This file is indexed.

/usr/include/dlib/svm/kkmeans.h is in libdlib-dev 18.18-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  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
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
// Copyright (C) 2008  Davis E. King (davis@dlib.net)
// License: Boost Software License   See LICENSE.txt for the full license.
#ifndef DLIB_KKMEANs_
#define DLIB_KKMEANs_

#include <cmath>
#include "../matrix/matrix_abstract.h"
#include "../algs.h"
#include "../serialize.h"
#include "kernel.h"
#include "../array.h"
#include "kcentroid.h"
#include "kkmeans_abstract.h"
#include "../noncopyable.h"
#include "../smart_pointers.h"
#include <vector>

namespace dlib
{

    template <
        typename kernel_type
        >
    class kkmeans : public noncopyable
    {
    public:
        typedef typename kernel_type::scalar_type scalar_type;
        typedef typename kernel_type::sample_type sample_type;
        typedef typename kernel_type::mem_manager_type mem_manager_type;

        kkmeans (
            const kcentroid<kernel_type>& kc_ 
        ):
            kc(kc_),
            min_change(0.01)
        {
            set_number_of_centers(1);
        }

        ~kkmeans()
        {
        }

        const kernel_type& get_kernel (
        ) const
        {
            return kc.get_kernel();
        }

        void set_kcentroid (
            const kcentroid<kernel_type>& kc_
        )
        {
            kc = kc_;
            set_number_of_centers(number_of_centers());
        }

        const kcentroid<kernel_type>& get_kcentroid (
            unsigned long i
        ) const
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(i < number_of_centers(),
                "\tkcentroid kkmeans::get_kcentroid(i)"
                << "\n\tYou have given an invalid value for i"
                << "\n\ti:                   " << i 
                << "\n\tnumber_of_centers(): " << number_of_centers() 
                << "\n\tthis:                " << this
                );

            return *centers[i];
        }

        void set_number_of_centers (
            unsigned long num
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(num > 0,
                "\tvoid kkmeans::set_number_of_centers()"
                << "\n\tYou can't set the number of centers to zero"
                << "\n\tthis: " << this
                );

            centers.set_max_size(num);
            centers.set_size(num);

            for (unsigned long i = 0; i < centers.size(); ++i)
            {
                centers[i].reset(new kcentroid<kernel_type>(kc));
            }
        }

        unsigned long number_of_centers (
        ) const
        {
            return centers.size();
        }

        template <typename T, typename U>
        void train (
            const T& samples,
            const U& initial_centers,
            long max_iter = 1000
        )
        {
            do_train(mat(samples),mat(initial_centers),max_iter);
        }

        unsigned long operator() (
            const sample_type& sample
        ) const
        {
            unsigned long label = 0;
            scalar_type best_score = (*centers[0])(sample);

            // figure out which center the given sample is closest too
            for (unsigned long i = 1; i < centers.size(); ++i)
            {
                scalar_type temp = (*centers[i])(sample);
                if (temp < best_score)
                {
                    label = i;
                    best_score = temp;
                }
            }

            return label;
        }

        void set_min_change (
            scalar_type min_change_
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT( 0 <= min_change_ < 1,
                "\tvoid kkmeans::set_min_change()"
                << "\n\tInvalid arguments to this function"
                << "\n\tthis: " << this
                << "\n\tmin_change_: " << min_change_ 
                );
            min_change = min_change_;
        }

        const scalar_type get_min_change (
        ) const
        {
            return min_change;
        }

        void swap (
            kkmeans& item
        )
        {
            centers.swap(item.centers);
            kc.swap(item.kc);
            assignments.swap(item.assignments);
            exchange(min_change, item.min_change);
        }

        friend void serialize(const kkmeans& item, std::ostream& out)
        {
            serialize(item.centers.size(),out);
            for (unsigned long i = 0; i < item.centers.size(); ++i)
            {
                serialize(*item.centers[i], out);
            }
            serialize(item.kc, out);
            serialize(item.min_change, out);
        }

        friend void deserialize(kkmeans& item, std::istream& in)
        {
            unsigned long num;
            deserialize(num, in);
            item.centers.resize(num);
            for (unsigned long i = 0; i < item.centers.size(); ++i)
            {
                scoped_ptr<kcentroid<kernel_type> > temp(new kcentroid<kernel_type>(kernel_type()));
                deserialize(*temp, in);
                item.centers[i].swap(temp);
            }

            deserialize(item.kc, in);
            deserialize(item.min_change, in);
        }

    private:

        template <typename matrix_type, typename matrix_type2>
        void do_train (
            const matrix_type& samples,
            const matrix_type2& initial_centers,
            long max_iter = 1000
        )
        {
            COMPILE_TIME_ASSERT((is_same_type<typename matrix_type::type, sample_type>::value));
            COMPILE_TIME_ASSERT((is_same_type<typename matrix_type2::type, sample_type>::value));

            // make sure requires clause is not broken
            DLIB_ASSERT(samples.nc() == 1 && initial_centers.nc() == 1 &&
                         initial_centers.nr() == static_cast<long>(number_of_centers()),
                "\tvoid kkmeans::train()"
                << "\n\tInvalid arguments to this function"
                << "\n\tthis: " << this
                << "\n\tsamples.nc(): " << samples.nc() 
                << "\n\tinitial_centers.nc(): " << initial_centers.nc() 
                << "\n\tinitial_centers.nr(): " << initial_centers.nr() 
                );

            // clear out the old data and initialize the centers
            for (unsigned long i = 0; i < centers.size(); ++i)
            {
                centers[i]->clear_dictionary();
                centers[i]->train(initial_centers(i));
            }

            assignments.resize(samples.size());

            bool assignment_changed = true;

            // loop until the centers stabilize 
            long count = 0;
            const unsigned long min_num_change = static_cast<unsigned long>(min_change*samples.size());
            unsigned long num_changed = min_num_change;
            while (assignment_changed && count < max_iter && num_changed >= min_num_change)
            {
                ++count;
                assignment_changed = false;
                num_changed = 0;

                // loop over all the samples and assign them to their closest centers
                for (long i = 0; i < samples.size(); ++i)
                {
                    // find the best center
                    unsigned long best_center = 0;
                    scalar_type best_score = (*centers[0])(samples(i));
                    for (unsigned long c = 1; c < centers.size(); ++c)
                    {
                        scalar_type temp = (*centers[c])(samples(i));
                        if (temp < best_score)
                        {
                            best_score = temp;
                            best_center = c;
                        }
                    }

                    // if the current sample changed centers then make note of that
                    if (assignments[i] != best_center)
                    {
                        assignments[i] = best_center;
                        assignment_changed = true;
                        ++num_changed;
                    }
                }

                if (assignment_changed)
                {
                    // now clear out the old data 
                    for (unsigned long i = 0; i < centers.size(); ++i)
                        centers[i]->clear_dictionary();

                    // recalculate the cluster centers 
                    for (unsigned long i = 0; i < assignments.size(); ++i)
                        centers[assignments[i]]->train(samples(i));
                }

            }


        }

        array<scoped_ptr<kcentroid<kernel_type> > > centers;
        kcentroid<kernel_type> kc;
        scalar_type min_change;

        // temp variables
        array<unsigned long> assignments;
    };

// ----------------------------------------------------------------------------------------

    template <typename kernel_type>
    void swap(kkmeans<kernel_type>& a, kkmeans<kernel_type>& b)
    { a.swap(b); }

// ----------------------------------------------------------------------------------------

    struct dlib_pick_initial_centers_data
    {
        dlib_pick_initial_centers_data():idx(0), dist(std::numeric_limits<double>::infinity()){}
        long idx;
        double dist;
        bool operator< (const dlib_pick_initial_centers_data& d) const { return dist < d.dist; }
    };

    template <
        typename vector_type1, 
        typename vector_type2, 
        typename kernel_type
        >
    void pick_initial_centers(
        long num_centers, 
        vector_type1& centers, 
        const vector_type2& samples, 
        const kernel_type& k, 
        double percentile = 0.01
    )
    {
        /*
            This function is basically just a non-randomized version of the kmeans++ algorithm
            described in the paper:
                kmeans++: The Advantages of Careful Seeding by Arthur and Vassilvitskii

        */


        // make sure requires clause is not broken
        DLIB_ASSERT(num_centers > 1 && 0 <= percentile && percentile < 1 && samples.size() > 1,
            "\tvoid pick_initial_centers()"
            << "\n\tYou passed invalid arguments to this function"
            << "\n\tnum_centers: " << num_centers 
            << "\n\tpercentile: " << percentile 
            << "\n\tsamples.size(): " << samples.size() 
            );

        std::vector<dlib_pick_initial_centers_data> scores(samples.size());
        std::vector<dlib_pick_initial_centers_data> scores_sorted(samples.size());
        centers.clear();

        // pick the first sample as one of the centers
        centers.push_back(samples[0]);

        const long best_idx = static_cast<long>(std::max(0.0,samples.size() - samples.size()*percentile - 1));

        // pick the next center
        for (long i = 0; i < num_centers-1; ++i)
        {
            // Loop over the samples and compare them to the most recent center.  Store
            // the distance from each sample to its closest center in scores.
            const double k_cc = k(centers[i], centers[i]);
            for (unsigned long s = 0; s < samples.size(); ++s)
            {
                // compute the distance between this sample and the current center
                const double dist = k_cc + k(samples[s],samples[s]) - 2*k(samples[s], centers[i]);

                if (dist < scores[s].dist)
                {
                    scores[s].dist = dist;
                    scores[s].idx = s;
                }
            }

            scores_sorted = scores;

            // now find the winning center and add it to centers.  It is the one that is 
            // far away from all the other centers.
            sort(scores_sorted.begin(), scores_sorted.end());
            centers.push_back(samples[scores_sorted[best_idx].idx]);
        }
        
    }

// ----------------------------------------------------------------------------------------

    template <
        typename vector_type1, 
        typename vector_type2
        >
    void pick_initial_centers(
        long num_centers, 
        vector_type1& centers, 
        const vector_type2& samples, 
        double percentile = 0.01
    )
    {
        typedef typename vector_type1::value_type sample_type;
        linear_kernel<sample_type> kern;
        pick_initial_centers(num_centers, centers, samples, kern, percentile);
    }

// ----------------------------------------------------------------------------------------

    template <
        typename array_type, 
        typename sample_type,
        typename alloc
        >
    void find_clusters_using_kmeans (
        const array_type& samples,
        std::vector<sample_type, alloc>& centers,
        unsigned long max_iter = 1000
    )
    {
        // make sure requires clause is not broken
        DLIB_ASSERT(samples.size() > 0 && centers.size() > 0,
            "\tvoid find_clusters_using_kmeans()"
            << "\n\tYou passed invalid arguments to this function"
            << "\n\t samples.size(): " << samples.size() 
            << "\n\t centers.size(): " << centers.size() 
            );

#ifdef ENABLE_ASSERTS
        {
        const long nr = samples[0].nr();
        const long nc = samples[0].nc();
        for (unsigned long i = 0; i < samples.size(); ++i)
        {
            DLIB_ASSERT(is_vector(samples[i]) && samples[i].nr() == nr && samples[i].nc() == nc,
                "\tvoid find_clusters_using_kmeans()"
                << "\n\t You passed invalid arguments to this function"
                << "\n\t is_vector(samples[i]): " << is_vector(samples[i])
                << "\n\t samples[i].nr():       " << samples[i].nr()
                << "\n\t nr:                    " << nr
                << "\n\t samples[i].nc():       " << samples[i].nc()
                << "\n\t nc:                    " << nc
                << "\n\t i:                     " << i
                );
        }
        }
#endif

        typedef typename sample_type::type scalar_type;

        sample_type zero(centers[0]);
        set_all_elements(zero, 0);

        std::vector<unsigned long> center_element_count;

        // tells which center a sample belongs to
        std::vector<unsigned long> assignments(samples.size(), samples.size());


        unsigned long iter = 0;
        bool centers_changed = true;
        while (centers_changed && iter < max_iter)
        {
            ++iter;
            centers_changed = false;
            center_element_count.assign(centers.size(), 0);

            // loop over each sample and see which center it is closest to
            for (unsigned long i = 0; i < samples.size(); ++i)
            {
                // find the best center for sample[i]
                scalar_type best_dist = std::numeric_limits<scalar_type>::max();
                unsigned long best_center = 0;
                for (unsigned long j = 0; j < centers.size(); ++j)
                {
                    scalar_type dist = length(centers[j] - samples[i]);
                    if (dist < best_dist)
                    {
                        best_dist = dist;
                        best_center = j;
                    }
                }

                if (assignments[i] != best_center)
                {
                    centers_changed = true;
                    assignments[i] = best_center;
                }

                center_element_count[best_center] += 1;
            }

            // now update all the centers
            centers.assign(centers.size(), zero);
            for (unsigned long i = 0; i < samples.size(); ++i)
            {
                centers[assignments[i]] += samples[i];
            }
            for (unsigned long i = 0; i < centers.size(); ++i)
            {
                if (center_element_count[i] != 0)
                    centers[i] /= center_element_count[i];
            }
        }


    }

// ----------------------------------------------------------------------------------------

    template <
        typename array_type, 
        typename sample_type,
        typename alloc
        >
    void find_clusters_using_angular_kmeans (
        const array_type& samples,
        std::vector<sample_type, alloc>& centers,
        unsigned long max_iter = 1000
    )
    {
        // make sure requires clause is not broken
        DLIB_ASSERT(samples.size() > 0 && centers.size() > 0,
            "\tvoid find_clusters_using_angular_kmeans()"
            << "\n\tYou passed invalid arguments to this function"
            << "\n\t samples.size(): " << samples.size() 
            << "\n\t centers.size(): " << centers.size() 
            );

#ifdef ENABLE_ASSERTS
        {
        const long nr = samples[0].nr();
        const long nc = samples[0].nc();
        for (unsigned long i = 0; i < samples.size(); ++i)
        {
            DLIB_ASSERT(is_vector(samples[i]) && samples[i].nr() == nr && samples[i].nc() == nc,
                "\tvoid find_clusters_using_angular_kmeans()"
                << "\n\t You passed invalid arguments to this function"
                << "\n\t is_vector(samples[i]): " << is_vector(samples[i])
                << "\n\t samples[i].nr():       " << samples[i].nr()
                << "\n\t nr:                    " << nr
                << "\n\t samples[i].nc():       " << samples[i].nc()
                << "\n\t nc:                    " << nc
                << "\n\t i:                     " << i
                );
        }
        }
#endif

        typedef typename sample_type::type scalar_type;

        sample_type zero(centers[0]);
        set_all_elements(zero, 0);

        unsigned long seed = 0;

        // tells which center a sample belongs to
        std::vector<unsigned long> assignments(samples.size(), samples.size());
        std::vector<double> lengths;
        for (unsigned long i = 0; i < samples.size(); ++i)
        {
            lengths.push_back(length(samples[i]));
            // If there are zero vectors in samples then just say their length is 1 so we
            // can avoid a division by zero check later on.  Also, this doesn't matter
            // since zero vectors can be assigned to any cluster randomly as there is no
            // basis for picking one based on angle.
            if (lengths.back() == 0)
                lengths.back() = 1;
        }

        // We will keep the centers as unit vectors at all times throughout the processing.
        for (unsigned long i = 0; i < centers.size(); ++i)
        {
            double len = length(centers[i]);
            // Avoid having length 0 centers.  If that is the case then pick another center
            // at random.
            while(len == 0)
            {
                centers[i] = matrix_cast<scalar_type>(gaussian_randm(centers[i].nr(), centers[i].nc(), seed++));
                len = length(centers[i]);
            }
            centers[i] /= len;
        }


        unsigned long iter = 0;
        bool centers_changed = true;
        while (centers_changed && iter < max_iter)
        {
            ++iter;
            centers_changed = false;

            // loop over each sample and see which center it is closest to
            for (unsigned long i = 0; i < samples.size(); ++i)
            {
                // find the best center for sample[i]
                scalar_type best_angle = std::numeric_limits<scalar_type>::max();
                unsigned long best_center = 0;
                for (unsigned long j = 0; j < centers.size(); ++j)
                {
                    scalar_type angle = -dot(centers[j],samples[i])/lengths[i];

                    if (angle < best_angle)
                    {
                        best_angle = angle;
                        best_center = j;
                    }
                }

                if (assignments[i] != best_center)
                {
                    centers_changed = true;
                    assignments[i] = best_center;
                }
            }

            // now update all the centers
            centers.assign(centers.size(), zero);
            for (unsigned long i = 0; i < samples.size(); ++i)
            {
                centers[assignments[i]] += samples[i];
            }
            // Now length normalize all the centers.
            for (unsigned long i = 0; i < centers.size(); ++i)
            {
                double len = length(centers[i]);
                // Avoid having length 0 centers.  If that is the case then pick another center
                // at random.
                while(len == 0)
                {
                    centers[i] = matrix_cast<scalar_type>(gaussian_randm(centers[i].nr(), centers[i].nc(), seed++));
                    len = length(centers[i]);
                    centers_changed = true;
                }
                centers[i] /= len;
            }
        }
    }

// ----------------------------------------------------------------------------------------

    template <
        typename array_type, 
        typename EXP 
        >
    unsigned long nearest_center (
        const array_type& centers,
        const matrix_exp<EXP>& sample
    )
    {
        // make sure requires clause is not broken
        DLIB_ASSERT(centers.size() > 0 && sample.size() > 0 && is_vector(sample),
            "\t unsigned long nearest_center()"
            << "\n\t You have given invalid inputs to this function."
            << "\n\t centers.size():    " << centers.size() 
            << "\n\t sample.size():     " << sample.size() 
            << "\n\t is_vector(sample): " << is_vector(sample) 
            );

        double best_dist = length_squared(centers[0] - sample);
        unsigned long best_idx = 0;
        for (unsigned long i = 1; i < centers.size(); ++i)
        {
            const double dist = length_squared(centers[i] - sample);
            if (dist < best_dist)
            {
                best_dist = dist;
                best_idx = i;
            }
        }
        return best_idx;
    }

// ----------------------------------------------------------------------------------------

}

#endif // DLIB_KKMEANs_