All Downloads are FREE. Search and download functionalities are using the official Maven repository.

org.apache.commons.math4.analysis.function.Logit Maven / Gradle / Ivy

Go to download

Statistical sampling library for use in virtdata libraries, based on apache commons math 4

There is a newer version: 5.17.0
Show newest version
/*
 * 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.analysis.function;

import org.apache.commons.math4.analysis.ParametricUnivariateFunction;
import org.apache.commons.math4.analysis.differentiation.DerivativeStructure;
import org.apache.commons.math4.analysis.differentiation.UnivariateDifferentiableFunction;
import org.apache.commons.math4.exception.DimensionMismatchException;
import org.apache.commons.math4.exception.NullArgumentException;
import org.apache.commons.math4.exception.OutOfRangeException;
import org.apache.commons.math4.util.FastMath;

/**
 * 
 *  Logit function.
 * It is the inverse of the {@link Sigmoid sigmoid} function.
 *
 * @since 3.0
 */
public class Logit implements UnivariateDifferentiableFunction {
    /** Lower bound. */
    private final double lo;
    /** Higher bound. */
    private final double hi;

    /**
     * Usual logit function, where the lower bound is 0 and the higher
     * bound is 1.
     */
    public Logit() {
        this(0, 1);
    }

    /**
     * Logit function.
     *
     * @param lo Lower bound of the function domain.
     * @param hi Higher bound of the function domain.
     */
    public Logit(double lo,
                 double hi) {
        this.lo = lo;
        this.hi = hi;
    }

    /** {@inheritDoc} */
    @Override
    public double value(double x)
        throws OutOfRangeException {
        return value(x, lo, hi);
    }

    /**
     * Parametric function where the input array contains the parameters of
     * the logit function, ordered as follows:
     * 
    *
  • Lower bound
  • *
  • Higher bound
  • *
*/ public static class Parametric implements ParametricUnivariateFunction { /** * Computes the value of the logit at {@code x}. * * @param x Value for which the function must be computed. * @param param Values of lower bound and higher bounds. * @return the value of the function. * @throws NullArgumentException if {@code param} is {@code null}. * @throws DimensionMismatchException if the size of {@code param} is * not 2. */ @Override public double value(double x, double ... param) throws NullArgumentException, DimensionMismatchException { validateParameters(param); return Logit.value(x, param[0], param[1]); } /** * Computes the value of the gradient at {@code x}. * The components of the gradient vector are the partial * derivatives of the function with respect to each of the * parameters (lower bound and higher bound). * * @param x Value at which the gradient must be computed. * @param param Values for lower and higher bounds. * @return the gradient vector at {@code x}. * @throws NullArgumentException if {@code param} is {@code null}. * @throws DimensionMismatchException if the size of {@code param} is * not 2. */ @Override public double[] gradient(double x, double ... param) throws NullArgumentException, DimensionMismatchException { validateParameters(param); final double lo = param[0]; final double hi = param[1]; return new double[] { 1 / (lo - x), 1 / (hi - x) }; } /** * Validates parameters to ensure they are appropriate for the evaluation of * the {@link #value(double,double[])} and {@link #gradient(double,double[])} * methods. * * @param param Values for lower and higher bounds. * @throws NullArgumentException if {@code param} is {@code null}. * @throws DimensionMismatchException if the size of {@code param} is * not 2. */ private void validateParameters(double[] param) throws NullArgumentException, DimensionMismatchException { if (param == null) { throw new NullArgumentException(); } if (param.length != 2) { throw new DimensionMismatchException(param.length, 2); } } } /** * @param x Value at which to compute the logit. * @param lo Lower bound. * @param hi Higher bound. * @return the value of the logit function at {@code x}. * @throws OutOfRangeException if {@code x < lo} or {@code x > hi}. */ private static double value(double x, double lo, double hi) throws OutOfRangeException { if (x < lo || x > hi) { throw new OutOfRangeException(x, lo, hi); } return FastMath.log((x - lo) / (hi - x)); } /** {@inheritDoc} * @since 3.1 * @exception OutOfRangeException if parameter is outside of function domain */ @Override public DerivativeStructure value(final DerivativeStructure t) throws OutOfRangeException { final double x = t.getValue(); if (x < lo || x > hi) { throw new OutOfRangeException(x, lo, hi); } double[] f = new double[t.getOrder() + 1]; // function value f[0] = FastMath.log((x - lo) / (hi - x)); if (Double.isInfinite(f[0])) { if (f.length > 1) { f[1] = Double.POSITIVE_INFINITY; } // fill the array with infinities // (for x close to lo the signs will flip between -inf and +inf, // for x close to hi the signs will always be +inf) // this is probably overkill, since the call to compose at the end // of the method will transform most infinities into NaN ... for (int i = 2; i < f.length; ++i) { f[i] = f[i - 2]; } } else { // function derivatives final double invL = 1.0 / (x - lo); double xL = invL; final double invH = 1.0 / (hi - x); double xH = invH; for (int i = 1; i < f.length; ++i) { f[i] = xL + xH; xL *= -i * invL; xH *= i * invH; } } return t.compose(f); } }




© 2015 - 2024 Weber Informatics LLC | Privacy Policy