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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* 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.function;
import java.util.Arrays;
import org.apache.commons.math3.analysis.FunctionUtils;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.DifferentiableUnivariateFunction;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.util.FastMath;
/**
*
* Sigmoid function.
* It is the inverse of the {@link Logit logit} function.
* A more flexible version, the generalised logistic, is implemented
* by the {@link Logistic} class.
*
* @since 3.0
* @version $Id: Sigmoid.java 1391927 2012-09-30 00:03:30Z erans $
*/
public class Sigmoid implements UnivariateDifferentiableFunction, DifferentiableUnivariateFunction {
/** Lower asymptote. */
private final double lo;
/** Higher asymptote. */
private final double hi;
/**
* Usual sigmoid function, where the lower asymptote is 0 and the higher
* asymptote is 1.
*/
public Sigmoid() {
this(0, 1);
}
/**
* Sigmoid function.
*
* @param lo Lower asymptote.
* @param hi Higher asymptote.
*/
public Sigmoid(double lo,
double hi) {
this.lo = lo;
this.hi = hi;
}
/** {@inheritDoc}
* @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)}
*/
@Deprecated
public UnivariateFunction derivative() {
return FunctionUtils.toDifferentiableUnivariateFunction(this).derivative();
}
/** {@inheritDoc} */
public double value(double x) {
return value(x, lo, hi);
}
/**
* Parametric function where the input array contains the parameters of
* the logit function, ordered as follows:
*
* - Lower asymptote
* - Higher asymptote
*
*/
public static class Parametric implements ParametricUnivariateFunction {
/**
* Computes the value of the sigmoid at {@code x}.
*
* @param x Value for which the function must be computed.
* @param param Values of lower asymptote and higher asymptote.
* @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.
*/
public double value(double x, double ... param)
throws NullArgumentException,
DimensionMismatchException {
validateParameters(param);
return Sigmoid.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 asymptote and higher asymptote).
*
* @param x Value at which the gradient must be computed.
* @param param Values for lower asymptote and higher asymptote.
* @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.
*/
public double[] gradient(double x, double ... param)
throws NullArgumentException,
DimensionMismatchException {
validateParameters(param);
final double invExp1 = 1 / (1 + FastMath.exp(-x));
return new double[] { 1 - invExp1, invExp1 };
}
/**
* 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 asymptotes.
* @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 sigmoid.
* @param lo Lower asymptote.
* @param hi Higher asymptote.
* @return the value of the sigmoid function at {@code x}.
*/
private static double value(double x,
double lo,
double hi) {
return lo + (hi - lo) / (1 + FastMath.exp(-x));
}
/** {@inheritDoc}
* @since 3.1
*/
public DerivativeStructure value(final DerivativeStructure t) {
double[] f = new double[t.getOrder() + 1];
final double exp = FastMath.exp(-t.getValue());
if (Double.isInfinite(exp)) {
// special handling near lower boundary, to avoid NaN
f[0] = lo;
Arrays.fill(f, 1, f.length, 0.0);
} else {
// the nth order derivative of sigmoid has the form:
// dn(sigmoid(x)/dxn = P_n(exp(-x)) / (1+exp(-x))^(n+1)
// where P_n(t) is a degree n polynomial with normalized higher term
// P_0(t) = 1, P_1(t) = t, P_2(t) = t^2 - t, P_3(t) = t^3 - 4 t^2 + t...
// the general recurrence relation for P_n is:
// P_n(x) = n t P_(n-1)(t) - t (1 + t) P_(n-1)'(t)
final double[] p = new double[f.length];
final double inv = 1 / (1 + exp);
double coeff = hi - lo;
for (int n = 0; n < f.length; ++n) {
// update and evaluate polynomial P_n(t)
double v = 0;
p[n] = 1;
for (int k = n; k >= 0; --k) {
v = v * exp + p[k];
if (k > 1) {
p[k - 1] = (n - k + 2) * p[k - 2] - (k - 1) * p[k - 1];
} else {
p[0] = 0;
}
}
coeff *= inv;
f[n] = coeff * v;
}
// fix function value
f[0] += lo;
}
return t.compose(f);
}
}