parameters: (self, X, weight=None)
return_statement: return super().clusterize(*get_data(x), int(x.shape[1]), weight)
docstring: Performs clustering of the given data.
  
          :param X: the input sample. Internally, it will be converted
              to ``dtype=np.float32``, and if a sparse matrix is provided -
              to a sparse ``scipy.csr_matrix``.
          :type X: {array-like, sparse matrix} of shape (n_samples, n_features)
  
          :param weight: sample weights. If `None`, then samples are equally weighted.
          :type weight: array-like of shape (n_samples,) or None, default=None
  
          :return:
              - **clusters** - cluster indices for each object of `X`;
              - **centers** - cluster centers;
              - **vars** - cluster variances.
          :rtype:
              - tuple(clusters, centers, vars)
              - **clusters** - *numpy.ndarray(numpy.int32) of shape (n_samples,)*
              - **centers** - *numpy.ndarray(numpy.float32) of shape (init_cluster_count, n_features)*
              - **vars** - *numpy.ndarray(numpy.float32) of shape (init_cluster_count, n_features)*
docstring_summary: Performs clustering of the given data.
docstring_tokens: Performs clustering of the given data .
function: def clusterize(self, X, weight=None):
          """Performs clustering of the given data.
  
          :param X: the input sample. Internally, it will be converted
              to ``dtype=np.float32``, and if a sparse matrix is provided -
              to a sparse ``scipy.csr_matrix``.
          :type X: {array-like, sparse matrix} of shape (n_samples, n_features)
  
          :param weight: sample weights. If `None`, then samples are equally weighted.
          :type weight: array-like of shape (n_samples,) or None, default=None
  
          :return:
              - **clusters** - cluster indices for each object of `X`;
              - **centers** - cluster centers;
              - **vars** - cluster variances.
          :rtype:
              - tuple(clusters, centers, vars)
              - **clusters** - *numpy.ndarray(numpy.int32) of shape (n_samples,)*
              - **centers** - *numpy.ndarray(numpy.float32) of shape (init_cluster_count, n_features)*
              - **vars** - *numpy.ndarray(numpy.float32) of shape (init_cluster_count, n_features)*
          """
          x = convert_data( X )
  
          if weight is None:
              weight = numpy.ones(x.shape[0], numpy.float32, order='C')
          else:
              weight = numpy.array(weight, dtype=numpy.float32, copy=False, order='C')
              if numpy.any(weight < 0):
                  raise ValueError('All `weight` elements must be >= 0.')
  
          return super().clusterize(*get_data(x), int(x.shape[1]), weight)
function_tokens: def clusterize ( self , X , weight = None ) : x = convert_data ( X ) if weight is None : weight = numpy . ones ( x . shape [ 0 ] , numpy . float32 , order = 'C' ) else : weight = numpy . array ( weight , dtype = numpy . float32 , copy = False , order = 'C' ) if numpy . any ( weight < 0 ) : raise ValueError ( 'All `weight` elements must be >= 0.' ) return super ( ) . clusterize ( * get_data ( x ) , int ( x . shape [ 1 ] ) , weight )