chart2/source/tools/ExponentialRegressionCurveCalculator.cxx

   1 /* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
   2 /*
   3  * This file is part of the LibreOffice project.
   4  *
   5  * This Source Code Form is subject to the terms of the Mozilla Public
   6  * License, v. 2.0. If a copy of the MPL was not distributed with this
   7  * file, You can obtain one at http://mozilla.org/MPL/2.0/.
   8  *
   9  * This file incorporates work covered by the following license notice:
  10  *
  11  *   Licensed to the Apache Software Foundation (ASF) under one or more
  12  *   contributor license agreements. See the NOTICE file distributed
  13  *   with this work for additional information regarding copyright
  14  *   ownership. The ASF licenses this file to you under the Apache
  15  *   License, Version 2.0 (the "License"); you may not use this file
  16  *   except in compliance with the License. You may obtain a copy of
  17  *   the License at http://www.apache.org/licenses/LICENSE-2.0 .
  18  */
  19
  20 #include <sal/config.h>
  21
  22 #include <limits>
  23 #include <string_view>
  24
  25 #include <ExponentialRegressionCurveCalculator.hxx>
  26 #include <RegressionCalculationHelper.hxx>
  27 #include <SpecialCharacters.hxx>
  28
  29 #include <rtl/math.hxx>
  30 #include <rtl/ustrbuf.hxx>
  31
  32 using namespace ::com::sun::star;
  33
  34 namespace chart
  35 {
  36
  37 ExponentialRegressionCurveCalculator::ExponentialRegressionCurveCalculator()
  38     : m_fLogSlope(std::numeric_limits<double>::quiet_NaN())
  39     , m_fLogIntercept(std::numeric_limits<double>::quiet_NaN())
  40     , m_fSign(1.0)
  41 {
  42 }
  43
  44 ExponentialRegressionCurveCalculator::~ExponentialRegressionCurveCalculator()
  45 {}
  46
  47 // ____ XRegressionCurveCalculator ____
  48 void SAL_CALL ExponentialRegressionCurveCalculator::recalculateRegression(
  49     const uno::Sequence< double >& aXValues,
  50     const uno::Sequence< double >& aYValues )
  51 {
  52     RegressionCalculationHelper::tDoubleVectorPair aValues(
  53         RegressionCalculationHelper::cleanup(
  54             aXValues, aYValues,
  55             RegressionCalculationHelper::isValidAndYPositive()));
  56     m_fSign = 1.0;
  57
  58     size_t nMax = aValues.first.size();
  59     if( nMax <= 1 ) // at least 2 points
  60     {
  61         aValues = RegressionCalculationHelper::cleanup(
  62                     aXValues, aYValues,
  63                     RegressionCalculationHelper::isValidAndYNegative());
  64         nMax = aValues.first.size();
  65         if( nMax <= 1 )
  66         {
  67             m_fLogSlope = std::numeric_limits<double>::quiet_NaN();
  68             m_fLogIntercept = std::numeric_limits<double>::quiet_NaN();
  69             m_fCorrelationCoefficient = std::numeric_limits<double>::quiet_NaN();// actual it is coefficient of determination
  70             return;
  71         }
  72         m_fSign = -1.0;
  73     }
  74
  75     double fAverageX = 0.0, fAverageY = 0.0;
  76     double fLogIntercept = ( mForceIntercept && (m_fSign * mInterceptValue)>0 ) ? log(m_fSign * mInterceptValue) : 0.0;
  77     std::vector<double> yVector;
  78     yVector.resize(nMax, 0.0);
  79
  80     size_t i = 0;
  81     for( i = 0; i < nMax; ++i )
  82     {
  83         double yValue = log( m_fSign *aValues.second[i] );
  84         if (mForceIntercept)
  85         {
  86             yValue -= fLogIntercept;
  87         }
  88         else
  89         {
  90             fAverageX += aValues.first[i];
  91             fAverageY += yValue;
  92         }
  93         yVector[i] = yValue;
  94     }
  95
  96     const double fN = static_cast< double >( nMax );
  97     fAverageX /= fN;
  98     fAverageY /= fN;
  99
 100     double fQx = 0.0, fQy = 0.0, fQxy = 0.0;
 101     for( i = 0; i < nMax; ++i )
 102     {
 103         double fDeltaX = aValues.first[i] - fAverageX;
 104         double fDeltaY = yVector[i] - fAverageY;
 105
 106         fQx  += fDeltaX * fDeltaX;
 107         fQy  += fDeltaY * fDeltaY;
 108         fQxy += fDeltaX * fDeltaY;
 109     }
 110
 111     m_fLogSlope = fQxy / fQx;
 112     m_fLogIntercept = mForceIntercept ? fLogIntercept : fAverageY - m_fLogSlope * fAverageX;
 113     m_fCorrelationCoefficient = fQxy / sqrt( fQx * fQy );
 114 }
 115
 116 double SAL_CALL ExponentialRegressionCurveCalculator::getCurveValue( double x )
 117 {
 118     if( ! ( std::isnan( m_fLogSlope ) ||
 119             std::isnan( m_fLogIntercept )))
 120     {
 121         return m_fSign * exp(m_fLogIntercept + x * m_fLogSlope);
 122     }
 123
 124     return std::numeric_limits<double>::quiet_NaN();
 125 }
 126
 127 uno::Sequence< geometry::RealPoint2D > SAL_CALL ExponentialRegressionCurveCalculator::getCurveValues(
 128     double min, double max, ::sal_Int32 nPointCount,
 129     const uno::Reference< chart2::XScaling >& xScalingX,
 130     const uno::Reference< chart2::XScaling >& xScalingY,
 131     sal_Bool bMaySkipPointsInCalculation )
 132 {
 133     if( bMaySkipPointsInCalculation &&
 134         isLinearScaling( xScalingX ) &&
 135         isLogarithmicScaling( xScalingY ))
 136     {
 137         // optimize result
 138         uno::Sequence< geometry::RealPoint2D > aResult{ { min, getCurveValue( min ) },
 139                                                         { max, getCurveValue( max ) } };
 140
 141         return aResult;
 142     }
 143
 144     return RegressionCurveCalculator::getCurveValues( min, max, nPointCount, xScalingX, xScalingY, bMaySkipPointsInCalculation );
 145 }
 146
 147 OUString ExponentialRegressionCurveCalculator::ImplGetRepresentation(
 148     const uno::Reference< util::XNumberFormatter >& xNumFormatter,
 149     sal_Int32 nNumberFormatKey, sal_Int32* pFormulaMaxWidth /* = nullptr */ ) const
 150 {
 151     double fIntercept = exp(m_fLogIntercept);
 152     bool bHasSlope = !rtl::math::approxEqual( exp(m_fLogSlope), 1.0 );
 153     bool bHasLogSlope = !rtl::math::approxEqual( fabs(m_fLogSlope), 1.0 );
 154     bool bHasIntercept = !rtl::math::approxEqual( fIntercept, 1.0 ) && fIntercept != 0.0;
 155
 156     OUStringBuffer aBuf( mYName + " = " );
 157     sal_Int32 nLineLength = aBuf.getLength();
 158     sal_Int32 nValueLength=0;
 159     if ( pFormulaMaxWidth && *pFormulaMaxWidth > 0 )
 160     {          // count characters different from coefficients
 161         sal_Int32 nCharMin = nLineLength + 10 + mXName.getLength();  // 10 = "exp( ", " x )" + 2 extra characters
 162         if ( m_fSign < 0.0 )
 163             nCharMin += 2;
 164         if ( fIntercept == 0.0 || ( !bHasSlope && m_fLogIntercept != 0.0 ) )
 165             nCharMin += 3; // " + " special case where equation is written exp( a + b x )
 166         if ( ( bHasIntercept || fIntercept == 0.0 || ( !bHasSlope && m_fLogIntercept != 0.0 ) ) &&
 167                bHasLogSlope )
 168             nValueLength = ( *pFormulaMaxWidth - nCharMin ) / 2;
 169         else
 170             nValueLength = *pFormulaMaxWidth - nCharMin;
 171         if ( nValueLength <= 0 )
 172             nValueLength = 1;
 173     }
 174                     // temporary buffer
 175     OUStringBuffer aTmpBuf("");
 176         // if nValueLength not calculated then nullptr
 177     sal_Int32* pValueLength = nValueLength ? &nValueLength : nullptr;
 178     if ( m_fSign < 0.0 )
 179         aTmpBuf.append( OUStringChar(aMinusSign) + " " );
 180     if ( bHasIntercept )
 181     {
 182         OUString aValueString = getFormattedString( xNumFormatter, nNumberFormatKey, fIntercept, pValueLength );
 183         if ( aValueString != "1" )  // aValueString may be rounded to 1 if nValueLength is small
 184         {
 185             aTmpBuf.append( aValueString + " " );
 186             addStringToEquation( aBuf, nLineLength, aTmpBuf, pFormulaMaxWidth );
 187             aTmpBuf.truncate();
 188         }
 189     }
 190     aTmpBuf.append( "exp( " );
 191     if ( !bHasIntercept )
 192     {
 193         if ( fIntercept == 0.0 ||  // underflow, a true zero is impossible
 194            ( !bHasSlope && m_fLogIntercept != 0.0 ) )   // show logarithmic output, if intercept and slope both are near one
 195         {                                               // otherwise drop output of intercept, which is 1 here
 196             OUString aValueString = getFormattedString( xNumFormatter, nNumberFormatKey, m_fLogIntercept, pValueLength );
 197             if ( aValueString != "0" )  // aValueString may be rounded to 0 if nValueLength is small
 198             {
 199                 aTmpBuf.append( aValueString ).append( (m_fLogSlope < 0.0) ? std::u16string_view(u" ") : std::u16string_view(u" + ") );
 200             }
 201         }
 202     }
 203     if ( m_fLogSlope < 0.0 )
 204         aTmpBuf.append( OUStringChar(aMinusSign) + " " );
 205     if ( bHasLogSlope )
 206     {
 207         OUString aValueString = getFormattedString( xNumFormatter, nNumberFormatKey, fabs(m_fLogSlope), pValueLength );
 208         if ( aValueString != "1" )  // aValueString may be rounded to 1 if nValueLength is small
 209         {
 210             aTmpBuf.append( aValueString + " " );
 211         }
 212     }
 213     aTmpBuf.append( mXName + " )");
 214     addStringToEquation( aBuf, nLineLength, aTmpBuf, pFormulaMaxWidth );
 215
 216     return aBuf.makeStringAndClear();
 217 }
 218
 219 } //  namespace chart
 220
 221 /* vim:set shiftwidth=4 softtabstop=4 expandtab: */