class_docstr: Principal Components Analysis
class_docstr_tok: ["Principal","Components","Analysis"]
docstr: Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of
  the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method
  (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model
  with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental)
  
  Type: ``Literal["gram_s_v_d", "power", "randomized", "glrm"]``, defaults to ``"gram_s_v_d"``.
  
  :examples:
  
  >>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
  >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1,
  ...                                                   transform="standardize",
  ...                                                   pca_method="power",
  ...                                                   impute_missing=True,
  ...                                                   max_iterations=800)
  >>> data_pca.train(x=data.names, training_frame=data)
  >>> data_pca.show()
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code: def pca_method(self):
      """
      Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of
      the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method
      (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model
      with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental)
  
      Type: ``Literal["gram_s_v_d", "power", "randomized", "glrm"]``, defaults to ``"gram_s_v_d"``.
  
      :examples:
  
      >>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
      >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1,
      ...                                                   transform="standardize",
      ...                                                   pca_method="power",
      ...                                                   impute_missing=True,
      ...                                                   max_iterations=800)
      >>> data_pca.train(x=data.names, training_frame=data)
      >>> data_pca.show()
      """
      return self._parms.get("pca_method")
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 14 for calling the H2OPrincipalComponentAnalysisEstimator obj's underlying member method code with example usage: obj.pca_method() and returns: self