FuzzyCMeans   A
last analyzed

Complexity

Total Complexity 28

Size/Duplication

Total Lines 222
Duplicated Lines 0 %

Importance

Changes 0
Metric Value
wmc 28
eloc 97
dl 0
loc 222
rs 10
c 0
b 0
f 0

10 Methods

Rating   Name   Duplication   Size   Complexity  
A getMembershipMatrix() 0 3 1
A cluster() 0 39 5
A __construct() 0 10 2
A getMembershipRowTotal() 0 13 3
A generateRandomMembership() 0 15 3
A getDistanceCalc() 0 20 2
A initClusters() 0 7 1
A updateClusters() 0 19 5
A updateMembershipMatrix() 0 6 3
A getObjective() 0 13 3
1
<?php
2
3
declare(strict_types=1);
4
5
namespace Phpml\Clustering;
6
7
use Phpml\Clustering\KMeans\Cluster;
8
use Phpml\Clustering\KMeans\Point;
9
use Phpml\Clustering\KMeans\Space;
10
use Phpml\Exception\InvalidArgumentException;
11
use Phpml\Math\Distance\Euclidean;
12
13
class FuzzyCMeans implements Clusterer
14
{
15
    /**
16
     * @var int
17
     */
18
    private $clustersNumber;
19
20
    /**
21
     * @var Cluster[]
22
     */
23
    private $clusters = [];
24
25
    /**
26
     * @var Space
27
     */
28
    private $space;
29
30
    /**
31
     * @var float[][]
32
     */
33
    private $membership = [];
34
35
    /**
36
     * @var float
37
     */
38
    private $fuzziness;
39
40
    /**
41
     * @var float
42
     */
43
    private $epsilon;
44
45
    /**
46
     * @var int
47
     */
48
    private $maxIterations;
49
50
    /**
51
     * @var int
52
     */
53
    private $sampleCount;
54
55
    /**
56
     * @var array
57
     */
58
    private $samples = [];
59
60
    /**
61
     * @throws InvalidArgumentException
62
     */
63
    public function __construct(int $clustersNumber, float $fuzziness = 2.0, float $epsilon = 1e-2, int $maxIterations = 100)
64
    {
65
        if ($clustersNumber <= 0) {
66
            throw new InvalidArgumentException('Invalid clusters number');
67
        }
68
69
        $this->clustersNumber = $clustersNumber;
70
        $this->fuzziness = $fuzziness;
71
        $this->epsilon = $epsilon;
72
        $this->maxIterations = $maxIterations;
73
    }
74
75
    public function getMembershipMatrix(): array
76
    {
77
        return $this->membership;
78
    }
79
80
    public function cluster(array $samples): array
81
    {
82
        // Initialize variables, clusters and membership matrix
83
        $this->sampleCount = count($samples);
84
        $this->samples = &$samples;
85
        $this->space = new Space(count($samples[0]));
86
        $this->initClusters();
87
88
        // Our goal is minimizing the objective value while
89
        // executing the clustering steps at a maximum number of iterations
90
        $lastObjective = 0.0;
91
        $iterations = 0;
92
        do {
93
            // Update the membership matrix and cluster centers, respectively
94
            $this->updateMembershipMatrix();
95
            $this->updateClusters();
96
97
            // Calculate the new value of the objective function
98
            $objectiveVal = $this->getObjective();
99
            $difference = abs($lastObjective - $objectiveVal);
100
            $lastObjective = $objectiveVal;
101
        } while ($difference > $this->epsilon && $iterations++ <= $this->maxIterations);
102
103
        // Attach (hard cluster) each data point to the nearest cluster
104
        for ($k = 0; $k < $this->sampleCount; ++$k) {
105
            $column = array_column($this->membership, $k);
106
            arsort($column);
107
            reset($column);
108
            $cluster = $this->clusters[key($column)];
109
            $cluster->attach(new Point($this->samples[$k]));
110
        }
111
112
        // Return grouped samples
113
        $grouped = [];
114
        foreach ($this->clusters as $cluster) {
115
            $grouped[] = $cluster->getPoints();
116
        }
117
118
        return $grouped;
119
    }
120
121
    protected function initClusters(): void
122
    {
123
        // Membership array is a matrix of cluster number by sample counts
124
        // We initilize the membership array with random values
125
        $dim = $this->space->getDimension();
126
        $this->generateRandomMembership($dim, $this->sampleCount);
127
        $this->updateClusters();
128
    }
129
130
    protected function generateRandomMembership(int $rows, int $cols): void
131
    {
132
        $this->membership = [];
133
        for ($i = 0; $i < $rows; ++$i) {
134
            $row = [];
135
            $total = 0.0;
136
            for ($k = 0; $k < $cols; ++$k) {
137
                $val = random_int(1, 5) / 10.0;
138
                $row[] = $val;
139
                $total += $val;
140
            }
141
142
            $this->membership[] = array_map(function ($val) use ($total) {
143
                return $val / $total;
144
            }, $row);
145
        }
146
    }
147
148
    protected function updateClusters(): void
149
    {
150
        $dim = $this->space->getDimension();
151
        if (count($this->clusters) === 0) {
152
            for ($i = 0; $i < $this->clustersNumber; ++$i) {
153
                $this->clusters[] = new Cluster($this->space, array_fill(0, $dim, 0.0));
154
            }
155
        }
156
157
        for ($i = 0; $i < $this->clustersNumber; ++$i) {
158
            $cluster = $this->clusters[$i];
159
            $center = $cluster->getCoordinates();
160
            for ($k = 0; $k < $dim; ++$k) {
161
                $a = $this->getMembershipRowTotal($i, $k, true);
162
                $b = $this->getMembershipRowTotal($i, $k, false);
163
                $center[$k] = $a / $b;
164
            }
165
166
            $cluster->setCoordinates($center);
167
        }
168
    }
169
170
    protected function getMembershipRowTotal(int $row, int $col, bool $multiply): float
171
    {
172
        $sum = 0.0;
173
        for ($k = 0; $k < $this->sampleCount; ++$k) {
174
            $val = $this->membership[$row][$k] ** $this->fuzziness;
175
            if ($multiply) {
176
                $val *= $this->samples[$k][$col];
177
            }
178
179
            $sum += $val;
180
        }
181
182
        return $sum;
183
    }
184
185
    protected function updateMembershipMatrix(): void
186
    {
187
        for ($i = 0; $i < $this->clustersNumber; ++$i) {
188
            for ($k = 0; $k < $this->sampleCount; ++$k) {
189
                $distCalc = $this->getDistanceCalc($i, $k);
190
                $this->membership[$i][$k] = 1.0 / $distCalc;
191
            }
192
        }
193
    }
194
195
    protected function getDistanceCalc(int $row, int $col): float
196
    {
197
        $sum = 0.0;
198
        $distance = new Euclidean();
199
        $dist1 = $distance->distance(
200
            $this->clusters[$row]->getCoordinates(),
201
            $this->samples[$col]
202
        );
203
204
        for ($j = 0; $j < $this->clustersNumber; ++$j) {
205
            $dist2 = $distance->distance(
206
                $this->clusters[$j]->getCoordinates(),
207
                $this->samples[$col]
208
            );
209
210
            $val = (($dist1 / $dist2) ** 2.0) / ($this->fuzziness - 1);
211
            $sum += $val;
212
        }
213
214
        return $sum;
215
    }
216
217
    /**
218
     * The objective is to minimize the distance between all data points
219
     * and all cluster centers. This method returns the summation of all
220
     * these distances
221
     */
222
    protected function getObjective(): float
223
    {
224
        $sum = 0.0;
225
        $distance = new Euclidean();
226
        for ($i = 0; $i < $this->clustersNumber; ++$i) {
227
            $clust = $this->clusters[$i]->getCoordinates();
228
            for ($k = 0; $k < $this->sampleCount; ++$k) {
229
                $point = $this->samples[$k];
230
                $sum += $distance->distance($clust, $point);
231
            }
232
        }
233
234
        return $sum;
235
    }
236
}
237