1 /************************************************************** 2 * 3 * Licensed to the Apache Software Foundation (ASF) under one 4 * or more contributor license agreements. See the NOTICE file 5 * distributed with this work for additional information 6 * regarding copyright ownership. The ASF licenses this file 7 * to you under the Apache License, Version 2.0 (the 8 * "License"); you may not use this file except in compliance 9 * with the License. You may obtain a copy of the License at 10 * 11 * http://www.apache.org/licenses/LICENSE-2.0 12 * 13 * Unless required by applicable law or agreed to in writing, 14 * software distributed under the License is distributed on an 15 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY 16 * KIND, either express or implied. See the License for the 17 * specific language governing permissions and limitations 18 * under the License. 19 * 20 *************************************************************/ 21 22 23 24 // MARKER(update_precomp.py): autogen include statement, do not remove 25 #include "precompiled_charttools.hxx" 26 #include "LogarithmicRegressionCurveCalculator.hxx" 27 #include "macros.hxx" 28 #include "RegressionCalculationHelper.hxx" 29 30 #include <rtl/math.hxx> 31 #include <rtl/ustrbuf.hxx> 32 33 using namespace ::com::sun::star; 34 35 using ::rtl::OUString; 36 using ::rtl::OUStringBuffer; 37 38 namespace chart 39 { 40 41 LogarithmicRegressionCurveCalculator::LogarithmicRegressionCurveCalculator() : 42 m_fSlope( 0.0 ), 43 m_fIntercept( 0.0 ) 44 { 45 ::rtl::math::setNan( & m_fSlope ); 46 ::rtl::math::setNan( & m_fIntercept ); 47 } 48 49 LogarithmicRegressionCurveCalculator::~LogarithmicRegressionCurveCalculator() 50 {} 51 52 // ____ XRegressionCurve ____ 53 void SAL_CALL LogarithmicRegressionCurveCalculator::recalculateRegression( 54 const uno::Sequence< double >& aXValues, 55 const uno::Sequence< double >& aYValues ) 56 { 57 RegressionCalculationHelper::tDoubleVectorPair aValues( 58 RegressionCalculationHelper::cleanup( 59 aXValues, aYValues, 60 RegressionCalculationHelper::isValidAndXPositive())); 61 62 const size_t nMax = aValues.first.size(); 63 if( nMax == 0 ) 64 { 65 ::rtl::math::setNan( & m_fSlope ); 66 ::rtl::math::setNan( & m_fIntercept ); 67 ::rtl::math::setNan( & m_fCorrelationCoeffitient ); 68 return; 69 } 70 71 double fAverageX = 0.0, fAverageY = 0.0; 72 size_t i = 0; 73 for( i = 0; i < nMax; ++i ) 74 { 75 fAverageX += log( aValues.first[i] ); 76 fAverageY += aValues.second[i]; 77 } 78 79 const double fN = static_cast< double >( nMax ); 80 fAverageX /= fN; 81 fAverageY /= fN; 82 83 double fQx = 0.0, fQy = 0.0, fQxy = 0.0; 84 for( i = 0; i < nMax; ++i ) 85 { 86 double fDeltaX = log( aValues.first[i] ) - fAverageX; 87 double fDeltaY = aValues.second[i] - fAverageY; 88 89 fQx += fDeltaX * fDeltaX; 90 fQy += fDeltaY * fDeltaY; 91 fQxy += fDeltaX * fDeltaY; 92 } 93 94 m_fSlope = fQxy / fQx; 95 m_fIntercept = fAverageY - m_fSlope * fAverageX; 96 m_fCorrelationCoeffitient = fQxy / sqrt( fQx * fQy ); 97 } 98 99 double SAL_CALL LogarithmicRegressionCurveCalculator::getCurveValue( double x ) 100 { 101 double fResult; 102 ::rtl::math::setNan( & fResult ); 103 104 if( ! ( ::rtl::math::isNan( m_fSlope ) || 105 ::rtl::math::isNan( m_fIntercept ))) 106 { 107 fResult = m_fSlope * log( x ) + m_fIntercept; 108 } 109 110 return fResult; 111 } 112 113 uno::Sequence< geometry::RealPoint2D > SAL_CALL LogarithmicRegressionCurveCalculator::getCurveValues( 114 double min, double max, ::sal_Int32 nPointCount, 115 const uno::Reference< chart2::XScaling >& xScalingX, 116 const uno::Reference< chart2::XScaling >& xScalingY, 117 ::sal_Bool bMaySkipPointsInCalculation ) 118 { 119 if( bMaySkipPointsInCalculation && 120 isLogarithmicScaling( xScalingX ) && 121 isLinearScaling( xScalingY )) 122 { 123 // optimize result 124 uno::Sequence< geometry::RealPoint2D > aResult( 2 ); 125 aResult[0].X = min; 126 aResult[0].Y = this->getCurveValue( min ); 127 aResult[1].X = max; 128 aResult[1].Y = this->getCurveValue( max ); 129 130 return aResult; 131 } 132 return RegressionCurveCalculator::getCurveValues( min, max, nPointCount, xScalingX, xScalingY, bMaySkipPointsInCalculation ); 133 } 134 135 OUString LogarithmicRegressionCurveCalculator::ImplGetRepresentation( 136 const uno::Reference< util::XNumberFormatter >& xNumFormatter, 137 ::sal_Int32 nNumberFormatKey ) const 138 { 139 OUStringBuffer aBuf( C2U( "f(x) = " )); 140 141 bool bHaveSlope = false; 142 143 if( m_fSlope != 0.0 ) 144 { 145 if( ::rtl::math::approxEqual( fabs( m_fSlope ), 1.0 )) 146 { 147 if( m_fSlope < 0 ) 148 aBuf.append( UC_MINUS_SIGN ); 149 } 150 else 151 { 152 aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fSlope )); 153 aBuf.append( UC_SPACE ); 154 } 155 aBuf.appendAscii( RTL_CONSTASCII_STRINGPARAM( "ln(x)" )); 156 bHaveSlope = true; 157 } 158 159 if( bHaveSlope ) 160 { 161 if( m_fIntercept < 0.0 ) 162 { 163 aBuf.append( UC_SPACE ); 164 aBuf.append( UC_MINUS_SIGN ); 165 aBuf.append( UC_SPACE ); 166 aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, fabs( m_fIntercept ))); 167 } 168 else if( m_fIntercept > 0.0 ) 169 { 170 aBuf.appendAscii( RTL_CONSTASCII_STRINGPARAM( " + " )); 171 aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fIntercept )); 172 } 173 } 174 else 175 { 176 aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fIntercept )); 177 } 178 179 return aBuf.makeStringAndClear(); 180 } 181 182 } // namespace chart 183