class_docstr: Singular Value Decomposition
docstr: RNG seed for k-means++ initialization
  
  Type: ``int``, defaults to ``-1``.
  
  :examples:
  
  >>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
  >>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, seed=-3)
  >>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
  >>> fit_h2o
docstr_tok: ["RNG","seed","for","k-means++","initialization","Type",":","``","int","``",",","defaults","to","``","-1","``",".",":","examples",":",">",">",">","arrests","=","h2o.import_file","(","``","https",":","\/\/s3.amazonaws.com\/h2o-public-test-data\/smalldata\/pca_test\/USArrests.csv","''",")",">",">",">","fit_h2o","=","H2OSingularValueDecompositionEstimator","(","nv=4",",","seed=-3",")",">",">",">","fit_h2o.train","(","x=list","(","range","(","4",")",")",",","training_frame=arrests",")",">",">",">","fit_h2o"]
code: def seed(self):
      """
      RNG seed for k-means++ initialization
  
      Type: ``int``, defaults to ``-1``.
  
      :examples:
  
      >>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
      >>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, seed=-3)
      >>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
      >>> fit_h2o
      """
      return self._parms.get("seed")
  
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 20 for calling the H2OSingularValueDecompositionEstimator obj's underlying member method code with example usage: obj.seed() and returns: self