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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* 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.differentiation.DerivativeStructure;
import org.apache.commons.math4.analysis.differentiation.UnivariateDifferentiableFunction;
import org.apache.commons.math4.exception.DimensionMismatchException;
import org.apache.commons.math4.util.FastMath;
/**
* Sinc function,
* defined by
*
* sinc(x) = 1 if x = 0,
* sin(x) / x otherwise.
*
*
* @since 3.0
*/
public class Sinc implements UnivariateDifferentiableFunction {
/**
* Value below which the computations are done using Taylor series.
*
* The Taylor series for sinc even order derivatives are:
*
* d^(2n)sinc/dx^(2n) = Sum_(k>=0) (-1)^(n+k) / ((2k)!(2n+2k+1)) x^(2k)
* = (-1)^n [ 1/(2n+1) - x^2/(4n+6) + x^4/(48n+120) - x^6/(1440n+5040) + O(x^8) ]
*
*
*
* The Taylor series for sinc odd order derivatives are:
*
* d^(2n+1)sinc/dx^(2n+1) = Sum_(k>=0) (-1)^(n+k+1) / ((2k+1)!(2n+2k+3)) x^(2k+1)
* = (-1)^(n+1) [ x/(2n+3) - x^3/(12n+30) + x^5/(240n+840) - x^7/(10080n+45360) + O(x^9) ]
*
*
*
* So the ratio of the fourth term with respect to the first term
* is always smaller than x^6/720, for all derivative orders.
* This implies that neglecting this term and using only the first three terms induces
* a relative error bounded by x^6/720. The SHORTCUT value is chosen such that this
* relative error is below double precision accuracy when |x| <= SHORTCUT.
*
*/
private static final double SHORTCUT = 6.0e-3;
/** For normalized sinc function. */
private final boolean normalized;
/**
* The sinc function, {@code sin(x) / x}.
*/
public Sinc() {
this(false);
}
/**
* Instantiates the sinc function.
*
* @param normalized If {@code true}, the function is
* sin(πx) / πx, otherwise {@code sin(x) / x}.
*/
public Sinc(boolean normalized) {
this.normalized = normalized;
}
/** {@inheritDoc} */
@Override
public double value(final double x) {
final double scaledX = normalized ? FastMath.PI * x : x;
if (FastMath.abs(scaledX) <= SHORTCUT) {
// use Taylor series
final double scaledX2 = scaledX * scaledX;
return ((scaledX2 - 20) * scaledX2 + 120) / 120;
} else {
// use definition expression
return FastMath.sin(scaledX) / scaledX;
}
}
/** {@inheritDoc}
* @since 3.1
*/
@Override
public DerivativeStructure value(final DerivativeStructure t)
throws DimensionMismatchException {
final double scaledX = (normalized ? FastMath.PI : 1) * t.getValue();
final double scaledX2 = scaledX * scaledX;
double[] f = new double[t.getOrder() + 1];
if (FastMath.abs(scaledX) <= SHORTCUT) {
for (int i = 0; i < f.length; ++i) {
final int k = i / 2;
if ((i & 0x1) == 0) {
// even derivation order
f[i] = (((k & 0x1) == 0) ? 1 : -1) *
(1.0 / (i + 1) - scaledX2 * (1.0 / (2 * i + 6) - scaledX2 / (24 * i + 120)));
} else {
// odd derivation order
f[i] = (((k & 0x1) == 0) ? -scaledX : scaledX) *
(1.0 / (i + 2) - scaledX2 * (1.0 / (6 * i + 24) - scaledX2 / (120 * i + 720)));
}
}
} else {
final double inv = 1 / scaledX;
final double cos = FastMath.cos(scaledX);
final double sin = FastMath.sin(scaledX);
f[0] = inv * sin;
// the nth order derivative of sinc has the form:
// dn(sinc(x)/dxn = [S_n(x) sin(x) + C_n(x) cos(x)] / x^(n+1)
// where S_n(x) is an even polynomial with degree n-1 or n (depending on parity)
// and C_n(x) is an odd polynomial with degree n-1 or n (depending on parity)
// S_0(x) = 1, S_1(x) = -1, S_2(x) = -x^2 + 2, S_3(x) = 3x^2 - 6...
// C_0(x) = 0, C_1(x) = x, C_2(x) = -2x, C_3(x) = -x^3 + 6x...
// the general recurrence relations for S_n and C_n are:
// S_n(x) = x S_(n-1)'(x) - n S_(n-1)(x) - x C_(n-1)(x)
// C_n(x) = x C_(n-1)'(x) - n C_(n-1)(x) + x S_(n-1)(x)
// as per polynomials parity, we can store both S_n and C_n in the same array
final double[] sc = new double[f.length];
sc[0] = 1;
double coeff = inv;
for (int n = 1; n < f.length; ++n) {
double s = 0;
double c = 0;
// update and evaluate polynomials S_n(x) and C_n(x)
final int kStart;
if ((n & 0x1) == 0) {
// even derivation order, S_n is degree n and C_n is degree n-1
sc[n] = 0;
kStart = n;
} else {
// odd derivation order, S_n is degree n-1 and C_n is degree n
sc[n] = sc[n - 1];
c = sc[n];
kStart = n - 1;
}
// in this loop, k is always even
for (int k = kStart; k > 1; k -= 2) {
// sine part
sc[k] = (k - n) * sc[k] - sc[k - 1];
s = s * scaledX2 + sc[k];
// cosine part
sc[k - 1] = (k - 1 - n) * sc[k - 1] + sc[k -2];
c = c * scaledX2 + sc[k - 1];
}
sc[0] *= -n;
s = s * scaledX2 + sc[0];
coeff *= inv;
f[n] = coeff * (s * sin + c * scaledX * cos);
}
}
if (normalized) {
double scale = FastMath.PI;
for (int i = 1; i < f.length; ++i) {
f[i] *= scale;
scale *= FastMath.PI;
}
}
return t.compose(f);
}
}