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 "PotentialRegressionCurveCalculator.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 PotentialRegressionCurveCalculator::PotentialRegressionCurveCalculator() : 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 PotentialRegressionCurveCalculator::~PotentialRegressionCurveCalculator() 50 {} 51 52 // ____ XRegressionCurveCalculator ____ 53 void SAL_CALL PotentialRegressionCurveCalculator::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::isValidAndBothPositive())); 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 += log( 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 = log( 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 m_fIntercept = exp( m_fIntercept ); 99 } 100 101 double SAL_CALL PotentialRegressionCurveCalculator::getCurveValue( double x ) 102 { 103 double fResult; 104 ::rtl::math::setNan( & fResult ); 105 106 if( ! ( ::rtl::math::isNan( m_fSlope ) || 107 ::rtl::math::isNan( m_fIntercept ))) 108 { 109 fResult = m_fIntercept * pow( x, m_fSlope ); 110 } 111 112 return fResult; 113 } 114 115 uno::Sequence< geometry::RealPoint2D > SAL_CALL PotentialRegressionCurveCalculator::getCurveValues( 116 double min, double max, ::sal_Int32 nPointCount, 117 const uno::Reference< chart2::XScaling >& xScalingX, 118 const uno::Reference< chart2::XScaling >& xScalingY, 119 ::sal_Bool bMaySkipPointsInCalculation ) 120 { 121 if( bMaySkipPointsInCalculation && 122 isLogarithmicScaling( xScalingX ) && 123 isLogarithmicScaling( xScalingY )) 124 { 125 // optimize result 126 uno::Sequence< geometry::RealPoint2D > aResult( 2 ); 127 aResult[0].X = min; 128 aResult[0].Y = this->getCurveValue( min ); 129 aResult[1].X = max; 130 aResult[1].Y = this->getCurveValue( max ); 131 132 return aResult; 133 } 134 return RegressionCurveCalculator::getCurveValues( min, max, nPointCount, xScalingX, xScalingY, bMaySkipPointsInCalculation ); 135 } 136 137 OUString PotentialRegressionCurveCalculator::ImplGetRepresentation( 138 const uno::Reference< util::XNumberFormatter >& xNumFormatter, 139 ::sal_Int32 nNumberFormatKey ) const 140 { 141 OUStringBuffer aBuf( C2U( "f(x) = " )); 142 143 if( m_fIntercept == 0.0 ) 144 { 145 aBuf.append( sal_Unicode( '0' )); 146 } 147 else if( m_fSlope == 0.0 ) 148 { 149 aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fIntercept )); 150 } 151 else 152 { 153 if( ! rtl::math::approxEqual( m_fIntercept, 1.0 ) ) 154 { 155 aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fIntercept )); 156 aBuf.append( sal_Unicode( ' ' )); 157 } 158 if( m_fSlope != 0.0 ) 159 { 160 aBuf.appendAscii( RTL_CONSTASCII_STRINGPARAM( "x^" )); 161 aBuf.append( getFormattedString( xNumFormatter, nNumberFormatKey, m_fSlope )); 162 } 163 } 164 165 return aBuf.makeStringAndClear(); 166 } 167 168 } // namespace chart 169