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AveragedPerceptron.h
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1 /*
2  * This program is free software; you can redistribute it and/or modify
3  * it under the terms of the GNU General Public License as published by
4  * the Free Software Foundation; either version 3 of the License, or
5  * (at your option) any later version.
6  *
7  * Written (W) 2011 Hidekazu Oiwa
8  */
9 
10 #ifndef _AVERAGEDPERCEPTRON_H___
11 #define _AVERAGEDPERCEPTRON_H___
12 
13 #include <shogun/lib/config.h>
14 
15 #include <stdio.h>
16 #include <shogun/lib/common.h>
19 
20 namespace shogun
21 {
33 {
34  public:
37 
40 
46  CAveragedPerceptron(CDotFeatures* traindat, CLabels* trainlab);
47  virtual ~CAveragedPerceptron();
48 
54 
56  inline void set_learn_rate(float64_t r)
57  {
58  learn_rate=r;
59  }
60 
62  inline void set_max_iter(int32_t i)
63  {
64  max_iter=i;
65  }
66 
68  virtual const char* get_name() const { return "AveragedPerceptron"; }
69 
70 protected:
71 
80  virtual bool train_machine(CFeatures* data=NULL);
81 
82  protected:
86  int32_t max_iter;
87 };
88 }
89 #endif
EMachineType
Definition: Machine.h:33
virtual const char * get_name() const
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
void set_learn_rate(float64_t r)
set learn rate of gradient descent training algorithm
Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is t...
virtual EMachineType get_classifier_type()
Features that support dot products among other operations.
Definition: DotFeatures.h:44
double float64_t
Definition: common.h:50
Class LinearMachine is a generic interface for all kinds of linear machines like classifiers.
Definition: LinearMachine.h:64
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual bool train_machine(CFeatures *data=NULL)
void set_max_iter(int32_t i)
set maximum number of iterations

SHOGUN Machine Learning Toolbox - Documentation