Container of output dynamic of the chaotic neural network where states of each neuron during simulation are stored. More...

Public Member Functions
def	__init__ (self, output=None, time=None)
	Costructor of the chaotic neural network output dynamic. More...

def	__len__ (self)
	(uint) Returns amount of simulation steps that are stored.

def	allocate_observation_matrix (self)
	Allocates observation matrix in line with output dynamic of the network. More...

def	allocate_sync_ensembles (self, steps)
	Allocate clusters in line with ensembles of synchronous neurons where each synchronous ensemble corresponds to only one cluster. More...

Public Attributes
	output
	Output value of each neuron on each iteration. More...

	time
	Sequence of simulation steps of the network. More...

Detailed Description

Container of output dynamic of the chaotic neural network where states of each neuron during simulation are stored.

See also: cnn_network

Definition at line 63 of file cnn.py.

Constructor & Destructor Documentation

◆ init()

def pyclustering.nnet.cnn.cnn_dynamic.__init__	(	self,
		output = `None`,
		time = `None`
	)

Costructor of the chaotic neural network output dynamic.

Parameters

[in]	output	(list): Dynamic of oscillators on each step of simulation.
[in]	time	(list): Simulation time.

Definition at line 71 of file cnn.py.

Member Function Documentation

◆ allocate_observation_matrix()

def pyclustering.nnet.cnn.cnn_dynamic.allocate_observation_matrix ( self )

Allocates observation matrix in line with output dynamic of the network.

Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.

Returns: (list) Observation matrix of the network dynamic.

Definition at line 95 of file cnn.py.

◆ allocate_sync_ensembles()

def pyclustering.nnet.cnn.cnn_dynamic.allocate_sync_ensembles	(	self,
		steps
	)

Allocate clusters in line with ensembles of synchronous neurons where each synchronous ensemble corresponds to only one cluster.

Parameters

[in] steps (double): Amount of steps from the end that is used for analysis. During specified period chaotic neural network should have stable output otherwise inccorect results are allocated.

Returns: (list) Grours (lists) of indexes of synchronous oscillators. For example [ [index_osc1, index_osc3], [index_osc2], [index_osc4, index_osc5] ].

Definition at line 136 of file cnn.py.

Referenced by pyclustering.gcolor.hysteresis.hysteresis_analyser.allocate_clusters(), pyclustering.cluster.syncnet.syncnet_analyser.allocate_clusters(), and pyclustering.gcolor.sync.syncgcolor_analyser.allocate_color_clusters().

Member Data Documentation

◆ output

pyclustering.nnet.cnn.cnn_dynamic.output

Output value of each neuron on each iteration.

Definition at line 81 of file cnn.py.

Referenced by pyclustering.nnet.sync.sync_dynamic.__getitem__(), pyclustering.nnet.cnn.cnn_dynamic.__len__(), pyclustering.nnet.cnn.cnn_dynamic.allocate_observation_matrix(), pyclustering.nnet.sync.sync_dynamic.allocate_phase_matrix(), pyclustering.nnet.cnn.cnn_dynamic.allocate_sync_ensembles(), pyclustering.nnet.sync.sync_dynamic.calculate_local_order_parameter(), and pyclustering.nnet.sync.sync_dynamic.calculate_order_parameter().

◆ time

pyclustering.nnet.cnn.cnn_dynamic.time

Sequence of simulation steps of the network.

Definition at line 84 of file cnn.py.

Referenced by pyclustering.nnet.sync.sync_dynamic.__getitem__().

The documentation for this class was generated from the following file:

pyclustering/nnet/cnn.py

Public Member Functions

Public Attributes