parameters: (self, X, Y, weight=None)
return_statement: return SvmClassificationModel(super().train_classifier(*get_data(x), int(x.shape[1]), y, weight))
docstring: Trains the SVM classification model.
  
          :param X: the training sample. The values will be converted 
              to ``dtype=np.float32``. If a sparse matrix is
              passed in, it will be converted to a sparse ``csr_matrix``.
          :type X: {array-like, sparse matrix} of shape (n_samples, n_features)
  
          :param Y: correct class labels (``int``) for the training set vectors.
          :type Y: array-like of shape (n_samples,)
  
          :param weight: sample weights. If None, then samples are equally weighted.
          :type weight: array-like of shape (n_samples,), default=None
  
          :return: the trained classification model.
          :rtype: neoml.SVM.SvmClassificationModel
docstring_summary: Trains the SVM classification model.
docstring_tokens: Trains the SVM classification model .
function: def train(self, X, Y, weight=None):
          """Trains the SVM classification model.
  
          :param X: the training sample. The values will be converted 
              to ``dtype=np.float32``. If a sparse matrix is
              passed in, it will be converted to a sparse ``csr_matrix``.
          :type X: {array-like, sparse matrix} of shape (n_samples, n_features)
  
          :param Y: correct class labels (``int``) for the training set vectors.
          :type Y: array-like of shape (n_samples,)
  
          :param weight: sample weights. If None, then samples are equally weighted.
          :type weight: array-like of shape (n_samples,), default=None
  
          :return: the trained classification model.
          :rtype: neoml.SVM.SvmClassificationModel
          """
          x = convert_data( X )
          y = numpy.array( Y, dtype=numpy.int32, copy=False, order='C' )
  
          if x.shape[0] != y.size:
              raise ValueError('The `X` and `Y` inputs must be the same length.')
  
          if weight is None:
              weight = numpy.ones(y.size, numpy.float32, order='C')
          else:
              weight = numpy.array( weight, dtype=numpy.float32, copy=False, order='C' )
  
          if numpy.any(y < 0):
              raise ValueError('All `Y` elements must be >= 0.')
  
          if numpy.any(weight < 0):
              raise ValueError('All `weight` elements must be >= 0.')
  
          return SvmClassificationModel(super().train_classifier(*get_data(x), int(x.shape[1]), y, weight))
function_tokens: def train ( self , X , Y , weight = None ) : x = convert_data ( X ) y = numpy . array ( Y , dtype = numpy . int32 , copy = False , order = 'C' ) if x . shape [ 0 ] != y . size : raise ValueError ( 'The `X` and `Y` inputs must be the same length.' ) if weight is None : weight = numpy . ones ( y . size , numpy . float32 , order = 'C' ) else : weight = numpy . array ( weight , dtype = numpy . float32 , copy = False , order = 'C' ) if numpy . any ( y < 0 ) : raise ValueError ( 'All `Y` elements must be >= 0.' ) if numpy . any ( weight < 0 ) : raise ValueError ( 'All `weight` elements must be >= 0.' ) return SvmClassificationModel ( super ( ) . train_classifier ( * get_data ( x ) , int ( x . shape [ 1 ] ) , y , weight ) )