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
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
|
/*
Copyright (c) by respective owners including Yahoo!, Microsoft, and
individual contributors. All rights reserved. Released under a BSD (revised)
license as described in the file LICENSE.
*/
#include <fstream>
#include <vector>
#include <float.h>
#ifdef _WIN32
#include <winsock2.h>
#else
#include <netdb.h>
#endif
#include <string.h>
#include <stdio.h>
#include <assert.h>
#include "parse_example.h"
#include "constant.h"
#include "sparse_dense.h"
#include "gd.h"
#include "lda_core.h"
#include "cache.h"
#include "simple_label.h"
#include "rand48.h"
#include "vw.h"
using namespace LEARNER;
namespace LDA {
class index_feature {
public:
uint32_t document;
feature f;
bool operator<(const index_feature b) const { return f.weight_index < b.f.weight_index; }
};
struct lda {
v_array<float> Elogtheta;
v_array<float> decay_levels;
v_array<float> total_new;
v_array<example* > examples;
v_array<float> total_lambda;
v_array<int> doc_lengths;
v_array<float> digammas;
v_array<float> v;
std::vector<index_feature> sorted_features;
bool total_lambda_init;
double example_t;
vw* all;
};
#ifdef _WIN32
inline float fmax(float f1, float f2) { return (f1 < f2 ? f2 : f1); }
inline float fmin(float f1, float f2) { return (f1 > f2 ? f2 : f1); }
#endif
#define MINEIRO_SPECIAL
#ifdef MINEIRO_SPECIAL
namespace {
inline float
fastlog2 (float x)
{
union { float f; uint32_t i; } vx = { x };
union { uint32_t i; float f; } mx = { (vx.i & 0x007FFFFF) | (0x7e << 23) };
float y = (float)vx.i;
y *= 1.0f / (float)(1 << 23);
return
y - 124.22544637f - 1.498030302f * mx.f - 1.72587999f / (0.3520887068f + mx.f);
}
inline float
fastlog (float x)
{
return 0.69314718f * fastlog2 (x);
}
inline float
fastpow2 (float p)
{
float offset = (p < 0) ? 1.0f : 0.0f;
float clipp = (p < -126) ? -126.0f : p;
int w = (int)clipp;
float z = clipp - w + offset;
union { uint32_t i; float f; } v = { (uint32_t)((1 << 23) * (clipp + 121.2740838f + 27.7280233f / (4.84252568f - z) - 1.49012907f * z)) };
return v.f;
}
inline float
fastexp (float p)
{
return fastpow2 (1.442695040f * p);
}
inline float
fastpow (float x,
float p)
{
return fastpow2 (p * fastlog2 (x));
}
inline float
fastlgamma (float x)
{
float logterm = fastlog (x * (1.0f + x) * (2.0f + x));
float xp3 = 3.0f + x;
return
-2.081061466f - x + 0.0833333f / xp3 - logterm + (2.5f + x) * fastlog (xp3);
}
inline float
fastdigamma (float x)
{
float twopx = 2.0f + x;
float logterm = fastlog (twopx);
return - (1.0f + 2.0f * x) / (x * (1.0f + x))
- (13.0f + 6.0f * x) / (12.0f * twopx * twopx)
+ logterm;
}
#define log fastlog
#define exp fastexp
#define powf fastpow
#define mydigamma fastdigamma
#define mylgamma fastlgamma
#if defined(__SSE2__) && !defined(VW_LDA_NO_SSE)
#include <emmintrin.h>
typedef __m128 v4sf;
typedef __m128i v4si;
#define v4si_to_v4sf _mm_cvtepi32_ps
#define v4sf_to_v4si _mm_cvttps_epi32
static inline float
v4sf_index (const v4sf x,
unsigned int i)
{
union { v4sf f; float array[4]; } tmp = { x };
return tmp.array[i];
}
static inline const v4sf
v4sfl (float x)
{
union { float array[4]; v4sf f; } tmp = { { x, x, x, x } };
return tmp.f;
}
static inline const v4si
v4sil (uint32_t x)
{
uint64_t wide = (((uint64_t) x) << 32) | x;
union { uint64_t array[2]; v4si f; } tmp = { { wide, wide } };
return tmp.f;
}
static inline v4sf
vfastpow2 (const v4sf p)
{
v4sf ltzero = _mm_cmplt_ps (p, v4sfl (0.0f));
v4sf offset = _mm_and_ps (ltzero, v4sfl (1.0f));
v4sf lt126 = _mm_cmplt_ps (p, v4sfl (-126.0f));
v4sf clipp = _mm_andnot_ps (lt126, p) + _mm_and_ps (lt126, v4sfl (-126.0f));
v4si w = v4sf_to_v4si (clipp);
v4sf z = clipp - v4si_to_v4sf (w) + offset;
const v4sf c_121_2740838 = v4sfl (121.2740838f);
const v4sf c_27_7280233 = v4sfl (27.7280233f);
const v4sf c_4_84252568 = v4sfl (4.84252568f);
const v4sf c_1_49012907 = v4sfl (1.49012907f);
union { v4si i; v4sf f; } v = {
v4sf_to_v4si (
v4sfl (1 << 23) *
(clipp + c_121_2740838 + c_27_7280233 / (c_4_84252568 - z) - c_1_49012907 * z)
)
};
return v.f;
}
inline v4sf
vfastexp (const v4sf p)
{
const v4sf c_invlog_2 = v4sfl (1.442695040f);
return vfastpow2 (c_invlog_2 * p);
}
inline v4sf
vfastlog2 (v4sf x)
{
union { v4sf f; v4si i; } vx = { x };
union { v4si i; v4sf f; } mx = { (vx.i & v4sil (0x007FFFFF)) | v4sil (0x3f000000) };
v4sf y = v4si_to_v4sf (vx.i);
y *= v4sfl (1.1920928955078125e-7f);
const v4sf c_124_22551499 = v4sfl (124.22551499f);
const v4sf c_1_498030302 = v4sfl (1.498030302f);
const v4sf c_1_725877999 = v4sfl (1.72587999f);
const v4sf c_0_3520087068 = v4sfl (0.3520887068f);
return y - c_124_22551499
- c_1_498030302 * mx.f
- c_1_725877999 / (c_0_3520087068 + mx.f);
}
inline v4sf
vfastlog (v4sf x)
{
const v4sf c_0_69314718 = v4sfl (0.69314718f);
return c_0_69314718 * vfastlog2 (x);
}
inline v4sf
vfastdigamma (v4sf x)
{
v4sf twopx = v4sfl (2.0f) + x;
v4sf logterm = vfastlog (twopx);
return (v4sfl (-48.0f) + x * (v4sfl (-157.0f) + x * (v4sfl (-127.0f) - v4sfl (30.0f) * x))) /
(v4sfl (12.0f) * x * (v4sfl (1.0f) + x) * twopx * twopx)
+ logterm;
}
void
vexpdigammify (vw& all, float* gamma)
{
unsigned int n = all.lda;
float extra_sum = 0.0f;
v4sf sum = v4sfl (0.0f);
size_t i;
for (i = 0; i < n && ((uintptr_t) (gamma + i)) % 16 > 0; ++i)
{
extra_sum += gamma[i];
gamma[i] = fastdigamma (gamma[i]);
}
for (; i + 4 < n; i += 4)
{
v4sf arg = _mm_load_ps (gamma + i);
sum += arg;
arg = vfastdigamma (arg);
_mm_store_ps (gamma + i, arg);
}
for (; i < n; ++i)
{
extra_sum += gamma[i];
gamma[i] = fastdigamma (gamma[i]);
}
extra_sum += v4sf_index (sum, 0) + v4sf_index (sum, 1) +
v4sf_index (sum, 2) + v4sf_index (sum, 3);
extra_sum = fastdigamma (extra_sum);
sum = v4sfl (extra_sum);
for (i = 0; i < n && ((uintptr_t) (gamma + i)) % 16 > 0; ++i)
{
gamma[i] = fmaxf (1e-10f, fastexp (gamma[i] - v4sf_index (sum, 0)));
}
for (; i + 4 < n; i += 4)
{
v4sf arg = _mm_load_ps (gamma + i);
arg -= sum;
arg = vfastexp (arg);
arg = _mm_max_ps (v4sfl (1e-10f), arg);
_mm_store_ps (gamma + i, arg);
}
for (; i < n; ++i)
{
gamma[i] = fmaxf (1e-10f, fastexp (gamma[i] - v4sf_index (sum, 0)));
}
}
void vexpdigammify_2(vw& all, float* gamma, const float* norm)
{
size_t n = all.lda;
size_t i;
for (i = 0; i < n && ((uintptr_t) (gamma + i)) % 16 > 0; ++i)
{
gamma[i] = fmaxf (1e-10f, fastexp (fastdigamma (gamma[i]) - norm[i]));
}
for (; i + 4 < n; i += 4)
{
v4sf arg = _mm_load_ps (gamma + i);
arg = vfastdigamma (arg);
v4sf vnorm = _mm_loadu_ps (norm + i);
arg -= vnorm;
arg = vfastexp (arg);
arg = _mm_max_ps (v4sfl (1e-10f), arg);
_mm_store_ps (gamma + i, arg);
}
for (; i < n; ++i)
{
gamma[i] = fmaxf (1e-10f, fastexp (fastdigamma (gamma[i]) - norm[i]));
}
}
#define myexpdigammify vexpdigammify
#define myexpdigammify_2 vexpdigammify_2
#else
#ifndef _WIN32
#warning "lda IS NOT using sse instructions"
#endif
#define myexpdigammify expdigammify
#define myexpdigammify_2 expdigammify_2
#endif // __SSE2__
} // end anonymous namespace
#else
#include <boost/math/special_functions/digamma.hpp>
#include <boost/math/special_functions/gamma.hpp>
using namespace boost::math::policies;
#define mydigamma boost::math::digamma
#define mylgamma boost::math::lgamma
#define myexpdigammify expdigammify
#define myexpdigammify_2 expdigammify_2
#endif // MINEIRO_SPECIAL
float decayfunc(float t, float old_t, float power_t) {
float result = 1;
for (float i = old_t+1; i <= t; i += 1)
result *= (1-powf(i, -power_t));
return result;
}
float decayfunc2(float t, float old_t, float power_t)
{
float power_t_plus_one = 1.f - power_t;
float arg = - ( powf(t, power_t_plus_one) -
powf(old_t, power_t_plus_one));
return exp ( arg
/ power_t_plus_one);
}
float decayfunc3(double t, double old_t, double power_t)
{
double power_t_plus_one = 1. - power_t;
double logt = log((float)t);
double logoldt = log((float)old_t);
return (float)((old_t / t) * exp((float)(0.5*power_t_plus_one*(-logt*logt + logoldt*logoldt))));
}
float decayfunc4(double t, double old_t, double power_t)
{
if (power_t > 0.99)
return decayfunc3(t, old_t, power_t);
else
return (float)decayfunc2((float)t, (float)old_t, (float)power_t);
}
void expdigammify(vw& all, float* gamma)
{
float sum=0;
for (size_t i = 0; i<all.lda; i++)
{
sum += gamma[i];
gamma[i] = mydigamma(gamma[i]);
}
sum = mydigamma(sum);
for (size_t i = 0; i<all.lda; i++)
gamma[i] = fmax(1e-6f, exp(gamma[i] - sum));
}
void expdigammify_2(vw& all, float* gamma, float* norm)
{
for (size_t i = 0; i<all.lda; i++)
{
gamma[i] = fmax(1e-6f, exp(mydigamma(gamma[i]) - norm[i]));
}
}
float average_diff(vw& all, float* oldgamma, float* newgamma)
{
float sum = 0.;
float normalizer = 0.;
for (size_t i = 0; i<all.lda; i++) {
sum += fabsf(oldgamma[i] - newgamma[i]);
normalizer += newgamma[i];
}
return sum / normalizer;
}
// Returns E_q[log p(\theta)] - E_q[log q(\theta)].
float theta_kl(vw& all, v_array<float>& Elogtheta, float* gamma)
{
float gammasum = 0;
Elogtheta.erase();
for (size_t k = 0; k < all.lda; k++) {
Elogtheta.push_back(mydigamma(gamma[k]));
gammasum += gamma[k];
}
float digammasum = mydigamma(gammasum);
gammasum = mylgamma(gammasum);
float kl = -(all.lda*mylgamma(all.lda_alpha));
kl += mylgamma(all.lda_alpha*all.lda) - gammasum;
for (size_t k = 0; k < all.lda; k++) {
Elogtheta[k] -= digammasum;
kl += (all.lda_alpha - gamma[k]) * Elogtheta[k];
kl += mylgamma(gamma[k]);
}
return kl;
}
float find_cw(vw& all, float* u_for_w, float* v)
{
float c_w = 0;
for (size_t k =0; k<all.lda; k++)
c_w += u_for_w[k]*v[k];
return 1.f / c_w;
}
v_array<float> new_gamma;
v_array<float> old_gamma;
// Returns an estimate of the part of the variational bound that
// doesn't have to do with beta for the entire corpus for the current
// setting of lambda based on the document passed in. The value is
// divided by the total number of words in the document This can be
// used as a (possibly very noisy) estimate of held-out likelihood.
float lda_loop(vw& all, v_array<float>& Elogtheta, float* v,weight* weights,example* ec, float power_t)
{
new_gamma.erase();
old_gamma.erase();
for (size_t i = 0; i < all.lda; i++)
{
new_gamma.push_back(1.f);
old_gamma.push_back(0.f);
}
size_t num_words =0;
for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++)
num_words += ec->atomics[*i].end - ec->atomics[*i].begin;
float xc_w = 0;
float score = 0;
float doc_length = 0;
do
{
memcpy(v,new_gamma.begin,sizeof(float)*all.lda);
myexpdigammify(all, v);
memcpy(old_gamma.begin,new_gamma.begin,sizeof(float)*all.lda);
memset(new_gamma.begin,0,sizeof(float)*all.lda);
score = 0;
size_t word_count = 0;
doc_length = 0;
for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++)
{
feature *f = ec->atomics[*i].begin;
for (; f != ec->atomics[*i].end; f++)
{
float* u_for_w = &weights[(f->weight_index&all.reg.weight_mask)+all.lda+1];
float c_w = find_cw(all, u_for_w,v);
xc_w = c_w * f->x;
score += -f->x*log(c_w);
size_t max_k = all.lda;
for (size_t k =0; k<max_k; k++) {
new_gamma[k] += xc_w*u_for_w[k];
}
word_count++;
doc_length += f->x;
}
}
for (size_t k =0; k<all.lda; k++)
new_gamma[k] = new_gamma[k]*v[k]+all.lda_alpha;
}
while (average_diff(all, old_gamma.begin, new_gamma.begin) > 0.001);
ec->topic_predictions.erase();
ec->topic_predictions.resize(all.lda);
memcpy(ec->topic_predictions.begin,new_gamma.begin,all.lda*sizeof(float));
score += theta_kl(all, Elogtheta, new_gamma.begin);
return score / doc_length;
}
size_t next_pow2(size_t x) {
int i = 0;
x = x > 0 ? x - 1 : 0;
while (x > 0) {
x >>= 1;
i++;
}
return ((size_t)1) << i;
}
void save_load(lda* l, io_buf& model_file, bool read, bool text)
{
vw* all = l->all;
uint32_t length = 1 << all->num_bits;
uint32_t stride = all->reg.stride;
if (read)
{
initialize_regressor(*all);
for (size_t j = 0; j < stride*length; j+=stride)
{
for (size_t k = 0; k < all->lda; k++) {
if (all->random_weights) {
all->reg.weight_vector[j+k] = (float)(-log(frand48()) + 1.0f);
all->reg.weight_vector[j+k] *= (float)(all->lda_D / all->lda / all->length() * 200);
}
}
all->reg.weight_vector[j+all->lda] = all->initial_t;
}
}
if (model_file.files.size() > 0)
{
uint32_t i = 0;
uint32_t text_len;
char buff[512];
size_t brw = 1;
do
{
brw = 0;
size_t K = all->lda;
text_len = sprintf(buff, "%d ", i);
brw += bin_text_read_write_fixed(model_file,(char *)&i, sizeof (i),
"", read,
buff, text_len, text);
if (brw != 0)
for (uint32_t k = 0; k < K; k++)
{
uint32_t ndx = stride*i+k;
weight* v = &(all->reg.weight_vector[ndx]);
text_len = sprintf(buff, "%f ", *v + all->lda_rho);
brw += bin_text_read_write_fixed(model_file,(char *)v, sizeof (*v),
"", read,
buff, text_len, text);
}
if (text)
brw += bin_text_read_write_fixed(model_file,buff,0,
"", read,
"\n",1,text);
if (!read)
i++;
}
while ((!read && i < length) || (read && brw >0));
}
}
void learn_batch(lda& l)
{
if (l.sorted_features.empty()) {
// This can happen when the socket connection is dropped by the client.
// If l.sorted_features is empty, then l.sorted_features[0] does not
// exist, so we should not try to take its address in the beginning of
// the for loops down there. Since it seems that there's not much to
// do in this case, we just return.
return;
}
float eta = -1;
float minuseta = -1;
if (l.total_lambda.size() == 0)
{
for (size_t k = 0; k < l.all->lda; k++)
l.total_lambda.push_back(0.f);
size_t stride = l.all->reg.stride;
for (size_t i =0; i <= l.all->reg.weight_mask;i+=stride)
for (size_t k = 0; k < l.all->lda; k++)
l.total_lambda[k] += l.all->reg.weight_vector[i+k];
}
l.example_t++;
l.total_new.erase();
for (size_t k = 0; k < l.all->lda; k++)
l.total_new.push_back(0.f);
size_t batch_size = l.examples.size();
sort(l.sorted_features.begin(), l.sorted_features.end());
eta = l.all->eta * powf((float)l.example_t, - l.all->power_t);
minuseta = 1.0f - eta;
eta *= l.all->lda_D / batch_size;
l.decay_levels.push_back(l.decay_levels.last() + log(minuseta));
l.digammas.erase();
float additional = (float)(l.all->length()) * l.all->lda_rho;
for (size_t i = 0; i<l.all->lda; i++) {
l.digammas.push_back(mydigamma(l.total_lambda[i] + additional));
}
weight* weights = l.all->reg.weight_vector;
size_t last_weight_index = -1;
for (index_feature* s = &l.sorted_features[0]; s <= &l.sorted_features.back(); s++)
{
if (last_weight_index == s->f.weight_index)
continue;
last_weight_index = s->f.weight_index;
float* weights_for_w = &(weights[s->f.weight_index & l.all->reg.weight_mask]);
float decay = fmin(1.0, exp(l.decay_levels.end[-2] - l.decay_levels.end[(int)(-1 - l.example_t+weights_for_w[l.all->lda])]));
float* u_for_w = weights_for_w + l.all->lda+1;
weights_for_w[l.all->lda] = (float)l.example_t;
for (size_t k = 0; k < l.all->lda; k++)
{
weights_for_w[k] *= decay;
u_for_w[k] = weights_for_w[k] + l.all->lda_rho;
}
myexpdigammify_2(*l.all, u_for_w, l.digammas.begin);
}
for (size_t d = 0; d < batch_size; d++)
{
float score = lda_loop(*l.all, l.Elogtheta, &(l.v[d*l.all->lda]), weights, l.examples[d],l.all->power_t);
if (l.all->audit)
GD::print_audit_features(*l.all, l.examples[d]);
// If the doc is empty, give it loss of 0.
if (l.doc_lengths[d] > 0) {
l.all->sd->sum_loss -= score;
l.all->sd->sum_loss_since_last_dump -= score;
}
return_simple_example(*l.all, NULL, l.examples[d]);
}
for (index_feature* s = &l.sorted_features[0]; s <= &l.sorted_features.back();)
{
index_feature* next = s+1;
while(next <= &l.sorted_features.back() && next->f.weight_index == s->f.weight_index)
next++;
float* word_weights = &(weights[s->f.weight_index & l.all->reg.weight_mask]);
for (size_t k = 0; k < l.all->lda; k++) {
float new_value = minuseta*word_weights[k];
word_weights[k] = new_value;
}
for (; s != next; s++) {
float* v_s = &(l.v[s->document*l.all->lda]);
float* u_for_w = &weights[(s->f.weight_index & l.all->reg.weight_mask) + l.all->lda + 1];
float c_w = eta*find_cw(*l.all, u_for_w, v_s)*s->f.x;
for (size_t k = 0; k < l.all->lda; k++) {
float new_value = u_for_w[k]*v_s[k]*c_w;
l.total_new[k] += new_value;
word_weights[k] += new_value;
}
}
}
for (size_t k = 0; k < l.all->lda; k++) {
l.total_lambda[k] *= minuseta;
l.total_lambda[k] += l.total_new[k];
}
l.sorted_features.resize(0);
l.examples.erase();
l.doc_lengths.erase();
}
void learn(lda* l, learner& base, example* ec)
{
size_t num_ex = l->examples.size();
l->examples.push_back(ec);
l->doc_lengths.push_back(0);
for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) {
feature* f = ec->atomics[*i].begin;
for (; f != ec->atomics[*i].end; f++) {
index_feature temp = {(uint32_t)num_ex, *f};
l->sorted_features.push_back(temp);
l->doc_lengths[num_ex] += (int)f->x;
}
}
if (++num_ex == l->all->minibatch && !ec->test_only)
learn_batch(*l);
}
// placeholder
void predict(lda* l, learner& base, example* ec)
{
bool test_only = ec->test_only;
ec->test_only = true;
learn(l, base, ec);
ec->test_only = test_only;
}
void end_pass(lda* l)
{
if (l->examples.size())
learn_batch(*l);
}
void end_examples(lda* l)
{
for (size_t i = 0; i < l->all->length(); i++) {
weight* weights_for_w = & (l->all->reg.weight_vector[i*l->all->reg.stride]);
float decay = fmin(1.0, exp(l->decay_levels.last() - l->decay_levels.end[(int)(-1- l->example_t +weights_for_w[l->all->lda])]));
for (size_t k = 0; k < l->all->lda; k++)
weights_for_w[k] *= decay;
}
}
void finish_example(vw& all, lda*, example*ec)
{}
learner* setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm)
{
lda* ld = new lda;
ld->total_lambda_init = 0;
ld->all = &all;
ld->example_t = all.initial_t;
po::options_description desc("LDA options");
desc.add_options()
("lda_alpha", po::value<float>(&all.lda_alpha), "Prior on sparsity of per-document topic weights")
("lda_rho", po::value<float>(&all.lda_rho), "Prior on sparsity of topic distributions")
("lda_D", po::value<float>(&all.lda_D), "Number of documents")
("minibatch", po::value<size_t>(&all.minibatch), "Minibatch size, for LDA");
po::parsed_options parsed = po::command_line_parser(opts).
style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
options(desc).allow_unregistered().run();
opts = po::collect_unrecognized(parsed.options, po::include_positional);
po::store(parsed, vm);
po::notify(vm);
all.p->sort_features = true;
float temp = ceilf(logf((float)(all.lda*2+1)) / logf (2.f));
all.reg.stride = ((size_t)1) << (int) temp;
all.random_weights = true;
all.add_constant = false;
if (vm.count("lda") && all.eta > 1.)
{
cerr << "your learning rate is too high, setting it to 1" << endl;
all.eta = min(all.eta,1.f);
}
if (vm.count("minibatch")) {
size_t minibatch2 = next_pow2(all.minibatch);
all.p->ring_size = all.p->ring_size > minibatch2 ? all.p->ring_size : minibatch2;
}
ld->v.resize(all.lda*all.minibatch);
ld->decay_levels.push_back(0.f);
learner* l = new learner(ld, all.reg.stride);
l->set_learn<lda,learn>();
l->set_predict<lda,predict>();
l->set_save_load<lda,save_load>();
l->set_finish_example<lda,finish_example>();
l->set_end_examples<lda,end_examples>();
l->set_end_pass<lda,end_pass>();
return l;
}
}
|