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#ifndef regressionline_h
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#define regressionline_h
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#include "doublevector.h" |
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enum LineType { FREE = 1, FIXEDSLOPE, FIXEDINTERCEPT, FIXED };
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/**
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* \class Regression
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* \brief This is the base class used to fit a regression line to compare 2 vectors
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* \note This will always be overridden by the derived classes that actually fit the regression line
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*/
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class Regression {
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public:
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/**
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* \brief This is the default Regression constructor
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*/
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Regression();
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/**
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* \brief This is the Regression constructor for a regression line of a specified LineType
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* \param ltype is the LineType of the regression line
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*/
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Regression(LineType ltype);
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/**
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* \brief This is the default Regression destructor
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*/
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~Regression() {};
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/**
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* \brief This is the function that stores 2 vectors that will be compared using a regression line
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* \param modData is the DoubleVector containing the modelled data
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* \param obsData is the DoubleVector containing the observed data
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*/
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virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData) = 0;
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/**
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* \brief This is the function that fits a regression line to compare the 2 vectors that have been stored, according to the LineType that has been defined
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*/
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void calcFit();
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/**
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* \brief This function will set the intercept of the regression line
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* \param intercept is the intercept of the regression line
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*/
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void setIntercept(double intercept) { a = intercept; };
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/**
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* \brief This function will set the slope of the regression line
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* \param slope is the slope of the regession line
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*/
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void setSlope(double slope) { b = slope; };
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/**
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* \brief This function will set the weights that can be used to fit the regression line
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* \param weights is the DoubleVector of weights to be used
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*/
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void setWeights(const DoubleVector& weights);
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/**
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* \brief This function will check to see if an error has occured
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* \return error
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*/
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int getError() { return error; };
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/**
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* \brief This function will return the sum of squares of errors calculated when fitting the regression line
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* \return sse
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*/
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double getSSE();
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/**
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* \brief This function will return the intercept of the regression line
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* \return a
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*/
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double getIntercept() { return a; };
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/**
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* \brief This function will return the slope of the regression line
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* \return b
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*/
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double getSlope() { return b; };
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/**
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* \brief This function will return the fit type for the regression line
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* \return fittype
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*/
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LineType getType() const { return linetype; };
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protected:
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/**
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* \brief This function will calculate the sum of squares of errors for the regession line
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*/
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void calcSSE();
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/**
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* \brief This function will calculate the weighted sum of squares of errors for the regession line
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*/
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void calcSSEWeights();
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/**
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* \brief This function will calculate the slope of the regession line
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*/
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void calcSlope();
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/**
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* \brief This function will calculate the intercept of the regession line
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*/
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void calcIntercept();
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/**
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* \brief This function will calculate both the slope and the intercept of the regession line
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*/
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void calcSlopeIntercept();
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/**
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* \brief This is the flag to denote whether an error has occured
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*/
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int error;
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/**
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* \brief This is the flag to denote whether the weights should be used when calculating the fit to the regression line
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*/
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int useweights;
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/**
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* \brief This is the sum of squares of errors from the regression line
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*/
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double sse;
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/**
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* \brief This is the intercept of the regression line
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*/
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double a;
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/**
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* \brief This is the slope of the regression line
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*/
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double b;
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/**
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* \brief This is the DoubleVector of weights that can be used to fit the regression line
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*/
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DoubleVector w;
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/**
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* \brief This is the DoubleVector that will contain the the modelled data to be used to fit the regression line
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*/
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DoubleVector x;
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/**
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* \brief This is the DoubleVector that will contain the the observed data to be used to fit the regression line
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*/
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DoubleVector y;
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/**
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* \brief This denotes what type of line fit is to be used for the regression line
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*/
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LineType linetype;
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};
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/**
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* \class LinearRegression
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* \brief This is the class used to fit a linear regression line to compare 2 vectors
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*/
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class LinearRegression : public Regression {
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public:
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/**
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* \brief This is the default LinearRegression constructor
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* \param ltype is the LineType of the regression line
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*/
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LinearRegression(LineType ltype);
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/**
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* \brief This is the default LinearRegression destructor
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*/
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~LinearRegression() {};
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/**
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* \brief This is the function that stores 2 vectors that will be compared using a linear regression line
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* \param modData is the DoubleVector containing the modelled data
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* \param obsData is the DoubleVector containing the observed data
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*/
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virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData);
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};
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/**
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* \class LogLinearRegression
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* \brief This is the class used to fit a log linear regression line to compare 2 vectors
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*/
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class LogLinearRegression : public Regression {
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public:
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/**
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* \brief This is the default LogLinearRegression constructor
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* \param ltype is the LineType of the regression line
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*/
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LogLinearRegression(LineType ltype);
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/**
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* \brief This is the default LogLinearRegression destructor
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*/
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~LogLinearRegression() {};
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/**
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* \brief This is the function that stores 2 vectors that will be compared using a log linear regression line
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* \param modData is the DoubleVector containing the modelled data
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* \param obsData is the DoubleVector containing the observed data
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*/
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virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData);
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};
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/**
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* \class WeightRegression
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* \brief This is the class used to fit a weighted linear regression line to compare 2 vectors
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*/
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class WeightRegression : public LinearRegression {
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public:
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/**
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* \brief This is the default WeightRegression constructor
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* \param ltype is the LineType of the regression line
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*/
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WeightRegression(LineType ltype);
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/**
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* \brief This is the default WeightRegression destructor
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*/
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~WeightRegression() {};
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/**
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* \brief This is the function that stores 2 vectors that will be compared using a linear regression line
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* \param modData is the DoubleVector containing the modelled data
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* \param obsData is the DoubleVector containing the observed data
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*/
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virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData);
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};
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/**
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* \class LogWeightRegression
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* \brief This is the class used to fit a weighted log linear regression line to compare 2 vectors
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*/
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class LogWeightRegression : public LogLinearRegression {
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public:
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/**
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* \brief This is the default LogWeightRegression constructor
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* \param ltype is the LineType of the regression line
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*/
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LogWeightRegression(LineType ltype);
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/**
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* \brief This is the default LogWeightRegression destructor
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*/
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~LogWeightRegression() {};
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/**
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* \brief This is the function that stores 2 vectors that will be compared using a log linear regression line
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* \param modData is the DoubleVector containing the modelled data
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* \param obsData is the DoubleVector containing the observed data
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*/
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virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData);
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};
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#endif
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