SHOGUN  v3.0.0
CLinearTimeMMD Class Reference

## Detailed Description

This class implements the linear time Maximum Mean Statistic as described in [1]. This statistic is in particular suitable for streaming data. Therefore, only streaming features may be passed. To process other feature types, construct streaming features from these (see constructor documentations). A blocksize has to be specified that determines how many examples are processed at once. This should be set as large as available memory allows to ensure faster computations.

The MMD is the distance of two probability distributions $$p$$ and $$q$$ in a RKHS.

$\text{MMD}}[\mathcal{F},p,q]^2=\textbf{E}_{x,x'}\left[ k(x,x')\right]- 2\textbf{E}_{x,y}\left[ k(x,y)\right] +\textbf{E}_{y,y'}\left[ k(y,y')\right]=||\mu_p - \mu_q||^2_\mathcal{F}$

Given two sets of samples $$\{x_i\}_{i=1}^m\sim p$$ and $$\{y_i\}_{i=1}^n\sim q$$ the (unbiased) statistic is computed as

$\text{MMD}_l^2[\mathcal{F},X,Y]=\frac{1}{m_2}\sum_{i=1}^{m_2} h(z_{2i},z_{2i+1})$

where

$h(z_{2i},z_{2i+1})=k(x_{2i},x_{2i+1})+k(y_{2i},y_{2i+1})-k(x_{2i},y_{2i+1})- k(x_{2i+1},y_{2i})$

and $$m_2=\lfloor\frac{m}{2} \rfloor$$.

Along with the statistic comes a method to compute a p-value based on a Gaussian approximation of the null-distribution which is also possible in linear time and constant space. Bootstrapping, is also possible (no permutations but new examples will be used here). If unsure which one to use, bootstrapping with 250 iterations always is correct (but slow). When the sample size is large (>1000) at least, the Gaussian approximation is an accurate and much faster choice than bootstrapping.

To choose, use set_null_approximation_method() and choose from

MMD1_GAUSSIAN: Approximates the null-distribution with a Gaussian. Only use from at least 1000 samples. If using, check if type I error equals the desired value.

BOOTSTRAPPING: For permuting available samples to sample null-distribution

For kernel selection see CMMDKernelSelection.

[1]: Gretton, A., Borgwardt, K. M., Rasch, M. J., Schoelkopf, B., & Smola, A. (2012). A Kernel Two-Sample Test. Journal of Machine Learning Research, 13, 671-721.

Definition at line 75 of file LinearTimeMMD.h.

Inheritance diagram for CLinearTimeMMD:
[legend]

## Public Member Functions

CLinearTimeMMD ()
CLinearTimeMMD (CKernel *kernel, CStreamingFeatures *p, CStreamingFeatures *q, index_t m, index_t blocksize=10000)
virtual ~CLinearTimeMMD ()
virtual float64_t compute_statistic ()
virtual SGVector< float64_tcompute_statistic (bool multiple_kernels)
virtual float64_t compute_p_value (float64_t statistic)
virtual float64_t perform_test ()
virtual float64_t compute_threshold (float64_t alpha)
virtual float64_t compute_variance_estimate ()
virtual void compute_statistic_and_variance (SGVector< float64_t > &statistic, SGVector< float64_t > &variance, bool multiple_kernels=false)
virtual void compute_statistic_and_Q (SGVector< float64_t > &statistic, SGMatrix< float64_t > &Q)
virtual SGVector< float64_tbootstrap_null ()
void set_blocksize (index_t blocksize)
virtual void set_p_and_q (CFeatures *p_and_q)
virtual CFeaturesget_p_and_q ()
virtual CStreamingFeaturesget_streaming_p ()
virtual CStreamingFeaturesget_streaming_q ()
virtual EStatisticType get_statistic_type () const
void set_simulate_h0 (bool simulate_h0)
virtual const char * get_name () const
virtual void set_kernel (CKernel *kernel)
virtual CKernelget_kernel ()
index_t get_m ()
bool perform_test (float64_t alpha)
virtual void set_bootstrap_iterations (index_t bootstrap_iterations)
virtual void set_null_approximation_method (ENullApproximationMethod null_approximation_method)
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
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="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
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 ()
SGStringList< char > get_modelsel_names ()
void print_modsel_params ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
virtual bool update_parameter_hash ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0)
virtual CSGObjectclone ()

## Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

## Protected Member Functions

virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
virtual void load_serializable_pre () throw (ShogunException)
virtual void load_serializable_post () throw (ShogunException)
virtual void save_serializable_pre () throw (ShogunException)
virtual void save_serializable_post () throw (ShogunException)

## Protected Attributes

CStreamingFeaturesm_streaming_p
CStreamingFeaturesm_streaming_q
index_t m_blocksize
bool m_simulate_h0
CKernelm_kernel
CFeaturesm_p_and_q
index_t m_m
index_t m_bootstrap_iterations
ENullApproximationMethod m_null_approximation_method

## Constructor & Destructor Documentation

 CLinearTimeMMD ( )

Definition at line 22 of file LinearTimeMMD.cpp.

 CLinearTimeMMD ( CKernel * kernel, CStreamingFeatures * p, CStreamingFeatures * q, index_t m, index_t blocksize = 10000 )

Constructor.

Parameters
 kernel kernel to use p streaming features p to use q streaming features q to use m index of first sample of q blocksize size of examples that are processed at once when computing statistic/threshold. If larger than m/2, all examples will be processed at once. Memory consumption increased linearly in the blocksize. Choose as large as possible regarding available memory.

Definition at line 28 of file LinearTimeMMD.cpp.

 ~CLinearTimeMMD ( )
virtual

Definition at line 43 of file LinearTimeMMD.cpp.

## Member Function Documentation

 SGVector< float64_t > bootstrap_null ( )
virtual

Mimics bootstrapping for the linear time MMD. However, samples are not permutated but constantly streamed and then merged. Usually, this is not necessary since there is the Gaussian approximation for the null distribution. However, in certain cases this may fail and sampling the null distribution might be numerically more stable. Ovewrite superclass method that merges samples.

Returns
vector of all statistics

Reimplemented from CKernelTwoSampleTestStatistic.

Definition at line 683 of file LinearTimeMMD.cpp.

 void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > * dict )
inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters
 dict dictionary of parameters to be built.

Definition at line 1196 of file SGObject.cpp.

 CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 1313 of file SGObject.cpp.

 float64_t compute_p_value ( float64_t statistic )
virtual

computes a p-value based on current method for approximating the null-distribution. The p-value is the 1-p quantile of the null- distribution where the given statistic lies in.

The method for computing the p-value can be set via set_null_approximation_method(). Since the null- distribution is normal, a Gaussian approximation is available.

Parameters
 statistic statistic value to compute the p-value for
Returns
p-value parameter statistic is the (1-p) percentile of the null distribution

Reimplemented from CTwoDistributionsTestStatistic.

Definition at line 608 of file LinearTimeMMD.cpp.

 float64_t compute_statistic ( )
virtual

Computes the squared linear time MMD for the current data. This is an unbiased estimate.

Note that the underlying streaming feature parser has to be started before this is called. Otherwise deadlock.

Returns
squared linear time MMD

Implements CKernelTwoSampleTestStatistic.

Definition at line 573 of file LinearTimeMMD.cpp.

 SGVector< float64_t > compute_statistic ( bool multiple_kernels )
virtual

Same as compute_statistic(), but with the possibility to perform on multiple kernels at once

Parameters
 multiple_kernels if true, and underlying kernel is K_COMBINED, method will be executed on all subkernels on the same data
Returns
vector of results for subkernels

Implements CKernelTwoSampleTestStatistic.

Definition at line 583 of file LinearTimeMMD.cpp.

 void compute_statistic_and_Q ( SGVector< float64_t > & statistic, SGMatrix< float64_t > & Q )
virtual

Same as compute_statistic_and_variance, but computes a linear time estimate of the covariance of the multiple-kernel-MMD. See [1] for details.

Definition at line 274 of file LinearTimeMMD.cpp.

 void compute_statistic_and_variance ( SGVector< float64_t > & statistic, SGVector< float64_t > & variance, bool multiple_kernels = false )
virtual

Computes MMD and a linear time variance estimate. If multiple_kernels is set to true, each subkernel is evaluated on the same data.

Parameters
 statistic return parameter for statistic, vector with entry for each kernel. May be allocated before but doesn not have to be variance return parameter for statistic, vector with entry for each kernel. May be allocated before but doesn not have to be multiple_kernels optional flag, if set to true, it is assumed that the underlying kernel is of type K_COMBINED. Then, the MMD is computed on all subkernel separately rather than computing it on the combination. This is used by kernel selection strategies that need to evaluate multiple kernels on the same data. Since the linear time MMD works on streaming data, one cannot simply compute MMD, change kernel since data would be different for every kernel.

Definition at line 68 of file LinearTimeMMD.cpp.

 float64_t compute_threshold ( float64_t alpha )
virtual

computes a threshold based on current method for approximating the null-distribution. The threshold is the value that a statistic has to have in ordner to reject the null-hypothesis.

The method for computing the p-value can be set via set_null_approximation_method(). Since the null- distribution is normal, a Gaussian approximation is available.

Parameters
 alpha test level to reject null-hypothesis
Returns
threshold for statistics to reject null-hypothesis

Reimplemented from CTwoDistributionsTestStatistic.

Definition at line 631 of file LinearTimeMMD.cpp.

 float64_t compute_variance_estimate ( )
virtual

computes a linear time estimate of the variance of the squared linear time mmd, which may be used for an approximation of the null-distribution The value is the variance of the vector of which the linear time MMD is the mean.

Returns
variance estimate

Definition at line 598 of file LinearTimeMMD.cpp.

 virtual CSGObject* deep_copy ( ) const
virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 160 of file SGObject.h.

 bool equals ( CSGObject * other, float64_t accuracy = 0.0 )
virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters
 other object to compare with accuracy accuracy to use for comparison (optional)
Returns
true if all parameters were equal, false if not

Definition at line 1217 of file SGObject.cpp.

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 214 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 249 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 262 of file SGObject.cpp.

 virtual CKernel* get_kernel ( )
virtualinherited
Returns
underlying kernel, is SG_REF'ed

Definition at line 80 of file KernelTwoSampleTestStatistic.h.

 index_t get_m ( )
inherited
Returns
number of to be used samples m

Definition at line 98 of file TwoDistributionsTestStatistic.h.

 SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 1100 of file SGObject.cpp.

 char * get_modsel_param_descr ( const char * param_name )
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters
 param_name name of the parameter
Returns
description of the parameter

Definition at line 1124 of file SGObject.cpp.

 index_t get_modsel_param_index ( const char * param_name )
inherited

Returns index of model selection parameter with provided index

Parameters
 param_name name of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 1137 of file SGObject.cpp.

 virtual const char* get_name ( ) const
virtual

Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed C'.

Returns
name of the SGSerializable

Implements CKernelTwoSampleTestStatistic.

Definition at line 242 of file LinearTimeMMD.h.

 CFeatures * get_p_and_q ( )
virtual

Not implemented for linear time MMD since it uses streaming feautres

Reimplemented from CTwoDistributionsTestStatistic.

Definition at line 715 of file LinearTimeMMD.cpp.

 virtual EStatisticType get_statistic_type ( ) const
virtual

returns the statistic type of this test statistic

Implements CTestStatistic.

Definition at line 231 of file LinearTimeMMD.h.

 CStreamingFeatures * get_streaming_p ( )
virtual

Getter for streaming features of p distribution.

Returns
streaming features object for p distribution, SG_REF'ed

Definition at line 722 of file LinearTimeMMD.cpp.

 CStreamingFeatures * get_streaming_q ( )
virtual

Getter for streaming features of q distribution.

Returns
streaming features object for q distribution, SG_REF'ed

Definition at line 728 of file LinearTimeMMD.cpp.

 bool is_generic ( EPrimitiveType * generic ) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
 generic set to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 268 of file SGObject.cpp.

 DynArray< TParameter * > * load_all_file_parameters ( int32_t file_version, int32_t current_version, CSerializableFile * file, const char * prefix = "" )
inherited

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters
 file_version parameter version of the file current_version version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) file file to load from prefix prefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 673 of file SGObject.cpp.

 DynArray< TParameter * > * load_file_parameters ( const SGParamInfo * param_info, int32_t file_version, CSerializableFile * file, const char * prefix = "" )
inherited

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

Parameters
 param_info information of parameter file_version parameter version of the file, must be <= provided parameter version file file to load from prefix prefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 514 of file SGObject.cpp.

 bool load_serializable ( CSerializableFile * file, const char * prefix = "", int32_t param_version = Version::get_version_parameter() )
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
 file where to load from prefix prefix for members param_version (optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 345 of file SGObject.cpp.

 void load_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1029 of file SGObject.cpp.

 void load_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1024 of file SGObject.cpp.

 void map_parameters ( DynArray< TParameter * > * param_base, int32_t & base_version, DynArray< const SGParamInfo * > * target_param_infos )
inherited

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

Parameters
 param_base set of TParameter instances that are mapped to the provided target parameter infos base_version version of the parameter base target_param_infos set of SGParamInfo instances that specify the target parameter base

Definition at line 711 of file SGObject.cpp.

 TParameter * migrate ( DynArray< TParameter * > * param_base, const SGParamInfo * target )
protectedvirtualinherited

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

Parameters
 param_base set of TParameter instances to use for migration target parameter info for the resulting TParameter
Returns
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 918 of file SGObject.cpp.

 void one_to_one_migration_prepare ( DynArray< TParameter * > * param_base, const SGParamInfo * target, TParameter *& replacement, TParameter *& to_migrate, char * old_name = NULL )
protectedvirtualinherited

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

Parameters
 param_base set of TParameter instances to use for migration target parameter info for the resulting TParameter replacement (used as output) here the TParameter instance which is returned by migration is created into to_migrate the only source that is used for migration old_name with this parameter, a name change may be specified

Definition at line 858 of file SGObject.cpp.

 bool perform_test ( float64_t alpha )
inherited

Performs the complete two-sample test on current data and returns a binary answer wheter null hypothesis is rejected or not.

This is just a wrapper for the above perform_test() method that returns a p-value. If this p-value lies below the test level alpha, the null hypothesis is rejected.

Should not be overwritten in subclasses. (Therefore not virtual)

Parameters
 alpha test level alpha.
Returns
true if null hypothesis is rejected and false otherwise

Definition at line 58 of file TestStatistic.cpp.

 float64_t perform_test ( )
virtual

Performs the complete two-sample test on current data and returns a p-value.

In case null distribution should be estimated with MMD1_GAUSSIAN, statistic and p-value are computed in the same loop, which is more efficient than first computing statistic and then computung p-values.

In case of bootstrapping, superclass method is called.

The method for computing the p-value can be set via set_null_approximation_method().

Returns
p-value such that computed statistic is the (1-p) quantile of the estimated null distribution

Reimplemented from CTestStatistic.

Definition at line 654 of file LinearTimeMMD.cpp.

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1076 of file SGObject.cpp.

 void print_serializable ( const char * prefix = "" )
virtualinherited

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 280 of file SGObject.cpp.

 bool save_serializable ( CSerializableFile * file, const char * prefix = "", int32_t param_version = Version::get_version_parameter()` )
virtualinherited

Save this object to file.

Parameters
 file where to save the object; will be closed during returning if PREFIX is an empty string. prefix prefix for members param_version (optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 286 of file SGObject.cpp.

 void save_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions
 ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1039 of file SGObject.cpp.

 void save_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1034 of file SGObject.cpp.

 void set_blocksize ( index_t blocksize )

Setter for the blocksize of examples to be processed at once

Parameters
 blocksize new blocksize to use

Definition at line 212 of file LinearTimeMMD.h.

 void set_bootstrap_iterations ( index_t bootstrap_iterations )
virtualinherited

sets the number of bootstrap iterations for bootstrap_null()

Parameters
 bootstrap_iterations how often bootstrapping shall be done

Definition at line 44 of file TestStatistic.cpp.

 void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 41 of file SGObject.cpp.

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 207 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 220 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 255 of file SGObject.cpp.

 virtual void set_kernel ( CKernel * kernel )
virtualinherited

Setter for the underlying kernel

Parameters
 kernel new kernel to use

Definition at line 71 of file KernelTwoSampleTestStatistic.h.

 void set_null_approximation_method ( ENullApproximationMethod null_approximation_method )
virtualinherited

sets the method how to approximate the null-distribution

Parameters
 null_approximation_method method to use

Definition at line 38 of file TestStatistic.cpp.

 void set_p_and_q ( CFeatures * p_and_q )
virtual

Not implemented for linear time MMD since it uses streaming feautres

Reimplemented from CTwoDistributionsTestStatistic.

Definition at line 709 of file LinearTimeMMD.cpp.

 void set_simulate_h0 ( bool simulate_h0 )
Parameters
 simulate_h0 if true, samples from p and q will be mixed and permuted

Definition at line 239 of file LinearTimeMMD.h.

 virtual CSGObject* shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 151 of file SGObject.h.

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 275 of file SGObject.cpp.

 bool update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination.

Returns
bool if parameter combination has changed since last update.

Definition at line 227 of file SGObject.cpp.

## Member Data Documentation

 SGIO* io
inherited

io

Definition at line 514 of file SGObject.h.

 index_t m_blocksize
protected

Number of examples processed at once, i.e. in one burst

Definition at line 258 of file LinearTimeMMD.h.

 index_t m_bootstrap_iterations
protectedinherited

number of iterations for bootstrapping null-distributions

Definition at line 138 of file TestStatistic.h.

 Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 529 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 535 of file SGObject.h.

 CKernel* m_kernel
protectedinherited

underlying kernel

Definition at line 115 of file KernelTwoSampleTestStatistic.h.

 index_t m_m
protectedinherited

defines the first index of samples of q

Definition at line 110 of file TwoDistributionsTestStatistic.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 526 of file SGObject.h.

 ENullApproximationMethod m_null_approximation_method
protectedinherited

Defines how the the null distribution is approximated

Definition at line 141 of file TestStatistic.h.

 CFeatures* m_p_and_q
protectedinherited

concatenated samples of the two distributions (two blocks)

Definition at line 107 of file TwoDistributionsTestStatistic.h.

 ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 532 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 523 of file SGObject.h.

 bool m_simulate_h0
protected

If this is true, samples will be mixed between p and q ind any method that computes the statistic

Definition at line 262 of file LinearTimeMMD.h.

 CStreamingFeatures* m_streaming_p
protected

Streaming feature objects that are used instead of merged samples

Definition at line 252 of file LinearTimeMMD.h.

 CStreamingFeatures* m_streaming_q
protected

Streaming feature objects that are used instead of merged samples

Definition at line 255 of file LinearTimeMMD.h.

 Parallel* parallel
inherited

parallel

Definition at line 517 of file SGObject.h.

 Version* version
inherited

version

Definition at line 520 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation