SHOGUN
v3.2.0
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Class KNN, an implementation of the standard k-nearest neigbor classifier.
An example is classified to belong to the class of which the majority of the k closest examples belong to. Formally, kNN is described as
\[ label for x = \arg \max_{l} \sum_{i=1}^{k} [label of i-th example = l] \]
This class provides a capability to do weighted classfication using:
\[ label for x = \arg \max_{l} \sum_{i=1}^{k} [label of i-th example = l] q^{i}, \]
where \(|q|<1\).
To avoid ties, k should be an odd number. To define how close examples are k-NN requires a CDistance object to work with (e.g., CEuclideanDistance ).
Note that k-NN has zero training time but classification times increase dramatically with the number of examples. Also note that k-NN is capable of multi-class-classification. And finally, in case of k=1 classification will take less time with an special optimization provided.
Public Member Functions | |
CKNN () | |
CKNN (int32_t k, CDistance *d, CLabels *trainlab) | |
virtual | ~CKNN () |
virtual EMachineType | get_classifier_type () |
SGMatrix< index_t > | nearest_neighbors () |
virtual CMulticlassLabels * | apply_multiclass (CFeatures *data=NULL) |
virtual float64_t | apply_one (int32_t vec_idx) |
get output for example "vec_idx" More... | |
SGMatrix< int32_t > | classify_for_multiple_k () |
virtual bool | load (FILE *srcfile) |
virtual bool | save (FILE *dstfile) |
void | set_k (int32_t k) |
int32_t | get_k () |
void | set_q (float64_t q) |
float64_t | get_q () |
void | set_use_covertree (bool use_covertree) |
bool | get_use_covertree () const |
virtual const char * | get_name () const |
void | set_distance (CDistance *d) |
CDistance * | get_distance () const |
void | distances_lhs (float64_t *result, int32_t idx_a1, int32_t idx_a2, int32_t idx_b) |
void | distances_rhs (float64_t *result, int32_t idx_b1, int32_t idx_b2, int32_t idx_a) |
virtual bool | train (CFeatures *data=NULL) |
virtual CLabels * | apply (CFeatures *data=NULL) |
virtual CBinaryLabels * | apply_binary (CFeatures *data=NULL) |
virtual CRegressionLabels * | apply_regression (CFeatures *data=NULL) |
virtual CStructuredLabels * | apply_structured (CFeatures *data=NULL) |
virtual CLatentLabels * | apply_latent (CFeatures *data=NULL) |
virtual void | set_labels (CLabels *lab) |
virtual CLabels * | get_labels () |
void | set_max_train_time (float64_t t) |
float64_t | get_max_train_time () |
void | set_solver_type (ESolverType st) |
ESolverType | get_solver_type () |
virtual void | set_store_model_features (bool store_model) |
virtual bool | train_locked (SGVector< index_t > indices) |
virtual CLabels * | apply_locked (SGVector< index_t > indices) |
virtual CBinaryLabels * | apply_locked_binary (SGVector< index_t > indices) |
virtual CRegressionLabels * | apply_locked_regression (SGVector< index_t > indices) |
virtual CMulticlassLabels * | apply_locked_multiclass (SGVector< index_t > indices) |
virtual CStructuredLabels * | apply_locked_structured (SGVector< index_t > indices) |
virtual CLatentLabels * | apply_locked_latent (SGVector< index_t > indices) |
virtual void | data_lock (CLabels *labs, CFeatures *features) |
virtual void | post_lock (CLabels *labs, CFeatures *features) |
virtual void | data_unlock () |
virtual bool | supports_locking () const |
bool | is_data_locked () const |
virtual EProblemType | get_machine_problem_type () const |
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="", int32_t param_version=Version::get_version_parameter()) |
virtual bool | load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter()) |
DynArray< TParameter * > * | load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="") |
DynArray< TParameter * > * | load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="") |
void | map_parameters (DynArray< TParameter *> *param_base, int32_t &base_version, DynArray< const SGParamInfo *> *target_param_infos) |
void | set_global_io (SGIO *io) |
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) |
virtual bool | update_parameter_hash () |
virtual bool | equals (CSGObject *other, float64_t accuracy=0.0) |
virtual CSGObject * | clone () |
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
ParameterMap * | m_parameter_map |
uint32_t | m_hash |
Protected Member Functions | |
virtual void | store_model_features () |
virtual CMulticlassLabels * | classify_NN () |
void | init_distance (CFeatures *data) |
virtual bool | train_machine (CFeatures *data=NULL) |
virtual bool | is_label_valid (CLabels *lab) const |
virtual bool | train_require_labels () const |
virtual TParameter * | migrate (DynArray< TParameter *> *param_base, const SGParamInfo *target) |
virtual void | one_to_one_migration_prepare (DynArray< TParameter *> *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL) |
virtual void | load_serializable_pre () throw (ShogunException) |
virtual void | load_serializable_post () throw (ShogunException) |
virtual void | save_serializable_pre () throw (ShogunException) |
virtual void | save_serializable_post () throw (ShogunException) |
Static Protected Member Functions | |
static void * | run_distance_thread_lhs (void *p) |
static void * | run_distance_thread_rhs (void *p) |
Protected Attributes | |
int32_t | m_k |
the k parameter in KNN More... | |
float64_t | m_q |
parameter q of rank weighting More... | |
bool | m_use_covertree |
parameter to enable cover tree support More... | |
int32_t | m_num_classes |
number of classes (i.e. number of values labels can take) More... | |
int32_t | m_min_label |
smallest label, i.e. -1 More... | |
SGVector< int32_t > | m_train_labels |
CDistance * | distance |
float64_t | m_max_train_time |
CLabels * | m_labels |
ESolverType | m_solver_type |
bool | m_store_model_features |
bool | m_data_locked |
apply machine to data if data is not specified apply to the current features
data | (test)data to be classified |
Definition at line 162 of file Machine.cpp.
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apply machine to data in means of binary classification problem
Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CLinearMachine, CDomainAdaptationSVMLinear, CPluginEstimate, CGaussianProcessBinaryClassification, and CBaggingMachine.
Definition at line 218 of file Machine.cpp.
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virtualinherited |
apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 242 of file Machine.cpp.
Applies a locked machine on a set of indices. Error if machine is not locked
indices | index vector (of locked features) that is predicted |
Definition at line 197 of file Machine.cpp.
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applies a locked machine on a set of indices for binary problems
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
Definition at line 248 of file Machine.cpp.
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applies a locked machine on a set of indices for latent problems
Definition at line 276 of file Machine.cpp.
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applies a locked machine on a set of indices for multiclass problems
Definition at line 262 of file Machine.cpp.
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applies a locked machine on a set of indices for regression problems
Reimplemented in CKernelMachine.
Definition at line 255 of file Machine.cpp.
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applies a locked machine on a set of indices for structured problems
Definition at line 269 of file Machine.cpp.
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classify objects
data | (test)data to be classified |
Reimplemented from CDistanceMachine.
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get output for example "vec_idx"
Reimplemented from CDistanceMachine.
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apply machine to data in means of regression problem
Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CGaussianProcessRegression, and CBaggingMachine.
Definition at line 224 of file Machine.cpp.
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apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 236 of file Machine.cpp.
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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 1156 of file SGObject.cpp.
SGMatrix< int32_t > classify_for_multiple_k | ( | ) |
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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.
Definition at line 1273 of file SGObject.cpp.
Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called
Only possible if supports_locking() returns true
labs | labels used for locking |
features | features used for locking |
Reimplemented in CKernelMachine.
Definition at line 122 of file Machine.cpp.
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Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 153 of file Machine.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 126 of file SGObject.h.
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get distance functions for lhs feature vectors going from a1 to a2 and rhs feature vector b
result | array of distance values |
idx_a1 | first feature vector a1 at idx_a1 |
idx_a2 | last feature vector a2 at idx_a2 |
idx_b | feature vector b at idx_b |
Definition at line 51 of file DistanceMachine.cpp.
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get distance functions for rhs feature vectors going from b1 to b2 and lhs feature vector a
result | array of distance values |
idx_b1 | first feature vector a1 at idx_b1 |
idx_b2 | last feature vector a2 at idx_b2 |
idx_a | feature vector a at idx_a |
Definition at line 113 of file DistanceMachine.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) |
Definition at line 1177 of file SGObject.cpp.
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returns type of problem machine solves
Reimplemented in CBaseMulticlassMachine.
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Definition at line 1060 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 1084 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 1097 of file SGObject.cpp.
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bool get_use_covertree | ( | ) | const |
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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 228 of file SGObject.cpp.
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check whether the labels is valid.
Subclasses can override this to implement their check of label types.
lab | the labels being checked, guaranteed to be non-NULL |
Reimplemented in CGaussianProcessBinaryClassification, CGaussianProcessRegression, and CBaseMulticlassMachine.
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maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)
file_version | parameter version of the file |
current_version | version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) |
file | file to load from |
prefix | prefix for members |
Definition at line 633 of file SGObject.cpp.
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loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned
param_info | information of parameter |
file_version | parameter version of the file, must be <= provided parameter version |
file | file to load from |
prefix | prefix for members |
Definition at line 474 of file SGObject.cpp.
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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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 305 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 occurres. |
Reimplemented in CWeightedDegreePositionStringKernel, CKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 989 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 984 of file SGObject.cpp.
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Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match
param_base | set of TParameter instances that are mapped to the provided target parameter infos |
base_version | version of the parameter base |
target_param_infos | set of SGParamInfo instances that specify the target parameter base |
Definition at line 671 of file SGObject.cpp.
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creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.
If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
Definition at line 878 of file SGObject.cpp.
for each example in the rhs features of the distance member, find the m_k nearest neighbors among the vectors in the lhs features
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This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
replacement | (used as output) here the TParameter instance which is returned by migration is created into |
to_migrate | the only source that is used for migration |
old_name | with this parameter, a name change may be specified |
Definition at line 818 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 1036 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 240 of file SGObject.cpp.
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thread function for computing distance values
p | thread parameter |
Definition at line 175 of file DistanceMachine.cpp.
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staticprotectedinherited |
thread function for computing distance values
p | thread parameter |
Definition at line 191 of file DistanceMachine.cpp.
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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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 246 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel.
Definition at line 999 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 994 of file SGObject.cpp.
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Definition at line 41 of file SGObject.cpp.
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Definition at line 46 of file SGObject.cpp.
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Definition at line 51 of file SGObject.cpp.
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Definition at line 56 of file SGObject.cpp.
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Definition at line 61 of file SGObject.cpp.
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Definition at line 66 of file SGObject.cpp.
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Definition at line 71 of file SGObject.cpp.
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Definition at line 76 of file SGObject.cpp.
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Definition at line 81 of file SGObject.cpp.
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Definition at line 86 of file SGObject.cpp.
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Definition at line 91 of file SGObject.cpp.
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Definition at line 96 of file SGObject.cpp.
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Definition at line 101 of file SGObject.cpp.
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Definition at line 106 of file SGObject.cpp.
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Definition at line 111 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 180 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 215 of file SGObject.cpp.
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set labels
lab | labels |
Reimplemented in CGaussianProcessMachine, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.
Definition at line 75 of file Machine.cpp.
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set maximum training time
t | maximimum training time |
Definition at line 92 of file Machine.cpp.
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Setter for store-model-features-after-training flag
store_model | whether model should be stored after training |
Definition at line 117 of file Machine.cpp.
void set_use_covertree | ( | bool | use_covertree | ) |
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A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 117 of file SGObject.h.
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Stores feature data of underlying model.
Replaces lhs and rhs of underlying distance with copies of themselves
Reimplemented from CDistanceMachine.
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Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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train machine
data | training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training. |
Reimplemented in CRelaxedTree, CSGDQN, and COnlineSVMSGD.
Definition at line 49 of file Machine.cpp.
Trains a locked machine on a set of indices. Error if machine is not locked
NOT IMPLEMENTED
indices | index vector (of locked features) that is used for training |
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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returns whether machine require labels for training
Reimplemented in COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 235 of file SGObject.cpp.
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Updates the hash of current parameter combination.
Definition at line 187 of file SGObject.cpp.
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the distance
Definition at line 133 of file DistanceMachine.h.
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io
Definition at line 473 of file SGObject.h.
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parameters wrt which we can compute gradients
Definition at line 488 of file SGObject.h.
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Hash of parameter values
Definition at line 494 of file SGObject.h.
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model selection parameters
Definition at line 485 of file SGObject.h.
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map for different parameter versions
Definition at line 491 of file SGObject.h.
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parameters
Definition at line 482 of file SGObject.h.
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parallel
Definition at line 476 of file SGObject.h.
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version
Definition at line 479 of file SGObject.h.