class_docstr: Mixin class for centroid-based clustering of time series.
class_docstr_tok: ["Mixin","class","for","centroid-based","clustering","of","time","series","."]
code: def _post_fit(self, X_fitted, centroids, inertia):
      if numpy.isfinite(inertia) and (centroids is not None):
          self.cluster_centers_ = centroids
          self._assign(X_fitted)
          self._X_fit = X_fitted
          self.inertia_ = inertia
      else:
          self._X_fit = None
  
file_path: tslearn/tslearn/clustering/utils.py
imports: from sklearn.metrics.cluster import silhouette_score
  from scipy.spatial.distance import cdist
  import numpy
  from tslearn.metrics import cdist_dtw, cdist_soft_dtw_normalized
  from tslearn.preprocessing import TimeSeriesResampler
  from tslearn.utils import to_time_series_dataset, to_time_series
label_desc: Use image node_id 1 for calling the TimeSeriesCentroidBasedClusteringMixin obj's underlying member method code with example usage: obj._post_fit(X_fitted, centroids, inertia) without return types