/usr/include/shogun/features/RandomFourierDotFeatures.h is in libshogun-dev 3.2.0-7.3build4.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | /*
* 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) 2013 Evangelos Anagnostopoulos
* Copyright (C) 2013 Evangelos Anagnostopoulos
*/
#ifndef _RANDOMFOURIER_DOTFEATURES__H__
#define _RANDOMFOURIER_DOTFEATURES__H__
#include <shogun/features/RandomKitchenSinksDotFeatures.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/features/DotFeatures.h>
namespace shogun
{
template <class ST> class CDenseFeatures;
class CDotFeatures;
/** names of kernels that can be approximated currently */
enum KernelName
{
/** approximate gaussian kernel
* expects one parameter to be specified :
* kernel width
*/
GAUSSIAN,
/** not specified */
NOT_SPECIFIED
};
/** @brief This class implements the random fourier features for the DotFeatures
* framework.
* Basically upon the object creation it computes the random coefficients, namely w and b,
* that are needed for this method and then every time a vector is required it is computed
* based on the following formula z(x) = sqrt(2/D) * cos(w'*x + b), where D is the number
* of samples that are used.
*
* For more detailed information you can take a look at this source:
* i) Random Features for Large-Scale Kernel Machines - Ali Rahimi and Ben Recht
*/
class CRandomFourierDotFeatures : public CRandomKitchenSinksDotFeatures
{
public:
/** default constructor */
CRandomFourierDotFeatures();
/** constructor that creates new random coefficients, basedon the kernel specified and the parameters
* of the kernel.
*
* @param features the dense features to use as a base
* @param D the number of random fourier samples to draw / dimensionality of new feature space
* @param kernel_name the name of the kernel to approximate
* @param params kernel parameters (see kernel's description in KernelName to see what each kernel expects)
*/
CRandomFourierDotFeatures(CDotFeatures* features, int32_t D, KernelName kernel_name,
SGVector<float64_t> params);
/** constructor that uses the specified random coefficients.
*
* @param features the dense features to use as a base
* @param D the number of random fourier samples to draw / dimensionality of new feature space
* @param kernel_name the name of the kernel to approximate
* @param params kernel parameters (see kernel's description in KernelName to see what each kernel expects)
* @param coeff pre-computed random coefficients to use
*/
CRandomFourierDotFeatures(CDotFeatures* features, int32_t D, KernelName kernel_name,
SGVector<float64_t> params, SGMatrix<float64_t> coeff);
/** constructor loading features from file
*
* @param loader File object via which to load data
*/
CRandomFourierDotFeatures(CFile* loader);
/** copy constructor */
CRandomFourierDotFeatures(const CRandomFourierDotFeatures& orig);
/** duplicate */
virtual CFeatures* duplicate() const;
/** destructor */
virtual ~CRandomFourierDotFeatures();
/** @return object name */
virtual const char* get_name() const;
protected:
/** subclass must override this to perform any operations
* on the dot result between a feature vector and a parameter vector w
*
* @param dot_result the result of the dot operation
* @param par_idx the idx of the parameter vector
* @return the (optionally) modified result
*/
virtual float64_t post_dot(float64_t dot_result, index_t par_idx);
/** Generates a random parameter vector, subclasses must override this
*
* @return a random parameter vector
*/
virtual SGVector<float64_t> generate_random_parameter_vector();
private:
void init(KernelName kernel_name, SGVector<float64_t> params);
private:
/** the kernel to approximate */
KernelName kernel;
/** The parameters of the kernel to approximate */
SGVector<float64_t> kernel_params;
/** norm const */
float64_t constant;
};
}
#endif // _RANDOMFOURIER_DOTFEATURES__H__
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