SHOGUN  6.0.0

## Detailed Description

This class implements the quadratic time Maximum Mean Statistic as described in [1]. The MMD is the distance of two probability distributions $$p$$ and $$q$$ in a RKHS which we denote by

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

.

Estimating variance of the asymptotic distribution of the statistic under null and alternative hypothesis can be done using compute_variance_h0() and compute_variance_h1() method.

Note that all these operations can be done for multiple kernels at once as well. To use this functionality, use multikernel() method to obtain a CMultiKernelQuadraticTimeMMD instance and then call methods on that.

If you do not know about your data, but want to use the MMD from a kernel matrix, just use the custom kernel constructor and initialize the features as CDummyFeatures. Everything else will work as usual.

To make the computation faster, this class always pre-computes the kernel and stores the Gram matrix using merged samples from p and q. It essentially keeps a backup of the old kernel and rather uses this pre-computed one as long as the present kernel is valid. Therefore, after a computation phase is executed, upon calling get_kernel() we will obtain the pre-computed kernel matrix as a CCustomKernel object. However, if subsequently the features are updated or the underlying kernel itself is updated, it discards the pre-computed kernel matrix (frees memory) and pulls the old kernel from backup (or, simply replace that if a new kernel is provided) and then pre-computes that in the next run.

It is possible to turn off the above feature by turning it off. However, it will affect the performance of the algorithms, since they are optimzied for pre-computed kernel matrices. Therefore, this should only be turned off if the storage of the kernel is a major concern. Please note that only the lower triangular part of the Gram matrix is stored, in order to exploit the symmetry.

Since the methods modifies the object's state, using the methods of this class from multiple threads may result in undesired/incorrect results/behavior.

NOTE: $$n_x$$ and $$n_y$$ are represented by $$m$$ and $$n$$, respectively in the implementation.

[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.

[2]: Gretton, A., Fukumizu, K., & Harchaoui, Z. (2011). A fast, consistent kernel two-sample test.

Definition at line 97 of file QuadraticTimeMMD.h.

[legend]

struct  Self

## Public Member Functions

virtual void set_p (CFeatures *samples_from_p)

virtual void set_q (CFeatures *samples_from_q)

CFeaturesget_p_and_q ()

virtual void set_kernel (CKernel *kernel)

virtual void select_kernel ()

virtual float64_t compute_statistic ()

virtual SGVector< float64_tsample_null ()

virtual float64_t compute_p_value (float64_t statistic)

virtual float64_t compute_threshold (float64_t alpha)

float64_t compute_variance_h0 ()

float64_t compute_variance_h1 ()

void spectrum_set_num_eigenvalues (index_t num_eigenvalues)

index_t spectrum_get_num_eigenvalues () const

void precompute_kernel_matrix (bool precompute)

void save_permutation_inds (bool save_inds)

SGMatrix< index_tget_permutation_inds () const

virtual const char * get_name () const

void set_kernel_selection_strategy (EKernelSelectionMethod method, bool weighted=false)

void set_kernel_selection_strategy (EKernelSelectionMethod method, index_t num_runs, index_t num_folds, float64_t alpha)

CKernelSelectionStrategy const * get_kernel_selection_strategy () const

void cleanup ()

void set_num_null_samples (index_t null_samples)

index_t get_num_null_samples () const

void set_statistic_type (EStatisticType stype)

EStatisticType get_statistic_type () const

void set_null_approximation_method (ENullApproximationMethod nmethod)

ENullApproximationMethod get_null_approximation_method () const

CKernelget_kernel () const

CFeaturesget_p () const

CFeaturesget_q () const

void set_num_samples_p (index_t num_samples_from_p)

const index_t get_num_samples_p () const

void set_num_samples_q (index_t num_samples_from_q)

const index_t get_num_samples_q () const

CCustomDistancecompute_distance (CDistance *distance)

CCustomDistancecompute_joint_distance (CDistance *distance)

void set_train_test_mode (bool on)

void set_train_test_ratio (float64_t ratio)

bool perform_test (float64_t alpha)

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

uint32_t m_hash

## Protected Member Functions

virtual float64_t normalize_statistic (float64_t statistic) const

internal::KernelManager & get_kernel_mgr ()

const internal::KernelManager & get_kernel_mgr () const

internal::DataManagerget_data_mgr ()

const internal::DataManagerget_data_mgr () const

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)

bool clone_parameters (CSGObject *other)

## Constructor & Destructor Documentation

Default constructor

Definition at line 181 of file QuadraticTimeMMD.cpp.

 CQuadraticTimeMMD ( CFeatures * samples_from_p, CFeatures * samples_from_q )

Convenience constructor. Initializes the features representing samples from both the distributions.

Parameters
 samples_from_p Samples from p. samples_from_q Samples from q.

Definition at line 186 of file QuadraticTimeMMD.cpp.

virtual

Destructor

Definition at line 197 of file QuadraticTimeMMD.cpp.

## Member Function Documentation

 void add_kernel ( CKernel * kernel )
inherited

Method that adds a kernel instance to be used for kernel selection. Please note that the kernels added by this method are NOT set as the main test kernel unless select_kernel() method is executed.

Parameters
 kernel One of the kernel instances with which learning algorithm will work.

Definition at line 109 of file MMD.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 630 of file SGObject.cpp.

 void cleanup ( )
inherited

Method that releases the pre-computed kernel that is used in the computation.

Definition at line 124 of file MMD.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 729 of file SGObject.cpp.

 bool clone_parameters ( CSGObject * other )
protectedinherited

Definition at line 754 of file SGObject.cpp.

 CCustomDistance * compute_distance ( CDistance * distance )
inherited

Method that pre-computes the pair-wise distance between the samples using the provided distance instance.

Parameters
 distance The distance instance used for pre-computing the pair-wise distance.
Returns
A newly created CCustomDistance instance representing the pre-computed pair-wise distance between the samples.

Definition at line 99 of file TwoDistributionTest.cpp.

 CCustomDistance * compute_joint_distance ( CDistance * distance )
inherited

Method that pre-computes the pair-wise distance between the joint samples using the provided distance instance. A temporary object appending the samples from both the distributions is created in order to perform the task.

Parameters
 distance The distance instance used for pre-computing the pair-wise distance.
Returns
A newly created CCustomDistance instance representing the pre-computed pair-wise distance between the joint samples.

Definition at line 128 of file TwoDistributionTest.cpp.

 float64_t compute_p_value ( float64_t statistic )
virtual

Method that computes the p-value from the provided statistic.

Parameters
 statistic The test statistic
Returns
The p-value computed using the null-appriximation method specified.

Reimplemented from CHypothesisTest.

Definition at line 532 of file QuadraticTimeMMD.cpp.

 float64_t compute_statistic ( )
virtual

Method that computes the estimator of MMD^2 (biased/unbiased/incomplete) as set from set_statistic_type() method. Default is unbiased.

Returns
A normalized value of the MMD^2 estimator.

Implements CMMD.

Definition at line 312 of file QuadraticTimeMMD.cpp.

 float64_t compute_threshold ( float64_t alpha )
virtual

Method that computes the threshold from the provided significance level (alpha).

Parameters
 alpha The significance level (value should be between 0 and 1)
Returns
The threshold computed using the null-approximation method specified.

Reimplemented from CHypothesisTest.

Definition at line 551 of file QuadraticTimeMMD.cpp.

 float64_t compute_variance_h0 ( )

Method that computes an estimate of the variance of the unbiased MMD^2 estimator under the assumption that the null hypothesis was true.

Returns
The variance estimate of the unbiased MMD^2 estimator under null.

Definition at line 499 of file QuadraticTimeMMD.cpp.

 float64_t compute_variance_h1 ( )

Method that computes an estimate of the variance of the unbiased MMD^2 estimator under the assumption that the alternative hypothesis was true.

Returns
The variance estimate of the unbiased MMD^2 estimator under alternative.

Definition at line 510 of file QuadraticTimeMMD.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
 other object to compare with accuracy accuracy to use for comparison (optional) tolerant allows 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
 _tag name and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 376 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
 name name of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 399 of file SGObject.h.

 DataManager & get_data_mgr ( )
protectedinherited

Definition at line 104 of file HypothesisTest.cpp.

 const DataManager & get_data_mgr ( ) const
protectedinherited

Definition at line 109 of file HypothesisTest.cpp.

 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.

 CKernel * get_kernel ( ) const
inherited
Returns
The kernel instance that is presently being used for performing the test

Definition at line 73 of file TwoSampleTest.cpp.

 KernelManager & get_kernel_mgr ( )
protectedinherited

Definition at line 83 of file TwoSampleTest.cpp.

 const KernelManager & get_kernel_mgr ( ) const
protectedinherited

Definition at line 88 of file TwoSampleTest.cpp.

 CKernelSelectionStrategy const * get_kernel_selection_strategy ( ) const
inherited

Method that returns the kernel selection strategy wrapper object that will be/ was used in the last kernel learning algorithm. Use this method when results of intermediate steps taken by the kernel selection algorithms are of interest.

Returns
The internal instance of CKernelSelectionStrategy that holds intermediate measures computed at the time of the last kernel selection algorithm invocation.

Definition at line 104 of file MMD.cpp.

 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_name name 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_name name 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.

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

Reimplemented from CMMD.

Definition at line 631 of file QuadraticTimeMMD.cpp.

 ENullApproximationMethod get_null_approximation_method ( ) const
inherited
Returns
The null-approximation method

Definition at line 154 of file MMD.cpp.

 index_t get_num_null_samples ( ) const
inherited
Returns
Number of null-samples

Definition at line 134 of file MMD.cpp.

 const index_t get_num_samples_p ( ) const
inherited
Returns
The number of samples from $$\mathbf{P}$$.

Definition at line 81 of file TwoDistributionTest.cpp.

 const index_t get_num_samples_q ( ) const
inherited
Returns
The number of samples from $$\mathbf{Q}$$.

Definition at line 93 of file TwoDistributionTest.cpp.

 CFeatures * get_p ( ) const
inherited
Returns
The samples from $$\mathbf{P}$$.

Definition at line 56 of file TwoDistributionTest.cpp.

 CFeatures * get_p_and_q ( )

Method that creates a merged copy of CFeatures instance from both the features, appending the samples from p and q. This method does not cache the merged copy from previous call. So, calling this method will create a new instance every time.

Returns
The merged samples.

Definition at line 246 of file QuadraticTimeMMD.cpp.

 SGMatrix< index_t > get_permutation_inds ( ) const

Method that returns the permutation indices, if that option was turned on by using the save_permutation_inds(true).

Returns
The permutation indices, one column per null-sample.

Definition at line 626 of file QuadraticTimeMMD.cpp.

 CFeatures * get_q ( ) const
inherited
Returns
The samples from $$\mathbf{Q}$$.

Definition at line 69 of file TwoDistributionTest.cpp.

 EStatisticType get_statistic_type ( ) const
inherited
Returns
The type of the estimator for MMD^2

Definition at line 144 of file MMD.cpp.

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

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

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

Definition at line 299 of file SGObject.h.

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

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

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

Definition at line 310 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
 name name of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 321 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
 generic set 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
 file where to load from prefix prefix 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
 ShogunException will be thrown if an error occurs.

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
 ShogunException will be thrown if an error occurs.

Definition at line 454 of file SGObject.cpp.

Method that returns the internal instance of CMultiKernelQuadraticTimeMMD which provides a similar API to this class to compute the estimates for multiple kernel all at once. This internal instance shares the same set of samples with this one but the kernel has to be added seperately using multikernel().add_kernel() method.

Returns

Definition at line 587 of file QuadraticTimeMMD.cpp.

 float64_t normalize_statistic ( float64_t statistic ) const
protectedvirtual

Implements CMMD.

Definition at line 304 of file QuadraticTimeMMD.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

Method that performs the complete hypothesis test on current data and returns a binary answer: wheter null hypothesis is rejected or not.

This is just a wrapper for the above compute_p_value() 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 92 of file HypothesisTest.cpp.

 void precompute_kernel_matrix ( bool precompute )

Use this method when pre-computation of the kernel matrix is NOT desired. By default this class always precomputes the Gram matrix. Please note that the performance will be slow if this option is turned off.

Parameters
 precompute Flag to whether pre-compute the kernel matrix internally or not. If false, the kernel matrix is NOT pre-computed, otherwise it is. Default is true.

Definition at line 602 of file QuadraticTimeMMD.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
 prefix prefix 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
 _tag name and type information of parameter value value of the parameter

Definition at line 450 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
 name name of the parameter value value of the parameter along with type information

Definition at line 463 of file SGObject.h.

 SGVector< float64_t > sample_null ( )
virtual

Method that returns a number of null-samples, based on the null approximation method that was set using set_null_approximation_method(). Default is permutation.

Returns
Normalized values of the MMD^2 estimates under null hypothesis.

Implements CMMD.

Definition at line 570 of file QuadraticTimeMMD.cpp.

 void save_permutation_inds ( bool save_inds )

Method that saves the permutation indices that will be used while sampling from the null distribution in case permutation approach was adopted. The indices will be available only after a successful run of the permutation test. By default, the indices are never saved.

Parameters
 save_inds Whether to save the permutation indices or not. If true, the indices are saved, otherwise not.

Definition at line 621 of file QuadraticTimeMMD.cpp.

 bool save_serializable ( CSerializableFile * file, const char * prefix = "" )
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
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
 ShogunException will 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
 ShogunException will be thrown if an error occurs.

Definition at line 464 of file SGObject.cpp.

 void select_kernel ( )
virtual

Method that learns/selects the kernel from a set of provided kernel instances added from the add_kernel() methods. Upon selection, it internally replaces the kernel instance, if any, that was already present.

Please make sure to set the train-test mode on before using this method.

Reimplemented from CMMD.

Definition at line 290 of file QuadraticTimeMMD.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
 _tag name and type information of parameter value value of the parameter

Definition at line 337 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
 name name of the parameter value value of the parameter along with type information

Definition at line 363 of file SGObject.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
 io io object to use

Definition at line 261 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 274 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 316 of file SGObject.cpp.

 void set_kernel ( CKernel * kernel )
virtual

Method that sets the kernel instance to be used. If a CCustomKernel is set, then the features passed would be effectively ignored. Therefore, if this is the intended behavior, simply passing two instances of CDummyFeatures would do (since they cannot be left null as of now).

If a pre-computed instance already exists from previous runs, this will invalidate that one and free memory.

Parameters
 kernel The kernel instance.

Reimplemented from CTwoSampleTest.

Definition at line 270 of file QuadraticTimeMMD.cpp.

 void set_kernel_selection_strategy ( EKernelSelectionMethod method, bool weighted = false )
inherited

Method that sets the specific kernel selection strategy based on the specific parameters provided. Please see class documentation for details. Use this method for every other strategy other than KSM_CROSS_VALIDATION.

Parameters
 method The kernel selection method as specified in EKernelSelectionMethod. weighted If true, then an weighted combination of the kernel is used after solving an optimization. If false, only a single kernel is selected among the provided ones.

Definition at line 89 of file MMD.cpp.

 void set_kernel_selection_strategy ( EKernelSelectionMethod method, index_t num_runs, index_t num_folds, float64_t alpha )
inherited

Method that sets the specific kernel selection strategy based on the specific parameters provided. Please see class documentation for details. Use this method for KSM_CROSS_VALIDATION.

Parameters
 method The kernel selection method as specified in EKernelSelectionMethod. num_runs The number of total runs of the cross-validation algorithm. num_folds The number of folds (k) to be used in k-fold stratified cross-validation. alpha The threshold to be used while performing test for the test-folds.

Definition at line 95 of file MMD.cpp.

 void set_null_approximation_method ( ENullApproximationMethod nmethod )
inherited

Method that sets the approach to be taken while approximating the null-samples.

The null-approximation method

Definition at line 149 of file MMD.cpp.

 void set_num_null_samples ( index_t null_samples )
inherited

Method that sets the number of null-samples used for computing p-value.

Parameters
 null_samples Number of null-samples.

Definition at line 129 of file MMD.cpp.

 void set_num_samples_p ( index_t num_samples_from_p )
inherited

Method that initializes the number of samples to be drawn from distribution $$\mathbf{P}$$. Please ensure to call this method if you are intending to use streaming data generators that generate the samples on the fly. For other types of features, the number of samples is set internally from the features object itself, therefore this method should not be used.

Parameters
 num_samples_from_p The CFeatures instance representing the samples from $$\mathbf{P}$$.

Definition at line 75 of file TwoDistributionTest.cpp.

 void set_num_samples_q ( index_t num_samples_from_q )
inherited

Method that initializes the number of samples to be drawn from distribution $$\mathbf{Q}$$. Please ensure to call this method if you are intending to use streaming data generators that generate the samples on the fly. For other types of features, the number of samples is set internally from the features object itself, therefore this method should not be used.

Parameters
 num_samples_from_q The CFeatures instance representing the samples from $$\mathbf{Q}$$.

Definition at line 87 of file TwoDistributionTest.cpp.

 void set_p ( CFeatures * samples_from_p )
virtual

Method that initializes/replaces samples from p. It will invalidate existing pre-computed kernel, if any, from previous run. However, if the underlying kernel, if set already by this point, is an instance of CCustomKernel itself, the supplied features will be ignored.

Parameters
 samples_from_p Samples from p.

Reimplemented from CTwoDistributionTest.

Definition at line 202 of file QuadraticTimeMMD.cpp.

 void set_q ( CFeatures * samples_from_q )
virtual

Method that initializes/replaces samples from q. It will invalidate existing pre-computed kernel, if any, from previous run. However, if the underlying kernel, if set already by this point, is an instance of CCustomKernel itself, the supplied features will be ignored.

Parameters
 samples_from_p Samples from q.

Reimplemented from CTwoDistributionTest.

Definition at line 224 of file QuadraticTimeMMD.cpp.

 void set_statistic_type ( EStatisticType stype )
inherited

Method that sets the type of the estimator for MMD^2

Parameters
 stype The type of the estimator for MMD^2

Definition at line 139 of file MMD.cpp.

 void set_train_test_mode ( bool on )
inherited

Method that enables/disables the training-testing mode. If this option is turned on, then the samples would be split in two pieces: one chunk would be used for training algorithms and the other chunk would be used for performing tests. If this option is turned off, the entire data would be used for performing the test. Before running any training algorithms, make sure to turn this mode on.

By default, the training-testing mode is turned off.

{set_train_test_ratio()}
Parameters
 on Whether to enable/disable the training-testing mode

Definition at line 66 of file HypothesisTest.cpp.

 void set_train_test_ratio ( float64_t ratio )
inherited

Method that specifies the ratio of training-testing data split for the algorithms. Note that this is NOT the percentage of samples to be used for training, rather the ratio of the number of samples to be used for training and that of testing.

By default, an equal 50-50 split (ratio = 1) is made.

{set_train_test_mode()}
Parameters
 ratio The ratio of the number of samples to be used for training and that of testing

Definition at line 71 of file HypothesisTest.cpp.

 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.

 index_t spectrum_get_num_eigenvalues ( ) const
Returns
The number of eigenvalues in use for the spectral test

Definition at line 597 of file QuadraticTimeMMD.cpp.

 void spectrum_set_num_eigenvalues ( index_t num_eigenvalues )

Method that sets the number of eigenvalues to be used when spectral estimation of the null samples is used. Will be ignored if null-approximation method was anything else.

Parameters
 num_eigenvalues The number of eigenvalues to be used from the eigenspectrum of the Gram matrix.

Definition at line 592 of file QuadraticTimeMMD.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.

## Friends And Related Function Documentation

friend

Definition at line 99 of file QuadraticTimeMMD.h.

## Member Data Documentation

 SGIO* io
inherited

io

Definition at line 558 of file SGObject.h.

inherited

parameters wrt which we can compute gradients

Definition at line 573 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 576 of file SGObject.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 570 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 567 of file SGObject.h.

 Parallel* parallel
inherited

parallel

Definition at line 561 of file SGObject.h.

 Version* version
inherited

version

Definition at line 564 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation