Enumeration of initialization types for SOM.
More...

int  random = 0 
 Weights are randomly distributed using Gaussian distribution (0, 1). More...


int  random_centroid = 1 
 Weights are randomly distributed using Gaussian distribution (input data centroid, 1). More...


int  random_surface = 2 
 Weights are randomly distrbiuted using Gaussian distribution (input data centroid, surface of input data). More...


int  uniform_grid = 3 
 Weights are distributed as a uniform grid that covers whole surface of the input data. More...


Enumeration of initialization types for SOM.
 See also
 som
Definition at line 69 of file som.py.
◆ random
int pyclustering.nnet.som.type_init.random = 0 

static 
Weights are randomly distributed using Gaussian distribution (0, 1).
Definition at line 78 of file som.py.
◆ random_centroid
int pyclustering.nnet.som.type_init.random_centroid = 1 

static 
Weights are randomly distributed using Gaussian distribution (input data centroid, 1).
Definition at line 81 of file som.py.
◆ random_surface
int pyclustering.nnet.som.type_init.random_surface = 2 

static 
Weights are randomly distrbiuted using Gaussian distribution (input data centroid, surface of input data).
Definition at line 84 of file som.py.
◆ uniform_grid
int pyclustering.nnet.som.type_init.uniform_grid = 3 

static 
Weights are distributed as a uniform grid that covers whole surface of the input data.
Definition at line 87 of file som.py.
The documentation for this class was generated from the following file: