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
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
|
use strict;
use warnings;
use Test::More;
use PDL::Stats::Basic;
use PDL::Stats::Kmeans;
use PDL::LiteF;
use Test::PDL qw(is_pdl eq_pdl);
{
my $a = iv_cluster( [qw(a a b b)] );
is_pdl $a, pdl([1,1,0,0], [0,0,1,1]), 'independent variable cluster';
}
is_pdl scalar iv_cluster([qw(a a BAD b b)]), pdl('1 1 BAD 0 0;0 0 BAD 1 1'),
'independent variable cluster with bad data';
is_pdl +(sequence(4,3) % 2)->assign(xvals(2,3)), short('1 0 1 0; 0 1 0 1');
{
my ($m, $ss) = sequence(4,3)->centroid(byte([1,0,1,0], [0,1,0,1]));
is_pdl $m, pdl([1,2], [5,6], [9,10]), "centroid";
is_pdl $ss, ones(2,3) * 2, "centroid";
}
{
my $centroid = pdl( [0,1], [0,1], [0,1] );
my $a = pdl '0 1 0 1 BAD; 1 0 1 0 1; 0 1 0 1 BAD';
is_pdl $a->assign($centroid), short([1,0,1,0,0], [0,1,0,1,1]),
"assign with bad data";
}
{
my $a = pdl '0 1 2 3 BAD; 5 6 7 8 9; 10 11 12 13 BAD';
my $cluster = pdl(byte, [1,0,1,0,0], [0,1,0,1,1]);
my ($m, $ss) = $a->centroid($cluster);
my $m_a = pdl([1,2], [6,7.6666667], [11,12]);
my $ss_a = pdl([1,1], [1,1.5555556], [1,1]);
is_pdl $m, $m_a, "centroid with bad data";
is_pdl $ss, $ss_a, "centroid with bad data";
}
# make kmeans deterministic:
srandom(5);
{
my $data = pdl '0 0 2 3 4 5 6; 7 0 9 10 11 12 13; 14 0 16 17 18 19 20';
my %m = $data->kmeans({NCLUS=>2, NSEED=>6, NTRY=>10, V=>0});
is_pdl $m{centroid}->sumover, pdl('3.3333333 10.333333 17.333333'), 'kmeans';
}
{
my $data = pdl '
[
[0 0 2 3 4 5 6; 0 0 9 10 11 12 13; 14 0 16 17 18 19 20]
[21 0 23 24 25 26 27; 28 0 30 31 32 33 34; 35 0 37 38 39 40 41]
]
[
[0 0 44 45 46 47 48; 0 0 51 52 53 54 55; 56 0 58 59 60 61 62]
[63 0 65 66 67 68 69; 70 0 72 73 74 75 76; 77 0 79 80 81 82 83]
]
';
my %m = $data->kmeans( {nclus=>[2,1,1], ntry=>20, v=>0} );
# print "$_\t$m{$_}\n" for sort keys %m;
my %a = (
'R2' => pdl
(
[ qw(0.74223245 0.97386667) ],
[ qw(0.84172845 0.99499377) ],
),
'ss_sum' => pdl (
[
[ qw( 10 10 108 )],
[ qw( 23.333333 23.333333 23.333333 )],
],
[
[ qw( 10 10 1578 )],
[ qw( 23.333333 23.333333 23.333333 )],
]
),
);
is_pdl $m{R2}, $a{R2}, "kmeans R2 result as expected";
is_pdl $m{ss}->sumover, $a{ss_sum}, {atol=>1e-3, test_name=>"kmeans ss result as expected"};
}
{
my $data = pdl '
[
[0 0 2 3 4 5 6; 0 0 9 10 11 12 13; 14 0 16 17 18 19 20]
[21 0 23 24 25 26 27; 28 0 30 31 32 33 34; 35 0 37 38 39 40 41]
]
[
[0 0 44 45 46 47 48; 0 0 51 52 53 54 55; 56 0 58 59 60 61 62]
[63 0 65 66 67 68 69; 70 0 72 73 74 75 76; 77 0 79 80 81 82 83]
]
';
# centroid intentionally has one less dim than data
my $centroid = pdl('[10 0; 10 0; 10 0] [20 0; 30 0; 30 0]');
# use dummy to match centroid dims to data dims
my %m = $data->kmeans( {cntrd=>$centroid->dummy(-1), v=>0} );
# print "$_\t$m{$_}\n" for sort keys %m;
my %a = (
R2 => pdl('0.74223245 0.97386667; 0.84172845 0.99499377'),
ss_sum => pdl('
[10 10 108; 23.333333 23.333333 23.333333]
[10 10 1578; 23.333333 23.333333 23.333333]
'),
);
is_pdl $m{R2}, $a{R2}, "kmeans R2 with manually seeded centroid";
is_pdl $m{ss}->sumover, $a{ss_sum}, {atol=>1e-3, test_name=>"kmeans ss with manually seeded centroid"};
}
{
my $data = sequence 7, 3;
$data = $data->setbadat(4,0);
my %m = $data->kmeans({NCLUS=>2, NTRY=>10, V=>0});
#print "$_\t$m{$_}\n" for sort keys %m;
is_pdl $m{ms}->sumover, pdl('1.5 1.9166667 1.9166667'), 'kmeans bad';
}
{
my $data = pdl '
[0 0 2 BAD 4 5 6; 0 0 9 10 11 12 13; 0 0 16 17 18 19 20]
[21 22 23 24 1 1 1; 28 29 30 31 1 1 1; 35 36 37 38 1 1 1]
';
my %m = $data->kmeans( {nclus=>[2,1], ntry=>20, v=>0} );
# print "$_\t$m{$_}\n" for sort keys %m;
my %a = (
'R2' => pdl( [ qw( 0.96879592 0.99698988 ) ] ),
'ms' => pdl('[2.1875 0; 2 0; 2 0] [0,1.25; 0 1.25; 0 1.25]'),
);
is_pdl $m{R2}, $a{R2}, "3d kmeans with bad data R2 is as expected";
is_pdl $m{ms}->sumover, $a{ms}->sumover, {atol=>1e-3, test_name=>"3d kmeans with bad data ss is as expected"};
}
{
my $l = pdl(
[qw( -0.798603 -0.61624 -0.906765 0.103116)],
[qw( 0.283269 -0.41041 0.131113 0.894118)],
[qw( -0.419717 0.649522 -0.0223668 0.434389)],
[qw( 0.325314 0.173015 -0.400108 0.0350236)],
);
my $c = $l->pca_cluster({v=>0,ncomp=>4,plot=>0});
is_pdl $c, pdl([1,0,1,0], [0,1,0,0], [0,0,0,1]),
"principal component analysis clustering";
}
{
my $a = pdl( [[3,1], [2,4]] );
my $b = pdl( [2,4], [3,1] );
my $c = pdl( 5,15 );
my $d = PDL::Stats::Kmeans::_d_point2line( $a, $b, $c );
is_pdl $d, pdl(1.754116, 1.4142136), '_d_point2line';
}
{
my $c0 = pdl(byte, [1,0,1,0], [0,1,0,1]);
my $c1 = pdl(byte, [0,0,0,1], [0,1,1,0]);
my $c = cat $c0, $c1;
my $ans = indx( [0,1,0,1], [-1,1,1,0] );
is_pdl $c->which_cluster, $ans, 'which_cluster';
}
done_testing();
|
