h2o4gpu.truncated_svd {h2o4gpu}

Truncated Singular Value Decomposition (TruncatedSVD)

Description

Truncated Singular Value Decomposition (TruncatedSVD)

Usage

h2o4gpu.truncated_svd(n_components = 2L, algorithm = "power",
  n_iter = 100L, random_state = NULL, tol = 1e-05, verbose = FALSE,
  backend = "h2o4gpu", n_gpus = 1L, gpu_id = 0L)

Arguments

n_components

Desired dimensionality of output data

algorithm

SVD solver to use. H2O4GPU options: Either "cusolver" (similar to ARPACK) or "power" for the power method. SKlearn options: Either "arpack" for the ARPACK wrapper in SciPy (scipy.sparse.linalg.svds), or "randomized" for the randomized algorithm due to Halko (2009).

n_iter

number of iterations (only relevant for power method) Should be at most 2147483647 due to INT_MAX in C++ backend.

random_state

seed (NULL for auto-generated)

tol

Tolerance for "power" method. Ignored by "cusolver". Should be > 0.0 to ensure convergence. Should be 0.0 to effectively ignore and only base convergence upon n_iter

verbose

Verbose or not

backend

Which backend to use. Options are 'auto', 'sklearn', 'h2o4gpu'. Saves as attribute for actual backend used.

n_gpus

How many gpus to use. If 0, use CPU backup method. Currently SVD only uses 1 GPU, so >1 has no effect compared to 1.

gpu_id

ID of the GPU on which the algorithm should run.