(self, n_iter, inplace=False, propagate_exception=False, **kwargs)
return new_model
Resume optimization for the current embedding.
Resume optimization for the current embedding.
Resume optimization for the current embedding .
def optimize(self, n_iter, inplace=False, propagate_exception=False, **kwargs):
        """Resume optimization for the current embedding."""
        kwargs = {"n_iter": n_iter, "inplace": inplace,
                  "propagate_exception": propagate_exception, **kwargs}
        if inplace:
            self.embedding_.optimize(**kwargs)
            return self

        # If not inplace, we return a new TSNEModel object
        new_embedding = self.embedding_.optimize(**kwargs)
        table = Table(self.embedding.domain, new_embedding.view(np.ndarray),
                      self.embedding.Y, self.embedding.metas)

        new_model = TSNEModel(new_embedding, table, self.pre_domain)
        new_model.name = self.name
        return new_model
def optimize ( self , n_iter , inplace = False , propagate_exception = False , * * kwargs ) : kwargs = { "n_iter" : n_iter , "inplace" : inplace , "propagate_exception" : propagate_exception , * * kwargs } if inplace : self . embedding_ . optimize ( * * kwargs ) return self # If not inplace, we return a new TSNEModel object new_embedding = self . embedding_ . optimize ( * * kwargs ) table = Table ( self . embedding . domain , new_embedding . view ( np . ndarray ) , self . embedding . Y , self . embedding . metas ) new_model = TSNEModel ( new_embedding , table , self . pre_domain ) new_model . name = self . name return new_model