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diff --git a/chart2/source/tools/MeanValueRegressionCurveCalculator.cxx b/chart2/source/tools/MeanValueRegressionCurveCalculator.cxx
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+/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
+/*
+ * This file is part of the LibreOffice project.
+ *
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/.
+ *
+ * This file incorporates work covered by the following license notice:
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed
+ * with this work for additional information regarding copyright
+ * ownership. The ASF licenses this file to you under the Apache
+ * License, Version 2.0 (the "License"); you may not use this file
+ * except in compliance with the License. You may obtain a copy of
+ * the License at http://www.apache.org/licenses/LICENSE-2.0 .
+ */
+
+#include <MeanValueRegressionCurveCalculator.hxx>
+
+#include <osl/diagnose.h>
+
+#include <cmath>
+#include <limits>
+
+using namespace ::com::sun::star;
+
+namespace chart
+{
+
+MeanValueRegressionCurveCalculator::MeanValueRegressionCurveCalculator() :
+ m_fMeanValue( std::numeric_limits<double>::quiet_NaN() )
+{
+}
+
+MeanValueRegressionCurveCalculator::~MeanValueRegressionCurveCalculator()
+{}
+
+// ____ XRegressionCurveCalculator ____
+void SAL_CALL MeanValueRegressionCurveCalculator::recalculateRegression(
+ const uno::Sequence< double >& /*aXValues*/,
+ const uno::Sequence< double >& aYValues )
+{
+ const sal_Int32 nDataLength = aYValues.getLength();
+ sal_Int32 nMax = nDataLength;
+ double fSumY = 0.0;
+ const double * pY = aYValues.getConstArray();
+
+ for( sal_Int32 i = 0; i < nDataLength; ++i )
+ {
+ if( std::isnan( pY[i] ) ||
+ std::isinf( pY[i] ))
+ --nMax;
+ else
+ fSumY += pY[i];
+ }
+
+ m_fCorrelationCoefficient = 0.0;
+
+ if( nMax == 0 )
+ {
+ m_fMeanValue = std::numeric_limits<double>::quiet_NaN();
+ }
+ else
+ {
+ m_fMeanValue = fSumY / static_cast< double >( nMax );
+
+ // correlation coefficient: standard deviation
+ if( nMax > 1 )
+ {
+ double fErrorSum = 0.0;
+ for( sal_Int32 i = 0; i < nDataLength; ++i )
+ {
+ if( !std::isnan( pY[i] ) &&
+ !std::isinf( pY[i] ))
+ {
+ double v = m_fMeanValue - pY[i];
+ fErrorSum += (v*v);
+ }
+ }
+ OSL_ASSERT( fErrorSum >= 0.0 );
+ m_fCorrelationCoefficient = sqrt( fErrorSum / (nMax - 1 ));
+ }
+ }
+}
+
+double SAL_CALL MeanValueRegressionCurveCalculator::getCurveValue( double /*x*/ )
+{
+ return m_fMeanValue;
+}
+
+uno::Sequence< geometry::RealPoint2D > SAL_CALL MeanValueRegressionCurveCalculator::getCurveValues(
+ double min, double max, ::sal_Int32 nPointCount,
+ const uno::Reference< chart2::XScaling >& xScalingX,
+ const uno::Reference< chart2::XScaling >& xScalingY,
+ sal_Bool bMaySkipPointsInCalculation )
+{
+ if( bMaySkipPointsInCalculation )
+ {
+ // optimize result
+ uno::Sequence< geometry::RealPoint2D > aResult{ { min, m_fMeanValue },
+ { max, m_fMeanValue } };
+
+ return aResult;
+ }
+ return RegressionCurveCalculator::getCurveValues( min, max, nPointCount, xScalingX, xScalingY, bMaySkipPointsInCalculation );
+}
+
+OUString MeanValueRegressionCurveCalculator::ImplGetRepresentation(
+ const uno::Reference< util::XNumberFormatter >& xNumFormatter,
+ sal_Int32 nNumberFormatKey, sal_Int32* pFormulaLength /* = nullptr */ ) const
+{
+ OUString aBuf(mYName + " = ");
+ if ( pFormulaLength )
+ {
+ *pFormulaLength -= aBuf.getLength();
+ if ( *pFormulaLength <= 0 )
+ return "###";
+ }
+ return ( aBuf + getFormattedString( xNumFormatter, nNumberFormatKey, m_fMeanValue, pFormulaLength ) );
+}
+
+} // namespace chart
+
+/* vim:set shiftwidth=4 softtabstop=4 expandtab: */