SHOGUN
v1.1.0
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KMeans clustering, partitions the data into k (a-priori specified) clusters.
It minimizes
where are the cluster centers and
are the index sets of the clusters.
Beware that this algorithm obtains only a local optimum.
Public Member Functions | |
CKMeans () | |
CKMeans (int32_t k, CDistance *d) | |
virtual | ~CKMeans () |
virtual EClassifierType | get_classifier_type () |
virtual bool | load (FILE *srcfile) |
virtual bool | save (FILE *dstfile) |
void | set_k (int32_t p_k) |
int32_t | get_k () |
void | set_max_iter (int32_t iter) |
float64_t | get_max_iter () |
SGVector< float64_t > | get_radiuses () |
SGMatrix< float64_t > | get_cluster_centers () |
int32_t | get_dimensions () |
virtual const char * | get_name () const |
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CDistanceMachine () | |
virtual | ~CDistanceMachine () |
void | set_distance (CDistance *d) |
CDistance * | get_distance () |
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 CLabels * | apply () |
virtual CLabels * | apply (CFeatures *data) |
virtual float64_t | apply (int32_t num) |
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CMachine () | |
virtual | ~CMachine () |
virtual bool | train (CFeatures *data=NULL) |
virtual void | set_labels (CLabels *lab) |
virtual CLabels * | get_labels () |
virtual float64_t | get_label (int32_t i) |
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) |
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CSGObject () | |
CSGObject (const CSGObject &orig) | |
virtual | ~CSGObject () |
virtual bool | is_generic (EPrimitiveType *generic) const |
template<class T > | |
void | set_generic () |
void | unset_generic () |
virtual void | print_serializable (const char *prefix="") |
virtual bool | save_serializable (CSerializableFile *file, const char *prefix="") |
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 () |
SGVector< char * > | get_modelsel_names () |
char * | get_modsel_param_descr (const char *param_name) |
index_t | get_modsel_param_index (const char *param_name) |
Protected Member Functions | |
void | clustknb (bool use_old_mus, float64_t *mus_start) |
virtual bool | train_machine (CFeatures *data=NULL) |
virtual void | store_model_features () |
Protected Attributes | |
int32_t | max_iter |
maximum number of iterations | |
int32_t | k |
the k parameter in KMeans | |
int32_t | dimensions |
number of dimensions | |
SGVector< float64_t > | R |
radi of the clusters (size k) | |
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CDistance * | distance |
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float64_t | max_train_time |
CLabels * | labels |
ESolverType | solver_type |
bool | m_store_model_features |
Additional Inherited Members | |
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SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
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static void * | run_distance_thread_lhs (void *p) |
static void * | run_distance_thread_rhs (void *p) |
CKMeans | ( | ) |
default constructor
Definition at line 29 of file KMeans.cpp.
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virtual |
Definition at line 43 of file KMeans.cpp.
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protected |
clustknb
use_old_mus | if old mus shall be used |
mus_start | mus start |
replace rhs feature vectors
set rhs to mus_start
update rhs
Definition at line 179 of file KMeans.cpp.
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virtual |
get centers
Definition at line 115 of file KMeans.cpp.
int32_t get_dimensions | ( | ) |
int32_t get_k | ( | ) |
float64_t get_max_iter | ( | ) |
get maximum number of iterations
Definition at line 105 of file KMeans.cpp.
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virtual |
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virtual |
load distance machine from file
srcfile | file to load from |
Reimplemented from CMachine.
Definition at line 73 of file KMeans.cpp.
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virtual |
save distance machine to file
dstfile | file to save to |
Reimplemented from CMachine.
Definition at line 80 of file KMeans.cpp.
void set_k | ( | int32_t | p_k | ) |
void set_max_iter | ( | int32_t | iter | ) |
set maximum number of iterations
iter | the new maximum |
Definition at line 99 of file KMeans.cpp.
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protectedvirtual |
Ensures cluster centers are in lhs of underlying distance
Reimplemented from CDistanceMachine.
Definition at line 464 of file KMeans.cpp.
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protectedvirtual |
train k-means
data | training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data) |
Reimplemented from CMachine.
Definition at line 48 of file KMeans.cpp.