Truncated Singular Value Decomposition (TruncatedSVD)
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)Desired dimensionality of output data
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).
number of iterations (only relevant for power method) Should be at most 2147483647 due to INT_MAX in C++ backend.
seed (NULL for auto-generated)
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 or not
Which backend to use. Options are 'auto', 'sklearn', 'h2o4gpu'. Saves as attribute for actual backend used.
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.
ID of the GPU on which the algorithm should run.