26 class CDistanceMachine;
96 virtual bool load(FILE* srcfile);
103 virtual bool save(FILE* dstfile);
109 void set_k(int32_t p_k);
172 virtual const char*
get_name()
const {
return "KMeans"; }
232 virtual bool train_machine(
CFeatures* data=NULL);
235 virtual void store_model_features();
237 virtual bool train_require_labels()
const {
return false; }
243 SGMatrix<float64_t> kmeanspp();
250 void set_random_centers(SGVector<float64_t> weights_set, SGVector<int32_t> ClList, int32_t XSize);
252 SGVector<int32_t> ClList, int32_t XSize);
253 void compute_cluster_variances();
269 SGVector<float64_t> R;
272 SGMatrix<float64_t> mus_initial;
284 SGMatrix<float64_t> mus;
int32_t get_mbKMeans_batch_size() const
virtual const char * get_name() const
int32_t get_mbKMeans_iter() const
virtual bool save(FILE *dstfile)
void set_mbKMeans_params(int32_t b, int32_t t)
Class Distance, a base class for all the distances used in the Shogun toolbox.
void set_mbKMeans_batch_size(int32_t b)
void set_mbKMeans_iter(int32_t t)
void set_use_kmeanspp(bool kmpp)
A generic DistanceMachine interface.
bool get_use_kmeanspp() const
SGVector< float64_t > get_radiuses()
KMeans clustering, partitions the data into k (a-priori specified) clusters.
#define MACHINE_PROBLEM_TYPE(PT)
virtual bool load(FILE *srcfile)
void set_max_iter(int32_t iter)
void set_fixed_centers(bool fixed)
virtual void set_initial_centers(SGMatrix< float64_t > centers)
virtual EMachineType get_classifier_type()
The class Features is the base class of all feature objects.
void set_train_method(EKMeansMethod f)
EKMeansMethod get_train_method() const
SGMatrix< float64_t > get_cluster_centers()