pyclustering
0.10.1
pyclustring is a Python, C++ data mining library.
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Represents output dynamic of hysteresis oscillatory network. More...
Public Member Functions | |
def | output (self) |
(list) Returns outputs of oscillator during simulation. | |
def | time (self) |
(list) Returns sampling times when dynamic is measured during simulation. | |
def | __init__ (self, amplitudes, time) |
Constructor of hysteresis neural network dynamic. More... | |
def | __len__ (self) |
(uint) Returns number of simulation steps that are stored in dynamic. | |
def | allocate_sync_ensembles (self, tolerance=0.1, threshold_steps=1) |
Allocate clusters in line with ensembles of synchronous oscillators where each synchronous ensemble corresponds to only one cluster. More... | |
Represents output dynamic of hysteresis oscillatory network.
Definition at line 21 of file hysteresis.py.
def pyclustering.nnet.hysteresis.hysteresis_dynamic.__init__ | ( | self, | |
amplitudes, | |||
time | |||
) |
Constructor of hysteresis neural network dynamic.
[in] | amplitudes | (list): Dynamic (amplitudes) of oscillators on each step of simulation. |
[in] | time | (list): Simulation time (timestamps of simulation steps) when amplitudes are stored. |
Reimplemented in pyclustering.gcolor.hysteresis.hysteresis_analyser.
Definition at line 46 of file hysteresis.py.
def pyclustering.nnet.hysteresis.hysteresis_dynamic.allocate_sync_ensembles | ( | self, | |
tolerance = 0.1 , |
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threshold_steps = 1 |
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) |
Allocate clusters in line with ensembles of synchronous oscillators where each synchronous ensemble corresponds to only one cluster.
[in] | tolerance | (double): Maximum error for allocation of synchronous ensemble oscillators. |
[in] | threshold_steps | (uint): Number of steps from the end of simulation that should be analysed for ensemble allocation. If amout of simulation steps has been less than threshold steps than amount of steps will be reduced to amout of simulation steps. |
Definition at line 72 of file hysteresis.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().