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/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.math3.analysis;

import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction;
import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction;
import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction;
import org.apache.commons.math3.analysis.function.Identity;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;

/**
 * Utilities for manipulating function objects.
 *
 * @since 3.0
 */
public class FunctionUtils {
    /**
     * Class only contains static methods.
     */
    private FunctionUtils() {}

    /**
     * Composes functions.
     * 

* The functions in the argument list are composed sequentially, in the * given order. For example, compose(f1,f2,f3) acts like f1(f2(f3(x))).

* * @param f List of functions. * @return the composite function. */ public static UnivariateFunction compose(final UnivariateFunction ... f) { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double r = x; for (int i = f.length - 1; i >= 0; i--) { r = f[i].value(r); } return r; } }; } /** * Composes functions. *

* The functions in the argument list are composed sequentially, in the * given order. For example, compose(f1,f2,f3) acts like f1(f2(f3(x))).

* * @param f List of functions. * @return the composite function. * @since 3.1 */ public static UnivariateDifferentiableFunction compose(final UnivariateDifferentiableFunction ... f) { return new UnivariateDifferentiableFunction() { /** {@inheritDoc} */ public double value(final double t) { double r = t; for (int i = f.length - 1; i >= 0; i--) { r = f[i].value(r); } return r; } /** {@inheritDoc} */ public DerivativeStructure value(final DerivativeStructure t) { DerivativeStructure r = t; for (int i = f.length - 1; i >= 0; i--) { r = f[i].value(r); } return r; } }; } /** * Composes functions. *

* The functions in the argument list are composed sequentially, in the * given order. For example, compose(f1,f2,f3) acts like f1(f2(f3(x))).

* * @param f List of functions. * @return the composite function. * @deprecated as of 3.1 replaced by {@link #compose(UnivariateDifferentiableFunction...)} */ @Deprecated public static DifferentiableUnivariateFunction compose(final DifferentiableUnivariateFunction ... f) { return new DifferentiableUnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double r = x; for (int i = f.length - 1; i >= 0; i--) { r = f[i].value(r); } return r; } /** {@inheritDoc} */ public UnivariateFunction derivative() { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double p = 1; double r = x; for (int i = f.length - 1; i >= 0; i--) { p *= f[i].derivative().value(r); r = f[i].value(r); } return p; } }; } }; } /** * Adds functions. * * @param f List of functions. * @return a function that computes the sum of the functions. */ public static UnivariateFunction add(final UnivariateFunction ... f) { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double r = f[0].value(x); for (int i = 1; i < f.length; i++) { r += f[i].value(x); } return r; } }; } /** * Adds functions. * * @param f List of functions. * @return a function that computes the sum of the functions. * @since 3.1 */ public static UnivariateDifferentiableFunction add(final UnivariateDifferentiableFunction ... f) { return new UnivariateDifferentiableFunction() { /** {@inheritDoc} */ public double value(final double t) { double r = f[0].value(t); for (int i = 1; i < f.length; i++) { r += f[i].value(t); } return r; } /** {@inheritDoc} * @throws DimensionMismatchException if functions are not consistent with each other */ public DerivativeStructure value(final DerivativeStructure t) throws DimensionMismatchException { DerivativeStructure r = f[0].value(t); for (int i = 1; i < f.length; i++) { r = r.add(f[i].value(t)); } return r; } }; } /** * Adds functions. * * @param f List of functions. * @return a function that computes the sum of the functions. * @deprecated as of 3.1 replaced by {@link #add(UnivariateDifferentiableFunction...)} */ @Deprecated public static DifferentiableUnivariateFunction add(final DifferentiableUnivariateFunction ... f) { return new DifferentiableUnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double r = f[0].value(x); for (int i = 1; i < f.length; i++) { r += f[i].value(x); } return r; } /** {@inheritDoc} */ public UnivariateFunction derivative() { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double r = f[0].derivative().value(x); for (int i = 1; i < f.length; i++) { r += f[i].derivative().value(x); } return r; } }; } }; } /** * Multiplies functions. * * @param f List of functions. * @return a function that computes the product of the functions. */ public static UnivariateFunction multiply(final UnivariateFunction ... f) { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double r = f[0].value(x); for (int i = 1; i < f.length; i++) { r *= f[i].value(x); } return r; } }; } /** * Multiplies functions. * * @param f List of functions. * @return a function that computes the product of the functions. * @since 3.1 */ public static UnivariateDifferentiableFunction multiply(final UnivariateDifferentiableFunction ... f) { return new UnivariateDifferentiableFunction() { /** {@inheritDoc} */ public double value(final double t) { double r = f[0].value(t); for (int i = 1; i < f.length; i++) { r *= f[i].value(t); } return r; } /** {@inheritDoc} */ public DerivativeStructure value(final DerivativeStructure t) { DerivativeStructure r = f[0].value(t); for (int i = 1; i < f.length; i++) { r = r.multiply(f[i].value(t)); } return r; } }; } /** * Multiplies functions. * * @param f List of functions. * @return a function that computes the product of the functions. * @deprecated as of 3.1 replaced by {@link #multiply(UnivariateDifferentiableFunction...)} */ @Deprecated public static DifferentiableUnivariateFunction multiply(final DifferentiableUnivariateFunction ... f) { return new DifferentiableUnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double r = f[0].value(x); for (int i = 1; i < f.length; i++) { r *= f[i].value(x); } return r; } /** {@inheritDoc} */ public UnivariateFunction derivative() { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { double sum = 0; for (int i = 0; i < f.length; i++) { double prod = f[i].derivative().value(x); for (int j = 0; j < f.length; j++) { if (i != j) { prod *= f[j].value(x); } } sum += prod; } return sum; } }; } }; } /** * Returns the univariate function * {@code h(x) = combiner(f(x), g(x)).} * * @param combiner Combiner function. * @param f Function. * @param g Function. * @return the composite function. */ public static UnivariateFunction combine(final BivariateFunction combiner, final UnivariateFunction f, final UnivariateFunction g) { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { return combiner.value(f.value(x), g.value(x)); } }; } /** * Returns a MultivariateFunction h(x[]) defined by
 
     * h(x[]) = combiner(...combiner(combiner(initialValue,f(x[0])),f(x[1]))...),f(x[x.length-1]))
     * 
* * @param combiner Combiner function. * @param f Function. * @param initialValue Initial value. * @return a collector function. */ public static MultivariateFunction collector(final BivariateFunction combiner, final UnivariateFunction f, final double initialValue) { return new MultivariateFunction() { /** {@inheritDoc} */ public double value(double[] point) { double result = combiner.value(initialValue, f.value(point[0])); for (int i = 1; i < point.length; i++) { result = combiner.value(result, f.value(point[i])); } return result; } }; } /** * Returns a MultivariateFunction h(x[]) defined by
 
     * h(x[]) = combiner(...combiner(combiner(initialValue,x[0]),x[1])...),x[x.length-1])
     * 
* * @param combiner Combiner function. * @param initialValue Initial value. * @return a collector function. */ public static MultivariateFunction collector(final BivariateFunction combiner, final double initialValue) { return collector(combiner, new Identity(), initialValue); } /** * Creates a unary function by fixing the first argument of a binary function. * * @param f Binary function. * @param fixed value to which the first argument of {@code f} is set. * @return the unary function h(x) = f(fixed, x) */ public static UnivariateFunction fix1stArgument(final BivariateFunction f, final double fixed) { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { return f.value(fixed, x); } }; } /** * Creates a unary function by fixing the second argument of a binary function. * * @param f Binary function. * @param fixed value to which the second argument of {@code f} is set. * @return the unary function h(x) = f(x, fixed) */ public static UnivariateFunction fix2ndArgument(final BivariateFunction f, final double fixed) { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(double x) { return f.value(x, fixed); } }; } /** * Samples the specified univariate real function on the specified interval. *

* The interval is divided equally into {@code n} sections and sample points * are taken from {@code min} to {@code max - (max - min) / n}; therefore * {@code f} is not sampled at the upper bound {@code max}.

* * @param f Function to be sampled * @param min Lower bound of the interval (included). * @param max Upper bound of the interval (excluded). * @param n Number of sample points. * @return the array of samples. * @throws NumberIsTooLargeException if the lower bound {@code min} is * greater than, or equal to the upper bound {@code max}. * @throws NotStrictlyPositiveException if the number of sample points * {@code n} is negative. */ public static double[] sample(UnivariateFunction f, double min, double max, int n) throws NumberIsTooLargeException, NotStrictlyPositiveException { if (n <= 0) { throw new NotStrictlyPositiveException( LocalizedFormats.NOT_POSITIVE_NUMBER_OF_SAMPLES, Integer.valueOf(n)); } if (min >= max) { throw new NumberIsTooLargeException(min, max, false); } final double[] s = new double[n]; final double h = (max - min) / n; for (int i = 0; i < n; i++) { s[i] = f.value(min + i * h); } return s; } /** * Convert a {@link UnivariateDifferentiableFunction} into a {@link DifferentiableUnivariateFunction}. * * @param f function to convert * @return converted function * @deprecated this conversion method is temporary in version 3.1, as the {@link * DifferentiableUnivariateFunction} interface itself is deprecated */ @Deprecated public static DifferentiableUnivariateFunction toDifferentiableUnivariateFunction(final UnivariateDifferentiableFunction f) { return new DifferentiableUnivariateFunction() { /** {@inheritDoc} */ public double value(final double x) { return f.value(x); } /** {@inheritDoc} */ public UnivariateFunction derivative() { return new UnivariateFunction() { /** {@inheritDoc} */ public double value(final double x) { return f.value(new DerivativeStructure(1, 1, 0, x)).getPartialDerivative(1); } }; } }; } /** * Convert a {@link DifferentiableUnivariateFunction} into a {@link UnivariateDifferentiableFunction}. *

* Note that the converted function is able to handle {@link DerivativeStructure} up to order one. * If the function is called with higher order, a {@link NumberIsTooLargeException} is thrown. *

* @param f function to convert * @return converted function * @deprecated this conversion method is temporary in version 3.1, as the {@link * DifferentiableUnivariateFunction} interface itself is deprecated */ @Deprecated public static UnivariateDifferentiableFunction toUnivariateDifferential(final DifferentiableUnivariateFunction f) { return new UnivariateDifferentiableFunction() { /** {@inheritDoc} */ public double value(final double x) { return f.value(x); } /** {@inheritDoc} * @exception NumberIsTooLargeException if derivation order is greater than 1 */ public DerivativeStructure value(final DerivativeStructure t) throws NumberIsTooLargeException { switch (t.getOrder()) { case 0 : return new DerivativeStructure(t.getFreeParameters(), 0, f.value(t.getValue())); case 1 : { final int parameters = t.getFreeParameters(); final double[] derivatives = new double[parameters + 1]; derivatives[0] = f.value(t.getValue()); final double fPrime = f.derivative().value(t.getValue()); int[] orders = new int[parameters]; for (int i = 0; i < parameters; ++i) { orders[i] = 1; derivatives[i + 1] = fPrime * t.getPartialDerivative(orders); orders[i] = 0; } return new DerivativeStructure(parameters, 1, derivatives); } default : throw new NumberIsTooLargeException(t.getOrder(), 1, true); } } }; } /** * Convert a {@link MultivariateDifferentiableFunction} into a {@link DifferentiableMultivariateFunction}. * * @param f function to convert * @return converted function * @deprecated this conversion method is temporary in version 3.1, as the {@link * DifferentiableMultivariateFunction} interface itself is deprecated */ @Deprecated public static DifferentiableMultivariateFunction toDifferentiableMultivariateFunction(final MultivariateDifferentiableFunction f) { return new DifferentiableMultivariateFunction() { /** {@inheritDoc} */ public double value(final double[] x) { return f.value(x); } /** {@inheritDoc} */ public MultivariateFunction partialDerivative(final int k) { return new MultivariateFunction() { /** {@inheritDoc} */ public double value(final double[] x) { final int n = x.length; // delegate computation to underlying function final DerivativeStructure[] dsX = new DerivativeStructure[n]; for (int i = 0; i < n; ++i) { if (i == k) { dsX[i] = new DerivativeStructure(1, 1, 0, x[i]); } else { dsX[i] = new DerivativeStructure(1, 1, x[i]); } } final DerivativeStructure y = f.value(dsX); // extract partial derivative return y.getPartialDerivative(1); } }; } /** {@inheritDoc} */ public MultivariateVectorFunction gradient() { return new MultivariateVectorFunction() { /** {@inheritDoc} */ public double[] value(final double[] x) { final int n = x.length; // delegate computation to underlying function final DerivativeStructure[] dsX = new DerivativeStructure[n]; for (int i = 0; i < n; ++i) { dsX[i] = new DerivativeStructure(n, 1, i, x[i]); } final DerivativeStructure y = f.value(dsX); // extract gradient final double[] gradient = new double[n]; final int[] orders = new int[n]; for (int i = 0; i < n; ++i) { orders[i] = 1; gradient[i] = y.getPartialDerivative(orders); orders[i] = 0; } return gradient; } }; } }; } /** * Convert a {@link DifferentiableMultivariateFunction} into a {@link MultivariateDifferentiableFunction}. *

* Note that the converted function is able to handle {@link DerivativeStructure} elements * that all have the same number of free parameters and order, and with order at most 1. * If the function is called with inconsistent numbers of free parameters or higher order, a * {@link DimensionMismatchException} or a {@link NumberIsTooLargeException} will be thrown. *

* @param f function to convert * @return converted function * @deprecated this conversion method is temporary in version 3.1, as the {@link * DifferentiableMultivariateFunction} interface itself is deprecated */ @Deprecated public static MultivariateDifferentiableFunction toMultivariateDifferentiableFunction(final DifferentiableMultivariateFunction f) { return new MultivariateDifferentiableFunction() { /** {@inheritDoc} */ public double value(final double[] x) { return f.value(x); } /** {@inheritDoc} * @exception NumberIsTooLargeException if derivation order is higher than 1 * @exception DimensionMismatchException if numbers of free parameters are inconsistent */ public DerivativeStructure value(final DerivativeStructure[] t) throws DimensionMismatchException, NumberIsTooLargeException { // check parameters and orders limits final int parameters = t[0].getFreeParameters(); final int order = t[0].getOrder(); final int n = t.length; if (order > 1) { throw new NumberIsTooLargeException(order, 1, true); } // check all elements in the array are consistent for (int i = 0; i < n; ++i) { if (t[i].getFreeParameters() != parameters) { throw new DimensionMismatchException(t[i].getFreeParameters(), parameters); } if (t[i].getOrder() != order) { throw new DimensionMismatchException(t[i].getOrder(), order); } } // delegate computation to underlying function final double[] point = new double[n]; for (int i = 0; i < n; ++i) { point[i] = t[i].getValue(); } final double value = f.value(point); final double[] gradient = f.gradient().value(point); // merge value and gradient into one DerivativeStructure final double[] derivatives = new double[parameters + 1]; derivatives[0] = value; final int[] orders = new int[parameters]; for (int i = 0; i < parameters; ++i) { orders[i] = 1; for (int j = 0; j < n; ++j) { derivatives[i + 1] += gradient[j] * t[j].getPartialDerivative(orders); } orders[i] = 0; } return new DerivativeStructure(parameters, order, derivatives); } }; } /** * Convert a {@link MultivariateDifferentiableVectorFunction} into a {@link DifferentiableMultivariateVectorFunction}. * * @param f function to convert * @return converted function * @deprecated this conversion method is temporary in version 3.1, as the {@link * DifferentiableMultivariateVectorFunction} interface itself is deprecated */ @Deprecated public static DifferentiableMultivariateVectorFunction toDifferentiableMultivariateVectorFunction(final MultivariateDifferentiableVectorFunction f) { return new DifferentiableMultivariateVectorFunction() { /** {@inheritDoc} */ public double[] value(final double[] x) { return f.value(x); } /** {@inheritDoc} */ public MultivariateMatrixFunction jacobian() { return new MultivariateMatrixFunction() { /** {@inheritDoc} */ public double[][] value(final double[] x) { final int n = x.length; // delegate computation to underlying function final DerivativeStructure[] dsX = new DerivativeStructure[n]; for (int i = 0; i < n; ++i) { dsX[i] = new DerivativeStructure(n, 1, i, x[i]); } final DerivativeStructure[] y = f.value(dsX); // extract Jacobian final double[][] jacobian = new double[y.length][n]; final int[] orders = new int[n]; for (int i = 0; i < y.length; ++i) { for (int j = 0; j < n; ++j) { orders[j] = 1; jacobian[i][j] = y[i].getPartialDerivative(orders); orders[j] = 0; } } return jacobian; } }; } }; } /** * Convert a {@link DifferentiableMultivariateVectorFunction} into a {@link MultivariateDifferentiableVectorFunction}. *

* Note that the converted function is able to handle {@link DerivativeStructure} elements * that all have the same number of free parameters and order, and with order at most 1. * If the function is called with inconsistent numbers of free parameters or higher order, a * {@link DimensionMismatchException} or a {@link NumberIsTooLargeException} will be thrown. *

* @param f function to convert * @return converted function * @deprecated this conversion method is temporary in version 3.1, as the {@link * DifferentiableMultivariateFunction} interface itself is deprecated */ @Deprecated public static MultivariateDifferentiableVectorFunction toMultivariateDifferentiableVectorFunction(final DifferentiableMultivariateVectorFunction f) { return new MultivariateDifferentiableVectorFunction() { /** {@inheritDoc} */ public double[] value(final double[] x) { return f.value(x); } /** {@inheritDoc} * @exception NumberIsTooLargeException if derivation order is higher than 1 * @exception DimensionMismatchException if numbers of free parameters are inconsistent */ public DerivativeStructure[] value(final DerivativeStructure[] t) throws DimensionMismatchException, NumberIsTooLargeException { // check parameters and orders limits final int parameters = t[0].getFreeParameters(); final int order = t[0].getOrder(); final int n = t.length; if (order > 1) { throw new NumberIsTooLargeException(order, 1, true); } // check all elements in the array are consistent for (int i = 0; i < n; ++i) { if (t[i].getFreeParameters() != parameters) { throw new DimensionMismatchException(t[i].getFreeParameters(), parameters); } if (t[i].getOrder() != order) { throw new DimensionMismatchException(t[i].getOrder(), order); } } // delegate computation to underlying function final double[] point = new double[n]; for (int i = 0; i < n; ++i) { point[i] = t[i].getValue(); } final double[] value = f.value(point); final double[][] jacobian = f.jacobian().value(point); // merge value and Jacobian into a DerivativeStructure array final DerivativeStructure[] merged = new DerivativeStructure[value.length]; for (int k = 0; k < merged.length; ++k) { final double[] derivatives = new double[parameters + 1]; derivatives[0] = value[k]; final int[] orders = new int[parameters]; for (int i = 0; i < parameters; ++i) { orders[i] = 1; for (int j = 0; j < n; ++j) { derivatives[i + 1] += jacobian[k][j] * t[j].getPartialDerivative(orders); } orders[i] = 0; } merged[k] = new DerivativeStructure(parameters, order, derivatives); } return merged; } }; } }




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