corresponding value in the multivariate optimizer.
- *
- * @param rel Relative threshold.
- * @param abs Absolute threshold.
- * @throws NotStrictlyPositiveException if {@code abs <= 0}.
- * @throws NumberIsTooSmallException if {@code rel < 2 * Math.ulp(1d)}.
- */
- public PowellOptimizer(double rel,
- double abs) {
- this(rel, abs, null);
- }
-
- /**
- * Builds an instance with the default convergence checking procedure.
- *
- * @param rel Relative threshold.
- * @param abs Absolute threshold.
- * @param lineRel Relative threshold for the internal line search optimizer.
- * @param lineAbs Absolute threshold for the internal line search optimizer.
- * @throws NotStrictlyPositiveException if {@code abs <= 0}.
- * @throws NumberIsTooSmallException if {@code rel < 2 * Math.ulp(1d)}.
- * @since 3.1
- */
- public PowellOptimizer(double rel,
- double abs,
- double lineRel,
- double lineAbs) {
- this(rel, abs, lineRel, lineAbs, null);
- }
-
- /** {@inheritDoc} */
- @Override
- protected PointValuePair doOptimize() {
- final GoalType goal = getGoalType();
- final double[] guess = getStartPoint();
- final int n = guess.length;
-
- final double[][] direc = new double[n][n];
- for (int i = 0; i < n; i++) {
- direc[i][i] = 1;
- }
-
- final ConvergenceChecker<PointValuePair> checker
- = getConvergenceChecker();
-
- double[] x = guess;
- double fVal = computeObjectiveValue(x);
- double[] x1 = x.clone();
- int iter = 0;
- while (true) {
- ++iter;
-
- double fX = fVal;
- double fX2 = 0;
- double delta = 0;
- int bigInd = 0;
- double alphaMin = 0;
-
- for (int i = 0; i < n; i++) {
- final double[] d = MathArrays.copyOf(direc[i]);
-
- fX2 = fVal;
-
- final UnivariatePointValuePair optimum = line.search(x, d);
- fVal = optimum.getValue();
- alphaMin = optimum.getPoint();
- final double[][] result = newPointAndDirection(x, d, alphaMin);
- x = result[0];
-
- if ((fX2 - fVal) > delta) {
- delta = fX2 - fVal;
- bigInd = i;
- }
- }
-
- // Default convergence check.
- boolean stop = 2 * (fX - fVal) <=
- (relativeThreshold * (FastMath.abs(fX) + FastMath.abs(fVal)) +
- absoluteThreshold);
-
- final PointValuePair previous = new PointValuePair(x1, fX);
- final PointValuePair current = new PointValuePair(x, fVal);
- if (!stop && checker != null) {
- stop = checker.converged(iter, previous, current);
- }
- if (stop) {
- if (goal == GoalType.MINIMIZE) {
- return (fVal < fX) ? current : previous;
- } else {
- return (fVal > fX) ? current : previous;
- }
- }
-
- final double[] d = new double[n];
- final double[] x2 = new double[n];
- for (int i = 0; i < n; i++) {
- d[i] = x[i] - x1[i];
- x2[i] = 2 * x[i] - x1[i];
- }
-
- x1 = x.clone();
- fX2 = computeObjectiveValue(x2);
-
- if (fX > fX2) {
- double t = 2 * (fX + fX2 - 2 * fVal);
- double temp = fX - fVal - delta;
- t *= temp * temp;
- temp = fX - fX2;
- t -= delta * temp * temp;
-
- if (t < 0.0) {
- final UnivariatePointValuePair optimum = line.search(x, d);
- fVal = optimum.getValue();
- alphaMin = optimum.getPoint();
- final double[][] result = newPointAndDirection(x, d, alphaMin);
- x = result[0];
-
- final int lastInd = n - 1;
- direc[bigInd] = direc[lastInd];
- direc[lastInd] = result[1];
- }
- }
- }
- }
-
- /**
- * Compute a new point (in the original space) and a new direction
- * vector, resulting from the line search.
- *
- * @param p Point used in the line search.
- * @param d Direction used in the line search.
- * @param optimum Optimum found by the line search.
- * @return a 2-element array containing the new point (at index 0) and
- * the new direction (at index 1).
- */
- private double[][] newPointAndDirection(double[] p,
- double[] d,
- double optimum) {
- final int n = p.length;
- final double[] nP = new double[n];
- final double[] nD = new double[n];
- for (int i = 0; i < n; i++) {
- nD[i] = d[i] * optimum;
- nP[i] = p[i] + nD[i];
- }
-
- final double[][] result = new double[2][];
- result[0] = nP;
- result[1] = nD;
-
- return result;
- }
-
- /**
- * Class for finding the minimum of the objective function along a given
- * direction.
- */
- private class LineSearch extends BrentOptimizer {
- /**
- * Value that will pass the precondition check for {@link BrentOptimizer}
- * but will not pass the convergence check, so that the custom checker
- * will always decide when to stop the line search.
- */
- private static final double REL_TOL_UNUSED = 1e-15;
- /**
- * Value that will pass the precondition check for {@link BrentOptimizer}
- * but will not pass the convergence check, so that the custom checker
- * will always decide when to stop the line search.
- */
- private static final double ABS_TOL_UNUSED = Double.MIN_VALUE;
- /**
- * Automatic bracketing.
- */
- private final BracketFinder bracket = new BracketFinder();
-
- /**
- * The "BrentOptimizer" default stopping criterion uses the tolerances
- * to check the domain (point) values, not the function values.
- * We thus create a custom checker to use function values.
- *
- * @param rel Relative threshold.
- * @param abs Absolute threshold.
- */
- LineSearch(double rel,
- double abs) {
- super(REL_TOL_UNUSED,
- ABS_TOL_UNUSED,
- new SimpleUnivariateValueChecker(rel, abs));
- }
-
- /**
- * Find the minimum of the function {@code f(p + alpha * d)}.
- *
- * @param p Starting point.
- * @param d Search direction.
- * @return the optimum.
- * @throws org.apache.commons.math4.exception.TooManyEvaluationsException
- * if the number of evaluations is exceeded.
- */
- public UnivariatePointValuePair search(final double[] p, final double[] d) {
- final int n = p.length;
- final UnivariateFunction f = new UnivariateFunction() {
- public double value(double alpha) {
- final double[] x = new double[n];
- for (int i = 0; i < n; i++) {
- x[i] = p[i] + alpha * d[i];
- }
- final double obj = PowellOptimizer.this.computeObjectiveValue(x);
- return obj;
- }
- };
-
- final GoalType goal = PowellOptimizer.this.getGoalType();
- bracket.search(f, goal, 0, 1);
- // Passing "MAX_VALUE" as a dummy value because it is the enclosing
- // class that counts the number of evaluations (and will eventually
- // generate the exception).
- return optimize(Integer.MAX_VALUE, f, goal,
- bracket.getLo(), bracket.getHi(), bracket.getMid());
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/direct/SimplexOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optimization/direct/SimplexOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/direct/SimplexOptimizer.java
deleted file mode 100644
index 0adcdb3..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/direct/SimplexOptimizer.java
+++ /dev/null
@@ -1,233 +0,0 @@
-/*
- * 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.math4.optimization.direct;
-
-import java.util.Comparator;
-
-import org.apache.commons.math4.analysis.MultivariateFunction;
-import org.apache.commons.math4.exception.NullArgumentException;
-import org.apache.commons.math4.optimization.ConvergenceChecker;
-import org.apache.commons.math4.optimization.GoalType;
-import org.apache.commons.math4.optimization.MultivariateOptimizer;
-import org.apache.commons.math4.optimization.OptimizationData;
-import org.apache.commons.math4.optimization.PointValuePair;
-import org.apache.commons.math4.optimization.SimpleValueChecker;
-
-/**
- * This class implements simplex-based direct search optimization.
- *
- * <p>
- * Direct search methods only use objective function values, they do
- * not need derivatives and don't either try to compute approximation
- * of the derivatives. According to a 1996 paper by Margaret H. Wright
- * (<a href="http://cm.bell-labs.com/cm/cs/doc/96/4-02.ps.gz">Direct
- * Search Methods: Once Scorned, Now Respectable</a>), they are used
- * when either the computation of the derivative is impossible (noisy
- * functions, unpredictable discontinuities) or difficult (complexity,
- * computation cost). In the first cases, rather than an optimum, a
- * <em>not too bad</em> point is desired. In the latter cases, an
- * optimum is desired but cannot be reasonably found. In all cases
- * direct search methods can be useful.
- * </p>
- * <p>
- * Simplex-based direct search methods are based on comparison of
- * the objective function values at the vertices of a simplex (which is a
- * set of n+1 points in dimension n) that is updated by the algorithms
- * steps.
- * <p>
- * <p>
- * The {@link #setSimplex(AbstractSimplex) setSimplex} method <em>must</em>
- * be called prior to calling the {@code optimize} method.
- * </p>
- * <p>
- * Each call to {@link #optimize(int,MultivariateFunction,GoalType,double[])
- * optimize} will re-use the start configuration of the current simplex and
- * move it such that its first vertex is at the provided start point of the
- * optimization. If the {@code optimize} method is called to solve a different
- * problem and the number of parameters change, the simplex must be
- * re-initialized to one with the appropriate dimensions.
- * </p>
- * <p>
- * Convergence is checked by providing the <em>worst</em> points of
- * previous and current simplex to the convergence checker, not the best
- * ones.
- * </p>
- * <p>
- * This simplex optimizer implementation does not directly support constrained
- * optimization with simple bounds, so for such optimizations, either a more
- * dedicated method must be used like {@link CMAESOptimizer} or {@link
- * BOBYQAOptimizer}, or the optimized method must be wrapped in an adapter like
- * {@link MultivariateFunctionMappingAdapter} or {@link
- * MultivariateFunctionPenaltyAdapter}.
- * </p>
- *
- * @see AbstractSimplex
- * @see MultivariateFunctionMappingAdapter
- * @see MultivariateFunctionPenaltyAdapter
- * @see CMAESOptimizer
- * @see BOBYQAOptimizer
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 3.0
- */
-@SuppressWarnings("boxing") // deprecated anyway
-@Deprecated
-public class SimplexOptimizer
- extends BaseAbstractMultivariateOptimizer<MultivariateFunction>
- implements MultivariateOptimizer {
- /** Simplex. */
- private AbstractSimplex simplex;
-
- /**
- * Constructor using a default {@link SimpleValueChecker convergence
- * checker}.
- * @deprecated See {@link SimpleValueChecker#SimpleValueChecker()}
- */
- @Deprecated
- public SimplexOptimizer() {
- this(new SimpleValueChecker());
- }
-
- /**
- * @param checker Convergence checker.
- */
- public SimplexOptimizer(ConvergenceChecker<PointValuePair> checker) {
- super(checker);
- }
-
- /**
- * @param rel Relative threshold.
- * @param abs Absolute threshold.
- */
- public SimplexOptimizer(double rel, double abs) {
- this(new SimpleValueChecker(rel, abs));
- }
-
- /**
- * Set the simplex algorithm.
- *
- * @param simplex Simplex.
- * @deprecated As of 3.1. The initial simplex can now be passed as an
- * argument of the {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])}
- * method.
- */
- @Deprecated
- public void setSimplex(AbstractSimplex simplex) {
- parseOptimizationData(simplex);
- }
-
- /**
- * Optimize an objective function.
- *
- * @param maxEval Allowed number of evaluations of the objective function.
- * @param f Objective function.
- * @param goalType Optimization type.
- * @param optData Optimization data. The following data will be looked for:
- * <ul>
- * <li>{@link org.apache.commons.math4.optimization.InitialGuess InitialGuess}</li>
- * <li>{@link AbstractSimplex}</li>
- * </ul>
- * @return the point/value pair giving the optimal value for objective
- * function.
- */
- @Override
- protected PointValuePair optimizeInternal(int maxEval, MultivariateFunction f,
- GoalType goalType,
- OptimizationData... optData) {
- // Scan "optData" for the input specific to this optimizer.
- parseOptimizationData(optData);
-
- // The parent's method will retrieve the common parameters from
- // "optData" and call "doOptimize".
- return super.optimizeInternal(maxEval, f, goalType, optData);
- }
-
- /**
- * Scans the list of (required and optional) optimization data that
- * characterize the problem.
- *
- * @param optData Optimization data. The following data will be looked for:
- * <ul>
- * <li>{@link AbstractSimplex}</li>
- * </ul>
- */
- private void parseOptimizationData(OptimizationData... optData) {
- // The existing values (as set by the previous call) are reused if
- // not provided in the argument list.
- for (OptimizationData data : optData) {
- if (data instanceof AbstractSimplex) {
- simplex = (AbstractSimplex) data;
- continue;
- }
- }
- }
-
- /** {@inheritDoc} */
- @Override
- protected PointValuePair doOptimize() {
- if (simplex == null) {
- throw new NullArgumentException();
- }
-
- // Indirect call to "computeObjectiveValue" in order to update the
- // evaluations counter.
- final MultivariateFunction evalFunc
- = new MultivariateFunction() {
- public double value(double[] point) {
- return computeObjectiveValue(point);
- }
- };
-
- final boolean isMinim = getGoalType() == GoalType.MINIMIZE;
- final Comparator<PointValuePair> comparator
- = new Comparator<PointValuePair>() {
- public int compare(final PointValuePair o1,
- final PointValuePair o2) {
- final double v1 = o1.getValue();
- final double v2 = o2.getValue();
- return isMinim ? Double.compare(v1, v2) : Double.compare(v2, v1);
- }
- };
-
- // Initialize search.
- simplex.build(getStartPoint());
- simplex.evaluate(evalFunc, comparator);
-
- PointValuePair[] previous = null;
- int iteration = 0;
- final ConvergenceChecker<PointValuePair> checker = getConvergenceChecker();
- while (true) {
- if (iteration > 0) {
- boolean converged = true;
- for (int i = 0; i < simplex.getSize(); i++) {
- PointValuePair prev = previous[i];
- converged = converged &&
- checker.converged(iteration, prev, simplex.getPoint(i));
- }
- if (converged) {
- // We have found an optimum.
- return simplex.getPoint(0);
- }
- }
-
- // We still need to search.
- previous = simplex.getPoints();
- simplex.iterate(evalFunc, comparator);
- ++iteration;
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/direct/package-info.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optimization/direct/package-info.java b/src/main/java/org/apache/commons/math4/optimization/direct/package-info.java
deleted file mode 100644
index 57b385d..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/direct/package-info.java
+++ /dev/null
@@ -1,24 +0,0 @@
-/*
- * 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.
- */
-/**
- *
- * <p>
- * This package provides optimization algorithms that don't require derivatives.
- * </p>
- *
- */
-package org.apache.commons.math4.optimization.direct;
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/fitting/CurveFitter.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optimization/fitting/CurveFitter.java b/src/main/java/org/apache/commons/math4/optimization/fitting/CurveFitter.java
deleted file mode 100644
index 7b3a429..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/fitting/CurveFitter.java
+++ /dev/null
@@ -1,298 +0,0 @@
-/*
- * 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.math4.optimization.fitting;
-
-import java.util.ArrayList;
-import java.util.List;
-
-import org.apache.commons.math4.analysis.DifferentiableMultivariateVectorFunction;
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.ParametricUnivariateFunction;
-import org.apache.commons.math4.analysis.differentiation.DerivativeStructure;
-import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableVectorFunction;
-import org.apache.commons.math4.optimization.DifferentiableMultivariateVectorOptimizer;
-import org.apache.commons.math4.optimization.MultivariateDifferentiableVectorOptimizer;
-import org.apache.commons.math4.optimization.PointVectorValuePair;
-
-/** Fitter for parametric univariate real functions y = f(x).
- * <br/>
- * When a univariate real function y = f(x) does depend on some
- * unknown parameters p<sub>0</sub>, p<sub>1</sub> ... p<sub>n-1</sub>,
- * this class can be used to find these parameters. It does this
- * by <em>fitting</em> the curve so it remains very close to a set of
- * observed points (x<sub>0</sub>, y<sub>0</sub>), (x<sub>1</sub>,
- * y<sub>1</sub>) ... (x<sub>k-1</sub>, y<sub>k-1</sub>). This fitting
- * is done by finding the parameters values that minimizes the objective
- * function ∑(y<sub>i</sub>-f(x<sub>i</sub>))<sup>2</sup>. This is
- * really a least squares problem.
- *
- * @param <T> Function to use for the fit.
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.0
- */
-@Deprecated
-public class CurveFitter<T extends ParametricUnivariateFunction> {
-
- /** Optimizer to use for the fitting.
- * @deprecated as of 3.1 replaced by {@link #optimizer}
- */
- @Deprecated
- private final DifferentiableMultivariateVectorOptimizer oldOptimizer;
-
- /** Optimizer to use for the fitting. */
- private final MultivariateDifferentiableVectorOptimizer optimizer;
-
- /** Observed points. */
- private final List<WeightedObservedPoint> observations;
-
- /** Simple constructor.
- * @param optimizer optimizer to use for the fitting
- * @deprecated as of 3.1 replaced by {@link #CurveFitter(MultivariateDifferentiableVectorOptimizer)}
- */
- @Deprecated
- public CurveFitter(final DifferentiableMultivariateVectorOptimizer optimizer) {
- this.oldOptimizer = optimizer;
- this.optimizer = null;
- observations = new ArrayList<WeightedObservedPoint>();
- }
-
- /** Simple constructor.
- * @param optimizer optimizer to use for the fitting
- * @since 3.1
- */
- public CurveFitter(final MultivariateDifferentiableVectorOptimizer optimizer) {
- this.oldOptimizer = null;
- this.optimizer = optimizer;
- observations = new ArrayList<WeightedObservedPoint>();
- }
-
- /** Add an observed (x,y) point to the sample with unit weight.
- * <p>Calling this method is equivalent to call
- * {@code addObservedPoint(1.0, x, y)}.</p>
- * @param x abscissa of the point
- * @param y observed value of the point at x, after fitting we should
- * have f(x) as close as possible to this value
- * @see #addObservedPoint(double, double, double)
- * @see #addObservedPoint(WeightedObservedPoint)
- * @see #getObservations()
- */
- public void addObservedPoint(double x, double y) {
- addObservedPoint(1.0, x, y);
- }
-
- /** Add an observed weighted (x,y) point to the sample.
- * @param weight weight of the observed point in the fit
- * @param x abscissa of the point
- * @param y observed value of the point at x, after fitting we should
- * have f(x) as close as possible to this value
- * @see #addObservedPoint(double, double)
- * @see #addObservedPoint(WeightedObservedPoint)
- * @see #getObservations()
- */
- public void addObservedPoint(double weight, double x, double y) {
- observations.add(new WeightedObservedPoint(weight, x, y));
- }
-
- /** Add an observed weighted (x,y) point to the sample.
- * @param observed observed point to add
- * @see #addObservedPoint(double, double)
- * @see #addObservedPoint(double, double, double)
- * @see #getObservations()
- */
- public void addObservedPoint(WeightedObservedPoint observed) {
- observations.add(observed);
- }
-
- /** Get the observed points.
- * @return observed points
- * @see #addObservedPoint(double, double)
- * @see #addObservedPoint(double, double, double)
- * @see #addObservedPoint(WeightedObservedPoint)
- */
- public WeightedObservedPoint[] getObservations() {
- return observations.toArray(new WeightedObservedPoint[observations.size()]);
- }
-
- /**
- * Remove all observations.
- */
- public void clearObservations() {
- observations.clear();
- }
-
- /**
- * Fit a curve.
- * This method compute the coefficients of the curve that best
- * fit the sample of observed points previously given through calls
- * to the {@link #addObservedPoint(WeightedObservedPoint)
- * addObservedPoint} method.
- *
- * @param f parametric function to fit.
- * @param initialGuess first guess of the function parameters.
- * @return the fitted parameters.
- * @throws org.apache.commons.math4.exception.DimensionMismatchException
- * if the start point dimension is wrong.
- */
- public double[] fit(T f, final double[] initialGuess) {
- return fit(Integer.MAX_VALUE, f, initialGuess);
- }
-
- /**
- * Fit a curve.
- * This method compute the coefficients of the curve that best
- * fit the sample of observed points previously given through calls
- * to the {@link #addObservedPoint(WeightedObservedPoint)
- * addObservedPoint} method.
- *
- * @param f parametric function to fit.
- * @param initialGuess first guess of the function parameters.
- * @param maxEval Maximum number of function evaluations.
- * @return the fitted parameters.
- * @throws org.apache.commons.math4.exception.TooManyEvaluationsException
- * if the number of allowed evaluations is exceeded.
- * @throws org.apache.commons.math4.exception.DimensionMismatchException
- * if the start point dimension is wrong.
- * @since 3.0
- */
- public double[] fit(int maxEval, T f,
- final double[] initialGuess) {
- // prepare least squares problem
- double[] target = new double[observations.size()];
- double[] weights = new double[observations.size()];
- int i = 0;
- for (WeightedObservedPoint point : observations) {
- target[i] = point.getY();
- weights[i] = point.getWeight();
- ++i;
- }
-
- // perform the fit
- final PointVectorValuePair optimum;
- if (optimizer == null) {
- // to be removed in 4.0
- optimum = oldOptimizer.optimize(maxEval, new OldTheoreticalValuesFunction(f),
- target, weights, initialGuess);
- } else {
- optimum = optimizer.optimize(maxEval, new TheoreticalValuesFunction(f),
- target, weights, initialGuess);
- }
-
- // extract the coefficients
- return optimum.getPointRef();
- }
-
- /** Vectorial function computing function theoretical values. */
- @Deprecated
- private class OldTheoreticalValuesFunction
- implements DifferentiableMultivariateVectorFunction {
- /** Function to fit. */
- private final ParametricUnivariateFunction f;
-
- /** Simple constructor.
- * @param f function to fit.
- */
- public OldTheoreticalValuesFunction(final ParametricUnivariateFunction f) {
- this.f = f;
- }
-
- /** {@inheritDoc} */
- public MultivariateMatrixFunction jacobian() {
- return new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- final double[][] jacobian = new double[observations.size()][];
-
- int i = 0;
- for (WeightedObservedPoint observed : observations) {
- jacobian[i++] = f.gradient(observed.getX(), point);
- }
-
- return jacobian;
- }
- };
- }
-
- /** {@inheritDoc} */
- public double[] value(double[] point) {
- // compute the residuals
- final double[] values = new double[observations.size()];
- int i = 0;
- for (WeightedObservedPoint observed : observations) {
- values[i++] = f.value(observed.getX(), point);
- }
-
- return values;
- }
- }
-
- /** Vectorial function computing function theoretical values. */
- private class TheoreticalValuesFunction implements MultivariateDifferentiableVectorFunction {
-
- /** Function to fit. */
- private final ParametricUnivariateFunction f;
-
- /** Simple constructor.
- * @param f function to fit.
- */
- public TheoreticalValuesFunction(final ParametricUnivariateFunction f) {
- this.f = f;
- }
-
- /** {@inheritDoc} */
- public double[] value(double[] point) {
- // compute the residuals
- final double[] values = new double[observations.size()];
- int i = 0;
- for (WeightedObservedPoint observed : observations) {
- values[i++] = f.value(observed.getX(), point);
- }
-
- return values;
- }
-
- /** {@inheritDoc} */
- public DerivativeStructure[] value(DerivativeStructure[] point) {
-
- // extract parameters
- final double[] parameters = new double[point.length];
- for (int k = 0; k < point.length; ++k) {
- parameters[k] = point[k].getValue();
- }
-
- // compute the residuals
- final DerivativeStructure[] values = new DerivativeStructure[observations.size()];
- int i = 0;
- for (WeightedObservedPoint observed : observations) {
-
- // build the DerivativeStructure by adding first the value as a constant
- // and then adding derivatives
- DerivativeStructure vi = new DerivativeStructure(point.length, 1, f.value(observed.getX(), parameters));
- for (int k = 0; k < point.length; ++k) {
- vi = vi.add(new DerivativeStructure(point.length, 1, k, 0.0));
- }
-
- values[i++] = vi;
-
- }
-
- return values;
- }
-
- }
-
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/fitting/GaussianFitter.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optimization/fitting/GaussianFitter.java b/src/main/java/org/apache/commons/math4/optimization/fitting/GaussianFitter.java
deleted file mode 100644
index bf0f03d..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/fitting/GaussianFitter.java
+++ /dev/null
@@ -1,365 +0,0 @@
-/*
- * 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.math4.optimization.fitting;
-
-import java.util.Arrays;
-import java.util.Comparator;
-
-import org.apache.commons.math4.analysis.function.Gaussian;
-import org.apache.commons.math4.exception.NotStrictlyPositiveException;
-import org.apache.commons.math4.exception.NullArgumentException;
-import org.apache.commons.math4.exception.NumberIsTooSmallException;
-import org.apache.commons.math4.exception.OutOfRangeException;
-import org.apache.commons.math4.exception.ZeroException;
-import org.apache.commons.math4.exception.util.LocalizedFormats;
-import org.apache.commons.math4.optimization.DifferentiableMultivariateVectorOptimizer;
-import org.apache.commons.math4.util.FastMath;
-
-/**
- * Fits points to a {@link
- * org.apache.commons.math4.analysis.function.Gaussian.Parametric Gaussian} function.
- * <p>
- * Usage example:
- * <pre>
- * GaussianFitter fitter = new GaussianFitter(
- * new LevenbergMarquardtOptimizer());
- * fitter.addObservedPoint(4.0254623, 531026.0);
- * fitter.addObservedPoint(4.03128248, 984167.0);
- * fitter.addObservedPoint(4.03839603, 1887233.0);
- * fitter.addObservedPoint(4.04421621, 2687152.0);
- * fitter.addObservedPoint(4.05132976, 3461228.0);
- * fitter.addObservedPoint(4.05326982, 3580526.0);
- * fitter.addObservedPoint(4.05779662, 3439750.0);
- * fitter.addObservedPoint(4.0636168, 2877648.0);
- * fitter.addObservedPoint(4.06943698, 2175960.0);
- * fitter.addObservedPoint(4.07525716, 1447024.0);
- * fitter.addObservedPoint(4.08237071, 717104.0);
- * fitter.addObservedPoint(4.08366408, 620014.0);
- * double[] parameters = fitter.fit();
- * </pre>
- *
- * @since 2.2
- * @deprecated As of 3.1 (to be removed in 4.0).
- */
-@Deprecated
-public class GaussianFitter extends CurveFitter<Gaussian.Parametric> {
- /**
- * Constructs an instance using the specified optimizer.
- *
- * @param optimizer Optimizer to use for the fitting.
- */
- public GaussianFitter(DifferentiableMultivariateVectorOptimizer optimizer) {
- super(optimizer);
- }
-
- /**
- * Fits a Gaussian function to the observed points.
- *
- * @param initialGuess First guess values in the following order:
- * <ul>
- * <li>Norm</li>
- * <li>Mean</li>
- * <li>Sigma</li>
- * </ul>
- * @return the parameters of the Gaussian function that best fits the
- * observed points (in the same order as above).
- * @since 3.0
- */
- public double[] fit(double[] initialGuess) {
- final Gaussian.Parametric f = new Gaussian.Parametric() {
- @Override
- public double value(double x, double ... p) {
- double v = Double.POSITIVE_INFINITY;
- try {
- v = super.value(x, p);
- } catch (NotStrictlyPositiveException e) { // NOPMD
- // Do nothing.
- }
- return v;
- }
-
- @Override
- public double[] gradient(double x, double ... p) {
- double[] v = { Double.POSITIVE_INFINITY,
- Double.POSITIVE_INFINITY,
- Double.POSITIVE_INFINITY };
- try {
- v = super.gradient(x, p);
- } catch (NotStrictlyPositiveException e) { // NOPMD
- // Do nothing.
- }
- return v;
- }
- };
-
- return fit(f, initialGuess);
- }
-
- /**
- * Fits a Gaussian function to the observed points.
- *
- * @return the parameters of the Gaussian function that best fits the
- * observed points (in the same order as above).
- */
- public double[] fit() {
- final double[] guess = (new ParameterGuesser(getObservations())).guess();
- return fit(guess);
- }
-
- /**
- * Guesses the parameters {@code norm}, {@code mean}, and {@code sigma}
- * of a {@link org.apache.commons.math4.analysis.function.Gaussian.Parametric}
- * based on the specified observed points.
- */
- public static class ParameterGuesser {
- /** Normalization factor. */
- private final double norm;
- /** Mean. */
- private final double mean;
- /** Standard deviation. */
- private final double sigma;
-
- /**
- * Constructs instance with the specified observed points.
- *
- * @param observations Observed points from which to guess the
- * parameters of the Gaussian.
- * @throws NullArgumentException if {@code observations} is
- * {@code null}.
- * @throws NumberIsTooSmallException if there are less than 3
- * observations.
- */
- public ParameterGuesser(WeightedObservedPoint[] observations) {
- if (observations == null) {
- throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY);
- }
- if (observations.length < 3) {
- throw new NumberIsTooSmallException(observations.length, 3, true);
- }
-
- final WeightedObservedPoint[] sorted = sortObservations(observations);
- final double[] params = basicGuess(sorted);
-
- norm = params[0];
- mean = params[1];
- sigma = params[2];
- }
-
- /**
- * Gets an estimation of the parameters.
- *
- * @return the guessed parameters, in the following order:
- * <ul>
- * <li>Normalization factor</li>
- * <li>Mean</li>
- * <li>Standard deviation</li>
- * </ul>
- */
- public double[] guess() {
- return new double[] { norm, mean, sigma };
- }
-
- /**
- * Sort the observations.
- *
- * @param unsorted Input observations.
- * @return the input observations, sorted.
- */
- private WeightedObservedPoint[] sortObservations(WeightedObservedPoint[] unsorted) {
- final WeightedObservedPoint[] observations = unsorted.clone();
- final Comparator<WeightedObservedPoint> cmp
- = new Comparator<WeightedObservedPoint>() {
- public int compare(WeightedObservedPoint p1,
- WeightedObservedPoint p2) {
- if (p1 == null && p2 == null) {
- return 0;
- }
- if (p1 == null) {
- return -1;
- }
- if (p2 == null) {
- return 1;
- }
- if (p1.getX() < p2.getX()) {
- return -1;
- }
- if (p1.getX() > p2.getX()) {
- return 1;
- }
- if (p1.getY() < p2.getY()) {
- return -1;
- }
- if (p1.getY() > p2.getY()) {
- return 1;
- }
- if (p1.getWeight() < p2.getWeight()) {
- return -1;
- }
- if (p1.getWeight() > p2.getWeight()) {
- return 1;
- }
- return 0;
- }
- };
-
- Arrays.sort(observations, cmp);
- return observations;
- }
-
- /**
- * Guesses the parameters based on the specified observed points.
- *
- * @param points Observed points, sorted.
- * @return the guessed parameters (normalization factor, mean and
- * sigma).
- */
- private double[] basicGuess(WeightedObservedPoint[] points) {
- final int maxYIdx = findMaxY(points);
- final double n = points[maxYIdx].getY();
- final double m = points[maxYIdx].getX();
-
- double fwhmApprox;
- try {
- final double halfY = n + ((m - n) / 2);
- final double fwhmX1 = interpolateXAtY(points, maxYIdx, -1, halfY);
- final double fwhmX2 = interpolateXAtY(points, maxYIdx, 1, halfY);
- fwhmApprox = fwhmX2 - fwhmX1;
- } catch (OutOfRangeException e) {
- // TODO: Exceptions should not be used for flow control.
- fwhmApprox = points[points.length - 1].getX() - points[0].getX();
- }
- final double s = fwhmApprox / (2 * FastMath.sqrt(2 * FastMath.log(2)));
-
- return new double[] { n, m, s };
- }
-
- /**
- * Finds index of point in specified points with the largest Y.
- *
- * @param points Points to search.
- * @return the index in specified points array.
- */
- private int findMaxY(WeightedObservedPoint[] points) {
- int maxYIdx = 0;
- for (int i = 1; i < points.length; i++) {
- if (points[i].getY() > points[maxYIdx].getY()) {
- maxYIdx = i;
- }
- }
- return maxYIdx;
- }
-
- /**
- * Interpolates using the specified points to determine X at the
- * specified Y.
- *
- * @param points Points to use for interpolation.
- * @param startIdx Index within points from which to start the search for
- * interpolation bounds points.
- * @param idxStep Index step for searching interpolation bounds points.
- * @param y Y value for which X should be determined.
- * @return the value of X for the specified Y.
- * @throws ZeroException if {@code idxStep} is 0.
- * @throws OutOfRangeException if specified {@code y} is not within the
- * range of the specified {@code points}.
- */
- private double interpolateXAtY(WeightedObservedPoint[] points,
- int startIdx,
- int idxStep,
- double y)
- throws OutOfRangeException {
- if (idxStep == 0) {
- throw new ZeroException();
- }
- final WeightedObservedPoint[] twoPoints
- = getInterpolationPointsForY(points, startIdx, idxStep, y);
- final WeightedObservedPoint p1 = twoPoints[0];
- final WeightedObservedPoint p2 = twoPoints[1];
- if (p1.getY() == y) {
- return p1.getX();
- }
- if (p2.getY() == y) {
- return p2.getX();
- }
- return p1.getX() + (((y - p1.getY()) * (p2.getX() - p1.getX())) /
- (p2.getY() - p1.getY()));
- }
-
- /**
- * Gets the two bounding interpolation points from the specified points
- * suitable for determining X at the specified Y.
- *
- * @param points Points to use for interpolation.
- * @param startIdx Index within points from which to start search for
- * interpolation bounds points.
- * @param idxStep Index step for search for interpolation bounds points.
- * @param y Y value for which X should be determined.
- * @return the array containing two points suitable for determining X at
- * the specified Y.
- * @throws ZeroException if {@code idxStep} is 0.
- * @throws OutOfRangeException if specified {@code y} is not within the
- * range of the specified {@code points}.
- */
- private WeightedObservedPoint[] getInterpolationPointsForY(WeightedObservedPoint[] points,
- int startIdx,
- int idxStep,
- double y)
- throws OutOfRangeException {
- if (idxStep == 0) {
- throw new ZeroException();
- }
- for (int i = startIdx;
- idxStep < 0 ? i + idxStep >= 0 : i + idxStep < points.length;
- i += idxStep) {
- final WeightedObservedPoint p1 = points[i];
- final WeightedObservedPoint p2 = points[i + idxStep];
- if (isBetween(y, p1.getY(), p2.getY())) {
- if (idxStep < 0) {
- return new WeightedObservedPoint[] { p2, p1 };
- } else {
- return new WeightedObservedPoint[] { p1, p2 };
- }
- }
- }
-
- // Boundaries are replaced by dummy values because the raised
- // exception is caught and the message never displayed.
- // TODO: Exceptions should not be used for flow control.
- throw new OutOfRangeException(y,
- Double.NEGATIVE_INFINITY,
- Double.POSITIVE_INFINITY);
- }
-
- /**
- * Determines whether a value is between two other values.
- *
- * @param value Value to test whether it is between {@code boundary1}
- * and {@code boundary2}.
- * @param boundary1 One end of the range.
- * @param boundary2 Other end of the range.
- * @return {@code true} if {@code value} is between {@code boundary1} and
- * {@code boundary2} (inclusive), {@code false} otherwise.
- */
- private boolean isBetween(double value,
- double boundary1,
- double boundary2) {
- return (value >= boundary1 && value <= boundary2) ||
- (value >= boundary2 && value <= boundary1);
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/fitting/HarmonicFitter.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optimization/fitting/HarmonicFitter.java b/src/main/java/org/apache/commons/math4/optimization/fitting/HarmonicFitter.java
deleted file mode 100644
index 938156d..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/fitting/HarmonicFitter.java
+++ /dev/null
@@ -1,384 +0,0 @@
-/*
- * 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.math4.optimization.fitting;
-
-import org.apache.commons.math4.analysis.function.HarmonicOscillator;
-import org.apache.commons.math4.exception.MathIllegalStateException;
-import org.apache.commons.math4.exception.NumberIsTooSmallException;
-import org.apache.commons.math4.exception.ZeroException;
-import org.apache.commons.math4.exception.util.LocalizedFormats;
-import org.apache.commons.math4.optimization.DifferentiableMultivariateVectorOptimizer;
-import org.apache.commons.math4.util.FastMath;
-
-/**
- * Class that implements a curve fitting specialized for sinusoids.
- *
- * Harmonic fitting is a very simple case of curve fitting. The
- * estimated coefficients are the amplitude a, the pulsation ω and
- * the phase φ: <code>f (t) = a cos (ω t + φ)</code>. They are
- * searched by a least square estimator initialized with a rough guess
- * based on integrals.
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.0
- */
-@Deprecated
-public class HarmonicFitter extends CurveFitter<HarmonicOscillator.Parametric> {
- /**
- * Simple constructor.
- * @param optimizer Optimizer to use for the fitting.
- */
- public HarmonicFitter(final DifferentiableMultivariateVectorOptimizer optimizer) {
- super(optimizer);
- }
-
- /**
- * Fit an harmonic function to the observed points.
- *
- * @param initialGuess First guess values in the following order:
- * <ul>
- * <li>Amplitude</li>
- * <li>Angular frequency</li>
- * <li>Phase</li>
- * </ul>
- * @return the parameters of the harmonic function that best fits the
- * observed points (in the same order as above).
- */
- public double[] fit(double[] initialGuess) {
- return fit(new HarmonicOscillator.Parametric(), initialGuess);
- }
-
- /**
- * Fit an harmonic function to the observed points.
- * An initial guess will be automatically computed.
- *
- * @return the parameters of the harmonic function that best fits the
- * observed points (see the other {@link #fit(double[]) fit} method.
- * @throws NumberIsTooSmallException if the sample is too short for the
- * the first guess to be computed.
- * @throws ZeroException if the first guess cannot be computed because
- * the abscissa range is zero.
- */
- public double[] fit() {
- return fit((new ParameterGuesser(getObservations())).guess());
- }
-
- /**
- * This class guesses harmonic coefficients from a sample.
- * <p>The algorithm used to guess the coefficients is as follows:</p>
- *
- * <p>We know f (t) at some sampling points t<sub>i</sub> and want to find a,
- * ω and φ such that f (t) = a cos (ω t + φ).
- * </p>
- *
- * <p>From the analytical expression, we can compute two primitives :
- * <pre>
- * If2 (t) = ∫ f<sup>2</sup> = a<sup>2</sup> × [t + S (t)] / 2
- * If'2 (t) = ∫ f'<sup>2</sup> = a<sup>2</sup> ω<sup>2</sup> × [t - S (t)] / 2
- * where S (t) = sin (2 (ω t + φ)) / (2 ω)
- * </pre>
- * </p>
- *
- * <p>We can remove S between these expressions :
- * <pre>
- * If'2 (t) = a<sup>2</sup> ω<sup>2</sup> t - ω<sup>2</sup> If2 (t)
- * </pre>
- * </p>
- *
- * <p>The preceding expression shows that If'2 (t) is a linear
- * combination of both t and If2 (t): If'2 (t) = A × t + B × If2 (t)
- * </p>
- *
- * <p>From the primitive, we can deduce the same form for definite
- * integrals between t<sub>1</sub> and t<sub>i</sub> for each t<sub>i</sub> :
- * <pre>
- * If2 (t<sub>i</sub>) - If2 (t<sub>1</sub>) = A × (t<sub>i</sub> - t<sub>1</sub>) + B × (If2 (t<sub>i</sub>) - If2 (t<sub>1</sub>))
- * </pre>
- * </p>
- *
- * <p>We can find the coefficients A and B that best fit the sample
- * to this linear expression by computing the definite integrals for
- * each sample points.
- * </p>
- *
- * <p>For a bilinear expression z (x<sub>i</sub>, y<sub>i</sub>) = A × x<sub>i</sub> + B × y<sub>i</sub>, the
- * coefficients A and B that minimize a least square criterion
- * ∑ (z<sub>i</sub> - z (x<sub>i</sub>, y<sub>i</sub>))<sup>2</sup> are given by these expressions:</p>
- * <pre>
- *
- * ∑y<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub>
- * A = ------------------------
- * ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>y<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>y<sub>i</sub>
- *
- * ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub>
- * B = ------------------------
- * ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>y<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>y<sub>i</sub>
- * </pre>
- * </p>
- *
- *
- * <p>In fact, we can assume both a and ω are positive and
- * compute them directly, knowing that A = a<sup>2</sup> ω<sup>2</sup> and that
- * B = - ω<sup>2</sup>. The complete algorithm is therefore:</p>
- * <pre>
- *
- * for each t<sub>i</sub> from t<sub>1</sub> to t<sub>n-1</sub>, compute:
- * f (t<sub>i</sub>)
- * f' (t<sub>i</sub>) = (f (t<sub>i+1</sub>) - f(t<sub>i-1</sub>)) / (t<sub>i+1</sub> - t<sub>i-1</sub>)
- * x<sub>i</sub> = t<sub>i</sub> - t<sub>1</sub>
- * y<sub>i</sub> = ∫ f<sup>2</sup> from t<sub>1</sub> to t<sub>i</sub>
- * z<sub>i</sub> = ∫ f'<sup>2</sup> from t<sub>1</sub> to t<sub>i</sub>
- * update the sums ∑x<sub>i</sub>x<sub>i</sub>, ∑y<sub>i</sub>y<sub>i</sub>, ∑x<sub>i</sub>y<sub>i</sub>, ∑x<sub>i</sub>z<sub>i</sub> and ∑y<sub>i</sub>z<sub>i</sub>
- * end for
- *
- * |--------------------------
- * \ | ∑y<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub>
- * a = \ | ------------------------
- * \| ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub>
- *
- *
- * |--------------------------
- * \ | ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub>
- * ω = \ | ------------------------
- * \| ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>y<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>y<sub>i</sub>
- *
- * </pre>
- * </p>
- *
- * <p>Once we know ω, we can compute:
- * <pre>
- * fc = ω f (t) cos (ω t) - f' (t) sin (ω t)
- * fs = ω f (t) sin (ω t) + f' (t) cos (ω t)
- * </pre>
- * </p>
- *
- * <p>It appears that <code>fc = a ω cos (φ)</code> and
- * <code>fs = -a ω sin (φ)</code>, so we can use these
- * expressions to compute φ. The best estimate over the sample is
- * given by averaging these expressions.
- * </p>
- *
- * <p>Since integrals and means are involved in the preceding
- * estimations, these operations run in O(n) time, where n is the
- * number of measurements.</p>
- */
- public static class ParameterGuesser {
- /** Amplitude. */
- private final double a;
- /** Angular frequency. */
- private final double omega;
- /** Phase. */
- private final double phi;
-
- /**
- * Simple constructor.
- *
- * @param observations Sampled observations.
- * @throws NumberIsTooSmallException if the sample is too short.
- * @throws ZeroException if the abscissa range is zero.
- * @throws MathIllegalStateException when the guessing procedure cannot
- * produce sensible results.
- */
- public ParameterGuesser(WeightedObservedPoint[] observations) {
- if (observations.length < 4) {
- throw new NumberIsTooSmallException(LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE,
- observations.length, 4, true);
- }
-
- final WeightedObservedPoint[] sorted = sortObservations(observations);
-
- final double aOmega[] = guessAOmega(sorted);
- a = aOmega[0];
- omega = aOmega[1];
-
- phi = guessPhi(sorted);
- }
-
- /**
- * Gets an estimation of the parameters.
- *
- * @return the guessed parameters, in the following order:
- * <ul>
- * <li>Amplitude</li>
- * <li>Angular frequency</li>
- * <li>Phase</li>
- * </ul>
- */
- public double[] guess() {
- return new double[] { a, omega, phi };
- }
-
- /**
- * Sort the observations with respect to the abscissa.
- *
- * @param unsorted Input observations.
- * @return the input observations, sorted.
- */
- private WeightedObservedPoint[] sortObservations(WeightedObservedPoint[] unsorted) {
- final WeightedObservedPoint[] observations = unsorted.clone();
-
- // Since the samples are almost always already sorted, this
- // method is implemented as an insertion sort that reorders the
- // elements in place. Insertion sort is very efficient in this case.
- WeightedObservedPoint curr = observations[0];
- for (int j = 1; j < observations.length; ++j) {
- WeightedObservedPoint prec = curr;
- curr = observations[j];
- if (curr.getX() < prec.getX()) {
- // the current element should be inserted closer to the beginning
- int i = j - 1;
- WeightedObservedPoint mI = observations[i];
- while ((i >= 0) && (curr.getX() < mI.getX())) {
- observations[i + 1] = mI;
- if (i-- != 0) {
- mI = observations[i];
- }
- }
- observations[i + 1] = curr;
- curr = observations[j];
- }
- }
-
- return observations;
- }
-
- /**
- * Estimate a first guess of the amplitude and angular frequency.
- * This method assumes that the {@link #sortObservations(WeightedObservedPoint[])} method
- * has been called previously.
- *
- * @param observations Observations, sorted w.r.t. abscissa.
- * @throws ZeroException if the abscissa range is zero.
- * @throws MathIllegalStateException when the guessing procedure cannot
- * produce sensible results.
- * @return the guessed amplitude (at index 0) and circular frequency
- * (at index 1).
- */
- private double[] guessAOmega(WeightedObservedPoint[] observations) {
- final double[] aOmega = new double[2];
-
- // initialize the sums for the linear model between the two integrals
- double sx2 = 0;
- double sy2 = 0;
- double sxy = 0;
- double sxz = 0;
- double syz = 0;
-
- double currentX = observations[0].getX();
- double currentY = observations[0].getY();
- double f2Integral = 0;
- double fPrime2Integral = 0;
- final double startX = currentX;
- for (int i = 1; i < observations.length; ++i) {
- // one step forward
- final double previousX = currentX;
- final double previousY = currentY;
- currentX = observations[i].getX();
- currentY = observations[i].getY();
-
- // update the integrals of f<sup>2</sup> and f'<sup>2</sup>
- // considering a linear model for f (and therefore constant f')
- final double dx = currentX - previousX;
- final double dy = currentY - previousY;
- final double f2StepIntegral =
- dx * (previousY * previousY + previousY * currentY + currentY * currentY) / 3;
- final double fPrime2StepIntegral = dy * dy / dx;
-
- final double x = currentX - startX;
- f2Integral += f2StepIntegral;
- fPrime2Integral += fPrime2StepIntegral;
-
- sx2 += x * x;
- sy2 += f2Integral * f2Integral;
- sxy += x * f2Integral;
- sxz += x * fPrime2Integral;
- syz += f2Integral * fPrime2Integral;
- }
-
- // compute the amplitude and pulsation coefficients
- double c1 = sy2 * sxz - sxy * syz;
- double c2 = sxy * sxz - sx2 * syz;
- double c3 = sx2 * sy2 - sxy * sxy;
- if ((c1 / c2 < 0) || (c2 / c3 < 0)) {
- final int last = observations.length - 1;
- // Range of the observations, assuming that the
- // observations are sorted.
- final double xRange = observations[last].getX() - observations[0].getX();
- if (xRange == 0) {
- throw new ZeroException();
- }
- aOmega[1] = 2 * Math.PI / xRange;
-
- double yMin = Double.POSITIVE_INFINITY;
- double yMax = Double.NEGATIVE_INFINITY;
- for (int i = 1; i < observations.length; ++i) {
- final double y = observations[i].getY();
- if (y < yMin) {
- yMin = y;
- }
- if (y > yMax) {
- yMax = y;
- }
- }
- aOmega[0] = 0.5 * (yMax - yMin);
- } else {
- if (c2 == 0) {
- // In some ill-conditioned cases (cf. MATH-844), the guesser
- // procedure cannot produce sensible results.
- throw new MathIllegalStateException(LocalizedFormats.ZERO_DENOMINATOR);
- }
-
- aOmega[0] = FastMath.sqrt(c1 / c2);
- aOmega[1] = FastMath.sqrt(c2 / c3);
- }
-
- return aOmega;
- }
-
- /**
- * Estimate a first guess of the phase.
- *
- * @param observations Observations, sorted w.r.t. abscissa.
- * @return the guessed phase.
- */
- private double guessPhi(WeightedObservedPoint[] observations) {
- // initialize the means
- double fcMean = 0;
- double fsMean = 0;
-
- double currentX = observations[0].getX();
- double currentY = observations[0].getY();
- for (int i = 1; i < observations.length; ++i) {
- // one step forward
- final double previousX = currentX;
- final double previousY = currentY;
- currentX = observations[i].getX();
- currentY = observations[i].getY();
- final double currentYPrime = (currentY - previousY) / (currentX - previousX);
-
- double omegaX = omega * currentX;
- double cosine = FastMath.cos(omegaX);
- double sine = FastMath.sin(omegaX);
- fcMean += omega * currentY * cosine - currentYPrime * sine;
- fsMean += omega * currentY * sine + currentYPrime * cosine;
- }
-
- return FastMath.atan2(-fsMean, fcMean);
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/fitting/PolynomialFitter.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optimization/fitting/PolynomialFitter.java b/src/main/java/org/apache/commons/math4/optimization/fitting/PolynomialFitter.java
deleted file mode 100644
index 3773acb..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/fitting/PolynomialFitter.java
+++ /dev/null
@@ -1,111 +0,0 @@
-/*
- * 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.math4.optimization.fitting;
-
-import org.apache.commons.math4.analysis.polynomials.PolynomialFunction;
-import org.apache.commons.math4.optimization.DifferentiableMultivariateVectorOptimizer;
-
-/**
- * Polynomial fitting is a very simple case of {@link CurveFitter curve fitting}.
- * The estimated coefficients are the polynomial coefficients (see the
- * {@link #fit(double[]) fit} method).
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.0
- */
-@Deprecated
-public class PolynomialFitter extends CurveFitter<PolynomialFunction.Parametric> {
- /** Polynomial degree.
- * @deprecated
- */
- @Deprecated
- private final int degree;
-
- /**
- * Simple constructor.
- * <p>The polynomial fitter built this way are complete polynomials,
- * ie. a n-degree polynomial has n+1 coefficients.</p>
- *
- * @param degree Maximal degree of the polynomial.
- * @param optimizer Optimizer to use for the fitting.
- * @deprecated Since 3.1 (to be removed in 4.0). Please use
- * {@link #PolynomialFitter(DifferentiableMultivariateVectorOptimizer)} instead.
- */
- @Deprecated
- public PolynomialFitter(int degree, final DifferentiableMultivariateVectorOptimizer optimizer) {
- super(optimizer);
- this.degree = degree;
- }
-
- /**
- * Simple constructor.
- *
- * @param optimizer Optimizer to use for the fitting.
- * @since 3.1
- */
- public PolynomialFitter(DifferentiableMultivariateVectorOptimizer optimizer) {
- super(optimizer);
- degree = -1; // To avoid compilation error until the instance variable is removed.
- }
-
- /**
- * Get the polynomial fitting the weighted (x, y) points.
- *
- * @return the coefficients of the polynomial that best fits the observed points.
- * @throws org.apache.commons.math4.exception.ConvergenceException
- * if the algorithm failed to converge.
- * @deprecated Since 3.1 (to be removed in 4.0). Please use {@link #fit(double[])} instead.
- */
- @Deprecated
- public double[] fit() {
- return fit(new PolynomialFunction.Parametric(), new double[degree + 1]);
- }
-
- /**
- * Get the coefficients of the polynomial fitting the weighted data points.
- * The degree of the fitting polynomial is {@code guess.length - 1}.
- *
- * @param guess First guess for the coefficients. They must be sorted in
- * increasing order of the polynomial's degree.
- * @param maxEval Maximum number of evaluations of the polynomial.
- * @return the coefficients of the polynomial that best fits the observed points.
- * @throws org.apache.commons.math4.exception.TooManyEvaluationsException if
- * the number of evaluations exceeds {@code maxEval}.
- * @throws org.apache.commons.math4.exception.ConvergenceException
- * if the algorithm failed to converge.
- * @since 3.1
- */
- public double[] fit(int maxEval, double[] guess) {
- return fit(maxEval, new PolynomialFunction.Parametric(), guess);
- }
-
- /**
- * Get the coefficients of the polynomial fitting the weighted data points.
- * The degree of the fitting polynomial is {@code guess.length - 1}.
- *
- * @param guess First guess for the coefficients. They must be sorted in
- * increasing order of the polynomial's degree.
- * @return the coefficients of the polynomial that best fits the observed points.
- * @throws org.apache.commons.math4.exception.ConvergenceException
- * if the algorithm failed to converge.
- * @since 3.1
- */
- public double[] fit(double[] guess) {
- return fit(new PolynomialFunction.Parametric(), guess);
- }
-}