/usr/share/octave/site/m/vlfeat/toolbox/xtest/vl_test_kmeans.m is in octave-vlfeat 0.9.17+dfsg0-6+b1.
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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 88 89 90 91 92 93 94 95 | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = randn(128, 100) ;
function test_basic(s)
[centers, assignments, en] = vl_kmeans(s.X, 10, 'NumRepetitions', 10) ;
[centers_, assignments_, en_] = simpleKMeans(s.X, 10) ;
assert(en_ <= 1.1 * en, 'vl_kmeans did not optimize enough') ;
function test_algorithms(s)
distances = {'l1', 'l2'} ;
dataTypes = {'single','double'} ;
for dataType = dataTypes
for distance = distances
distance = char(distance) ;
conversion = str2func(char(dataType)) ;
X = conversion(s.X) ;
vl_twister('state',0) ;
[centers, assignments, en] = vl_kmeans(X, 10, ...
'NumRepetitions', 1, ...
'MaxNumIterations', 10, ...
'Algorithm', 'Lloyd', ...
'Distance', distance) ;
vl_twister('state',0) ;
[centers_, assignments_, en_] = vl_kmeans(X, 10, ...
'NumRepetitions', 1, ...
'MaxNumIterations', 10, ...
'Algorithm', 'Elkan', ...
'Distance', distance) ;
vl_twister('state',0) ;
[centers__, assignments__, en__] = vl_kmeans(X, 10, ...
'NumRepetitions', 1, ...
'MaxNumIterations', 10, ...
'Algorithm', 'ANN', ...
'Distance', distance, ...
'NumTrees', 3, ...
'MaxNumComparisons',0) ;
vl_assert_almost_equal(centers, centers_, 1e-5) ;
vl_assert_almost_equal(assignments, assignments_, 1e-5) ;
vl_assert_almost_equal(en, en_, 1e-5) ;
vl_assert_almost_equal(centers, centers__, 1e-5) ;
vl_assert_almost_equal(assignments, assignments__, 1e-5) ;
vl_assert_almost_equal(en, en__, 1e-5) ;
vl_assert_almost_equal(centers_, centers__, 1e-5) ;
vl_assert_almost_equal(assignments_, assignments__, 1e-5) ;
vl_assert_almost_equal(en_, en__, 1e-5) ;
end
end
function test_patterns(s)
distances = {'l1', 'l2'} ;
dataTypes = {'single','double'} ;
for dataType = dataTypes
for distance = distances
distance = char(distance) ;
conversion = str2func(char(dataType)) ;
data = [1 1 0 0 ;
1 0 1 0] ;
data = conversion(data) ;
[centers, assignments, en] = vl_kmeans(data, 4, ...
'NumRepetitions', 100, ...
'Distance', distance) ;
assert(isempty(setdiff(data', centers', 'rows'))) ;
end
end
function [centers, assignments, en] = simpleKMeans(X, numCenters)
[dimension, numData] = size(X) ;
centers = randn(dimension, numCenters) ;
for iter = 1:10
[dists, assignments] = min(vl_alldist(centers, X)) ;
en = sum(dists) ;
centers = [zeros(dimension, numCenters) ; ones(1, numCenters)] ;
centers = vl_binsum(centers, ...
[X ; ones(1,numData)], ...
repmat(assignments, dimension+1, 1), 2) ;
centers = centers(1:end-1, :) ./ repmat(centers(end,:), dimension, 1) ;
end
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