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CStreamingMMD Class Referenceabstract

Detailed Description

Abstract base class that provides an interface for performing kernel two-sample test on streaming data using Maximum Mean Discrepancy (MMD) as the test statistic. The MMD is the distance of two probability distributions \(p\) and \(q\) in a RKHS (see [1] for formal description).

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

where \(x,x'\sim p\) and \(y,y'\sim q\). The data has to be provided as streaming features, which are processed in blocks for a given blocksize. The blocksize determines how many examples are processed at once. A method for getting a specified number of blocks of data is provided which can optionally merge and permute the data within the current burst. The exact computation of kernel functions for MMD computation is abstract and has to be defined by its subclasses, which should return a vector of function values. Please note that for streaming MMD, the number of data points from both the distributions has to be equal.

Along with the statistic comes a method to compute a p-value based on a Gaussian approximation of the null-distribution which is possible in linear time and constant space. Sampling from null is also possible (no permutations but new examples will be used here). If unsure which one to use, sampling 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.

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.

PERMUTATION: 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 88 of file StreamingMMD.h.

Inheritance diagram for CStreamingMMD:
[legend]

Public Member Functions

 CStreamingMMD ()
 
 CStreamingMMD (CKernel *kernel, CStreamingFeatures *p, CStreamingFeatures *q, index_t m, index_t blocksize=10000)
 
virtual ~CStreamingMMD ()
 
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)=0
 
virtual void compute_statistic_and_Q (SGVector< float64_t > &statistic, SGMatrix< float64_t > &Q)=0
 
virtual SGVector< float64_tsample_null ()
 
void set_blocksize (index_t blocksize)
 
CListstream_data_blocks (index_t num_blocks, index_t num_this_run)
 
virtual void set_p_and_q (CFeatures *p_and_q)
 
virtual CFeaturesget_p_and_q ()
 
virtual CStreamingFeaturesget_streaming_p ()
 
virtual CStreamingFeaturesget_streaming_q ()
 
void set_simulate_h0 (bool simulate_h0)
 
virtual const char * get_name () const
 
virtual void set_kernel (CKernel *kernel)
 
virtual CKernelget_kernel ()
 
void set_m (index_t m)
 
index_t get_m ()
 
bool perform_test (float64_t alpha)
 
virtual void set_num_null_samples (index_t num_null_samples)
 
virtual void set_null_approximation_method (ENullApproximationMethod null_approximation_method)
 
virtual EStatisticType get_statistic_type () const =0
 
virtual CSGObjectshallow_copy () const
 
virtual CSGObjectdeep_copy () const
 
virtual bool is_generic (EPrimitiveType *generic) const
 
template<class T >
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
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 ()
 
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)
 
bool has (const std::string &name) const
 
template<typename T >
bool has (const Tag< T > &tag) const
 
template<typename T , typename U = void>
bool has (const std::string &name) const
 
template<typename T >
void set (const Tag< T > &_tag, const T &value)
 
template<typename T , typename U = void>
void set (const std::string &name, const T &value)
 
template<typename T >
get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
get (const std::string &name) const
 
virtual void update_parameter_hash ()
 
virtual bool parameter_hash_changed ()
 
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
 
virtual CSGObjectclone ()
 

Public Attributes

SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_gradient_parameters
 
uint32_t m_hash
 

Protected Member Functions

virtual SGVector< float64_tcompute_squared_mmd (CKernel *kernel, CList *data, index_t num_this_run)=0
 
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)
 
template<typename T >
void register_param (Tag< T > &_tag, const T &value)
 
template<typename T >
void register_param (const std::string &name, const T &value)
 

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_num_null_samples
 
ENullApproximationMethod m_null_approximation_method
 

Constructor & Destructor Documentation

default constructor

Definition at line 40 of file StreamingMMD.cpp.

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

constructor.

Parameters
kernelkernel to use
pstreaming features p to use
qstreaming features q to use
mnumber of samples from each distribution
blocksizesize of examples that are processed at once when computing statistic/threshold.

Definition at line 45 of file StreamingMMD.cpp.

~CStreamingMMD ( )
virtual

destructor

Definition at line 60 of file StreamingMMD.cpp.

Member Function Documentation

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
dictdictionary of parameters to be built.

Definition at line 630 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 747 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
statisticstatistic value to compute the p-value for
Returns
p-value parameter statistic is the (1-p) percentile of the null distribution

Reimplemented from CTwoSampleTest.

Definition at line 119 of file StreamingMMD.cpp.

virtual SGVector<float64_t> compute_squared_mmd ( CKernel kernel,
CList data,
index_t  num_this_run 
)
protectedpure virtual

abstract method that computes the squared MMD

Parameters
kernelthe kernel to be used for computing MMD. This will be useful when multiple kernels are used
datathe list of data on which kernels are computed. The order of data in the list is \(x,x',\cdots\sim p\) followed by \(y,y',\cdots\sim q\). It is assumed that detele_data flag is set inside the list
num_this_runnumber of data points in current blocks
Returns
the MMD values

Implemented in CLinearTimeMMD.

float64_t compute_statistic ( )
virtual

Computes the squared MMD for the current data. This is an unbiased estimate. This method relies on compute_statistic_and_variance which has to be defined in the subclasses

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

Returns
squared MMD

Implements CKernelTwoSampleTest.

Definition at line 85 of file StreamingMMD.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_kernelsif 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 CKernelTwoSampleTest.

Definition at line 95 of file StreamingMMD.cpp.

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

Implemented in CLinearTimeMMD.

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

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

Parameters
statisticreturn parameter for statistic, vector with entry for each kernel. May be allocated before but doesn not have to be
variancereturn parameter for statistic, vector with entry for each kernel. May be allocated before but doesn not have to be
multiple_kernelsoptional 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.

Implemented in CLinearTimeMMD.

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
alphatest level to reject null-hypothesis
Returns
threshold for statistics to reject null-hypothesis

Reimplemented from CTwoSampleTest.

Definition at line 142 of file StreamingMMD.cpp.

float64_t compute_variance_estimate ( )
virtual

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

Returns
variance estimate

Definition at line 109 of file StreamingMMD.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 of file SGObject.cpp.

bool equals ( CSGObject other,
float64_t  accuracy = 0.0,
bool  tolerant = false 
)
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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 651 of file SGObject.cpp.

T get ( const Tag< T > &  _tag) const
inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

T get ( const std::string &  name) const
inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 323 of file SGObject.cpp.

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

Definition at line 86 of file KernelTwoSampleTest.h.

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

Definition at line 127 of file TwoSampleTest.h.

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

Definition at line 531 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_namename of the parameter
Returns
description of the parameter

Definition at line 555 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_namename of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 568 of file SGObject.cpp.

virtual const char* get_name ( ) const
virtual
Returns
the class name

Implements CKernelTwoSampleTest.

Reimplemented in CLinearTimeMMD.

Definition at line 269 of file StreamingMMD.h.

CFeatures * get_p_and_q ( )
virtual

Not implemented for streaming MMD since it uses streaming feautres

Reimplemented from CTwoSampleTest.

Definition at line 307 of file StreamingMMD.cpp.

virtual EStatisticType get_statistic_type ( ) const
pure virtualinherited

returns the statistic type of this test statistic

Implemented in CQuadraticTimeMMD, CNOCCO, CHSIC, and CLinearTimeMMD.

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 314 of file StreamingMMD.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 320 of file StreamingMMD.cpp.

bool has ( const std::string &  name) const
inherited

Checks if object has a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

bool has ( const Tag< T > &  tag) const
inherited

Checks if object has a class parameter identified by a Tag.

Parameters
tagtag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

bool has ( const std::string &  name) const
inherited

Checks if a type exists for a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.h.

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

Definition at line 329 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "" 
)
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
filewhere to load from
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 402 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
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 459 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
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 454 of file SGObject.cpp.

bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 295 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
alphatest level alpha.
Returns
true if null hypothesis is rejected and false otherwise

Definition at line 121 of file HypothesisTest.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 sampling null, 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 CHypothesisTest.

Definition at line 165 of file StreamingMMD.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 341 of file SGObject.cpp.

void register_param ( Tag< T > &  _tag,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 439 of file SGObject.h.

void register_param ( const std::string &  name,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 452 of file SGObject.h.

SGVector< float64_t > sample_null ( )
virtual

Mimics sampling null for 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 CKernelTwoSampleTest.

Definition at line 194 of file StreamingMMD.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "" 
)
virtualinherited

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 347 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
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 469 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
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 464 of file SGObject.cpp.

void set ( const Tag< T > &  _tag,
const T &  value 
)
inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 328 of file SGObject.h.

void set ( const std::string &  name,
const T &  value 
)
inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 354 of file SGObject.h.

void set_blocksize ( index_t  blocksize)

Setter for the blocksize of examples to be processed at once

Parameters
blocksizenew blocksize to use

Definition at line 226 of file StreamingMMD.h.

void set_generic ( )
inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 144 of file SGObject.cpp.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 of file SGObject.cpp.

virtual void set_kernel ( CKernel kernel)
virtualinherited

Setter for the underlying kernel

Parameters
kernelnew kernel to use

Definition at line 77 of file KernelTwoSampleTest.h.

void set_m ( index_t  m)
inherited
Parameters
mnumber of samples from first distribution p

Definition at line 162 of file TwoSampleTest.cpp.

void set_null_approximation_method ( ENullApproximationMethod  null_approximation_method)
virtualinherited

sets the method how to approximate the null-distribution

Parameters
null_approximation_methodmethod to use

Definition at line 61 of file HypothesisTest.cpp.

void set_num_null_samples ( index_t  num_null_samples)
virtualinherited

sets the number of permutation iterations for sample_null()

Parameters
num_null_sampleshow often permutation shall be done

Definition at line 67 of file HypothesisTest.cpp.

void set_p_and_q ( CFeatures p_and_q)
virtual

Not implemented for streaming MMD since it uses streaming feautres

Reimplemented from CTwoSampleTest.

Definition at line 301 of file StreamingMMD.cpp.

void set_simulate_h0 ( bool  simulate_h0)
Parameters
simulate_h0if true, samples from p and q will be mixed and permuted

Definition at line 263 of file StreamingMMD.h.

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 225 of file SGObject.cpp.

CList * stream_data_blocks ( index_t  num_blocks,
index_t  num_this_run 
)

Streams num_blocks data from each distribution with blocks of size num_this_run. If m_simulate_h0 is set, it merges the blocks together, shuffles and redistributes between the blocks.

Parameters
num_blocksnumber of blocks to be streamed from each distribution
num_this_runnumber of data points to be streamed for one block
Returns
an ordered list of blocks of data. The order in the list is \(x,x',\cdots\sim p\) followed by \(y,y',\cdots\sim q\). The features inside the list are SG_REF'ed and delete_data is set in the list, which will destroy the at CList's destructor call

Definition at line 220 of file StreamingMMD.cpp.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 336 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

index_t m_blocksize
protected

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

Definition at line 296 of file StreamingMMD.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

CKernel* m_kernel
protectedinherited

underlying kernel

Definition at line 121 of file KernelTwoSampleTest.h.

index_t m_m
protectedinherited

defines the first index of samples of q

Definition at line 139 of file TwoSampleTest.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

ENullApproximationMethod m_null_approximation_method
protectedinherited

Defines how the the null distribution is approximated

Definition at line 177 of file HypothesisTest.h.

index_t m_num_null_samples
protectedinherited

number of iterations for sampling from null-distributions

Definition at line 174 of file HypothesisTest.h.

CFeatures* m_p_and_q
protectedinherited

concatenated samples of the two distributions (two blocks)

Definition at line 136 of file TwoSampleTest.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

bool m_simulate_h0
protected

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

Definition at line 300 of file StreamingMMD.h.

CStreamingFeatures* m_streaming_p
protected

Streaming feature objects that are used instead of merged samples

Definition at line 290 of file StreamingMMD.h.

CStreamingFeatures* m_streaming_q
protected

Streaming feature objects that are used instead of merged samples

Definition at line 293 of file StreamingMMD.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation