pyclustering.nnet.cnn.cnn_network Class Reference

Chaotic neural network based on system of logistic map where clustering phenomenon can be observed. More...

Public Member Functions
def	__init__ (self, num_osc, conn_type=type_conn.ALL_TO_ALL, amount_neighbors=3)
	Constructor of chaotic neural network. More...
def	__len__ (self)
	Returns size of the chaotic neural network that is defined by amount of neurons.
def	simulate (self, steps, stimulus)
	Simulates chaotic neural network with extrnal stimulus during specified steps. More...
def	show_network (self)
	Shows structure of the network: neurons and connections between them.

Detailed Description

Chaotic neural network based on system of logistic map where clustering phenomenon can be observed.

Example:

# load stimulus from file
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);

# create chaotic neural network, amount of neurons should be equal to amout of stimulus
network_instance = cnn_network(len(stimulus));

# simulate it during 100 steps
output_dynamic = network_instance.simulate(steps, stimulus);

# display output dynamic of the network
cnn_visualizer.show_output_dynamic(output_dynamic);

# dysplay dynamic matrix and observation matrix to show clustering
# phenomenon.
cnn_visualizer.show_dynamic_matrix(output_dynamic);
cnn_visualizer.show_observation_matrix(output_dynamic);

Definition at line 238 of file cnn.py.

Constructor & Destructor Documentation

__init__()

def pyclustering.nnet.cnn.cnn_network.__init__	(		self,
			num_osc,
			conn_type = type_conn.ALL_TO_ALL,
			amount_neighbors = 3
	)

Constructor of chaotic neural network.

Parameters

[in]	num_osc	(uint): Amount of neurons in the chaotic neural network.
[in]	conn_type	(type_conn): CNN type connection for the network.
[in]	amount_neighbors	(uint): k-nearest neighbors for calculation scaling constant of weights.

Definition at line 264 of file cnn.py.

Member Function Documentation

simulate()

def pyclustering.nnet.cnn.cnn_network.simulate	(		self,
			steps,
			stimulus
	)

Simulates chaotic neural network with extrnal stimulus during specified steps.

Stimulus are considered as a coordinates of neurons and in line with that weights are initialized.

Parameters

[in]	steps	(uint): Amount of steps for simulation.
[in]	stimulus	(list): Stimulus that are used for simulation.

Returns

(cnn_dynamic) Output dynamic of the chaotic neural network.

Definition at line 296 of file cnn.py.

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

• pyclustering/nnet/cnn.py