SHOGUN  6.1.3
FirstOrderStochasticMinimizer.cpp
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3  * Written (w) 2015 Wu Lin
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31 
35 #include <shogun/base/Parameter.h>
36 using namespace shogun;
37 
39 {
40  REQUIRE(gradient_updater, "Gradient updater must set\n");
41  if(m_gradient_updater != gradient_updater)
42  {
43  SG_REF(gradient_updater);
45  m_gradient_updater=gradient_updater;
46  }
47 }
48 
50 {
53 }
54 
56 {
57  REQUIRE(num_passes>0, "The number (%d) to go through data must be positive\n", num_passes);
58  m_num_passes=num_passes;
59 }
60 
62 {
63  if(m_learning_rate != learning_rate)
64  {
65  SG_REF(learning_rate);
67  m_learning_rate=learning_rate;
68  }
69 }
70 
72 {
73  ProximalPenalty* proximal_penalty=dynamic_cast<ProximalPenalty*>(m_penalty_type);
74  if(proximal_penalty)
75  {
76  float64_t proximal_weight=m_penalty_weight;
77  SparsePenalty* sparse_penalty=dynamic_cast<SparsePenalty*>(m_penalty_type);
78  if(sparse_penalty)
79  {
80  REQUIRE(m_learning_rate, "Learning rate must set when Sparse Penalty (eg, L1) is used\n");
82  }
83  proximal_penalty->update_variable_for_proximity(variable_reference,proximal_weight);
84  }
85 }
86 
88 {
89  REQUIRE(m_fun,"Cost function must set\n");
90  REQUIRE(m_gradient_updater,"Descend updater must set\n");
91  REQUIRE(m_num_passes>0, "The number to go through data must set\n");
92  m_cur_passes=0;
93 }
94 
95 void FirstOrderStochasticMinimizer::init()
96 {
97  m_gradient_updater=NULL;
98  m_learning_rate=NULL;
99  m_num_passes=0;
100  m_cur_passes=0;
101  m_iter_counter=0;
102 
103  SG_ADD((CSGObject **)&m_learning_rate, "FirstOrderMinimizer__m_learning_rate",
104  "learning_rate in FirstOrderStochasticMinimizer", MS_NOT_AVAILABLE);
105  SG_ADD((CSGObject **)&m_gradient_updater, "FirstOrderMinimizer__m_gradient_updater",
106  "gradient_updater in FirstOrderStochasticMinimizer", MS_NOT_AVAILABLE);
107  SG_ADD(&m_num_passes, "FirstOrderMinimizer__m_num_passes",
108  "num_passes in FirstOrderStochasticMinimizer", MS_NOT_AVAILABLE);
109  SG_ADD(&m_cur_passes, "FirstOrderMinimizer__m_cur_passes",
110  "cur_passes in FirstOrderStochasticMinimizer", MS_NOT_AVAILABLE);
111  SG_ADD(&m_iter_counter, "FirstOrderMinimizer__m_iter_counter",
112  "m_iter_counter in FirstOrderStochasticMinimizer", MS_NOT_AVAILABLE);
113 }
virtual void set_learning_rate(LearningRate *learning_rate)
The base class about learning rate for descent-based minimizers.
Definition: LearningRate.h:47
FirstOrderCostFunction * m_fun
#define REQUIRE(x,...)
Definition: SGIO.h:181
The base class for sparse penalty/regularization used in minimization.
Definition: SparsePenalty.h:46
The base class for sparse penalty/regularization used in minimization.
#define SG_REF(x)
Definition: SGObject.h:52
Class SGObject is the base class of all shogun objects.
Definition: SGObject.h:124
double float64_t
Definition: common.h:60
virtual void set_number_passes(int32_t num_passes)
virtual void update_variable_for_proximity(SGVector< float64_t > variable, float64_t proximal_weight)=0
virtual void do_proximal_operation(SGVector< float64_t >variable_reference)
virtual void set_gradient_updater(DescendUpdater *gradient_updater)
This is a base class for descend update.
#define SG_UNREF(x)
Definition: SGObject.h:53
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual float64_t get_learning_rate(int32_t iter_counter)=0
#define SG_ADD(...)
Definition: SGObject.h:93

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