(self, affinities, initialization)
return openTSNE.TSNEEmbedding(
            initialization,
            affinities,
            learning_rate=self.learning_rate,
            theta=self.theta,
            min_num_intervals=self.min_num_intervals,
            ints_in_interval=self.ints_in_interval,
            n_jobs=self.n_jobs,
            negative_gradient_method=self.negative_gradient_method,
            callbacks=self.callbacks,
            callbacks_every_iters=self.callbacks_every_iters,
        )
Prepare an embedding object with appropriate parameters, given some
        affinities and initialization.
Prepare an embedding object with appropriate parameters, given some
        affinities and initialization.
Prepare an embedding object with appropriate parameters given some affinities and initialization .
def prepare_embedding(self, affinities, initialization):
        """Prepare an embedding object with appropriate parameters, given some
        affinities and initialization."""
        return openTSNE.TSNEEmbedding(
            initialization,
            affinities,
            learning_rate=self.learning_rate,
            theta=self.theta,
            min_num_intervals=self.min_num_intervals,
            ints_in_interval=self.ints_in_interval,
            n_jobs=self.n_jobs,
            negative_gradient_method=self.negative_gradient_method,
            callbacks=self.callbacks,
            callbacks_every_iters=self.callbacks_every_iters,
        )
def prepare_embedding ( self , affinities , initialization ) : return openTSNE . TSNEEmbedding ( initialization , affinities , learning_rate = self . learning_rate , theta = self . theta , min_num_intervals = self . min_num_intervals , ints_in_interval = self . ints_in_interval , n_jobs = self . n_jobs , negative_gradient_method = self . negative_gradient_method , callbacks = self . callbacks , callbacks_every_iters = self . callbacks_every_iters , )