X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Ftsa-4.2.4%2Flpc.m;fp=octave_packages%2Ftsa-4.2.4%2Flpc.m;h=ffd60c2a353c984cfdd354601745ee51a3b6caaf;hp=0000000000000000000000000000000000000000;hb=f5f7a74bd8a4900f0b797da6783be80e11a68d86;hpb=1705066eceaaea976f010f669ce8e972f3734b05

diff --git a/octave_packages/tsa-4.2.4/lpc.m b/octave_packages/tsa-4.2.4/lpc.m
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+function [A] = lpc(Y,P,mode);
+% LPC Linear prediction coefficients 
+% The Burg-method is used to estimate the prediction coefficients
+%
+% A = lpc(Y [,P]) finds the coefficients  A=[ 1 A(2) ... A(N+1) ],
+%     	of an Pth order forward linear predictor
+%     
+% 	 Xp(n) = -A(2)*X(n-1) - A(3)*X(n-2) - ... - A(N+1)*X(n-P)
+%	    
+% 	such that the sum of the squares of the errors
+%		
+%       err(n) = X(n) - Xp(n)
+%	       
+%	is minimized.  X can be a vector or a matrix.  If X is a matrix
+%       containing a separate signal in each column, LPC returns a model
+%	estimate for each column in the rows of A. N specifies the order
+%	of the polynomial A(z).
+%				       
+%	If you do not specify a value for P, LPC uses a default P = length(X)-1.
+%
+%
+% see also ACOVF ACORF AR2POLY RC2AR DURLEV SUMSKIPNAN LATTICE 
+% 
+
+% REFERENCE(S):
+%  J.P. Burg, "Maximum Entropy Spectral Analysis" Proc. 37th Meeting of the Society of Exp. Geophysiscists, Oklahoma City, OK 1967
+%  J.P. Burg, "Maximum Entropy Spectral Analysis" PhD-thesis, Dept. of Geophysics, Stanford University, Stanford, CA. 1975.
+%  P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
+%  S.   Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996.
+%  M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981. 
+%  W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
+
+%	$Id: lpc.m 5090 2008-06-05 08:12:04Z schloegl $
+%	Copyright (C) 1996-2002,2008 by Alois Schloegl <a.schloegl@ieee.org>
+%       This is part of the TSA-toolbox. See also 
+%       http://hci.tugraz.at/schloegl/matlab/tsa/
+%
+%    This program is free software: you can redistribute it and/or modify
+%    it under the terms of the GNU General Public License as published by
+%    the Free Software Foundation, either version 3 of the License, or
+%    (at your option) any later version.
+%
+%    This program is distributed in the hope that it will be useful,
+%    but WITHOUT ANY WARRANTY; without even the implied warranty of
+%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+%    GNU General Public License for more details.
+%
+%    You should have received a copy of the GNU General Public License
+%    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+
+[yr,yc] = size(Y);
+if yr < yc,
+	fprintf(2,'Warning LCP: data vector Y must be a column not a row vector\n');
+end;
+
+if nargin < 2,
+        P = yr-1;
+end;
+
+% you can use any of the following routines. 
+% the lattice methods are preferable for stochastic time series. 
+% but can fail for deterministic signals see: 
+% http://sourceforge.net/mailarchive/message.php?msg_name=20080516115110.GB20642%40localhost
+
+% [AR,RC,PE] = lattice(Y.',P);		% Burg method
+% [AR,RC,PE] = lattice(Y.',P,'GEOL');	% geometric lattice
+[AR,RC,PE] = durlev(acovf(Y.',P));  	% Yule-Walker
+
+A = ar2poly(AR);
+