Revision 304

trunk/contrib/statslib/dnorm.cpp (revision 304)
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* where \f$\mu\f$ is the mean and \f$\sigma\f$
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* is the standard deviation.
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* 
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* The concentrated likelihood is implemented as:
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* \f[
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*  0.5 n \ln(\sum_{i=1}^{n}\epsilon^2)
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* \f]
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* where \f$ \epsilon \f$ is a vector of residuals with an assumed mean 0.
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* 
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* 
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*/
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/** 
......
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}
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/** 
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	\author Steven James Dean Martell
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	\date 2011-06-21
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	\param  residual a variable vector of residuals
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	\return returns the concentrated likelihood for the normal distribution.
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	\sa
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**/
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dvariable dnorm( const dvar_vector& residual )
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{
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	RETURN_ARRAYS_INCREMENT();
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	int n              = size_count(residual);
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	dvariable SS       = norm2(residual);
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	dvariable nloglike = 0.5*n*log(SS);
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	RETURN_ARRAYS_DECREMENT();
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	return(nloglike);
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}
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/** 
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	\author Steven James Dean Martell
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	\date 2011-06-21
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	\param  obs a matrix of observed values
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	\param  pred a variable matrix of predicted values
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	\return returns the concentrated likelihood for the normal distribution.
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	\sa
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**/
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dvariable dnorm( const dmatrix& obs, const dvar_matrix& pred)
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{
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	RETURN_ARRAYS_INCREMENT();
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	int n = size_count(obs);
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	dvariable SS = sum(elem_div(square(obs-pred),0.01+pred));
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	RETURN_ARRAYS_DECREMENT();
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	return 0.5*n*log(SS);
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}
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/** 
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	\author Steven James Dean Martell
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	\date 2011-06-21
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	\param  residual a variable vector of residuals
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	\param  std a variable vector of standard deviations
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	\return returns the sum of negative loglikelihoods of the normal distribution
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	\sa
trunk/contrib/statslib/vcubicspline.cpp (revision 304)
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   ptr=0;
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 }
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/** 
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	\author Steven James Dean Martell
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	\date 2011-06-21
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	\brief A Wrapper for the vcubic_spline_function
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	\param  spline_nodes a vector of spline knots
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	\param  ip is a vector of interpreted points
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	\return returns a vector of interpreted points 
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	\sa
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**/
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dvar_vector cubic_spline(const dvar_vector& spline_nodes, const dvector& ip)
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{
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	RETURN_ARRAYS_INCREMENT();                                                              
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	int nodes=size_count(spline_nodes);
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	dvector ia(1,nodes);
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	ia.fill_seqadd(0,1./(nodes-1));
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	dvector fa = (ip-min(ip))/(max(ip)-min(ip));
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	vcubic_spline_function ffa(ia,spline_nodes);
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	RETURN_ARRAYS_DECREMENT();
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	return(ffa(fa));
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}
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void bicubic_spline(const dvector& x, const dvector& y, dvar_matrix& knots, dvar_matrix& S)
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