chart2/source/inc/PolynomialRegressionCurveCalculator.hxx | 10
chart2/source/tools/PolynomialRegressionCurveCalculator.cxx | 137 ++++++++----
2 files changed, 111 insertions(+), 36 deletions(-)
New commits:
commit 8df6f6ec12972ce2c14a162e6f4dd2c0d32367ef
Author: Deena Francis <deena.fran...@gmail.com>
AuthorDate: Mon Dec 16 18:47:11 2019 +0530
Commit: Tomaž Vajngerl <qui...@gmail.com>
CommitDate: Wed Dec 18 22:52:17 2019 +0100
tdf#128995: Special case for single variable regression...
like in LINEST implementation in Calc. Use a straightforward
regression solver in this case, so that it is easier to
handle the numerical error in the intercept term using
::rtl::math::approxSub().
Change-Id: I627c0c48e377cac5385a85050c4f472ee963f3d6
Reviewed-on: https://gerrit.libreoffice.org/85222
Tested-by: Jenkins
Reviewed-by: Tomaž Vajngerl <qui...@gmail.com>
diff --git a/chart2/source/inc/PolynomialRegressionCurveCalculator.hxx
b/chart2/source/inc/PolynomialRegressionCurveCalculator.hxx
index 6037fc742a78..e47d882d4903 100644
--- a/chart2/source/inc/PolynomialRegressionCurveCalculator.hxx
+++ b/chart2/source/inc/PolynomialRegressionCurveCalculator.hxx
@@ -22,6 +22,11 @@
#include "RegressionCurveCalculator.hxx"
#include <vector>
+namespace RegressionCalculationHelper
+{
+ typedef std::pair< std::vector< double >, std::vector< double > >
tDoubleVectorPair;
+}
+
namespace chart
{
@@ -44,6 +49,11 @@ private:
const css::uno::Sequence<double>& aXValues,
const css::uno::Sequence<double>& aYValues ) override;
+ void computeCorrelationCoefficient(
+ RegressionCalculationHelper::tDoubleVectorPair& rValues,
+ const sal_Int32 aNoValues,
+ double yAverage );
+
std::vector<double> mCoefficients;
};
diff --git a/chart2/source/tools/PolynomialRegressionCurveCalculator.cxx
b/chart2/source/tools/PolynomialRegressionCurveCalculator.cxx
index 8658f6004c1e..c1e17594a316 100644
--- a/chart2/source/tools/PolynomialRegressionCurveCalculator.cxx
+++ b/chart2/source/tools/PolynomialRegressionCurveCalculator.cxx
@@ -31,12 +31,60 @@ using namespace com::sun::star;
namespace chart
{
+static double lcl_GetDotProduct(std::vector<double>& aVec1,
std::vector<double>& aVec2)
+{
+ double fResult = 0.0;
+ assert(aVec1.size() == aVec2.size());
+ for (size_t i = 0; i < aVec1.size(); ++i)
+ fResult += aVec1[i] * aVec2[i];
+ return fResult;
+}
+
PolynomialRegressionCurveCalculator::PolynomialRegressionCurveCalculator()
{}
PolynomialRegressionCurveCalculator::~PolynomialRegressionCurveCalculator()
{}
+void PolynomialRegressionCurveCalculator::computeCorrelationCoefficient(
+ RegressionCalculationHelper::tDoubleVectorPair& rValues,
+ const sal_Int32 aNoValues,
+ double yAverage )
+{
+ double aSumError = 0.0;
+ double aSumTotal = 0.0;
+ double aSumYpred2 = 0.0;
+
+ for( sal_Int32 i = 0; i < aNoValues; i++ )
+ {
+ double xValue = rValues.first[i];
+ double yActual = rValues.second[i];
+ double yPredicted = getCurveValue( xValue );
+ aSumTotal += (yActual - yAverage) * (yActual - yAverage);
+ aSumError += (yActual - yPredicted) * (yActual - yPredicted);
+ if(mForceIntercept)
+ aSumYpred2 += (yPredicted - mInterceptValue) * (yPredicted -
mInterceptValue);
+ }
+
+ double aRSquared = 0.0;
+ if(mForceIntercept)
+ {
+ if (auto const div = aSumError + aSumYpred2)
+ {
+ aRSquared = aSumYpred2 / div;
+ }
+ }
+ else if (aSumTotal != 0.0)
+ {
+ aRSquared = 1.0 - (aSumError / aSumTotal);
+ }
+
+ if (aRSquared > 0.0)
+ m_fCorrelationCoeffitient = std::sqrt(aRSquared);
+ else
+ m_fCorrelationCoeffitient = 0.0;
+}
+
// ____ XRegressionCurveCalculator ____
void SAL_CALL PolynomialRegressionCurveCalculator::recalculateRegression(
const uno::Sequence< double >& aXValues,
@@ -56,9 +104,6 @@ void SAL_CALL
PolynomialRegressionCurveCalculator::recalculateRegression(
double yAverage = 0.0;
- std::vector<double> aQRTransposed;
- aQRTransposed.resize(aNoValues * aNoPowers, 0.0);
-
std::vector<double> yVector;
yVector.resize(aNoValues, 0.0);
@@ -75,6 +120,57 @@ void SAL_CALL
PolynomialRegressionCurveCalculator::recalculateRegression(
yAverage /= aNoValues;
}
+ // Special case for single variable regression like in LINEST
+ // implementation in Calc.
+ if (mDegree == 1)
+ {
+ std::vector<double> xVector;
+ xVector.resize(aNoValues, 0.0);
+ double xAverage = 0.0;
+
+ for(sal_Int32 i = 0; i < aNoValues; ++i)
+ {
+ double xValue = aValues.first[i];
+ xVector[i] = xValue;
+ xAverage += xValue;
+ }
+ if (aNoValues != 0)
+ {
+ xAverage /= aNoValues;
+ }
+
+ if (!mForceIntercept)
+ {
+ for (sal_Int32 i = 0; i < aNoValues; ++i)
+ {
+ xVector[i] -= xAverage;
+ yVector[i] -= yAverage;
+ }
+ }
+ double fSumXY = lcl_GetDotProduct(xVector, yVector);
+ double fSumX2 = lcl_GetDotProduct(xVector, xVector);
+
+ double fSlope = fSumXY / fSumX2;
+
+ if (!mForceIntercept)
+ {
+ mInterceptValue = ::rtl::math::approxSub(yAverage, fSlope *
xAverage);
+ mCoefficients[0] = mInterceptValue;
+ mCoefficients[1] = fSlope;
+ }
+ else
+ {
+ mCoefficients[0] = fSlope;
+ mCoefficients.insert(mCoefficients.begin(), mInterceptValue);
+ }
+
+ computeCorrelationCoefficient(aValues, aNoValues, yAverage);
+ return;
+ }
+
+ std::vector<double> aQRTransposed;
+ aQRTransposed.resize(aNoValues * aNoPowers, 0.0);
+
for(sal_Int32 j = 0; j < aNoPowers; j++)
{
sal_Int32 aPower = mForceIntercept ? j+1 : j;
@@ -167,39 +263,8 @@ void SAL_CALL
PolynomialRegressionCurveCalculator::recalculateRegression(
mCoefficients.insert(mCoefficients.begin(), mInterceptValue);
}
- // Calculate correlation coeffitient
- double aSumError = 0.0;
- double aSumTotal = 0.0;
- double aSumYpred2 = 0.0;
-
- for( sal_Int32 i = 0; i < aNoValues; i++ )
- {
- double xValue = aValues.first[i];
- double yActual = aValues.second[i];
- double yPredicted = getCurveValue( xValue );
- aSumTotal += (yActual - yAverage) * (yActual - yAverage);
- aSumError += (yActual - yPredicted) * (yActual - yPredicted);
- if(mForceIntercept)
- aSumYpred2 += (yPredicted - mInterceptValue) * (yPredicted -
mInterceptValue);
- }
-
- double aRSquared = 0.0;
- if(mForceIntercept)
- {
- if (auto const div = aSumError + aSumYpred2)
- {
- aRSquared = aSumYpred2 / div;
- }
- }
- else if (aSumTotal != 0.0)
- {
- aRSquared = 1.0 - (aSumError / aSumTotal);
- }
-
- if (aRSquared > 0.0)
- m_fCorrelationCoeffitient = std::sqrt(aRSquared);
- else
- m_fCorrelationCoeffitient = 0.0;
+ // Calculate correlation coefficient
+ computeCorrelationCoefficient(aValues, aNoValues, yAverage);
}
double SAL_CALL PolynomialRegressionCurveCalculator::getCurveValue( double x )
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