From 940b4d1848e8c70ab7642901a68594e8016caffc Mon Sep 17 00:00:00 2001
From: Daniel Baumann <daniel.baumann@progress-linux.org>
Date: Sat, 27 Apr 2024 18:51:28 +0200
Subject: Adding upstream version 1:7.0.4.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
---
 .../tools/MeanValueRegressionCurveCalculator.cxx   | 128 +++++++++++++++++++++
 1 file changed, 128 insertions(+)
 create mode 100644 chart2/source/tools/MeanValueRegressionCurveCalculator.cxx

(limited to 'chart2/source/tools/MeanValueRegressionCurveCalculator.cxx')

diff --git a/chart2/source/tools/MeanValueRegressionCurveCalculator.cxx b/chart2/source/tools/MeanValueRegressionCurveCalculator.cxx
new file mode 100644
index 000000000..c9821343b
--- /dev/null
+++ b/chart2/source/tools/MeanValueRegressionCurveCalculator.cxx
@@ -0,0 +1,128 @@
+/* -*- 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 <rtl/math.hxx>
+
+using namespace ::com::sun::star;
+
+namespace chart
+{
+
+MeanValueRegressionCurveCalculator::MeanValueRegressionCurveCalculator() :
+        m_fMeanValue( 0.0 )
+{
+    ::rtl::math::setNan( & m_fMeanValue );
+}
+
+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 )
+    {
+        ::rtl::math::setNan( & m_fMeanValue );
+    }
+    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( 2 );
+        aResult[0].X = min;
+        aResult[0].Y = m_fMeanValue;
+        aResult[1].X = max;
+        aResult[1].Y = 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: */
-- 
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