SHOGUN  3.2.1
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VwAdaptiveLearner.cpp
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1 /*
2  * Copyright (c) 2009 Yahoo! Inc. All rights reserved. The copyrights
3  * embodied in the content of this file are licensed under the BSD
4  * (revised) open source license.
5  *
6  * This program is free software; you can redistribute it and/or modify
7  * it under the terms of the GNU General Public License as published by
8  * the Free Software Foundation; either version 3 of the License, or
9  * (at your option) any later version.
10  *
11  * Written (W) 2011 Shashwat Lal Das
12  * Adaptation of Vowpal Wabbit v5.1.
13  * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society.
14  */
15 
17 
18 using namespace shogun;
19 
21  : CVwLearner()
22 {
23 }
24 
26  : CVwLearner(regressor, vw_env)
27 {
28 }
29 
31 {
32 }
33 
35 {
36  if (fabs(update) == 0.)
37  return;
38 
39  vw_size_t thread_num = 0;
40 
41  vw_size_t thread_mask = env->thread_mask;
42  float32_t* weights = reg->weight_vectors[thread_num];
43 
45  vw_size_t ctr = 0;
46  for (vw_size_t* i = ex->indices.begin; i != ex->indices.end; i++)
47  {
48  for (VwFeature *f = ex->atomics[*i].begin; f != ex->atomics[*i].end; f++)
49  {
50  float32_t* w = &weights[f->weight_index & thread_mask];
51  w[1] += g * f->x * f->x;
52  float32_t t = f->x * CMath::invsqrt(w[1]);
53  w[0] += update * t;
54  }
55  }
56 
57  for (int32_t k = 0; k < env->pairs.get_num_elements(); k++)
58  {
59  char* i = env->pairs.get_element(k);
60 
61  v_array<VwFeature> temp = ex->atomics[(int32_t)(i[0])];
62  temp.begin = ex->atomics[(int32_t)(i[0])].begin;
63  temp.end = ex->atomics[(int32_t)(i[0])].end;
64  for (; temp.begin != temp.end; temp.begin++)
65  quad_update(weights, *temp.begin, ex->atomics[(int32_t)(i[1])], thread_mask, update, g, ex, ctr);
66  }
67 }
68 
69 void CVwAdaptiveLearner::quad_update(float32_t* weights, VwFeature& page_feature,
70  v_array<VwFeature> &offer_features, vw_size_t mask,
72 {
73  vw_size_t halfhash = quadratic_constant * page_feature.weight_index;
74  update *= page_feature.x;
75  float32_t update2 = g * page_feature.x * page_feature.x;
76 
77  for (VwFeature* elem = offer_features.begin; elem != offer_features.end; elem++)
78  {
79  float32_t* w = &weights[(halfhash + elem->weight_index) & mask];
80  w[1] += update2 * elem->x * elem->x;
81  float32_t t = elem->x * CMath::invsqrt(w[1]);
82  w[0] += update * t;
83  }
84 }

SHOGUN Machine Learning Toolbox - Documentation