pyclustering.nnet.hysteresis.hysteresis_network Class Reference

Hysteresis oscillatory network that uses relaxation oscillators that are represented by objective hysteresis neurons whose output in range [-1, +1]. More...

Inheritance diagram for pyclustering.nnet.hysteresis.hysteresis_network:

Collaboration diagram for pyclustering.nnet.hysteresis.hysteresis_network:

Public Member Functions
def	outputs (self)
	Returns current outputs of neurons. More...
def	outputs (self, values)
	Sets outputs of neurons.
def	states (self)
	Return current states of neurons. More...
def	states (self, values)
	Set current states of neurons.
def	__init__ (self, num_osc, own_weight=-4, neigh_weight=-1, type_conn=conn_type.ALL_TO_ALL, type_conn_represent=conn_represent.MATRIX)
	Constructor of hysteresis oscillatory network. More...
def	simulate (self, steps, time, solution=solve_type.RK4, collect_dynamic=True)
	Performs static simulation of hysteresis oscillatory network. More...
def	simulate_static (self, steps, time, solution=solve_type.RK4, collect_dynamic=False)
	Performs static simulation of hysteresis oscillatory network. More...
Public Member Functions inherited from pyclustering.nnet.network
def	height (self)
	Height of the network grid (that is defined by amout of oscillators in each column), this value is zero in case of non-grid structure. More...
def	width (self)
	Width of the network grid, this value is zero in case of non-grid structure. More...
def	structure (self)
	Type of network structure that is used for connecting oscillators.
def	__len__ (self)
	Returns size of the network that is defined by amount of oscillators.
def	has_connection (self, i, j)
	Returns True if there is connection between i and j oscillators and False - if connection doesn't exist. More...
def	set_connection (self, i, j)
	Couples two specified oscillators in the network with dynamic connections. More...
def	get_neighbors (self, index)
	Finds neighbors of the oscillator with specified index. More...

Detailed Description

Hysteresis oscillatory network that uses relaxation oscillators that are represented by objective hysteresis neurons whose output in range [-1, +1].

Examples:

# create hysteresis oscillatory network with 5 oscillators.
network = hysteresis_network(5);
 
# set initial states (from range [-1, +1]).
network.states = [1 0 1 0 1];
 
# set initial outputs.
network.outputs = [1 1 1 1 1];
 
# perform simulation of the network during 1000 steps in 10 time units.
output_dynamic = network.simulate(1000, 10);
 
# show output dynamic of the network.
hysteresis_visualizer.show_output_dynamic(output_dynamic);
 
# obtain synchronous ensembles.
ensembles = output_dynamic.allocate_sync_ensembles(tolerance = 0.5, threshold_steps = 5);
print(ensembles);

Definition at line 137 of file hysteresis.py.

Constructor & Destructor Documentation

__init__()

def pyclustering.nnet.hysteresis.hysteresis_network.__init__	(		self,
			num_osc,
			own_weight = -4,
			neigh_weight = -1,
			type_conn = conn_type.ALL_TO_ALL,
			type_conn_represent = conn_represent.MATRIX
	)

Constructor of hysteresis oscillatory network.

Parameters

[in]	num_osc	(uint): Number of oscillators in the network.
[in]	own_weight	(double): Weight of connection from oscillator to itself - own weight.
[in]	neigh_weight	(double): Weight of connection between oscillators.
[in]	type_conn	(conn_type): Type of connection between oscillators in the network.
[in]	type_conn_represent	(conn_represent): Internal representation of connection in the network: matrix or list.

Reimplemented from pyclustering.nnet.network.

Definition at line 207 of file hysteresis.py.

Member Function Documentation

outputs()

def pyclustering.nnet.hysteresis.hysteresis_network.outputs

(

self

)

Returns current outputs of neurons.

Returns

(list) Current outputs of neurons.

Definition at line 166 of file hysteresis.py.

Referenced by pyclustering.nnet.hysteresis.hysteresis_network.outputs().

simulate()

def pyclustering.nnet.hysteresis.hysteresis_network.simulate	(		self,
			steps,
			time,
			solution = solve_type.RK4,
			collect_dynamic = True
	)

Performs static simulation of hysteresis oscillatory network.

Parameters

[in]	steps	(uint): Number steps of simulations during simulation.
[in]	time	(double): Time of simulation.
[in]	solution	(solve_type): Type of solution (solving).
[in]	collect_dynamic	(bool): If True - returns whole dynamic of oscillatory network, otherwise returns only last values of dynamics.

Returns

(hysteresis_dynamic) Dynamic of oscillatory network. If argument 'collect_dynamic' = True, than return dynamic for the whole simulation time, otherwise returns only last values (last step of simulation) of dynamic.

Definition at line 267 of file hysteresis.py.

Referenced by pyclustering.gcolor.hysteresis.hysteresisgcolor.process().

simulate_static()

def pyclustering.nnet.hysteresis.hysteresis_network.simulate_static	(		self,
			steps,
			time,
			solution = solve_type.RK4,
			collect_dynamic = False
	)

Performs static simulation of hysteresis oscillatory network.

Parameters

[in]	steps	(uint): Number steps of simulations during simulation.
[in]	time	(double): Time of simulation.
[in]	solution	(solve_type): Type of solution (solving).
[in]	collect_dynamic	(bool): If True - returns whole dynamic of oscillatory network, otherwise returns only last values of dynamics.

Returns

(hysteresis_dynamic) Dynamic of oscillatory network. If argument 'collect_dynamic' = True, than return dynamic for the whole simulation time, otherwise returns only last values (last step of simulation) of dynamic.

Definition at line 283 of file hysteresis.py.

Referenced by pyclustering.nnet.hysteresis.hysteresis_network.simulate(), pyclustering.nnet.syncpr.syncpr.simulate(), and pyclustering.nnet.sync.sync_network.simulate().

states()

def pyclustering.nnet.hysteresis.hysteresis_network.states

(

self

)

Return current states of neurons.

Returns

(list) States of neurons.

Definition at line 187 of file hysteresis.py.

Referenced by pyclustering.nnet.hysteresis.hysteresis_network.states().

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

• pyclustering/nnet/hysteresis.py