/usr/share/octave/packages/statistics-1.2.3/pcares.m is in octave-statistics 1.2.3-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | ## Copyright (C) 2013 Fernando Damian Nieuwveldt <fdnieuwveldt@gmail.com>
##
## 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,
## 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/>.
## -*- texinfo -*-
## @deftypefn {Function File} {[@var{residuals},@var{reconstructed}]}=pcares(@var{X}, @var{NDIM})
## @itemize @bullet
## @item
## @var{X} : N x P Matrix with N observations and P variables, the variables will be mean centered
## @item
## @var{ndim} : Is a scalar indicating the number of principal components to use and should be <= P
## @end itemize
##
## @subheading References
##
## @enumerate
## @item
## Jolliffe, I. T., Principal Component Analysis, 2nd Edition, Springer, 2002
##
## @end enumerate
## @end deftypefn
## Author: Fernando Damian Nieuwveldt <fdnieuwveldt@gmail.com>
## Description: Residuals from Principal Components Analysis
function [residuals,reconstructed] = pcares(X,NDIM)
if (nargin ~= 2)
error('pcares takes two inputs: The data Matrix X and number of principal components NDIM')
endif
# Mean center data
Xcentered = bsxfun(@minus,X,mean(X));
# Apply svd to get the principal component coefficients
[U,S,V] = svd(Xcentered);
# Use only the first ndim PCA components
v = V(:,1:NDIM);
if (nargout == 2)
# Calculate the residuals
residuals = Xcentered - Xcentered * (v*v');
# Reconstructed data using ndim PCA components
reconstructed = X - residuals;
else
# Calculate the residuals
residuals = Xcentered - Xcentered * (v*v');
endif
endfunction
%!demo
%! X = [ 7 26 6 60;
%! 1 29 15 52;
%! 11 56 8 20;
%! 11 31 8 47;
%! 7 52 6 33;
%! 11 55 9 22;
%! 3 71 17 6;
%! 1 31 22 44;
%! 2 54 18 22;
%! 21 47 4 26;
%! 1 40 23 34;
%! 11 66 9 12;
%! 10 68 8 12
%! ];
%! # As we increase the number of principal components, the norm
%! # of the residuals matrix will decrease
%! r1 = pcares(X,1);
%! n1 = norm(r1)
%! r2 = pcares(X,2);
%! n2 = norm(r2)
%! r3 = pcares(X,3);
%! n3 = norm(r3)
%! r4 = pcares(X,4);
%! n4 = norm(r4)
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