/usr/include/openturns/DistFunc.hxx is in libopenturns-dev 1.7-3.
This file is owned by root:root, with mode 0o644.
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/**
* @brief OpenTURNS wrapper to a library of special functions
*
* Copyright 2005-2015 Airbus-EDF-IMACS-Phimeca
*
* This library is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* along with this library. If not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef OPENTURNS_DISTFUNC_HXX
#define OPENTURNS_DISTFUNC_HXX
#include "OTprivate.hxx"
#include "ResourceMap.hxx"
#include "NumericalPoint.hxx"
#include "Indices.hxx"
#include "NumericalSample.hxx"
BEGIN_NAMESPACE_OPENTURNS
class OT_API DistFunc
{
public:
static const UnsignedInteger NumberOfBandNormalZigurrat;
static const NumericalScalar NormalZigguratTail;
// The array NormalZigguratAbscissa stores the abscissas of the bands:
// the ith band is [NormalZigguratAbscissa[0], NormalZigguratAbscissa[i+1]] with
// NormalZigguratAbscissa[0] = 0.0 and
// NormalZigguratAbscissa[NumberOfBandNormalZigurrat] = NormalZigguratTail
static const NumericalScalar NormalZigguratAbscissa[];
// The array NormalZigguratRatio stores the ratio between a band and the next one,
// starting from the smallest one:
// NormalZigguratRatio[i] = NormalZigguratAbscissa[i] / NormalZigguratAbscissa[i+1]
static const NumericalScalar NormalZigguratRatio[];
// For Beta distribution. WARNING: here we adopt the classical definition
// If tail=false, compute P(X<=x), else P(X>x)
static NumericalScalar pBeta(const NumericalScalar p1,
const NumericalScalar p2,
const NumericalScalar x,
const Bool tail = false);
// If tail=false, compute x such that P(X<=x)=q, else x such that P(X>x)=q
static NumericalScalar qBeta(const NumericalScalar p1,
const NumericalScalar p2,
const NumericalScalar p,
const Bool tail = false);
static NumericalScalar rBeta(const NumericalScalar p1,
const NumericalScalar p2);
static NumericalPoint rBeta(const NumericalScalar p1,
const NumericalScalar p2,
const UnsignedInteger size);
// For the Binomial distribution
private:
static NumericalScalar fcBinomial(const UnsignedInteger k);
public:
static UnsignedInteger rBinomial(const UnsignedInteger n,
const NumericalScalar p);
static Indices rBinomial(const UnsignedInteger n,
const NumericalScalar p,
const UnsignedInteger size);
// For Gamma distribution
static NumericalScalar pGamma(const NumericalScalar k,
const NumericalScalar x,
const Bool tail = false);
static NumericalScalar qGamma(const NumericalScalar k,
const NumericalScalar p,
const Bool tail = false);
static NumericalScalar rGamma(const NumericalScalar k);
static NumericalPoint rGamma(const NumericalScalar k,
const UnsignedInteger size);
// For Kolmogorov distribution
static NumericalScalar pKolmogorov(const UnsignedInteger n,
const NumericalScalar x,
const Bool tail = false);
// For NonCentralChiSquare distribution
static NumericalScalar dNonCentralChiSquare(const NumericalScalar nu,
const NumericalScalar lambda,
const NumericalScalar x,
const NumericalScalar precision = ResourceMap::GetAsNumericalScalar("DistFunc-Precision"),
const UnsignedInteger maximumIteration = ResourceMap::GetAsNumericalScalar("DistFunc-MaximumIteration"));
static NumericalScalar pNonCentralChiSquare(const NumericalScalar nu,
const NumericalScalar lambda,
const NumericalScalar x,
const Bool tail = false,
const NumericalScalar precision = ResourceMap::GetAsNumericalScalar("DistFunc-Precision"),
const UnsignedInteger maximumIteration = ResourceMap::GetAsNumericalScalar("DistFunc-MaximumIteration"));
static NumericalScalar rNonCentralChiSquare(const NumericalScalar nu,
const NumericalScalar lambda);
static NumericalPoint rNonCentralChiSquare(const NumericalScalar nu,
const NumericalScalar lambda,
const UnsignedInteger size);
// For NonCentralStudent distribution
static NumericalScalar dNonCentralStudent(const NumericalScalar nu,
const NumericalScalar delta,
const NumericalScalar x);
static NumericalScalar dNonCentralStudentAlt0(const NumericalScalar nu,
const NumericalScalar delta,
const NumericalScalar x,
const NumericalScalar precision = ResourceMap::GetAsNumericalScalar("DistFunc-Precision"),
const UnsignedInteger maximumIteration = ResourceMap::GetAsNumericalScalar("DistFunc-MaximumIteration"));
static NumericalScalar pNonCentralStudent(const NumericalScalar nu,
const NumericalScalar delta,
const NumericalScalar x,
const Bool tail = false);
static NumericalScalar rNonCentralStudent(const NumericalScalar nu,
const NumericalScalar delta);
static NumericalPoint rNonCentralStudent(const NumericalScalar nu,
const NumericalScalar delta,
const UnsignedInteger size);
// For Normal distribution
static NumericalScalar pNormal(const NumericalScalar x,
const Bool tail = false);
static NumericalScalar pNormal2D(const NumericalScalar x1,
const NumericalScalar x2,
const NumericalScalar rho,
const Bool tail = false);
static NumericalScalar pNormal3D(const NumericalScalar x1,
const NumericalScalar x2,
const NumericalScalar x3,
const NumericalScalar rho12,
const NumericalScalar rho13,
const NumericalScalar rho23,
const Bool tail = false);
static NumericalScalar qNormal(const NumericalScalar p,
const Bool tail = false);
static NumericalScalar rNormal();
static NumericalPoint rNormal(const UnsignedInteger size);
// For Poisson distribution
static NumericalScalar qPoisson(const NumericalScalar lambda,
const NumericalScalar p,
const Bool tail = false);
static UnsignedInteger rPoisson(const NumericalScalar lambda);
static Indices rPoisson(const NumericalScalar lambda,
const UnsignedInteger size);
// For Student distribution
static NumericalScalar pStudent(const NumericalScalar nu,
const NumericalScalar x,
const Bool tail = false);
static NumericalScalar qStudent(const NumericalScalar nu,
const NumericalScalar p,
const Bool tail = false);
static NumericalScalar rStudent(const NumericalScalar nu);
static NumericalPoint rStudent(const NumericalScalar nu,
const UnsignedInteger size);
static NumericalPoint rUniformTriangle(const NumericalPoint & a,
const NumericalPoint & b,
const NumericalPoint & c);
static NumericalSample rUniformTriangle(const NumericalPoint & a,
const NumericalPoint & b,
const NumericalPoint & c,
const UnsignedInteger size);
// For TruncatedNormal distribution
// static NumericalScalar rTruncatedNormal(const NumericalScalar a, const NumericalScalar b);
// Compute the expectation of the min of n independent standard normal random variables
static NumericalScalar eZ1(const UnsignedInteger n);
// Asymptotic distribution of the DickeyFuller distribution
static NumericalScalar pDickeyFullerTrend(const NumericalScalar x,
const Bool tail = false);
static NumericalScalar pDickeyFullerConstant(const NumericalScalar x,
const Bool tail = false);
static NumericalScalar pDickeyFullerNoConstant(const NumericalScalar x,
const Bool tail = false);
static NumericalScalar qDickeyFullerTrend(const NumericalScalar p,
const Bool tail = false);
static NumericalScalar qDickeyFullerConstant(const NumericalScalar p,
const Bool tail = false);
static NumericalScalar qDickeyFullerNoConstant(const NumericalScalar p,
const Bool tail = false);
}; /* class DistFunc */
END_NAMESPACE_OPENTURNS
#endif /* OPENTURNS_DISTFUNC_HXX */
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