chart2/source/tools/ExponentialRegressionCurveCalculator.cxx

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  30
  31 // MARKER(update_precomp.py): autogen include statement, do not remove
  32 #include "precompiled_chart2.hxx"
  33 #include "ExponentialRegressionCurveCalculator.hxx"
  34 #include "macros.hxx"
  35 #include "RegressionCalculationHelper.hxx"
  36
  37 #include <rtl/math.hxx>
  38 #include <rtl/ustrbuf.hxx>
  39
  40 using namespace ::com::sun::star;
  41
  42 using ::rtl::OUString;
  43 using ::rtl::OUStringBuffer;
  44
  45 namespace chart
  46 {
  47
  48 ExponentialRegressionCurveCalculator::ExponentialRegressionCurveCalculator() :
  49         m_fSlope( 0.0 ),
  50         m_fIntercept( 0.0 )
  51 {
  52     ::rtl::math::setNan( & m_fSlope );
  53     ::rtl::math::setNan( & m_fIntercept );
  54 }
  55
  56 ExponentialRegressionCurveCalculator::~ExponentialRegressionCurveCalculator()
  57 {}
  58
  59 // ____ XRegressionCurveCalculator ____
  60 void SAL_CALL ExponentialRegressionCurveCalculator::recalculateRegression(
  61     const uno::Sequence< double >& aXValues,
  62     const uno::Sequence< double >& aYValues )
  63     throw (uno::RuntimeException)
  64 {
  65     RegressionCalculationHelper::tDoubleVectorPair aValues(
  66         RegressionCalculationHelper::cleanup(
  67             aXValues, aYValues,
  68             RegressionCalculationHelper::isValidAndYPositive()));
  69
  70     const size_t nMax = aValues.first.size();
  71     if( nMax == 0 )
  72     {
  73         ::rtl::math::setNan( & m_fSlope );
  74         ::rtl::math::setNan( & m_fIntercept );
  75         ::rtl::math::setNan( & m_fCorrelationCoeffitient );
  76         return;
  77     }
  78
  79     double fAverageX = 0.0, fAverageY = 0.0;
  80     size_t i = 0;
  81     for( i = 0; i < nMax; ++i )
  82     {
  83         fAverageX += aValues.first[i];
  84         fAverageY += log( aValues.second[i] );
  85     }
  86
  87     const double fN = static_cast< double >( nMax );
  88     fAverageX /= fN;
  89     fAverageY /= fN;
  90
  91     double fQx = 0.0, fQy = 0.0, fQxy = 0.0;
  92     for( i = 0; i < nMax; ++i )
  93     {
  94         double fDeltaX = aValues.first[i] - fAverageX;
  95         double fDeltaY = log( aValues.second[i] ) - fAverageY;
  96
  97         fQx  += fDeltaX * fDeltaX;
  98         fQy  += fDeltaY * fDeltaY;
  99         fQxy += fDeltaX * fDeltaY;
 100     }
 101
 102     m_fSlope = fQxy / fQx;
 103     m_fIntercept = fAverageY - m_fSlope * fAverageX;
 104     m_fCorrelationCoeffitient = fQxy / sqrt( fQx * fQy );
 105
 106     m_fSlope = exp( m_fSlope );
 107     m_fIntercept = exp( m_fIntercept );
 108 }
 109
 110 double SAL_CALL ExponentialRegressionCurveCalculator::getCurveValue( double x )
 111     throw (lang::IllegalArgumentException,
 112            uno::RuntimeException)
 113 {
 114     double fResult;
 115     ::rtl::math::setNan( & fResult );
 116
 117     if( ! ( ::rtl::math::isNan( m_fSlope ) ||
 118             ::rtl::math::isNan( m_fIntercept )))
 119     {
 120         fResult = m_fIntercept * pow( m_fSlope, x );
 121     }
 122
 123     return fResult;
 124 }
 125
 126 uno::Sequence< geometry::RealPoint2D > SAL_CALL ExponentialRegressionCurveCalculator::getCurveValues(
 127     double min, double max, ::sal_Int32 nPointCount,
 128     const uno::Reference< chart2::XScaling >& xScalingX,
 129     const uno::Reference< chart2::XScaling >& xScalingY,
 130     ::sal_Bool bMaySkipPointsInCalculation )
 131     throw (lang::IllegalArgumentException,
 132            uno::RuntimeException)
 133 {
 134     if( bMaySkipPointsInCalculation &&
 135         isLinearScaling( xScalingX ) &&
 136         isLogarithmicScaling( xScalingY ))
 137     {
 138         // optimize result
 139         uno::Sequence< geometry::RealPoint2D > aResult( 2 );
 140         aResult[0].X = min;
 141         aResult[0].Y = this->getCurveValue( min );
 142         aResult[1].X = max;
 143         aResult[1].Y = this->getCurveValue( max );
 144
 145         return aResult;
 146     }
 147
 148     return RegressionCurveCalculator::getCurveValues( min, max, nPointCount, xScalingX, xScalingY, bMaySkipPointsInCalculation );
 149 }
 150
 151
 152 OUString ExponentialRegressionCurveCalculator::ImplGetRepresentation(
 153     const uno::Reference< util::XNumberFormatter >& xNumFormatter,
 154     ::sal_Int32 nNumberFormatKey ) const
 155 {
 156     OUStringBuffer aBuf( C2U( "f(x) = " ));
 157
 158     if( m_fIntercept == 0.0 ||
 159         m_fSlope == 0.0 )
 160     {
 161         aBuf.append( sal_Unicode( '0' ));
 162     }
 163     else if( rtl::math::approxEqual( m_fSlope, 1.0 ) )
 164     {
 165         aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fIntercept ));
 166     }
 167     else
 168     {
 169         if( ! rtl::math::approxEqual( m_fIntercept, 1.0 ) )
 170         {
 171             aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fIntercept ));
 172             aBuf.append( sal_Unicode( 0x00b7 ));
 173         }
 174
 175         if( m_fSlope < 0.0 )
 176             aBuf.append( sal_Unicode( '(' ));
 177         aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fSlope ));
 178         if( m_fSlope < 0.0 )
 179             aBuf.append( sal_Unicode( ')' ));
 180         aBuf.appendAscii( RTL_CONSTASCII_STRINGPARAM( "^x" ));
 181     }
 182
 183     return aBuf.makeStringAndClear();
 184 }
 185
 186 } //  namespace chart