class_docstr: !
  @brief Oscillatory network based on Kuramoto model with negative and positive connections for graph coloring problem.
class_docstr_tok: ["!","@","brief","Oscillatory","network","based","on","Kuramoto","model","with","negative","and","positive","connections","for","graph","coloring","problem","."]
docstr: !
  @brief Constructor of the oscillatory network syncgcolor for graph coloring problem.
  
  @param[in] graph_matrix (list): Graph represented by matrix.
  @param[in] positive_weight (double): Value of weight of positive connections.
  @param[in] negative_weight (double): Value of weight of negative connections.
  @param[in] reduction (bool): Inverse degree of the processed graph.
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code: def __init__(
      self,
      graph_matrix,
      positive_weight,
      negative_weight,
      reduction=None,
  ):
      """!
      @brief Constructor of the oscillatory network syncgcolor for graph coloring problem.
  
      @param[in] graph_matrix (list): Graph represented by matrix.
      @param[in] positive_weight (double): Value of weight of positive connections.
      @param[in] negative_weight (double): Value of weight of negative connections.
      @param[in] reduction (bool): Inverse degree of the processed graph.
  
      """
      number_oscillators = len(graph_matrix)
      super().__init__(
          number_oscillators, type_conn=conn_type.DYNAMIC, ccore=False
      )
  
      if reduction == None:
          self._reduction = self._num_osc
      else:
          self._reduction = reduction
  
      self._positive_weight = positive_weight
      self._negative_weight = negative_weight
  
      self._create_connections(graph_matrix)
  
file_path: pyclustering/pyclustering/gcolor/sync.py
imports: from pyclustering.nnet import *
  from pyclustering.nnet.sync import sync_network
  from pyclustering.nnet.sync import sync_dynamic
label_desc: Use image node_id 1 to create a new syncgcolor object from inherited base classes: sync_network with example: obj = syncgcolor(graph_matrix, positive_weight, negative_weight, reduction)