/usr/include/shogun/classifier/Perceptron.h is in libshogun-dev 3.2.0-7.3build4.
This file is owned by root:root, with mode 0o644.
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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 | /*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 1999-2009 Soeren Sonnenburg
* Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef _PERCEPTRON_H___
#define _PERCEPTRON_H___
#include <stdio.h>
#include <shogun/lib/common.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/machine/LinearMachine.h>
namespace shogun
{
/** @brief Class Perceptron implements the standard linear (online) perceptron.
*
* Given a maximum number of iterations (the standard perceptron algorithm is
* not guaranteed to converge) and a fixed lerning rate, the result is a linear
* classifier.
*
* \sa CLinearMachine
* \sa http://en.wikipedia.org/wiki/Perceptron
*/
class CPerceptron : public CLinearMachine
{
public:
/** problem type */
MACHINE_PROBLEM_TYPE(PT_BINARY);
/** default constructor */
CPerceptron();
/** constructor
*
* @param traindat training features
* @param trainlab labels for training features
*/
CPerceptron(CDotFeatures* traindat, CLabels* trainlab);
virtual ~CPerceptron();
/** get classifier type
*
* @return classifier type PERCEPTRON
*/
virtual EMachineType get_classifier_type() { return CT_PERCEPTRON; }
/// set learn rate of gradient descent training algorithm
inline void set_learn_rate(float64_t r)
{
learn_rate=r;
}
/// set maximum number of iterations
inline void set_max_iter(int32_t i)
{
max_iter=i;
}
/// set if the hyperplane should be initialized
void set_initialize_hyperplane(bool initialize_hyperplane);
/// get if the hyperplane should be initialized
bool get_initialize_hyperplane();
/** @return object name */
virtual const char* get_name() const { return "Perceptron"; }
protected:
/** train classifier
*
* @param data training data (parameter can be avoided if distance or
* kernel-based classifiers are used and distance/kernels are
* initialized with train data)
*
* @return whether training was successful
*/
virtual bool train_machine(CFeatures* data=NULL);
protected:
/** learning rate */
float64_t learn_rate;
/** maximum number of iterations */
int32_t max_iter;
private:
/** whether the hyperplane should be initialized in train_machine
*
* this allows to initialize the hyperplane externally using set_w and set_b
*/
bool m_initialize_hyperplane;
};
}
#endif
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