/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Header: /cvsroot/bayesclasses/Bayes++/BayesFilter/infRtFlt.cpp,v 1.27.2.2 2004/07/18 18:38:04 mistevens Exp $ * $NoKeywords: $ *//* * Information Root Filter. */#include "infRtFlt.hpp"#include "matSup.hpp"#include "uLAPACK.hpp"	// Common LAPACK interface#include <algorithm>/* Filter namespace */namespace Bayesian_filter{	using namespace Bayesian_filter_matrix;Information_root_scheme::Information_root_scheme (std::size_t x_size, std::size_t /*z_initialsize*/) :		Kalman_state_filter(x_size),		r(x_size), R(x_size,x_size)/* * Set the size of things we know about */{}Information_root_info_scheme::Information_root_info_scheme (std::size_t x_size, std::size_t z_initialsize) :		Kalman_state_filter(x_size), Information_state_filter (x_size), 		Information_root_scheme (x_size, z_initialsize){}void Information_root_scheme::init ()/* * Inialise the filter from x,X * Predcondition: *		x,X * Postcondition: *		inv(R)*inv(R)' = X is PSD *		r = R*x */{						// Information Root	Float rcond = UCfactor (R, X);	rclimit.check_PD(rcond, "Initial X not PD");	bool singular = UTinverse (R);	assert (!singular); (void)singular;						// Information Root state r=R*x	noalias(r) = prod(R,x);}void Information_root_info_scheme::init_yY ()/* * Special Initialisation directly from Information form * Predcondition: *		y,Y * Postcondition: *		R'*R = Y is PSD *		r = inv(R)'*y */{					// Temporary R Matrix for factorisation	const std::size_t n = x.size();	LTriMatrix LC(n,n);					// Information Root	Float rcond = LdLfactor (LC, Y);	rclimit.check_PD(rcond, "Initial Y not PD");	{				// Lower triangular Choleksy factor of LdL'		std::size_t i,j;		for (i = 0; i < n; ++i)		{			using namespace std;		// for sqrt			LTriMatrix::value_type sd = LC(i,i);			sd = sqrt(sd);			LC(i,i) = sd;						// Multiply columns by square of non zero diagonal. TODO use column operation			for (j = i+1; j < n; ++j)			{				LC(j,i) *= sd;			}		}	}	R = FM::trans(LC);			// R = (L*sqrt(d))'	UTriMatrix RI(n,n);	RI = R;	bool singular = UTinverse(RI);	assert (!singular); (void)singular;	noalias(r) = prod(FM::trans(RI),y);}void Information_root_scheme::update ()/* * Recompute x,X from r,R * Precondition: *		r(k|k),R(k|k) * Postcondition: *		r(k|k),R(k|k) *		x = inv(R)*r *		X = inv(R)*inv(R)' */{	UTriMatrix RI (R);	// Invert Cholesky factor	bool singular = UTinverse (RI);	if (singular)		error (Numeric_exception("R not PD"));	noalias(X) = prod_SPD(RI);		// X = RI*RI'	noalias(x) = prod(RI,r);}void Information_root_info_scheme::update_yY ()/* * Recompute y,Y from r,R * Precondition: *		r(k|k),R(k|k) * Postcondition: *		r(k|k),R(k|k) *		Y = R'*R *		y = Y*x */{	Information_root_scheme::update();	noalias(Y) = prod(trans(R),R);		// Y = R'*R	noalias(y) = prod(Y,x);}void Information_root_scheme::inverse_Fx (FM::DenseColMatrix& invFx, const FM::Matrix& Fx)/* * Numerical Inversion of Fx using LU factorisation * Required LAPACK getrf (with PIVOTING) and getrs */{								// LU factorise with pivots	DenseColMatrix FxLU (Fx);	LAPACK::pivot_t ipivot(FxLU.size1());		// Pivots initialised to zero	ipivot.clear();	int info = LAPACK::getrf(FxLU, ipivot);	if (info < 0)		error (Numeric_exception("Fx not LU factorisable"));	FM::identity(invFx);				// Invert	info = LAPACK::getrs('N', FxLU, ipivot, invFx);	if (info != 0)		error (Numeric_exception("Predict Fx not LU invertable"));}Bayes_base::Float Information_root_scheme::predict (Linrz_predict_model& f, const FM::ColMatrix& invFx, bool linear_r)/* Linrz Prediction: using precomputed inverse of f.Fx * Precondition: *   r(k|k),R(k|k) * Postcondition: *   r(k+1|k) computed using QR decomposition see Ref[1] *   R(k+1|k) * * r can be computed in two was: * Either directly in the linear form or  using extended form via R*f.f(x) * * Requires LAPACK geqrf for QR decomposition (without PIVOTING) */{	if (!linear_r)		update ();		// x is required for f(x);						// Require Root of correlated predict noise (may be semidefinite)	Matrix Gqr (f.G);	for (Vec::const_iterator qi = f.q.begin(); qi != f.q.end(); ++qi)	{		if (*qi < 0)			error (Numeric_exception("Predict q Not PSD"));		column(Gqr, qi.index()) *= std::sqrt(*qi);	}						// Form Augmented matrix for factorisation	const std::size_t x_size = x.size();	const std::size_t q_size = f.q.size();						// Column major required for LAPACK, also this property is using in indexing	DenseColMatrix A(q_size+x_size, q_size+x_size+unsigned(linear_r));	FM::identity (A);	// Prefill with identity for topleft and zero's in off diagonals	Matrix RFxI (prod(R, invFx));	A.sub_matrix(q_size,q_size+x_size, 0,q_size) .assign (prod(RFxI, Gqr));	A.sub_matrix(q_size,q_size+x_size, q_size,q_size+x_size) .assign (RFxI);	if (linear_r)		A.sub_column(q_size,q_size+x_size, q_size+x_size) .assign (r);						// Calculate factorisation so we have and upper triangular R	DenseVec tau(q_size+x_size);	int info = LAPACK::geqrf (A, tau);	if (info != 0)			error (Numeric_exception("Predict no QR factor"));						// Extract the roots, junk in strict lower triangle	R = UpperTri( A.sub_matrix(q_size,q_size+x_size, q_size,q_size+x_size) );    if (linear_r)		noalias(r) = A.sub_column(q_size,q_size+x_size, q_size+x_size);	else		noalias(r) = prod(R,f.f(x));	// compute r using f(x)	return UCrcond(R);	// compute rcond of result}Bayes_base::Float Information_root_scheme::predict (Linrz_predict_model& f)/* Linrz Prediction: computes inverse model using inverse_Fx */{						// Require inverse(Fx)	DenseColMatrix FxI(f.Fx.size1(), f.Fx.size2());	inverse_Fx (FxI, f.Fx);	return predict (f, ColMatrix(FxI), false);}Bayes_base::Float Information_root_scheme::predict (Linear_predict_model& f)/* Linear Prediction: computes inverse model using inverse_Fx */{						// Require inverse(Fx)	DenseColMatrix FxI(f.Fx.size1(), f.Fx.size2());	inverse_Fx (FxI, f.Fx);	return predict (f, ColMatrix(FxI), true);}Bayes_base::Float Information_root_scheme::observe_innovation (Linrz_correlated_observe_model& h, const FM::Vec& s)/* Extended linrz correlated observe * Precondition: *		r(k+1|k),R(k+1|k) * Postcondition: *		r(k+1|k+1),R(k+1|k+1) * * Uses LAPACK geqrf for QR decomposigtion (without PIVOTING) * ISSUE correctness of linrz form needs validation */{	const std::size_t x_size = x.size();	const std::size_t z_size = s.size();						// Size consistency, z to model	if (z_size != h.Z.size1())		error (Logic_exception("observation and model size inconsistent"));						// Require Inverse of Root of uncorrelated observe noise	UTriMatrix Zir(z_size,z_size);	Float rcond = UCfactor (Zir, h.Z);	rclimit.check_PD(rcond, "Z not PD");	bool singular = UTinverse (Zir);	assert (!singular); (void)singular;						// Form Augmented matrix for factorisation	DenseColMatrix A(x_size+z_size, x_size+1);	// Column major required for LAPACK, also this property is using in indexing	A.sub_matrix(0,x_size, 0,x_size) .assign (R);	A.sub_matrix(x_size,x_size+z_size, 0,x_size) .assign (prod(Zir, h.Hx));	A.sub_column(0,x_size, x_size) .assign (r);	A.sub_column(x_size,x_size+z_size, x_size) .assign (prod(Zir, s+prod(h.Hx,x)));						// Calculate factorisation so we have and upper triangular R	DenseVec tau(x_size+1);	int info = LAPACK::geqrf (A, tau);	if (info != 0)			error (Numeric_exception("Observe no QR factor"));						// Extract the roots, junk in strict lower triangle	noalias(R) = UpperTri( A.sub_matrix(0,x_size, 0,x_size) );	noalias(r) = A.sub_column(0,x_size, x_size);	return UCrcond(R);	// compute rcond of result}Bayes_base::Float Information_root_scheme::observe_innovation (Linrz_uncorrelated_observe_model& h, const FM::Vec& s)/* Extended linrz uncorrelated observe * Precondition: *		r(k+1|k),R(k+1|k) * Postcondition: *		r(k+1|k+1),R(k+1|k+1) * * Uses LAPACK geqrf for QR decomposigtion (without PIVOTING) * ISSUE correctness of linrz form needs validation * ISSUE Efficiency. Product of Zir can be simplified */{	const std::size_t x_size = x.size();	const std::size_t z_size = s.size();						// Size consistency, z to model	if (z_size != h.Zv.size())		error (Logic_exception("observation and model size inconsistent"));						// Require Inverse of Root of uncorrelated observe noise	DiagMatrix Zir(z_size,z_size);	Zir.clear();	for (std::size_t i = 0; i < z_size; ++i)	{		Zir(i,i) = 1 / std::sqrt(h.Zv[i]);	}						// Form Augmented matrix for factorisation	DenseColMatrix A(x_size+z_size, x_size+1);	// Column major required for LAPACK, also this property is using in indexing	A.sub_matrix(0,x_size, 0,x_size) .assign(R);	A.sub_matrix(x_size,x_size+z_size, 0,x_size) .assign (prod(Zir, h.Hx));	A.sub_column(0,x_size, x_size) .assign (r);	A.sub_column(x_size,x_size+z_size, x_size) .assign (prod(Zir, s+prod(h.Hx,x)));						// Calculate factorisation so we have and upper triangular R	DenseVec tau(x_size+1);	int info = LAPACK::geqrf (A, tau);	if (info != 0)			error (Numeric_exception("Observe no QR factor"));						// Extract the roots, junk in strict lower triangle	noalias(R) = UpperTri( A.sub_matrix(0,x_size, 0,x_size) );	noalias(r) = A.sub_column(0,x_size, x_size);	return UCrcond(R);	// compute rcond of result}}//namespace