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