mirror of
https://github.com/OPM/ResInsight.git
synced 2025-02-25 18:55:39 -06:00
66 lines
2.5 KiB
C++
66 lines
2.5 KiB
C++
/////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// Copyright (C) 2011- Statoil ASA
|
|
// Copyright (C) 2013- Ceetron Solutions AS
|
|
// Copyright (C) 2011-2012 Ceetron AS
|
|
//
|
|
// ResInsight is free software: you can redistribute it and/or modify
|
|
// it under the terms of the GNU General Public License as published by
|
|
// the Free Software Foundation, either version 3 of the License, or
|
|
// (at your option) any later version.
|
|
//
|
|
// ResInsight 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 General Public License at <http://www.gnu.org/licenses/gpl.html>
|
|
// for more details.
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////
|
|
|
|
#include "RiaStatisticsTools.h"
|
|
|
|
#include "RigStatisticsMath.h"
|
|
|
|
//--------------------------------------------------------------------------------------------------
|
|
///
|
|
//--------------------------------------------------------------------------------------------------
|
|
double RiaStatisticsTools::pearsonCorrelation( const std::vector<double>& xValues, const std::vector<double>& yValues )
|
|
{
|
|
const double eps = 1.0e-8;
|
|
double rangeX = 0.0, rangeY = 0.0;
|
|
RigStatisticsMath::calculateBasicStatistics( xValues, nullptr, nullptr, nullptr, &rangeX, nullptr, nullptr );
|
|
RigStatisticsMath::calculateBasicStatistics( yValues, nullptr, nullptr, nullptr, &rangeY, nullptr, nullptr );
|
|
if ( rangeX < eps || rangeY < eps ) return 0.0;
|
|
|
|
if ( xValues.size() != yValues.size() ) return 0.0;
|
|
if ( xValues.empty() ) return 0.0;
|
|
|
|
size_t sampleSize = xValues.size();
|
|
|
|
double meanX = 0.0, meanY = 0.0;
|
|
for ( size_t i = 0; i < sampleSize; ++i )
|
|
{
|
|
meanX += xValues[i];
|
|
meanY += yValues[i];
|
|
}
|
|
meanX /= sampleSize;
|
|
meanY /= sampleSize;
|
|
|
|
double sumNumerator = 0.0;
|
|
double sumxDiffSquared = 0.0, sumyDiffSquared = 0.0;
|
|
for ( size_t i = 0; i < sampleSize; ++i )
|
|
{
|
|
double xDiff = xValues[i] - meanX;
|
|
double yDiff = yValues[i] - meanY;
|
|
sumNumerator += xDiff * yDiff;
|
|
sumxDiffSquared += xDiff * xDiff;
|
|
sumyDiffSquared += yDiff * yDiff;
|
|
}
|
|
|
|
if ( sumxDiffSquared < eps && sumyDiffSquared < eps ) return 1.0;
|
|
if ( sumxDiffSquared < eps || sumyDiffSquared < eps ) return 0.0;
|
|
|
|
return sumNumerator / ( std::sqrt( sumxDiffSquared ) * std::sqrt( sumyDiffSquared ) );
|
|
}
|