/usr/include/ql/math/statistics/sequencestatistics.hpp is in libquantlib0-dev 1.1-2build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | /* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2003, 2004, 2005, 2006, 2007 Ferdinando Ametrano
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program 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 license for more details.
*/
/*! \file sequencestatistics.hpp
\brief Statistics tools for sequence (vector, list, array) samples
*/
#ifndef quantlib_sequence_statistics_hpp
#define quantlib_sequence_statistics_hpp
#include <ql/math/statistics/statistics.hpp>
#include <ql/math/statistics/incrementalstatistics.hpp>
#include <ql/math/matrix.hpp>
namespace QuantLib {
//! Statistics analysis of N-dimensional (sequence) data
/*! It provides 1-dimensional statistics as discrepancy plus
N-dimensional (sequence) statistics (e.g. mean,
variance, skewness, kurtosis, etc.) with one component for each
dimension of the sample space.
For most of the statistics this class relies on
the StatisticsType underlying class to provide 1-D methods that
will be iterated for all the components of the N-D data. These
lifted methods are the union of all the methods that might be
requested to the 1-D underlying StatisticsType class, with the
usual compile-time checks provided by the template approach.
\test the correctness of the returned values is tested by
checking them against numerical calculations.
*/
template <class StatisticsType>
class GenericSequenceStatistics {
public:
// typedefs
typedef StatisticsType statistics_type;
typedef std::vector<typename StatisticsType::value_type> value_type;
// constructor
GenericSequenceStatistics(Size dimension = 0);
//! \name inspectors
//@{
Size size() const { return dimension_; }
//@}
//! \name covariance and correlation
//@{
//! returns the covariance Matrix
Disposable<Matrix> covariance() const;
//! returns the correlation Matrix
Disposable<Matrix> correlation() const;
//@}
//! \name 1-D inspectors lifted from underlying statistics class
//@{
Size samples() const;
Real weightSum() const;
//@}
//! \name N-D inspectors lifted from underlying statistics class
//@{
// void argument list
std::vector<Real> mean() const;
std::vector<Real> variance() const;
std::vector<Real> standardDeviation() const;
std::vector<Real> downsideVariance() const;
std::vector<Real> downsideDeviation() const;
std::vector<Real> semiVariance() const;
std::vector<Real> semiDeviation() const;
std::vector<Real> errorEstimate() const;
std::vector<Real> skewness() const;
std::vector<Real> kurtosis() const;
std::vector<Real> min() const;
std::vector<Real> max() const;
// single argument list
std::vector<Real> gaussianPercentile(Real y) const;
std::vector<Real> percentile(Real y) const;
std::vector<Real> gaussianPotentialUpside(Real percentile) const;
std::vector<Real> potentialUpside(Real percentile) const;
std::vector<Real> gaussianValueAtRisk(Real percentile) const;
std::vector<Real> valueAtRisk(Real percentile) const;
std::vector<Real> gaussianExpectedShortfall(Real percentile) const;
std::vector<Real> expectedShortfall(Real percentile) const;
std::vector<Real> regret(Real target) const;
std::vector<Real> gaussianShortfall(Real target) const;
std::vector<Real> shortfall(Real target) const;
std::vector<Real> gaussianAverageShortfall(Real target) const;
std::vector<Real> averageShortfall(Real target) const;
//@}
//! \name Modifiers
//@{
void reset(Size dimension = 0);
template <class Sequence>
void add(const Sequence& sample,
Real weight = 1.0) {
add(sample.begin(), sample.end(), weight);
}
template <class Iterator>
void add(Iterator begin,
Iterator end,
Real weight = 1.0) {
if (dimension_ == 0) {
// stat wasn't initialized yet
QL_REQUIRE(end>begin, "sample error: end<=begin");
Size dimension = std::distance(begin, end);
reset(dimension);
}
QL_REQUIRE(std::distance(begin, end) == Integer(dimension_),
"sample size mismatch: " << dimension_ <<
" required, " << std::distance(begin, end) <<
" provided");
quadraticSum_ += weight * outerProduct(begin, end,
begin, end);
for (Size i=0; i<dimension_; ++begin, ++i)
stats_[i].add(*begin, weight);
}
//@}
protected:
Size dimension_;
std::vector<statistics_type> stats_;
mutable std::vector<Real> results_;
Matrix quadraticSum_;
};
//! default multi-dimensional statistics tool
/*! \test the correctness of the returned values is tested by
checking them against numerical calculations.
*/
typedef GenericSequenceStatistics<Statistics> SequenceStatistics;
typedef GenericSequenceStatistics<IncrementalStatistics> SequenceStatisticsInc;
// inline definitions
template <class Stat>
inline GenericSequenceStatistics<Stat>::GenericSequenceStatistics(Size dimension)
: dimension_(0) {
reset(dimension);
}
template <class Stat>
inline Size GenericSequenceStatistics<Stat>::samples() const {
return (stats_.size() == 0) ? 0 : stats_[0].samples();
}
template <class Stat>
inline Real GenericSequenceStatistics<Stat>::weightSum() const {
return (stats_.size() == 0) ? 0.0 : stats_[0].weightSum();
}
// macros for the implementation of the lifted methods
// N-D methods' definition with void argument list
#define DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(METHOD) \
template <class Stat> \
std::vector<Real> \
GenericSequenceStatistics<Stat>::METHOD() const { \
for (Size i=0; i<dimension_; i++) \
results_[i] = stats_[i].METHOD(); \
return results_; \
}
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(mean)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(variance)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(standardDeviation)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(downsideVariance)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(downsideDeviation)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(semiVariance)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(semiDeviation)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(errorEstimate)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(skewness)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(kurtosis)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(min)
DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID(max)
#undef DEFINE_SEQUENCE_STAT_CONST_METHOD_VOID
// N-D methods' definition with single argument
#define DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(METHOD) \
template <class Stat> \
std::vector<Real> \
GenericSequenceStatistics<Stat>::METHOD(Real x) const { \
for (Size i=0; i<dimension_; i++) \
results_[i] = stats_[i].METHOD(x); \
return results_; \
}
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(gaussianPercentile)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(gaussianPotentialUpside)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(gaussianValueAtRisk)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(gaussianExpectedShortfall)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(gaussianShortfall)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(gaussianAverageShortfall)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(percentile)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(potentialUpside)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(valueAtRisk)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(expectedShortfall)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(regret)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(shortfall)
DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE(averageShortfall)
#undef DEFINE_SEQUENCE_STAT_CONST_METHOD_DOUBLE
template <class Stat>
void GenericSequenceStatistics<Stat>::reset(Size dimension) {
// (re-)initialize
if (dimension > 0) {
if (dimension == dimension_) {
for (Size i=0; i<dimension_; ++i)
stats_[i].reset();
} else {
dimension_ = dimension;
stats_ = std::vector<Stat>(dimension);
results_ = std::vector<Real>(dimension);
}
quadraticSum_ = Matrix(dimension_, dimension_, 0.0);
} else {
dimension_ = dimension;
}
}
template <class Stat>
Disposable<Matrix> GenericSequenceStatistics<Stat>::covariance() const {
Real sampleWeight = weightSum();
QL_REQUIRE(sampleWeight > 0.0,
"sampleWeight=0, unsufficient");
Real sampleNumber = static_cast<Real>(samples());
QL_REQUIRE(sampleNumber > 1.0,
"sample number <=1, unsufficient");
std::vector<Real> m = mean();
Real inv = 1.0/sampleWeight;
Matrix result = inv*quadraticSum_;
result -= outerProduct(m.begin(), m.end(),
m.begin(), m.end());
result *= (sampleNumber/(sampleNumber-1.0));
return result;
}
template <class Stat>
Disposable<Matrix> GenericSequenceStatistics<Stat>::correlation() const {
Matrix correlation = covariance();
Array variances = correlation.diagonal();
for (Size i=0; i<dimension_; i++){
for (Size j=0; j<dimension_; j++){
if (i==j) {
if (variances[i]==0.0) {
correlation[i][j] = 1.0;
} else {
correlation[i][j] *=
1.0/std::sqrt(variances[i]*variances[j]);
}
} else {
if (variances[i]==0.0 && variances[j]==0) {
correlation[i][j] = 1.0;
} else if (variances[i]==0.0 || variances[j]==0.0) {
correlation[i][j] = 0.0;
} else {
correlation[i][j] *=
1.0/std::sqrt(variances[i]*variances[j]);
}
}
} // j for
} // i for
return correlation;
}
}
#endif
|