Class represents clustering algorithm HSyncNet. More...
Public Member Functions | |
def | __init__ (self, source_data, number_clusters, osc_initial_phases=initial_type.RANDOM_GAUSSIAN, initial_neighbors=3, increase_persent=0.15, ccore=True) |
Costructor of the oscillatory network hSyncNet for cluster analysis. More... | |
def | __del__ (self) |
Destructor of oscillatory network HSyncNet. | |
def | process (self, order=0.998, solution=solve_type.FAST, collect_dynamic=False) |
Performs clustering of input data set in line with input parameters. More... | |
Public Member Functions inherited from pyclustering.cluster.syncnet.syncnet | |
def | __init__ (self, sample, radius, conn_repr=conn_represent.MATRIX, initial_phases=initial_type.RANDOM_GAUSSIAN, enable_conn_weight=False, ccore=True) |
Contructor of the oscillatory network SYNC for cluster analysis. More... | |
def | __del__ (self) |
Destructor of oscillatory network is based on Kuramoto model. | |
def | process (self, order=0.998, solution=solve_type.FAST, collect_dynamic=True) |
Peforms cluster analysis using simulation of the oscillatory network. More... | |
def | show_network (self) |
Shows connections in the network. More... | |
Public Member Functions inherited from pyclustering.nnet.sync.sync_network | |
def | __init__ (self, num_osc, weight=1, frequency=0, type_conn=conn_type.ALL_TO_ALL, representation=conn_represent.MATRIX, initial_phases=initial_type.RANDOM_GAUSSIAN, ccore=True) |
Constructor of oscillatory network is based on Kuramoto model. More... | |
def | __del__ (self) |
Destructor of oscillatory network is based on Kuramoto model. | |
def | sync_order (self) |
Calculates current level of global synchorization (order parameter) in the network. More... | |
def | sync_local_order (self) |
Calculates current level of local (partial) synchronization in the network. More... | |
def | simulate (self, steps, time, solution=solve_type.FAST, collect_dynamic=True) |
Performs static simulation of Sync oscillatory network. More... | |
def | simulate_dynamic (self, order=0.998, solution=solve_type.FAST, collect_dynamic=False, step=0.1, int_step=0.01, threshold_changes=0.0000001) |
Performs dynamic simulation of the network until stop condition is not reached. More... | |
def | simulate_static (self, steps, time, solution=solve_type.FAST, collect_dynamic=False) |
Performs static simulation of oscillatory network. More... | |
def | get_neighbors (self, index) |
Finds neighbors of the oscillator with specified index. More... | |
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... | |
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 | __init__ (self, num_osc, type_conn=conn_type.ALL_TO_ALL, conn_repr=conn_represent.MATRIX, height=None, width=None) |
Constructor of the network. More... | |
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... | |
Class represents clustering algorithm HSyncNet.
HSyncNet is bio-inspired algorithm that is based on oscillatory network that uses modified Kuramoto model.
CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
Example:
Definition at line 39 of file hsyncnet.py.
def pyclustering.cluster.hsyncnet.hsyncnet.__init__ | ( | self, | |
source_data, | |||
number_clusters, | |||
osc_initial_phases = initial_type.RANDOM_GAUSSIAN , |
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initial_neighbors = 3 , |
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increase_persent = 0.15 , |
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ccore = True |
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) |
Costructor of the oscillatory network hSyncNet for cluster analysis.
[in] | source_data | (list): Input data set defines structure of the network. |
[in] | number_clusters | (uint): Number of clusters that should be allocated. |
[in] | osc_initial_phases | (initial_type): Type of initialization of initial values of phases of oscillators. |
[in] | initial_neighbors | (uint): Defines initial radius connectivity by calculation average distance to connect specify number of oscillators. |
[in] | increase_persent | (double): Percent of increasing of radius connectivity on each step (input values in range (0.0; 1.0) correspond to (0%; 100%)). |
[in] | ccore | (bool): If True than DLL CCORE (C++ solution) will be used for solving. |
Definition at line 64 of file hsyncnet.py.
def pyclustering.cluster.hsyncnet.hsyncnet.process | ( | self, | |
order = 0.998 , |
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solution = solve_type.FAST , |
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collect_dynamic = False |
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) |
Performs clustering of input data set in line with input parameters.
[in] | order | (double): Level of local synchronization between oscillator that defines end of synchronization process, range [0..1]. |
[in] | solution | (solve_type) Type of solving differential equation. |
[in] | collect_dynamic | (bool): If True - returns whole history of process synchronization otherwise - only final state (when process of clustering is over). |
Definition at line 103 of file hsyncnet.py.