SHOGUN  6.1.3
SGDMinimizer.cpp
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1  /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2015 Wu Lin
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33 #include <shogun/lib/config.h>
34 using namespace shogun;
35 
38 {
39  init();
40 }
41 
43 {
44 }
45 
48 {
49  init();
50 }
51 
53 {
55 
58  REQUIRE(fun,"the cost function must be a stochastic cost function\n");
60  {
61  fun->begin_sample();
62  while(fun->next_sample())
63  {
65  float64_t learning_rate=1.0;
66  if(m_learning_rate)
69  update_gradient(grad,variable_reference);
70  m_gradient_updater->update_variable(variable_reference,grad,learning_rate);
71 
72  do_proximal_operation(variable_reference);
73  }
74  }
75  float64_t cost=m_fun->get_cost();
76  return cost+get_penalty(variable_reference);
77 }
78 
79 void SGDMinimizer::init()
80 {
81 }
82 
84 {
86 }
virtual SGVector< float64_t > get_gradient()=0
FirstOrderCostFunction * m_fun
#define REQUIRE(x,...)
Definition: SGIO.h:181
virtual void init_minimization()
virtual void update_variable(SGVector< float64_t > variable_reference, SGVector< float64_t > negative_descend_direction, float64_t learning_rate)=0
The first order stochastic cost function base class.
virtual float64_t minimize()
The base class for stochastic first-order gradient-based minimizers.
virtual void update_gradient(SGVector< float64_t > gradient, SGVector< float64_t > var)
double float64_t
Definition: common.h:60
virtual void do_proximal_operation(SGVector< float64_t >variable_reference)
virtual float64_t get_penalty(SGVector< float64_t > var)
virtual float64_t get_cost()=0
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
virtual SGVector< float64_t > obtain_variable_reference()=0

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