def predict_sample(self, keys, values, output_distance=False): if self.cluster_centers_ is None: raise Exception("need to fit before predict!") if self.assign_c_obj is None: self.assign_c_obj = _assign_c(self.cluster_centers_) closest_clust, closest_dist = self.assign_c_obj.assign_sparse_vector(keys, values) if not output_distance: return closest_clust else: return closest_clust, closest_dist
def predict(self, X, output_distance=False, output_numpy=None): if self.cluster_centers_ is None: raise Exception("need to fit before predict!") if self.assign_c_obj is None: self.assign_c_obj = _assign_c(self.cluster_centers_) assignments, distances \ = self.assign_c_obj.assign_matrix(X, result_as_numpy=self._retrieve_numpy(output_numpy)) if not output_distance: return assignments else: return assignments, distances