SHOGUN
6.1.3
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Abstract base class that provides an interface for performing kernel two-sample test 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).
\[ \text{MMD}[\mathcal{F},p,q]^2=||\mu_p - \mu_q||^2_\mathcal{F}= \textbf{E}_{x,x'}\left[ k(x,x')\right] -2\textbf{E}_{x,y}\left[ k(x,y)\right] +\textbf{E}_{y,y'}\left[ k(y,y')\right] \]
where \(x,x'\sim p\) and \(y,y'\sim q\).
Given two sets of samples \(\{x_i\}_{i=1}^{n_x}\sim p\) and \(\{y_i\}_{i=1}^{n_y}\sim q\), \(n_x+n_y=n\), the unbiased estimate of the above statistic is computed as
\[ \hat{\eta}_{k,U}=\frac{1}{n_x(n_x-1)}\sum_{i=1}^{n_x}\sum_{j\neq i} k(x_i,x_j)+\frac{1}{n_y(n_y-1)}\sum_{i=1}^{n_y}\sum_{j\neq i}k(y_i,y_j) -\frac{2}{n_xn_y}\sum_{i=1}^{n_x}\sum_{j=1}^{n_y}k(x_i,y_j) \]
A biased version is
\[ \hat{\eta}_{k,V}=\frac{1}{n_x^2}\sum_{i=1}^{n_x}\sum_{j=1}^{n_x} k(x_i,x_j)+\frac{1}{n_y^2}\sum_{i=1}^{n_y}\sum_{j=1}^{n_y}k(y_i,y_j) -\frac{2}{n_xn_y}\sum_{i=1}^{n_x}\sum_{j=1}^{n_y}k(x_i,y_j) \]
When \(n_x=n_y=\frac{n}{2}\), an incomplete version can also be computed as the following
\[ \hat{\eta}_{k,U^-}=\frac{1}{\frac{n}{2}(\frac{n}{2}-1)}\sum_{i\neq j} h(z_i,z_j) \]
where for each pair \(z=(x,y)\), \(h(z,z')=k(x,x')+k(y,y')-k(x,y')- k(x',y)\).
The type (biased/unbiased/incomplete) can be selected via set_statistic_type() via the enum values from EStatisticType, ST_BIASED, ST_UNBIASED and ST_INCOMPLETE, respectively. The estimate returned by compute_statistic() is \(\frac{n_xn_y}{n_x+n_y}\hat{\eta}_k\).
This class provides an interface for adding multiple kernels and then selecting the best kernel based on specified strategies. To know more in details about various learning algorithms for optimal kernel selection, please refer to [2].
Along with the statistic comes a method to compute a p-value based on different methods. Permutation test is possible. If unsure which one to use, sampling with 250 permutation iterations usually always is correct.
To choose, use set_null_approximation_method() and choose from.
NAM_MMD2_SPECTRUM: For a fast, consistent test based on the spectrum of the kernel matrix, as described in [2]. Only supported if Eigen3 is installed. Only applicable for CQuadraticTimeMMD.
NAM_MMD2_GAMMA: for a very fast, but not consistent test based on moment matching of a Gamma distribution, as described in [2]. Only applicable for CQuadraticTimeMMD.
NAM_PERMUTATION: For permuting available samples to sample null-distribution
[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] Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu: Optimal kernel choice for large-scale two-sample tests. NIPS 2012: 1214-1222.
Classes | |
struct | Self |
Public Types | |
typedef rxcpp::subjects::subject< ObservedValue > | SGSubject |
typedef rxcpp::observable< ObservedValue, rxcpp::dynamic_observable< ObservedValue > > | SGObservable |
typedef rxcpp::subscriber< ObservedValue, rxcpp::observer< ObservedValue, void, void, void, void > > | SGSubscriber |
Public Member Functions | |
CMMD () | |
CMMD (CFeatures *samples_from_p, CFeatures *samples_from_q) | |
virtual | ~CMMD () |
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) |
void | add_kernel (CKernel *kernel) |
virtual void | select_kernel () |
CKernelSelectionStrategy const * | get_kernel_selection_strategy () const |
virtual float64_t | compute_statistic ()=0 |
virtual SGVector< float64_t > | sample_null ()=0 |
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 |
virtual const char * | get_name () const |
virtual void | set_kernel (CKernel *kernel) |
CKernel * | get_kernel () const |
virtual void | set_p (CFeatures *samples_from_p) |
CFeatures * | get_p () const |
virtual void | set_q (CFeatures *samples_from_q) |
CFeatures * | get_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 |
CCustomDistance * | compute_distance (CDistance *distance) |
CCustomDistance * | compute_joint_distance (CDistance *distance) |
void | set_train_test_mode (bool on) |
void | set_train_test_ratio (float64_t ratio) |
virtual float64_t | compute_p_value (float64_t statistic) |
virtual float64_t | compute_threshold (float64_t alpha) |
bool | perform_test (float64_t alpha) |
int32_t | ref () |
int32_t | ref_count () |
int32_t | unref () |
virtual CSGObject * | shallow_copy () const |
virtual CSGObject * | deep_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) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_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 > | |
T | get (const Tag< T > &_tag) const |
template<typename T , typename U = void> | |
T | get (const std::string &name) const |
SGObservable * | get_parameters_observable () |
void | subscribe_to_parameters (ParameterObserverInterface *obs) |
void | list_observable_parameters () |
virtual void | update_parameter_hash () |
virtual bool | parameter_hash_changed () |
virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
virtual CSGObject * | clone () |
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
uint32_t | m_hash |
Protected Member Functions | |
virtual float64_t | normalize_statistic (float64_t statistic) const =0 |
internal::KernelManager & | get_kernel_mgr () |
const internal::KernelManager & | get_kernel_mgr () const |
internal::DataManager & | get_data_mgr () |
const internal::DataManager & | get_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) |
void | observe (const ObservedValue value) |
void | register_observable_param (const std::string &name, const SG_OBS_VALUE_TYPE type, const std::string &description) |
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Definition at line 130 of file SGObject.h.
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inherited |
Definition at line 127 of file SGObject.h.
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inherited |
Definition at line 133 of file SGObject.h.
void add_kernel | ( | CKernel * | kernel | ) |
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.
This method is NOT thread safe. Please DO NOT use this method from multiple threads.
kernel | One of the kernel instances with which learning algorithm will work. |
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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.
dict | dictionary of parameters to be built. |
Definition at line 635 of file SGObject.cpp.
void cleanup | ( | ) |
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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.
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicObjectArray, CAlphabet, and CMKL.
Definition at line 734 of file SGObject.cpp.
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protectedinherited |
Definition at line 759 of file SGObject.cpp.
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Method that pre-computes the pair-wise distance between the samples using the provided distance instance.
distance | The distance instance used for pre-computing the pair-wise distance. |
Definition at line 99 of file TwoDistributionTest.cpp.
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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.
distance | The distance instance used for pre-computing the pair-wise distance. |
Definition at line 128 of file TwoDistributionTest.cpp.
Method that 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.
This method depends on the implementation of sample_null method which should be implemented by the sub-classes.
statistic | statistic value to compute the p-value for |
Reimplemented in CQuadraticTimeMMD, CBTestMMD, and CLinearTimeMMD.
Definition at line 77 of file HypothesisTest.cpp.
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pure virtual |
Interface for computing the test-statistic for the hypothesis test.
Implements CTwoSampleTest.
Implemented in CQuadraticTimeMMD, and CStreamingMMD.
Method that 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.
This method depends on the implementation of sample_null method which should be implemented by the sub-classes.
alpha | test level to reject null-hypothesis |
Reimplemented in CQuadraticTimeMMD, CBTestMMD, and CLinearTimeMMD.
Definition at line 85 of file HypothesisTest.cpp.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 232 of file SGObject.cpp.
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.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
Definition at line 656 of file SGObject.cpp.
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inherited |
Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
_tag | name and type information of parameter |
Definition at line 381 of file SGObject.h.
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inherited |
Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
name | name of the parameter |
Definition at line 404 of file SGObject.h.
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protectedinherited |
Definition at line 104 of file HypothesisTest.cpp.
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protectedinherited |
Definition at line 109 of file HypothesisTest.cpp.
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inherited |
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inherited |
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Definition at line 73 of file TwoSampleTest.cpp.
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protectedinherited |
Definition at line 83 of file TwoSampleTest.cpp.
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protectedinherited |
Definition at line 88 of file TwoSampleTest.cpp.
CKernelSelectionStrategy const * get_kernel_selection_strategy | ( | ) | const |
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.
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inherited |
Definition at line 536 of file SGObject.cpp.
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inherited |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 560 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 573 of file SGObject.cpp.
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virtual |
Reimplemented from CTwoSampleTest.
Reimplemented in CQuadraticTimeMMD, CStreamingMMD, CBTestMMD, and CLinearTimeMMD.
ENullApproximationMethod get_null_approximation_method | ( | ) | const |
index_t get_num_null_samples | ( | ) | const |
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inherited |
Definition at line 81 of file TwoDistributionTest.cpp.
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inherited |
Definition at line 93 of file TwoDistributionTest.cpp.
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inherited |
Definition at line 56 of file TwoDistributionTest.cpp.
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inherited |
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inherited |
Definition at line 69 of file TwoDistributionTest.cpp.
EStatisticType get_statistic_type | ( | ) | const |
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inherited |
Checks if object has a class parameter identified by a name.
name | name of the parameter |
Definition at line 304 of file SGObject.h.
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inherited |
Checks if object has a class parameter identified by a Tag.
tag | tag of the parameter containing name and type information |
Definition at line 315 of file SGObject.h.
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inherited |
Checks if a type exists for a class parameter identified by a name.
name | name of the parameter |
Definition at line 326 of file SGObject.h.
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virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
Definition at line 330 of file SGObject.cpp.
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inherited |
Print to stdout a list of observable parameters
Definition at line 878 of file SGObject.cpp.
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virtualinherited |
Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
file | where to load from |
prefix | prefix for members |
Definition at line 403 of file SGObject.cpp.
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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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 460 of file SGObject.cpp.
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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.
ShogunException | will 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 455 of file SGObject.cpp.
Implemented in CQuadraticTimeMMD, and CStreamingMMD.
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protectedinherited |
Observe a parameter value and emit them to observer.
value | Observed parameter's value |
Definition at line 828 of file SGObject.cpp.
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virtualinherited |
Definition at line 296 of file SGObject.cpp.
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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)
alpha | test level alpha. |
Definition at line 92 of file HypothesisTest.cpp.
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inherited |
prints all parameter registered for model selection and their type
Definition at line 512 of file SGObject.cpp.
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virtualinherited |
prints registered parameters out
prefix | prefix for members |
Definition at line 342 of file SGObject.cpp.
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inherited |
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inherited |
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protectedinherited |
Register which params this object can emit.
name | the param name |
type | the param type |
description | a user oriented description |
Definition at line 871 of file SGObject.cpp.
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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.
_tag | name and type information of parameter |
value | value of the parameter |
Definition at line 472 of file SGObject.h.
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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.
name | name of the parameter |
value | value of the parameter along with type information |
Definition at line 485 of file SGObject.h.
Interface for computing the samples under the null-hypothesis.
Implements CTwoSampleTest.
Implemented in CQuadraticTimeMMD, and CStreamingMMD.
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virtualinherited |
Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
Definition at line 348 of file SGObject.cpp.
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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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 470 of file SGObject.cpp.
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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.
ShogunException | will 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 465 of file SGObject.cpp.
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virtual |
Method that selects/learns the kernel based on the defined kernel selection strategy. If no explicit kernel selection strategy was set using set_kernel_selection_strategy() method, then a default strategy is used. Please see EKernelSelectionMethod for the default strategy.
This method is NOT thread safe. It replaces the internel kernel set by set_kernel() method, if there was any. Please DO NOT use this method from multiple threads.
The learned/selected kernel can be obtained from a subsequent get_kernel() call.
This method expects train-test mode to be turned on at the time of invocation. Please see the class documentation of CHypothesisTest.
Reimplemented in CQuadraticTimeMMD.
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inherited |
Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
_tag | name and type information of parameter |
value | value of the parameter |
Definition at line 342 of file SGObject.h.
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inherited |
Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
name | name of the parameter |
value | value of the parameter along with type information |
Definition at line 368 of file SGObject.h.
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inherited |
Definition at line 73 of file SGObject.cpp.
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Definition at line 78 of file SGObject.cpp.
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Definition at line 83 of file SGObject.cpp.
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Definition at line 88 of file SGObject.cpp.
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Definition at line 93 of file SGObject.cpp.
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Definition at line 98 of file SGObject.cpp.
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Definition at line 103 of file SGObject.cpp.
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Definition at line 108 of file SGObject.cpp.
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Definition at line 113 of file SGObject.cpp.
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Definition at line 118 of file SGObject.cpp.
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Definition at line 123 of file SGObject.cpp.
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Definition at line 128 of file SGObject.cpp.
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Definition at line 133 of file SGObject.cpp.
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Definition at line 138 of file SGObject.cpp.
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Definition at line 143 of file SGObject.cpp.
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set generic type to T
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set the parallel object
parallel | parallel object to use |
Definition at line 275 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 317 of file SGObject.cpp.
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virtualinherited |
Method that sets the kernel that is used for performing the two-sample test. It is kept virtual so that sub-classes can perform other initialization tasks that has to be trigger every time a kernel is set/updated.
kernel | The kernel instance. |
Reimplemented in CQuadraticTimeMMD.
Definition at line 66 of file TwoSampleTest.cpp.
void set_kernel_selection_strategy | ( | EKernelSelectionMethod | method, |
bool | weighted = false |
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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.
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. |
void set_kernel_selection_strategy | ( | EKernelSelectionMethod | method, |
index_t | num_runs, | ||
index_t | num_folds, | ||
float64_t | alpha | ||
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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.
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. |
void set_null_approximation_method | ( | ENullApproximationMethod | nmethod | ) |
void set_num_null_samples | ( | index_t | null_samples | ) |
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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.
num_samples_from_p | The CFeatures instance representing the samples from \(\mathbf{P}\). |
Definition at line 75 of file TwoDistributionTest.cpp.
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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.
num_samples_from_q | The CFeatures instance representing the samples from \(\mathbf{Q}\). |
Definition at line 87 of file TwoDistributionTest.cpp.
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virtualinherited |
Method that initializes the samples from \(\mathbf{P}\). This method is kept virtual for the sub-classes to perform additional initialization tasks that have to be performed every time features are set/updated.
samples_from_p | The CFeatures instance representing the samples from \(\mathbf{P}\). |
Reimplemented in CQuadraticTimeMMD.
Definition at line 49 of file TwoDistributionTest.cpp.
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virtualinherited |
Method that initializes the samples from \(\mathbf{Q}\). This method is kept virtual for the sub-classes to perform additional initialization tasks that have to be performed every time features are set/updated.
samples_from_q | The CFeatures instance representing the samples from \(\mathbf{Q}\). |
Reimplemented in CQuadraticTimeMMD.
Definition at line 62 of file TwoDistributionTest.cpp.
void set_statistic_type | ( | EStatisticType | stype | ) |
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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.
on | Whether to enable/disable the training-testing mode |
Definition at line 66 of file HypothesisTest.cpp.
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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.
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.
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virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 226 of file SGObject.cpp.
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inherited |
Subscribe a parameter observer to watch over params
Definition at line 811 of file SGObject.cpp.
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inherited |
decrement reference counter and deallocate object if refcount is zero before or after decrementing it
Definition at line 200 of file SGObject.cpp.
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inherited |
unset generic type
this has to be called in classes specializing a template class
Definition at line 337 of file SGObject.cpp.
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virtualinherited |
Updates the hash of current parameter combination
Definition at line 282 of file SGObject.cpp.
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inherited |
io
Definition at line 600 of file SGObject.h.
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inherited |
parameters wrt which we can compute gradients
Definition at line 615 of file SGObject.h.
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inherited |
Hash of parameter values
Definition at line 618 of file SGObject.h.
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inherited |
model selection parameters
Definition at line 612 of file SGObject.h.
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inherited |
parameters
Definition at line 609 of file SGObject.h.
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inherited |
parallel
Definition at line 603 of file SGObject.h.
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inherited |
version
Definition at line 606 of file SGObject.h.