class_docstr: Singular Value Decomposition 
class_docstr_tok: ["Singular","Value","Decomposition"]
docstr: Singular value decomposition of an H2OFrame.
  
  :param int nv: The number of right singular vectors to be computed. This must be between 1 and
      min(ncol(training_frame), snrow(training_frame)) inclusive.
  :param int max_iterations: The maximum number of iterations to run each power iteration loop. Must be
      between 1 and 1e6 inclusive.
  :param str transform: A character string that indicates how the training data should be transformed
      before running SVD. Possible values are:
  
      - ``"none"``: for no transformation,
      - ``"demean"``: for subtracting the mean of each column,
      - ``"descale"``: for dividing by the standard deviation of each column,
      - ``"standardize"``: for demeaning and descaling, and
      - ``"normalize"``: for demeaning and dividing each column by its range (max - min).
  
  :param int seed: Random seed used to initialize the right singular vectors at the beginning of each
      power method iteration.
  :param bool use_all_factor_levels: A logical value indicating whether all factor levels should be included
      in each categorical column expansion. If False, the indicator column corresponding to the first factor
      level of every categorical variable will be dropped. Defaults to True.
  :param str svd_method: A character string that indicates how SVD should be calculated. Possible values are:
  
      - ``"gram_svd"``: distributed computation of the Gram matrix followed by a local SVD
        using the JAMA package,
      - ``"power"``: computation of the SVD using the power iteration method,
      - ``"randomized"``: approximate SVD by projecting onto a random subspace.
  
  :returns: a new H2OSVD model
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code: def __init__(
      self,
      nv=None,
      max_iterations=None,
      transform="none",
      seed=None,
      use_all_factor_levels=None,
      svd_method="gram_svd",
  ):
      """
      Singular value decomposition of an H2OFrame.
  
      :param int nv: The number of right singular vectors to be computed. This must be between 1 and
          min(ncol(training_frame), snrow(training_frame)) inclusive.
      :param int max_iterations: The maximum number of iterations to run each power iteration loop. Must be
          between 1 and 1e6 inclusive.
      :param str transform: A character string that indicates how the training data should be transformed
          before running SVD. Possible values are:
  
          - ``"none"``: for no transformation,
          - ``"demean"``: for subtracting the mean of each column,
          - ``"descale"``: for dividing by the standard deviation of each column,
          - ``"standardize"``: for demeaning and descaling, and
          - ``"normalize"``: for demeaning and dividing each column by its range (max - min).
  
      :param int seed: Random seed used to initialize the right singular vectors at the beginning of each
          power method iteration.
      :param bool use_all_factor_levels: A logical value indicating whether all factor levels should be included
          in each categorical column expansion. If False, the indicator column corresponding to the first factor
          level of every categorical variable will be dropped. Defaults to True.
      :param str svd_method: A character string that indicates how SVD should be calculated. Possible values are:
  
          - ``"gram_svd"``: distributed computation of the Gram matrix followed by a local SVD
            using the JAMA package,
          - ``"power"``: computation of the SVD using the power iteration method,
          - ``"randomized"``: approximate SVD by projecting onto a random subspace.
  
      :returns: a new H2OSVD model
      """
      allowed_params = locals().keys()
      super(H2OSVD, self).__init__(
          delegate=H2OSingularValueDecompositionEstimator(
              nv=nv,
              max_iterations=max_iterations,
              transform=transform,
              seed=seed,
              use_all_factor_levels=use_all_factor_levels,
              svd_method=svd_method,
          ),
          allowed_params=allowed_params,
      )
  
file_path: h2o-3/h2o-py/h2o/transforms/decomposition.py
imports: from h2o.frame import H2OFrame
  from ..estimators import H2OPrincipalComponentAnalysisEstimator, H2OSingularValueDecompositionEstimator
  from ..model import ModelBase
  from .transform_base import H2OTransformer
label_desc: Use image node_id 1 to create a new H2OSVD object from inherited base classes: _H2OTransformerProxy with example: obj = H2OSVD(nv, max_iterations, transform, seed, use_all_factor_levels, svd_method)