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/*
Copyright (C) 2000, 2001, 2002 RiskMap srl
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 ferdinando@ametrano.net
The license is also available online at http://quantlib.org/html/license.html
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 multivariateaccumulator.cpp
\brief A simple accumulator for vector-type samples
\fullpath
ql/Math/%multivariateaccumulator.cpp
*/
// $Id: multivariateaccumulator.cpp,v 1.12 2002/03/14 15:51:00 aleppo Exp $
#include <ql/Math/multivariateaccumulator.hpp>
namespace QuantLib {
namespace Math {
MultivariateAccumulator::MultivariateAccumulator()
: size_(0) {
reset();
}
MultivariateAccumulator::MultivariateAccumulator(Size size)
: size_(size){
reset();
}
void MultivariateAccumulator::reset() {
sampleNumber_ = 0;
sampleWeight_ = 0.0;
sum_ = Array(size_,0.0);
quadraticSum_ = Matrix(size_, size_, 0.0);
}
void MultivariateAccumulator::add(const Array &value, double weight) {
/*! \pre weights must be positive or null */
if(size_ == 0){
size_ = value.size();
reset();
} else {
QL_REQUIRE(value.size() == size_,
"MultivariateAccumulator::add : "
"wrong size for input array");
}
QL_REQUIRE(weight >= 0.0,
"MultivariateAccumulator::add : negative weight (" +
DoubleFormatter::toString(weight) + ") not allowed");
Size oldSamples = sampleNumber_;
sampleNumber_++;
QL_ENSURE(sampleNumber_ > oldSamples,
"MultivariateAccumulator::add : "
"maximum number of samples reached");
sampleWeight_ += weight;
Array weighedValue = weight*value;
sum_ += weighedValue;
quadraticSum_ += outerProduct(weighedValue, value);
}
Matrix MultivariateAccumulator::covariance() const {
QL_REQUIRE(sampleWeight_ > 0.0,
"Stat::variance() : sampleWeight_=0, unsufficient");
QL_REQUIRE(sampleNumber_ > 1,
"Stat::variance() : sample number <=1, unsufficient");
double inv = 1/sampleWeight_;
return (sampleNumber_/(sampleNumber_-1.0))*
inv*(quadraticSum_ - inv*outerProduct(sum_,sum_) );
}
Matrix MultivariateAccumulator::correlation() const {
Matrix correlation = covariance();
Array variances = correlation.diagonal();
size_t dimension = variances.size();
for (size_t i=0; i < dimension; ++i){
for (size_t j=0 ; j < dimension; ++j){
if( i == j){
if(variances[i] == 0.0){
correlation[i][j] = 1.0;
}
else{
correlation[i][j] *=1.0/QL_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/QL_SQRT(variances[i]*variances[j]);
}
}
}
}
return correlation;
}
std::vector<double> MultivariateAccumulator::meanVector() const {
Array ma(mean());
std::vector<double> mv(ma.size());
std::copy(ma.begin(), ma.end(), mv.begin());
return mv;
}
void MultivariateAccumulator::add(const std::vector<double> &vec,
double wei){
Array arr(vec.size());
std::copy(vec.begin(), vec.end(), arr.begin());
add(arr, wei);
}
}
}
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