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2 *
3 * Licensed to the Apache Software Foundation (ASF) under one
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7 * to you under the Apache License, Version 2.0 (the
8 * "License"); you may not use this file except in compliance
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11 * http://www.apache.org/licenses/LICENSE-2.0
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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 *
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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
LogarithmicRegressionCurveCalculator()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
~LogarithmicRegressionCurveCalculator()49 LogarithmicRegressionCurveCalculator::~LogarithmicRegressionCurveCalculator()
50 {}
51
52 // ____ XRegressionCurve ____
recalculateRegression(const uno::Sequence<double> & aXValues,const uno::Sequence<double> & aYValues)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
getCurveValue(double x)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
getCurveValues(double min,double max,::sal_Int32 nPointCount,const uno::Reference<chart2::XScaling> & xScalingX,const uno::Reference<chart2::XScaling> & xScalingY,::sal_Bool bMaySkipPointsInCalculation)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
ImplGetRepresentation(const uno::Reference<util::XNumberFormatter> & xNumFormatter,::sal_Int32 nNumberFormatKey) const135 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