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CRandomFourierGaussPreproc Class Reference

Detailed Description

Preprocessor CRandomFourierGaussPreproc implements Random Fourier Features for the Gauss kernel a la Ali Rahimi and Ben Recht Nips2007 after preprocessing the features using them in a linear kernel approximates a gaussian kernel.

approximation quality depends on dimension of feature space, NOT on number of data.

effectively it requires two parameters for initialization: (A) the dimension of the input features stored in dim_input_space (B) the dimension of the output feature space

in order to make it work there are two ways: (1) if you have already previously computed random fourier features which you want to use together with newly computed ones, then you have to take the random coefficients from the previous computation and provide them via void set_randomcoefficients(...) for the new computation this case is important for example if you compute separately features on training and testing data in two feature objects

in this case you have to set 1a) void set_randomcoefficients(...)

(2) if you compute random fourier features from scratch in this case you have to set 2a) set_kernelwidth(...) 2b) void set_dim_feature_space(const int32_t dim); 2c) set_dim_input_space(const int32_t dim); 2d) init_randomcoefficients() or apply_to_feature_matrix(...)

Definition at line 49 of file RandomFourierGaussPreproc.h.

Inheritance diagram for CRandomFourierGaussPreproc:
Inheritance graph
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Public Member Functions

 CRandomFourierGaussPreproc ()
 CRandomFourierGaussPreproc (const CRandomFourierGaussPreproc &pr)
 ~CRandomFourierGaussPreproc ()
virtual SGMatrix< float64_tapply_to_feature_matrix (CFeatures *features)
virtual SGVector< float64_tapply_to_feature_vector (SGVector< float64_t > vector)
virtual EFeatureType get_feature_type ()
virtual EFeatureClass get_feature_class ()
virtual bool init (CFeatures *f)
void set_kernelwidth (const float64_t width)
float64_t get_kernelwidth () const
void get_randomcoefficients (float64_t **randomcoeff_additive2, float64_t **randomcoeff_multiplicative2, int32_t *dim_feature_space2, int32_t *dim_input_space2, float64_t *kernelwidth2) const
void set_randomcoefficients (float64_t *randomcoeff_additive2, float64_t *randomcoeff_multiplicative2, const int32_t dim_feature_space2, const int32_t dim_input_space2, const float64_t kernelwidth2)
void set_dim_input_space (const int32_t dim)
void set_dim_feature_space (const int32_t dim)
bool init_randomcoefficients ()
int32_t get_dim_input_space () const
int32_t get_dim_feature_space () const
void cleanup ()
virtual const char * get_name () const
 return the name of the preprocessor
virtual EPreprocessorType get_type () const
 return a type of preprocessor
- Public Member Functions inherited from CSimplePreprocessor< float64_t >
 CSimplePreprocessor ()
- Public Member Functions inherited from CPreprocessor
 CPreprocessor ()
virtual ~CPreprocessor ()
- Public Member Functions inherited from CSGObject
 CSGObject ()
 CSGObject (const CSGObject &orig)
virtual ~CSGObject ()
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="")
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
void set_global_io (SGIO *io)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_global_version ()
SGVector< char * > get_modelsel_names ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)

Protected Member Functions

void copy (const CRandomFourierGaussPreproc &feats)
bool test_rfinited () const

Protected Attributes

float64_t kernelwidth
float64_t cur_kernelwidth
int32_t dim_input_space
int32_t cur_dim_input_space
int32_t dim_feature_space
int32_t cur_dim_feature_space
float64_trandomcoeff_additive
float64_trandomcoeff_multiplicative

Additional Inherited Members

- Public Attributes inherited from CSGObject
SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters

Constructor & Destructor Documentation

default constructor

Definition at line 62 of file RandomFourierGaussPreproc.cpp.

alternative constructor

Definition at line 93 of file RandomFourierGaussPreproc.cpp.

default destructor takes care for float64_t* randomcoeff_additive,float64_t* randomcoeff_multiplicative;

Definition at line 118 of file RandomFourierGaussPreproc.cpp.

Member Function Documentation

SGMatrix< float64_t > apply_to_feature_matrix ( CFeatures features)
virtual

default processing routine, inherited from base class

Parameters
featuresthe features to be processed, must be of type CSimpleFeatures<float64_t>
Returns
the processed feature matrix from the CSimpleFeatures<float64_t> class in case (2) (see description above) this routine requires only steps 2a) and 2b), the rest is determined automatically

Implements CSimplePreprocessor< float64_t >.

Definition at line 359 of file RandomFourierGaussPreproc.cpp.

SGVector< float64_t > apply_to_feature_vector ( SGVector< float64_t vector)
virtual

alternative processing routine, inherited from base class

Parameters
vectorthe feature vector to be processed
Returns
processed feature vector in order to work this routine requires the steps described above under cases (1) or two (2) before calling this routine

Implements CSimplePreprocessor< float64_t >.

Definition at line 341 of file RandomFourierGaussPreproc.cpp.

void cleanup ( )
virtual

inherited from base class does nothing

Implements CPreprocessor.

Definition at line 407 of file RandomFourierGaussPreproc.cpp.

void copy ( const CRandomFourierGaussPreproc feats)
protected

helper for copy constructor and assignment operator=

Definition at line 17 of file RandomFourierGaussPreproc.cpp.

int32_t get_dim_feature_space ( ) const

a getter

Returns
the set value of protected member dim_feature_space

Definition at line 133 of file RandomFourierGaussPreproc.cpp.

int32_t get_dim_input_space ( ) const

a getter

Returns
the set value of protected member dim_input_space

Definition at line 147 of file RandomFourierGaussPreproc.cpp.

EFeatureClass get_feature_class ( )
virtual

inherited from base class

Returns
F_DREAL

Reimplemented from CSimplePreprocessor< float64_t >.

Definition at line 125 of file RandomFourierGaussPreproc.cpp.

EFeatureType get_feature_type ( )
virtual

inherited from base class

Returns
C_SIMPLE

Reimplemented from CSimplePreprocessor< float64_t >.

Definition at line 129 of file RandomFourierGaussPreproc.cpp.

float64_t get_kernelwidth ( ) const

getter for kernel width

Returns
kernel width throws exception if kernelwidth <=0

Definition at line 159 of file RandomFourierGaussPreproc.cpp.

virtual const char* get_name ( ) const
virtual

return the name of the preprocessor

Implements CSGObject.

Definition at line 169 of file RandomFourierGaussPreproc.h.

void get_randomcoefficients ( float64_t **  randomcoeff_additive2,
float64_t **  randomcoeff_multiplicative2,
int32_t *  dim_feature_space2,
int32_t *  dim_input_space2,
float64_t kernelwidth2 
) const

getter for the random coefficients necessary for creating random fourier features compatible to the current ones returns values of internal members randomcoeff_additive and randomcoeff_multiplicative

Definition at line 247 of file RandomFourierGaussPreproc.cpp.

virtual EPreprocessorType get_type ( ) const
virtual

return a type of preprocessor

Reimplemented from CSimplePreprocessor< float64_t >.

Definition at line 172 of file RandomFourierGaussPreproc.h.

bool init ( CFeatures f)
virtual

initializer routine calls set_dim_input_space(const int32_t dim); with the proper value calls init_randomcoefficients(); this call does NOT override a previous call to void set_randomcoefficients(...) IF and ONLY IF the dimensions of input AND feature space are equal to the values from the previous call to void set_randomcoefficients(...)

Parameters
fthe features to be processed, must be of type CSimpleFeatures<float64_t>
Returns
true if new random coefficients were generated, false if old ones from a call to set_randomcoefficients(...) are kept

Implements CPreprocessor.

Definition at line 309 of file RandomFourierGaussPreproc.cpp.

bool init_randomcoefficients ( )

computes new random coefficients IF test_rfinited() evaluates to false test_rfinited() evaluates to TRUE if void set_randomcoefficients(...) hase been called and the values set by set_dim_input_space(...) , set_dim_feature_space(...) and set_kernelwidth(...) are consistent to the call of void set_randomcoefficients(...)

throws shogun exception if dim_feature_space <= 0 or dim_input_space <= 0

Returns
returns true if test_rfinited() evaluates to false and new coefficients are computed returns false if test_rfinited() evaluates to true and old random coefficients are kept which were set by a previous call to void set_randomcoefficients(...)

this function is useful if you want to use apply_to_feature_vector but cannot call before it init(CFeatures *f)

Definition at line 189 of file RandomFourierGaussPreproc.cpp.

void set_dim_feature_space ( const int32_t  dim)

a setter

Parameters
dimthe value of protected member dim_feature_space throws a shogun exception if dim<=0

Definition at line 137 of file RandomFourierGaussPreproc.cpp.

void set_dim_input_space ( const int32_t  dim)

a setter

Parameters
dimthe value of protected member dim_input_space throws a shogun exception if dim<=0

Definition at line 163 of file RandomFourierGaussPreproc.cpp.

void set_kernelwidth ( const float64_t  width)

setter for kernel width

Parameters
widthkernel width to be set

Definition at line 151 of file RandomFourierGaussPreproc.cpp.

void set_randomcoefficients ( float64_t randomcoeff_additive2,
float64_t randomcoeff_multiplicative2,
const int32_t  dim_feature_space2,
const int32_t  dim_input_space2,
const float64_t  kernelwidth2 
)

setter for the random coefficients necessary for creating random fourier features compatible to the previous ones sets values of internal members randomcoeff_additive and randomcoeff_multiplicative simply use as input what you got from get_random_coefficients(...)

Definition at line 279 of file RandomFourierGaussPreproc.cpp.

bool test_rfinited ( ) const
protected

tests whether rf features have already been initialized

Definition at line 173 of file RandomFourierGaussPreproc.cpp.

Member Data Documentation

int32_t cur_dim_feature_space
protected

actual dimension of output features as set by bool init_randomcoefficients() or void set_randomcoefficients

Definition at line 211 of file RandomFourierGaussPreproc.h.

int32_t cur_dim_input_space
protected

actual dimension of input features as set by bool init_randomcoefficients() or void set_randomcoefficients

Definition at line 200 of file RandomFourierGaussPreproc.h.

float64_t cur_kernelwidth
protected

dimension of input features width of gaussian kernel in the form of exp(-x^2 / (2.0 kernelwidth^2) ) NOTE the 2.0 and the power ^2 !

Definition at line 190 of file RandomFourierGaussPreproc.h.

int32_t dim_feature_space
protected

desired dimension of output features as set by void set_dim_feature_space(const int32_t dim)

Definition at line 206 of file RandomFourierGaussPreproc.h.

int32_t dim_input_space
protected

desired dimension of input features as set by void set_dim_input_space(const int32_t dim)

Definition at line 195 of file RandomFourierGaussPreproc.h.

float64_t kernelwidth
protected

dimension of input features width of gaussian kernel in the form of exp(-x^2 / (2.0 kernelwidth^2) ) NOTE the 2.0 and the power ^2 !

Definition at line 185 of file RandomFourierGaussPreproc.h.

float64_t* randomcoeff_additive
protected

random coefficient length = cur_dim_feature_space

Definition at line 222 of file RandomFourierGaussPreproc.h.

float64_t* randomcoeff_multiplicative
protected

random coefficient length = cur_dim_feature_space* cur_dim_input_space

Definition at line 228 of file RandomFourierGaussPreproc.h.


The documentation for this class was generated from the following files:

SHOGUN Machine Learning Toolbox - Documentation