class_docstr: Principal Components Analysis
class_docstr_tok: ["Principal","Components","Analysis"]
docstr: Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue
  decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix
  using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue
  decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ -
  https://github.com/fommil/matrix-toolkits-java/
  
  Type: ``Literal["mtj_evd_densematrix", "mtj_evd_symmmatrix", "mtj_svd_densematrix", "jama"]``.
  
  :examples:
  
  >>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
  >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3,
  ...                                                   pca_impl="jama",
  ...                                                   impute_missing=True,
  ...                                                   max_iterations=1200)
  >>> data_pca.train(x=data.names, training_frame=data)
  >>> data_pca.show()
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code: def pca_impl(self):
      """
      Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue
      decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix
      using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue
      decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ -
      https://github.com/fommil/matrix-toolkits-java/
  
      Type: ``Literal["mtj_evd_densematrix", "mtj_evd_symmmatrix", "mtj_svd_densematrix", "jama"]``.
  
      :examples:
  
      >>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
      >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3,
      ...                                                   pca_impl="jama",
      ...                                                   impute_missing=True,
      ...                                                   max_iterations=1200)
      >>> data_pca.train(x=data.names, training_frame=data)
      >>> data_pca.show()
      """
      return self._parms.get("pca_impl")
  
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 16 for calling the H2OPrincipalComponentAnalysisEstimator obj's underlying member method code with example usage: obj.pca_impl() and returns: self