parameters: (self, X)
docstring: Check values of the basic parameters.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
docstring_summary: Check values of the basic parameters.
docstring_tokens: Check values of the basic parameters .
function: def _check_initial_parameters(self, X):
          """Check values of the basic parameters.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
          """
          if self.n_components < 1:
              raise ValueError("Invalid value for 'n_components': %d "
                               "Estimation requires at least one component"
                               % self.n_components)
  
          if self.tol < 0.:
              raise ValueError("Invalid value for 'tol': %.5f "
                               "Tolerance used by the EM must be non-negative"
                               % self.tol)
  
          if self.n_init < 1:
              raise ValueError("Invalid value for 'n_init': %d "
                               "Estimation requires at least one run"
                               % self.n_init)
  
          if self.max_iter < 1:
              raise ValueError("Invalid value for 'max_iter': %d "
                               "Estimation requires at least one iteration"
                               % self.max_iter)
  
          if self.reg_covar < 0.:
              raise ValueError("Invalid value for 'reg_covar': %.5f "
                               "regularization on covariance must be "
                               "non-negative"
                               % self.reg_covar)
  
          # Check all the parameters values of the derived class
          self._check_parameters(X)
function_tokens: def _check_initial_parameters ( self , X ) : if self . n_components < 1 : raise ValueError ( "Invalid value for 'n_components': %d " "Estimation requires at least one component" % self . n_components ) if self . tol < 0. : raise ValueError ( "Invalid value for 'tol': %.5f " "Tolerance used by the EM must be non-negative" % self . tol ) if self . n_init < 1 : raise ValueError ( "Invalid value for 'n_init': %d " "Estimation requires at least one run" % self . n_init ) if self . max_iter < 1 : raise ValueError ( "Invalid value for 'max_iter': %d " "Estimation requires at least one iteration" % self . max_iter ) if self . reg_covar < 0. : raise ValueError ( "Invalid value for 'reg_covar': %.5f " "regularization on covariance must be " "non-negative" % self . reg_covar ) # Check all the parameters values of the derived class self . _check_parameters ( X )