pdl::linpred(3pm) [debian man page]

LinPred(3pm)						User Contributed Perl Documentation					      LinPred(3pm)

NAME

       PDL::Filter::LinPred - Linear predictive filtering

SYNOPSIS

	       $a = new PDL::Filter::LinPred(
		       {NLags => 10,
			LagInterval => 2,
			LagsBehind => 2,
			Data => $dat});

	       ($pd,$corrslic) = $a->predict($dat);

DESCRIPTION

       A filter by doing linear prediction: tries to predict the next value in a data stream as accurately as possible. The filtered data is the
       predicted value. The parameters are

       NLags   Number of time lags used for prediction

       LagInterval
	       How many points each lag should be

       LagsBehind
	       If, for some strange reason, you wish to predict not the next but the one after that (i.e. usually f(t) is predicted from f(t-1)
	       and f(t-2) etc., but with LagsBehind => 2, f(t) is predicted from f(t-2) and f(t-3)).

       Data    The input data, which may contain other dimensions past the first (time).  The extraneous dimensions are assumed to represent
	       epochs so the data is just concatenated.

       AutoCovar
	       As an alternative to Data, you can just give the temporal autocorrelation function.

       Smooth  Don't do prediction or filtering but smoothing.

       The method predict gives a prediction for some data plus a corresponding slice of the data, if evaluated in list context. This slice is
       given so that you may, if you wish, easily plot them atop each other.

       The rest of the documentation is under lazy evaluation.

AUTHOR

       Copyright (C) Tuomas J. Lukka 1997.  All rights reserved. There is no warranty. You are allowed to redistribute this software /
       documentation under certain conditions. For details, see the file COPYING in the PDL distribution. If this file is separated from the PDL
       distribution, the copyright notice should be included in the file.

perl v5.14.2							    2011-03-30							      LinPred(3pm)

Simplex(3pm)						User Contributed Perl Documentation					      Simplex(3pm)

NAME

       PDL::Opt::Simplex -- Simplex optimization routines

SYNOPSIS

	use PDL::Opt::Simplex;

	($optimum,$ssize,$optval) = simplex($init,$initsize,$minsize,
			$maxiter,
			sub {evaluate_func_at($_[0])},
			sub {display_simplex($_[0])}
			);

DESCRIPTION

       This package implements the commonly used simplex optimization algorithm. The basic idea of the algorithm is to move a "simplex" of N+1
       points in the N-dimensional search space according to certain rules. The main benefit of the algorithm is that you do not need to calculate
       the derivatives of your function.

       $init is a 1D vector holding the initial values of the N fitted parameters, $optimum is a vector holding the final solution.  $optval is
       the evaluation of the final solution.

       $initsize is the size of $init (more...)

       $minsize is some sort of convergence criterion (more...)  - e.g. $minsize = 1e-6

       The sub is assumed to understand more than 1 dimensions and threading.  Its signature is 'inp(nparams); [ret]out()'. An example would be

	       sub evaluate_func_at {
		       my($xv) = @_;
		       my $x1 = $xv->slice("(0)");
		       my $x2 = $xv->slice("(1)");
		       return $x1**4 + ($x2-5)**4 + $x1*$x2;
	       }

       Here $xv is a vector holding the current values of the parameters being fitted which are then sliced out explicitly as $x1 and $x2.

       $ssize gives a very very approximate estimate of how close we might be - it might be miles wrong. It is the euclidean distance between the
       best and the worst vertices. If it is not very small, the algorithm has not converged.

FUNCTIONS

   simplex
       Simplex optimization routine

	($optimum,$ssize,$optval) = simplex($init,$initsize,$minsize,
			$maxiter,
			sub {evaluate_func_at($_[0])},
			sub {display_simplex($_[0])}
			);

       See module "PDL::Opt::Simplex" for more information.

CAVEATS

       Do not use the simplex method if your function has local minima.  It will not work. Use genetic algorithms or simulated annealing or
       conjugate gradient or momentum gradient descent.

       They will not really work either but they are not guaranteed not to work ;) (if you have infinite time, simulated annealing is guaranteed
       to work but only after it has visited every point in your space).

SEE ALSO

       Ron Shaffer's chemometrics web page and references therein: "http://chem1.nrl.navy.mil/~shaffer/chemoweb.html".

       Numerical Recipes (bla bla bla XXX ref).

       The demonstration (Examples/Simplex/tsimp.pl and tsimp2.pl).

AUTHOR

       Copyright(C) 1997 Tuomas J. Lukka.  All rights reserved. There is no warranty. You are allowed to redistribute this software /
       documentation under certain conditions. For details, see the file COPYING in the PDL distribution. If this file is separated from the PDL
       distribution, the copyright notice should be included in the file.

perl v5.14.2							    2012-01-02							      Simplex(3pm)