chart2/source/tools/LogarithmicRegressionCurveCalculator.cxx

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  27
  28 // MARKER(update_precomp.py): autogen include statement, do not remove
  29 #include "precompiled_chart2.hxx"
  30 #include "LogarithmicRegressionCurveCalculator.hxx"
  31 #include "macros.hxx"
  32 #include "RegressionCalculationHelper.hxx"
  33
  34 #include <rtl/math.hxx>
  35 #include <rtl/ustrbuf.hxx>
  36
  37 using namespace ::com::sun::star;
  38
  39 using ::rtl::OUString;
  40 using ::rtl::OUStringBuffer;
  41
  42 namespace chart
  43 {
  44
  45 LogarithmicRegressionCurveCalculator::LogarithmicRegressionCurveCalculator() :
  46         m_fSlope( 0.0 ),
  47         m_fIntercept( 0.0 )
  48 {
  49     ::rtl::math::setNan( & m_fSlope );
  50     ::rtl::math::setNan( & m_fIntercept );
  51 }
  52
  53 LogarithmicRegressionCurveCalculator::~LogarithmicRegressionCurveCalculator()
  54 {}
  55
  56 // ____ XRegressionCurve ____
  57 void SAL_CALL LogarithmicRegressionCurveCalculator::recalculateRegression(
  58     const uno::Sequence< double >& aXValues,
  59     const uno::Sequence< double >& aYValues )
  60     throw (uno::RuntimeException)
  61 {
  62     RegressionCalculationHelper::tDoubleVectorPair aValues(
  63         RegressionCalculationHelper::cleanup(
  64             aXValues, aYValues,
  65             RegressionCalculationHelper::isValidAndXPositive()));
  66
  67     const size_t nMax = aValues.first.size();
  68     if( nMax == 0 )
  69     {
  70         ::rtl::math::setNan( & m_fSlope );
  71         ::rtl::math::setNan( & m_fIntercept );
  72         ::rtl::math::setNan( & m_fCorrelationCoeffitient );
  73         return;
  74     }
  75
  76     double fAverageX = 0.0, fAverageY = 0.0;
  77     size_t i = 0;
  78     for( i = 0; i < nMax; ++i )
  79     {
  80         fAverageX += log( aValues.first[i] );
  81         fAverageY += aValues.second[i];
  82     }
  83
  84     const double fN = static_cast< double >( nMax );
  85     fAverageX /= fN;
  86     fAverageY /= fN;
  87
  88     double fQx = 0.0, fQy = 0.0, fQxy = 0.0;
  89     for( i = 0; i < nMax; ++i )
  90     {
  91         double fDeltaX = log( aValues.first[i] ) - fAverageX;
  92         double fDeltaY = aValues.second[i] - fAverageY;
  93
  94         fQx  += fDeltaX * fDeltaX;
  95         fQy  += fDeltaY * fDeltaY;
  96         fQxy += fDeltaX * fDeltaY;
  97     }
  98
  99     m_fSlope = fQxy / fQx;
 100     m_fIntercept = fAverageY - m_fSlope * fAverageX;
 101     m_fCorrelationCoeffitient = fQxy / sqrt( fQx * fQy );
 102 }
 103
 104 double SAL_CALL LogarithmicRegressionCurveCalculator::getCurveValue( double x )
 105     throw (lang::IllegalArgumentException,
 106            uno::RuntimeException)
 107 {
 108     double fResult;
 109     ::rtl::math::setNan( & fResult );
 110
 111     if( ! ( ::rtl::math::isNan( m_fSlope ) ||
 112             ::rtl::math::isNan( m_fIntercept )))
 113     {
 114         fResult = m_fSlope * log( x ) + m_fIntercept;
 115     }
 116
 117     return fResult;
 118 }
 119
 120 uno::Sequence< geometry::RealPoint2D > SAL_CALL LogarithmicRegressionCurveCalculator::getCurveValues(
 121     double min, double max, ::sal_Int32 nPointCount,
 122     const uno::Reference< chart2::XScaling >& xScalingX,
 123     const uno::Reference< chart2::XScaling >& xScalingY,
 124     ::sal_Bool bMaySkipPointsInCalculation )
 125     throw (lang::IllegalArgumentException,
 126            uno::RuntimeException)
 127 {
 128     if( bMaySkipPointsInCalculation &&
 129         isLogarithmicScaling( xScalingX ) &&
 130         isLinearScaling( xScalingY ))
 131     {
 132         // optimize result
 133         uno::Sequence< geometry::RealPoint2D > aResult( 2 );
 134         aResult[0].X = min;
 135         aResult[0].Y = this->getCurveValue( min );
 136         aResult[1].X = max;
 137         aResult[1].Y = this->getCurveValue( max );
 138
 139         return aResult;
 140     }
 141     return RegressionCurveCalculator::getCurveValues( min, max, nPointCount, xScalingX, xScalingY, bMaySkipPointsInCalculation );
 142 }
 143
 144 OUString LogarithmicRegressionCurveCalculator::ImplGetRepresentation(
 145     const uno::Reference< util::XNumberFormatter >& xNumFormatter,
 146     ::sal_Int32 nNumberFormatKey ) const
 147 {
 148     OUStringBuffer aBuf( C2U( "f(x) = " ));
 149
 150     bool bHaveSlope = false;
 151
 152     if( m_fSlope != 0.0 )
 153     {
 154         if( ::rtl::math::approxEqual( fabs( m_fSlope ), 1.0 ))
 155         {
 156             if( m_fSlope < 0 )
 157                 aBuf.append( UC_MINUS_SIGN );
 158         }
 159         else
 160         {
 161             aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fSlope ));
 162             aBuf.append( UC_SPACE );
 163         }
 164         aBuf.appendAscii( RTL_CONSTASCII_STRINGPARAM( "ln(x)" ));
 165         bHaveSlope = true;
 166     }
 167
 168     if( bHaveSlope )
 169     {
 170         if( m_fIntercept < 0.0 )
 171         {
 172             aBuf.append( UC_SPACE );
 173             aBuf.append( UC_MINUS_SIGN );
 174             aBuf.append( UC_SPACE );
 175             aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, fabs( m_fIntercept )));
 176         }
 177         else if( m_fIntercept > 0.0 )
 178         {
 179             aBuf.appendAscii( RTL_CONSTASCII_STRINGPARAM( " + " ));
 180             aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fIntercept ));
 181         }
 182     }
 183     else
 184     {
 185         aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fIntercept ));
 186     }
 187
 188     return aBuf.makeStringAndClear();
 189 }
 190
 191 } //  namespace chart