parameters: (self)
return_statement: return hyperparameters
docstring: Generate a hyperparameter set for an InceptionTime model.
  
          Returns
          ----------
          hyperparameters : dict
              Hyperparameter ranges for a InceptionTime model
docstring_summary: Generate a hyperparameter set for an InceptionTime model.
docstring_tokens: Generate a hyperparameter set for an InceptionTime model .
function: def generate_hyperparameters(self):
          """Generate a hyperparameter set for an InceptionTime model.
  
          Returns
          ----------
          hyperparameters : dict
              Hyperparameter ranges for a InceptionTime model
          """
          params = Namespace(**self.settings)
          hyperparameters = generate_base_hyperparameter_set(params.low_lr,
                                                             params.high_lr,
                                                             params.low_reg,
                                                             params.high_reg)
          hyperparameters['network_depth'] = np.random.randint(params.IT_min_network_depth,
                                                               params.IT_max_network_depth + 1)
          hyperparameters['filters_number'] = np.random.randint(params.IT_min_filters_number,
                                                                params.IT_max_filters_number + 1)
          hyperparameters['max_kernel_size'] = np.random.randint(params.IT_min_max_kernel_size,
                                                                 params.IT_max_max_kernel_size + 1)
          return hyperparameters
function_tokens: def generate_hyperparameters ( self ) : params = Namespace ( * * self . settings ) hyperparameters = generate_base_hyperparameter_set ( params . low_lr , params . high_lr , params . low_reg , params . high_reg ) hyperparameters [ 'network_depth' ] = np . random . randint ( params . IT_min_network_depth , params . IT_max_network_depth + 1 ) hyperparameters [ 'filters_number' ] = np . random . randint ( params . IT_min_filters_number , params . IT_max_filters_number + 1 ) hyperparameters [ 'max_kernel_size' ] = np . random . randint ( params . IT_min_max_kernel_size , params . IT_max_max_kernel_size + 1 ) return hyperparameters