class_docstr: Clustering by k-means for each class with SPD matrices as inputs.
  
  See Also
  --------
  Kmeans
docstr: Init.
docstr_tok: ["Init","."]
code: def __init__(self, n_clusters=2, **params):
      """Init."""
      params["n_clusters"] = n_clusters
      self.km = Kmeans(**params)
      self.metric = self.km.metric
  
imports: import numpy
  from joblib import Parallel, delayed
  from scipy.stats import norm, chi2
  import sklearn
  from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin, ClusterMixin, clone
  from sklearn.cluster import KMeans
  from .classification import MDM, _check_metric
  from .utils.mean import mean_covariance
  from .utils.geodesic import geodesic
label_desc: Use image node_id 1 to create a new KmeansPerClassTransform object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = KmeansPerClassTransform(n_clusters)