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Posted to commits@commons.apache.org by tn...@apache.org on 2015/02/25 22:49:39 UTC
[11/18] [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/fitting/WeightedObservedPoint.java
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diff --git a/src/main/java/org/apache/commons/math4/optimization/fitting/WeightedObservedPoint.java b/src/main/java/org/apache/commons/math4/optimization/fitting/WeightedObservedPoint.java
deleted file mode 100644
index 5c2c6d2..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/fitting/WeightedObservedPoint.java
+++ /dev/null
@@ -1,76 +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.io.Serializable;
-
-/** This class is a simple container for weighted observed point in
- * {@link CurveFitter curve fitting}.
- * <p>Instances of this class are guaranteed to be immutable.</p>
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.0
- */
-@Deprecated
-public class WeightedObservedPoint implements Serializable {
-
- /** Serializable version id. */
- private static final long serialVersionUID = 5306874947404636157L;
-
- /** Weight of the measurement in the fitting process. */
- private final double weight;
-
- /** Abscissa of the point. */
- private final double x;
-
- /** Observed value of the function at x. */
- private final double y;
-
- /** Simple constructor.
- * @param weight weight of the measurement in the fitting process
- * @param x abscissa of the measurement
- * @param y ordinate of the measurement
- */
- public WeightedObservedPoint(final double weight, final double x, final double y) {
- this.weight = weight;
- this.x = x;
- this.y = y;
- }
-
- /** Get the weight of the measurement in the fitting process.
- * @return weight of the measurement in the fitting process
- */
- public double getWeight() {
- return weight;
- }
-
- /** Get the abscissa of the point.
- * @return abscissa of the point
- */
- public double getX() {
- return x;
- }
-
- /** Get the observed value of the function at x.
- * @return observed value of the function at x
- */
- public double getY() {
- return y;
- }
-
-}
-
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/fitting/package-info.java
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diff --git a/src/main/java/org/apache/commons/math4/optimization/fitting/package-info.java b/src/main/java/org/apache/commons/math4/optimization/fitting/package-info.java
deleted file mode 100644
index 98683a8..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/fitting/package-info.java
+++ /dev/null
@@ -1,30 +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.
- */
-/**
- *
- * This package provides classes to perform curve fitting.
- *
- * <p>Curve fitting is a special case of a least squares problem
- * were the parameters are the coefficients of a function <code>f</code>
- * whose graph <code>y=f(x)</code> should pass through sample points, and
- * were the objective function is the squared sum of residuals
- * <code>f(x<sub>i</sub>)-y<sub>i</sub></code> for observed points
- * (x<sub>i</sub>, y<sub>i</sub>).</p>
- *
- *
- */
-package org.apache.commons.math4.optimization.fitting;
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/AbstractDifferentiableOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optimization/general/AbstractDifferentiableOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/general/AbstractDifferentiableOptimizer.java
deleted file mode 100644
index 1fbcbcb..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/general/AbstractDifferentiableOptimizer.java
+++ /dev/null
@@ -1,90 +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.general;
-
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.analysis.differentiation.GradientFunction;
-import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableFunction;
-import org.apache.commons.math4.optimization.ConvergenceChecker;
-import org.apache.commons.math4.optimization.GoalType;
-import org.apache.commons.math4.optimization.InitialGuess;
-import org.apache.commons.math4.optimization.OptimizationData;
-import org.apache.commons.math4.optimization.PointValuePair;
-import org.apache.commons.math4.optimization.direct.BaseAbstractMultivariateOptimizer;
-
-/**
- * Base class for implementing optimizers for multivariate scalar
- * differentiable functions.
- * It contains boiler-plate code for dealing with gradient evaluation.
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 3.1
- */
-@Deprecated
-public abstract class AbstractDifferentiableOptimizer
- extends BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction> {
- /**
- * Objective function gradient.
- */
- private MultivariateVectorFunction gradient;
-
- /**
- * @param checker Convergence checker.
- */
- protected AbstractDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) {
- super(checker);
- }
-
- /**
- * Compute the gradient vector.
- *
- * @param evaluationPoint Point at which the gradient must be evaluated.
- * @return the gradient at the specified point.
- */
- protected double[] computeObjectiveGradient(final double[] evaluationPoint) {
- return gradient.value(evaluationPoint);
- }
-
- /**
- * {@inheritDoc}
- *
- * @deprecated In 3.1. Please use
- * {@link #optimizeInternal(int,MultivariateDifferentiableFunction,GoalType,OptimizationData[])}
- * instead.
- */
- @Override@Deprecated
- protected PointValuePair optimizeInternal(final int maxEval,
- final MultivariateDifferentiableFunction f,
- final GoalType goalType,
- final double[] startPoint) {
- return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint));
- }
-
- /** {@inheritDoc} */
- @Override
- protected PointValuePair optimizeInternal(final int maxEval,
- final MultivariateDifferentiableFunction f,
- final GoalType goalType,
- final OptimizationData... optData) {
- // Store optimization problem characteristics.
- gradient = new GradientFunction(f);
-
- // Perform optimization.
- return super.optimizeInternal(maxEval, f, goalType, optData);
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/AbstractLeastSquaresOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optimization/general/AbstractLeastSquaresOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/general/AbstractLeastSquaresOptimizer.java
deleted file mode 100644
index 3bc9a05..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/general/AbstractLeastSquaresOptimizer.java
+++ /dev/null
@@ -1,577 +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.general;
-
-import org.apache.commons.math4.analysis.DifferentiableMultivariateVectorFunction;
-import org.apache.commons.math4.analysis.FunctionUtils;
-import org.apache.commons.math4.analysis.differentiation.DerivativeStructure;
-import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableVectorFunction;
-import org.apache.commons.math4.exception.DimensionMismatchException;
-import org.apache.commons.math4.exception.NumberIsTooSmallException;
-import org.apache.commons.math4.exception.util.LocalizedFormats;
-import org.apache.commons.math4.linear.ArrayRealVector;
-import org.apache.commons.math4.linear.DecompositionSolver;
-import org.apache.commons.math4.linear.DiagonalMatrix;
-import org.apache.commons.math4.linear.EigenDecomposition;
-import org.apache.commons.math4.linear.MatrixUtils;
-import org.apache.commons.math4.linear.QRDecomposition;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optimization.ConvergenceChecker;
-import org.apache.commons.math4.optimization.DifferentiableMultivariateVectorOptimizer;
-import org.apache.commons.math4.optimization.InitialGuess;
-import org.apache.commons.math4.optimization.OptimizationData;
-import org.apache.commons.math4.optimization.PointVectorValuePair;
-import org.apache.commons.math4.optimization.Target;
-import org.apache.commons.math4.optimization.Weight;
-import org.apache.commons.math4.optimization.direct.BaseAbstractMultivariateVectorOptimizer;
-import org.apache.commons.math4.util.FastMath;
-
-/**
- * Base class for implementing least squares optimizers.
- * It handles the boilerplate methods associated to thresholds settings,
- * Jacobian and error estimation.
- * <br/>
- * This class constructs the Jacobian matrix of the function argument in method
- * {@link BaseAbstractMultivariateVectorOptimizer#optimize(int,
- * org.apache.commons.math4.analysis.MultivariateVectorFunction,OptimizationData[])
- * optimize} and assumes that the rows of that matrix iterate on the model
- * functions while the columns iterate on the parameters; thus, the numbers
- * of rows is equal to the dimension of the
- * {@link org.apache.commons.math4.optimization.Target Target} while
- * the number of columns is equal to the dimension of the
- * {@link org.apache.commons.math4.optimization.InitialGuess InitialGuess}.
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 1.2
- */
-@Deprecated
-public abstract class AbstractLeastSquaresOptimizer
- extends BaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>
- implements DifferentiableMultivariateVectorOptimizer {
- /**
- * Singularity threshold (cf. {@link #getCovariances(double)}).
- * @deprecated As of 3.1.
- */
- @Deprecated
- private static final double DEFAULT_SINGULARITY_THRESHOLD = 1e-14;
- /**
- * Jacobian matrix of the weighted residuals.
- * This matrix is in canonical form just after the calls to
- * {@link #updateJacobian()}, but may be modified by the solver
- * in the derived class (the {@link LevenbergMarquardtOptimizer
- * Levenberg-Marquardt optimizer} does this).
- * @deprecated As of 3.1. To be removed in 4.0. Please use
- * {@link #computeWeightedJacobian(double[])} instead.
- */
- @Deprecated
- protected double[][] weightedResidualJacobian;
- /** Number of columns of the jacobian matrix.
- * @deprecated As of 3.1.
- */
- @Deprecated
- protected int cols;
- /** Number of rows of the jacobian matrix.
- * @deprecated As of 3.1.
- */
- @Deprecated
- protected int rows;
- /** Current point.
- * @deprecated As of 3.1.
- */
- @Deprecated
- protected double[] point;
- /** Current objective function value.
- * @deprecated As of 3.1.
- */
- @Deprecated
- protected double[] objective;
- /** Weighted residuals
- * @deprecated As of 3.1.
- */
- @Deprecated
- protected double[] weightedResiduals;
- /** Cost value (square root of the sum of the residuals).
- * @deprecated As of 3.1. Field to become "private" in 4.0.
- * Please use {@link #setCost(double)}.
- */
- @Deprecated
- protected double cost;
- /** Objective function derivatives. */
- private MultivariateDifferentiableVectorFunction jF;
- /** Number of evaluations of the Jacobian. */
- private int jacobianEvaluations;
- /** Square-root of the weight matrix. */
- private RealMatrix weightMatrixSqrt;
-
- /**
- * Simple constructor with default settings.
- * The convergence check is set to a {@link
- * org.apache.commons.math4.optimization.SimpleVectorValueChecker}.
- * @deprecated See {@link org.apache.commons.math4.optimization.SimpleValueChecker#SimpleValueChecker()}
- */
- @Deprecated
- protected AbstractLeastSquaresOptimizer() {}
-
- /**
- * @param checker Convergence checker.
- */
- protected AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
- super(checker);
- }
-
- /**
- * @return the number of evaluations of the Jacobian function.
- */
- public int getJacobianEvaluations() {
- return jacobianEvaluations;
- }
-
- /**
- * Update the jacobian matrix.
- *
- * @throws DimensionMismatchException if the Jacobian dimension does not
- * match problem dimension.
- * @deprecated As of 3.1. Please use {@link #computeWeightedJacobian(double[])}
- * instead.
- */
- @Deprecated
- protected void updateJacobian() {
- final RealMatrix weightedJacobian = computeWeightedJacobian(point);
- weightedResidualJacobian = weightedJacobian.scalarMultiply(-1).getData();
- }
-
- /**
- * Computes the Jacobian matrix.
- *
- * @param params Model parameters at which to compute the Jacobian.
- * @return the weighted Jacobian: W<sup>1/2</sup> J.
- * @throws DimensionMismatchException if the Jacobian dimension does not
- * match problem dimension.
- * @since 3.1
- */
- protected RealMatrix computeWeightedJacobian(double[] params) {
- ++jacobianEvaluations;
-
- final DerivativeStructure[] dsPoint = new DerivativeStructure[params.length];
- final int nC = params.length;
- for (int i = 0; i < nC; ++i) {
- dsPoint[i] = new DerivativeStructure(nC, 1, i, params[i]);
- }
- final DerivativeStructure[] dsValue = jF.value(dsPoint);
- final int nR = getTarget().length;
- if (dsValue.length != nR) {
- throw new DimensionMismatchException(dsValue.length, nR);
- }
- final double[][] jacobianData = new double[nR][nC];
- for (int i = 0; i < nR; ++i) {
- int[] orders = new int[nC];
- for (int j = 0; j < nC; ++j) {
- orders[j] = 1;
- jacobianData[i][j] = dsValue[i].getPartialDerivative(orders);
- orders[j] = 0;
- }
- }
-
- return weightMatrixSqrt.multiply(MatrixUtils.createRealMatrix(jacobianData));
- }
-
- /**
- * Update the residuals array and cost function value.
- * @throws DimensionMismatchException if the dimension does not match the
- * problem dimension.
- * @throws org.apache.commons.math4.exception.TooManyEvaluationsException
- * if the maximal number of evaluations is exceeded.
- * @deprecated As of 3.1. Please use {@link #computeResiduals(double[])},
- * {@link #computeObjectiveValue(double[])}, {@link #computeCost(double[])}
- * and {@link #setCost(double)} instead.
- */
- @Deprecated
- protected void updateResidualsAndCost() {
- objective = computeObjectiveValue(point);
- final double[] res = computeResiduals(objective);
-
- // Compute cost.
- cost = computeCost(res);
-
- // Compute weighted residuals.
- final ArrayRealVector residuals = new ArrayRealVector(res);
- weightedResiduals = weightMatrixSqrt.operate(residuals).toArray();
- }
-
- /**
- * Computes the cost.
- *
- * @param residuals Residuals.
- * @return the cost.
- * @see #computeResiduals(double[])
- * @since 3.1
- */
- protected double computeCost(double[] residuals) {
- final ArrayRealVector r = new ArrayRealVector(residuals);
- return FastMath.sqrt(r.dotProduct(getWeight().operate(r)));
- }
-
- /**
- * Get the Root Mean Square value.
- * Get the Root Mean Square value, i.e. the root of the arithmetic
- * mean of the square of all weighted residuals. This is related to the
- * criterion that is minimized by the optimizer as follows: if
- * <em>c</em> if the criterion, and <em>n</em> is the number of
- * measurements, then the RMS is <em>sqrt (c/n)</em>.
- *
- * @return RMS value
- */
- public double getRMS() {
- return FastMath.sqrt(getChiSquare() / rows);
- }
-
- /**
- * Get a Chi-Square-like value assuming the N residuals follow N
- * distinct normal distributions centered on 0 and whose variances are
- * the reciprocal of the weights.
- * @return chi-square value
- */
- public double getChiSquare() {
- return cost * cost;
- }
-
- /**
- * Gets the square-root of the weight matrix.
- *
- * @return the square-root of the weight matrix.
- * @since 3.1
- */
- public RealMatrix getWeightSquareRoot() {
- return weightMatrixSqrt.copy();
- }
-
- /**
- * Sets the cost.
- *
- * @param cost Cost value.
- * @since 3.1
- */
- protected void setCost(double cost) {
- this.cost = cost;
- }
-
- /**
- * Get the covariance matrix of the optimized parameters.
- *
- * @return the covariance matrix.
- * @throws org.apache.commons.math4.linear.SingularMatrixException
- * if the covariance matrix cannot be computed (singular problem).
- * @see #getCovariances(double)
- * @deprecated As of 3.1. Please use {@link #computeCovariances(double[],double)}
- * instead.
- */
- @Deprecated
- public double[][] getCovariances() {
- return getCovariances(DEFAULT_SINGULARITY_THRESHOLD);
- }
-
- /**
- * Get the covariance matrix of the optimized parameters.
- * <br/>
- * Note that this operation involves the inversion of the
- * <code>J<sup>T</sup>J</code> matrix, where {@code J} is the
- * Jacobian matrix.
- * The {@code threshold} parameter is a way for the caller to specify
- * that the result of this computation should be considered meaningless,
- * and thus trigger an exception.
- *
- * @param threshold Singularity threshold.
- * @return the covariance matrix.
- * @throws org.apache.commons.math4.linear.SingularMatrixException
- * if the covariance matrix cannot be computed (singular problem).
- * @deprecated As of 3.1. Please use {@link #computeCovariances(double[],double)}
- * instead.
- */
- @Deprecated
- public double[][] getCovariances(double threshold) {
- return computeCovariances(point, threshold);
- }
-
- /**
- * Get the covariance matrix of the optimized parameters.
- * <br/>
- * Note that this operation involves the inversion of the
- * <code>J<sup>T</sup>J</code> matrix, where {@code J} is the
- * Jacobian matrix.
- * The {@code threshold} parameter is a way for the caller to specify
- * that the result of this computation should be considered meaningless,
- * and thus trigger an exception.
- *
- * @param params Model parameters.
- * @param threshold Singularity threshold.
- * @return the covariance matrix.
- * @throws org.apache.commons.math4.linear.SingularMatrixException
- * if the covariance matrix cannot be computed (singular problem).
- * @since 3.1
- */
- public double[][] computeCovariances(double[] params,
- double threshold) {
- // Set up the Jacobian.
- final RealMatrix j = computeWeightedJacobian(params);
-
- // Compute transpose(J)J.
- final RealMatrix jTj = j.transpose().multiply(j);
-
- // Compute the covariances matrix.
- final DecompositionSolver solver
- = new QRDecomposition(jTj, threshold).getSolver();
- return solver.getInverse().getData();
- }
-
- /**
- * <p>
- * Returns an estimate of the standard deviation of each parameter. The
- * returned values are the so-called (asymptotic) standard errors on the
- * parameters, defined as {@code sd(a[i]) = sqrt(S / (n - m) * C[i][i])},
- * where {@code a[i]} is the optimized value of the {@code i}-th parameter,
- * {@code S} is the minimized value of the sum of squares objective function
- * (as returned by {@link #getChiSquare()}), {@code n} is the number of
- * observations, {@code m} is the number of parameters and {@code C} is the
- * covariance matrix.
- * </p>
- * <p>
- * See also
- * <a href="http://en.wikipedia.org/wiki/Least_squares">Wikipedia</a>,
- * or
- * <a href="http://mathworld.wolfram.com/LeastSquaresFitting.html">MathWorld</a>,
- * equations (34) and (35) for a particular case.
- * </p>
- *
- * @return an estimate of the standard deviation of the optimized parameters
- * @throws org.apache.commons.math4.linear.SingularMatrixException
- * if the covariance matrix cannot be computed.
- * @throws NumberIsTooSmallException if the number of degrees of freedom is not
- * positive, i.e. the number of measurements is less or equal to the number of
- * parameters.
- * @deprecated as of version 3.1, {@link #computeSigma(double[],double)} should be used
- * instead. It should be emphasized that {@code guessParametersErrors} and
- * {@code computeSigma} are <em>not</em> strictly equivalent.
- */
- @Deprecated
- public double[] guessParametersErrors() {
- if (rows <= cols) {
- throw new NumberIsTooSmallException(LocalizedFormats.NO_DEGREES_OF_FREEDOM,
- rows, cols, false);
- }
- double[] errors = new double[cols];
- final double c = FastMath.sqrt(getChiSquare() / (rows - cols));
- double[][] covar = computeCovariances(point, 1e-14);
- for (int i = 0; i < errors.length; ++i) {
- errors[i] = FastMath.sqrt(covar[i][i]) * c;
- }
- return errors;
- }
-
- /**
- * Computes an estimate of the standard deviation of the parameters. The
- * returned values are the square root of the diagonal coefficients of the
- * covariance matrix, {@code sd(a[i]) ~= sqrt(C[i][i])}, where {@code a[i]}
- * is the optimized value of the {@code i}-th parameter, and {@code C} is
- * the covariance matrix.
- *
- * @param params Model parameters.
- * @param covarianceSingularityThreshold Singularity threshold (see
- * {@link #computeCovariances(double[],double) computeCovariances}).
- * @return an estimate of the standard deviation of the optimized parameters
- * @throws org.apache.commons.math4.linear.SingularMatrixException
- * if the covariance matrix cannot be computed.
- * @since 3.1
- */
- public double[] computeSigma(double[] params,
- double covarianceSingularityThreshold) {
- final int nC = params.length;
- final double[] sig = new double[nC];
- final double[][] cov = computeCovariances(params, covarianceSingularityThreshold);
- for (int i = 0; i < nC; ++i) {
- sig[i] = FastMath.sqrt(cov[i][i]);
- }
- return sig;
- }
-
- /** {@inheritDoc}
- * @deprecated As of 3.1. Please use
- * {@link BaseAbstractMultivariateVectorOptimizer#optimize(int,
- * org.apache.commons.math4.analysis.MultivariateVectorFunction,OptimizationData[])
- * optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)}
- * instead.
- */
- @Override
- @Deprecated
- public PointVectorValuePair optimize(int maxEval,
- final DifferentiableMultivariateVectorFunction f,
- final double[] target, final double[] weights,
- final double[] startPoint) {
- return optimizeInternal(maxEval,
- FunctionUtils.toMultivariateDifferentiableVectorFunction(f),
- new Target(target),
- new Weight(weights),
- new InitialGuess(startPoint));
- }
-
- /**
- * Optimize an objective function.
- * Optimization is considered to be a weighted least-squares minimization.
- * The cost function to be minimized is
- * <code>∑weight<sub>i</sub>(objective<sub>i</sub> - target<sub>i</sub>)<sup>2</sup></code>
- *
- * @param f Objective function.
- * @param target Target value for the objective functions at optimum.
- * @param weights Weights for the least squares cost computation.
- * @param startPoint Start point for optimization.
- * @return the point/value pair giving the optimal value for objective
- * function.
- * @param maxEval Maximum number of function evaluations.
- * @throws org.apache.commons.math4.exception.DimensionMismatchException
- * if the start point dimension is wrong.
- * @throws org.apache.commons.math4.exception.TooManyEvaluationsException
- * if the maximal number of evaluations is exceeded.
- * @throws org.apache.commons.math4.exception.NullArgumentException if
- * any argument is {@code null}.
- * @deprecated As of 3.1. Please use
- * {@link BaseAbstractMultivariateVectorOptimizer#optimize(int,
- * org.apache.commons.math4.analysis.MultivariateVectorFunction,OptimizationData[])
- * optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)}
- * instead.
- */
- @Deprecated
- public PointVectorValuePair optimize(final int maxEval,
- final MultivariateDifferentiableVectorFunction f,
- final double[] target, final double[] weights,
- final double[] startPoint) {
- return optimizeInternal(maxEval, f,
- new Target(target),
- new Weight(weights),
- new InitialGuess(startPoint));
- }
-
- /**
- * Optimize an objective function.
- * Optimization is considered to be a weighted least-squares minimization.
- * The cost function to be minimized is
- * <code>∑weight<sub>i</sub>(objective<sub>i</sub> - target<sub>i</sub>)<sup>2</sup></code>
- *
- * @param maxEval Allowed number of evaluations of the objective function.
- * @param f Objective function.
- * @param optData Optimization data. The following data will be looked for:
- * <ul>
- * <li>{@link Target}</li>
- * <li>{@link Weight}</li>
- * <li>{@link InitialGuess}</li>
- * </ul>
- * @return the point/value pair giving the optimal value of the objective
- * function.
- * @throws org.apache.commons.math4.exception.TooManyEvaluationsException if
- * the maximal number of evaluations is exceeded.
- * @throws DimensionMismatchException if the target, and weight arguments
- * have inconsistent dimensions.
- * @see BaseAbstractMultivariateVectorOptimizer#optimizeInternal(int,
- * org.apache.commons.math4.analysis.MultivariateVectorFunction,OptimizationData[])
- * @since 3.1
- * @deprecated As of 3.1. Override is necessary only until this class's generic
- * argument is changed to {@code MultivariateDifferentiableVectorFunction}.
- */
- @Deprecated
- protected PointVectorValuePair optimizeInternal(final int maxEval,
- final MultivariateDifferentiableVectorFunction f,
- OptimizationData... optData) {
- // XXX Conversion will be removed when the generic argument of the
- // base class becomes "MultivariateDifferentiableVectorFunction".
- return super.optimizeInternal(maxEval, FunctionUtils.toDifferentiableMultivariateVectorFunction(f), optData);
- }
-
- /** {@inheritDoc} */
- @Override
- protected void setUp() {
- super.setUp();
-
- // Reset counter.
- jacobianEvaluations = 0;
-
- // Square-root of the weight matrix.
- weightMatrixSqrt = squareRoot(getWeight());
-
- // Store least squares problem characteristics.
- // XXX The conversion won't be necessary when the generic argument of
- // the base class becomes "MultivariateDifferentiableVectorFunction".
- // XXX "jF" is not strictly necessary anymore but is currently more
- // efficient than converting the value returned from "getObjectiveFunction()"
- // every time it is used.
- jF = FunctionUtils.toMultivariateDifferentiableVectorFunction((DifferentiableMultivariateVectorFunction) getObjectiveFunction());
-
- // Arrays shared with "private" and "protected" methods.
- point = getStartPoint();
- rows = getTarget().length;
- cols = point.length;
- }
-
- /**
- * Computes the residuals.
- * The residual is the difference between the observed (target)
- * values and the model (objective function) value.
- * There is one residual for each element of the vector-valued
- * function.
- *
- * @param objectiveValue Value of the the objective function. This is
- * the value returned from a call to
- * {@link #computeObjectiveValue(double[]) computeObjectiveValue}
- * (whose array argument contains the model parameters).
- * @return the residuals.
- * @throws DimensionMismatchException if {@code params} has a wrong
- * length.
- * @since 3.1
- */
- protected double[] computeResiduals(double[] objectiveValue) {
- final double[] target = getTarget();
- if (objectiveValue.length != target.length) {
- throw new DimensionMismatchException(target.length,
- objectiveValue.length);
- }
-
- final double[] residuals = new double[target.length];
- for (int i = 0; i < target.length; i++) {
- residuals[i] = target[i] - objectiveValue[i];
- }
-
- return residuals;
- }
-
- /**
- * Computes the square-root of the weight matrix.
- *
- * @param m Symmetric, positive-definite (weight) matrix.
- * @return the square-root of the weight matrix.
- */
- private RealMatrix squareRoot(RealMatrix m) {
- if (m instanceof DiagonalMatrix) {
- final int dim = m.getRowDimension();
- final RealMatrix sqrtM = new DiagonalMatrix(dim);
- for (int i = 0; i < dim; i++) {
- sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
- }
- return sqrtM;
- } else {
- final EigenDecomposition dec = new EigenDecomposition(m);
- return dec.getSquareRoot();
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/AbstractScalarDifferentiableOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optimization/general/AbstractScalarDifferentiableOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/general/AbstractScalarDifferentiableOptimizer.java
deleted file mode 100644
index 1bb8cc0..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/general/AbstractScalarDifferentiableOptimizer.java
+++ /dev/null
@@ -1,114 +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.general;
-
-import org.apache.commons.math4.analysis.DifferentiableMultivariateFunction;
-import org.apache.commons.math4.analysis.FunctionUtils;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableFunction;
-import org.apache.commons.math4.optimization.ConvergenceChecker;
-import org.apache.commons.math4.optimization.DifferentiableMultivariateOptimizer;
-import org.apache.commons.math4.optimization.GoalType;
-import org.apache.commons.math4.optimization.PointValuePair;
-import org.apache.commons.math4.optimization.direct.BaseAbstractMultivariateOptimizer;
-
-/**
- * Base class for implementing optimizers for multivariate scalar
- * differentiable functions.
- * It contains boiler-plate code for dealing with gradient evaluation.
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.0
- */
-@Deprecated
-public abstract class AbstractScalarDifferentiableOptimizer
- extends BaseAbstractMultivariateOptimizer<DifferentiableMultivariateFunction>
- implements DifferentiableMultivariateOptimizer {
- /**
- * Objective function gradient.
- */
- private MultivariateVectorFunction gradient;
-
- /**
- * Simple constructor with default settings.
- * The convergence check is set to a
- * {@link org.apache.commons.math4.optimization.SimpleValueChecker
- * SimpleValueChecker}.
- * @deprecated See {@link org.apache.commons.math4.optimization.SimpleValueChecker#SimpleValueChecker()}
- */
- @Deprecated
- protected AbstractScalarDifferentiableOptimizer() {}
-
- /**
- * @param checker Convergence checker.
- */
- protected AbstractScalarDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) {
- super(checker);
- }
-
- /**
- * Compute the gradient vector.
- *
- * @param evaluationPoint Point at which the gradient must be evaluated.
- * @return the gradient at the specified point.
- * @throws org.apache.commons.math4.exception.TooManyEvaluationsException
- * if the allowed number of evaluations is exceeded.
- */
- protected double[] computeObjectiveGradient(final double[] evaluationPoint) {
- return gradient.value(evaluationPoint);
- }
-
- /** {@inheritDoc} */
- @Override
- protected PointValuePair optimizeInternal(int maxEval,
- final DifferentiableMultivariateFunction f,
- final GoalType goalType,
- final double[] startPoint) {
- // Store optimization problem characteristics.
- gradient = f.gradient();
-
- return super.optimizeInternal(maxEval, f, goalType, startPoint);
- }
-
- /**
- * Optimize an objective function.
- *
- * @param f Objective function.
- * @param goalType Type of optimization goal: either
- * {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}.
- * @param startPoint Start point for optimization.
- * @param maxEval Maximum number of function evaluations.
- * @return the point/value pair giving the optimal value for objective
- * function.
- * @throws org.apache.commons.math4.exception.DimensionMismatchException
- * if the start point dimension is wrong.
- * @throws org.apache.commons.math4.exception.TooManyEvaluationsException
- * if the maximal number of evaluations is exceeded.
- * @throws org.apache.commons.math4.exception.NullArgumentException if
- * any argument is {@code null}.
- */
- public PointValuePair optimize(final int maxEval,
- final MultivariateDifferentiableFunction f,
- final GoalType goalType,
- final double[] startPoint) {
- return optimizeInternal(maxEval,
- FunctionUtils.toDifferentiableMultivariateFunction(f),
- goalType,
- startPoint);
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/ConjugateGradientFormula.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optimization/general/ConjugateGradientFormula.java b/src/main/java/org/apache/commons/math4/optimization/general/ConjugateGradientFormula.java
deleted file mode 100644
index fae7419..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/general/ConjugateGradientFormula.java
+++ /dev/null
@@ -1,50 +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.general;
-
-/**
- * Available choices of update formulas for the β parameter
- * in {@link NonLinearConjugateGradientOptimizer}.
- * <p>
- * The β parameter is used to compute the successive conjugate
- * search directions. For non-linear conjugate gradients, there are
- * two formulas to compute β:
- * <ul>
- * <li>Fletcher-Reeves formula</li>
- * <li>Polak-Ribière formula</li>
- * </ul>
- * On the one hand, the Fletcher-Reeves formula is guaranteed to converge
- * if the start point is close enough of the optimum whether the
- * Polak-Ribière formula may not converge in rare cases. On the
- * other hand, the Polak-Ribière formula is often faster when it
- * does converge. Polak-Ribière is often used.
- * <p>
- * @see NonLinearConjugateGradientOptimizer
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.0
- */
-@Deprecated
-public enum ConjugateGradientFormula {
-
- /** Fletcher-Reeves formula. */
- FLETCHER_REEVES,
-
- /** Polak-Ribière formula. */
- POLAK_RIBIERE
-
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizer.java
deleted file mode 100644
index 9a44084..0000000
--- a/src/main/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizer.java
+++ /dev/null
@@ -1,194 +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.general;
-
-import org.apache.commons.math4.exception.ConvergenceException;
-import org.apache.commons.math4.exception.MathInternalError;
-import org.apache.commons.math4.exception.NullArgumentException;
-import org.apache.commons.math4.exception.util.LocalizedFormats;
-import org.apache.commons.math4.linear.ArrayRealVector;
-import org.apache.commons.math4.linear.BlockRealMatrix;
-import org.apache.commons.math4.linear.DecompositionSolver;
-import org.apache.commons.math4.linear.LUDecomposition;
-import org.apache.commons.math4.linear.QRDecomposition;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.linear.SingularMatrixException;
-import org.apache.commons.math4.optimization.ConvergenceChecker;
-import org.apache.commons.math4.optimization.PointVectorValuePair;
-import org.apache.commons.math4.optimization.SimpleVectorValueChecker;
-
-/**
- * Gauss-Newton least-squares solver.
- * <p>
- * This class solve a least-square problem by solving the normal equations
- * of the linearized problem at each iteration. Either LU decomposition or
- * QR decomposition can be used to solve the normal equations. LU decomposition
- * is faster but QR decomposition is more robust for difficult problems.
- * </p>
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.0
- *
- */
-@Deprecated
-public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
- /** Indicator for using LU decomposition. */
- private final boolean useLU;
-
- /**
- * Simple constructor with default settings.
- * The normal equations will be solved using LU decomposition and the
- * convergence check is set to a {@link SimpleVectorValueChecker}
- * with default tolerances.
- * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()}
- */
- @Deprecated
- public GaussNewtonOptimizer() {
- this(true);
- }
-
- /**
- * Simple constructor with default settings.
- * The normal equations will be solved using LU decomposition.
- *
- * @param checker Convergence checker.
- */
- public GaussNewtonOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
- this(true, checker);
- }
-
- /**
- * Simple constructor with default settings.
- * The convergence check is set to a {@link SimpleVectorValueChecker}
- * with default tolerances.
- *
- * @param useLU If {@code true}, the normal equations will be solved
- * using LU decomposition, otherwise they will be solved using QR
- * decomposition.
- * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()}
- */
- @Deprecated
- public GaussNewtonOptimizer(final boolean useLU) {
- this(useLU, new SimpleVectorValueChecker());
- }
-
- /**
- * @param useLU If {@code true}, the normal equations will be solved
- * using LU decomposition, otherwise they will be solved using QR
- * decomposition.
- * @param checker Convergence checker.
- */
- public GaussNewtonOptimizer(final boolean useLU,
- ConvergenceChecker<PointVectorValuePair> checker) {
- super(checker);
- this.useLU = useLU;
- }
-
- /** {@inheritDoc} */
- @Override
- public PointVectorValuePair doOptimize() {
- final ConvergenceChecker<PointVectorValuePair> checker
- = getConvergenceChecker();
-
- // Computation will be useless without a checker (see "for-loop").
- if (checker == null) {
- throw new NullArgumentException();
- }
-
- final double[] targetValues = getTarget();
- final int nR = targetValues.length; // Number of observed data.
-
- final RealMatrix weightMatrix = getWeight();
- // Diagonal of the weight matrix.
- final double[] residualsWeights = new double[nR];
- for (int i = 0; i < nR; i++) {
- residualsWeights[i] = weightMatrix.getEntry(i, i);
- }
-
- final double[] currentPoint = getStartPoint();
- final int nC = currentPoint.length;
-
- // iterate until convergence is reached
- PointVectorValuePair current = null;
- int iter = 0;
- for (boolean converged = false; !converged;) {
- ++iter;
-
- // evaluate the objective function and its jacobian
- PointVectorValuePair previous = current;
- // Value of the objective function at "currentPoint".
- final double[] currentObjective = computeObjectiveValue(currentPoint);
- final double[] currentResiduals = computeResiduals(currentObjective);
- final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
- current = new PointVectorValuePair(currentPoint, currentObjective);
-
- // build the linear problem
- final double[] b = new double[nC];
- final double[][] a = new double[nC][nC];
- for (int i = 0; i < nR; ++i) {
-
- final double[] grad = weightedJacobian.getRow(i);
- final double weight = residualsWeights[i];
- final double residual = currentResiduals[i];
-
- // compute the normal equation
- final double wr = weight * residual;
- for (int j = 0; j < nC; ++j) {
- b[j] += wr * grad[j];
- }
-
- // build the contribution matrix for measurement i
- for (int k = 0; k < nC; ++k) {
- double[] ak = a[k];
- double wgk = weight * grad[k];
- for (int l = 0; l < nC; ++l) {
- ak[l] += wgk * grad[l];
- }
- }
- }
-
- try {
- // solve the linearized least squares problem
- RealMatrix mA = new BlockRealMatrix(a);
- DecompositionSolver solver = useLU ?
- new LUDecomposition(mA).getSolver() :
- new QRDecomposition(mA).getSolver();
- final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
- // update the estimated parameters
- for (int i = 0; i < nC; ++i) {
- currentPoint[i] += dX[i];
- }
- } catch (SingularMatrixException e) {
- throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
- }
-
- // Check convergence.
- if (previous != null) {
- converged = checker.converged(iter, previous, current);
- if (converged) {
- cost = computeCost(currentResiduals);
- // Update (deprecated) "point" field.
- point = current.getPoint();
- return current;
- }
- }
- }
- // Must never happen.
- throw new MathInternalError();
- }
-}