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Fixed texinfo header. Added two internal tests.

time-shift
Stéphane Adjemian (Charybdis) 2012-02-22 18:08:15 +01:00
parent f9671be3d2
commit 8f95711445
1 changed files with 41 additions and 11 deletions
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52
matlab/particle/residual_resampling.m
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@ -1,20 +1,17 @@
function indx = residual_resampling(weights)
% Resamples particles.
%@info:
%! @deftypefn {Function File} {@var{indx} =} resample (@var{weights},@var{noise})
%! @anchor{particle/traditional_resampling}
%! @deftypefn {Function File} {@var{indx} =} residual_resampling (@var{weights})
%! @anchor{particle/residual_resampling}
%! @sp 1
%! Resamples particles (Resampling à la Kitagawa or stratified resampling).
%!
%! Resamples particles.
%! @sp 2
%! @strong{Inputs}
%! @sp 1
%! @table @ @var
%! @item weights
%! n*1 vector of doubles, particles' weights.
%! @item noise
%! n*1 vector of doubles sampled from a [0,1] uniform distribution (stratified resampling) or scalar double
%! sampled from a [0,1] uniform distribution (Kitagawa resampling).
%! @end table
%! @sp 2
%! @strong{Outputs}
@ -25,14 +22,15 @@ function indx = residual_resampling(weights)
%! @end table
%! @sp 2
%! @strong{This function is called by:}
%! @sp 1
%! @ref{particle/resample}
%! @sp 2
%! @strong{This function calls:}
%!
%! @sp 2
%! @end deftypefn
%@eod:
% Copyright (C) 2011 Dynare Team
% Copyright (C) 2011, 2012 Dynare Team
%
% This file is part of Dynare.
%
@ -50,7 +48,7 @@ function indx = residual_resampling(weights)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR(S) frederic DOT karame AT univ DASH evry DOT fr
% stephane DOT adjemian AT univ DASH lemans DOT fr
% stephane DOT adjemian AT univ DASH lemans DOT fr
% What is the number of particles?
number_of_particles = length(weights);
@ -89,4 +87,36 @@ for i=1:number_of_particles
end
end
k = k + iWEIGHTS(i);
end
end
%@test:1
%$ % Define the weights
%$ weights = randn(2000,1).^2;
%$ weights = weights/sum(weights);
%$ % Initialize t.
%$ t = ones(1,1);
%$
%$ try
%$ indx1 = residual_resampling(weights);
%$ catch
%$ t(1) = 0;
%$ end
%$
%$ T = all(t);
%@eof:1
%@test:2
%$ % Define the weights
%$ weights = exp(randn(2000,1));
%$ weights = weights/sum(weights);
%$ % Initialize t.
%$ t = ones(1,1);
%$
%$ try
%$ indx1 = residual_resampling(weights);
%$ catch
%$ t(1) = 0;
%$ end
%$
%$ T = all(t);
%@eof:2