Singular value decomposition of an H2O data frame using the power method
h2o.svd( training_frame, x, destination_key, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), svd_method = c("GramSVD", "Power", "Randomized"), nv = 1, max_iterations = 1000, seed = -1, keep_u = TRUE, u_name = NULL, use_all_factor_levels = TRUE, max_runtime_secs = 0, export_checkpoints_dir = NULL )
training_frame | Id of the training data frame. |
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x | A vector containing the |
destination_key | (Optional) The unique key assigned to the resulting model. Automatically generated if none is provided. |
model_id | Destination id for this model; auto-generated if not specified. |
validation_frame | Id of the validation data frame. |
ignore_const_cols |
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score_each_iteration |
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transform | Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE. |
svd_method | Method for computing SVD (Caution: Randomized is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults to GramSVD. |
nv | Number of right singular vectors Defaults to 1. |
max_iterations | Maximum iterations Defaults to 1000. |
seed | Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
keep_u |
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u_name | Frame key to save left singular vectors |
use_all_factor_levels |
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max_runtime_secs | Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
export_checkpoints_dir | Automatically export generated models to this directory. |
an object of class H2ODimReductionModel.
N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions[https://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.
# NOT RUN { library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) h2o.svd(training_frame = australia, nv = 8) # }