Principal components analysis of an H2O data frame using the power method to calculate the singular value decomposition of the Gram matrix.
h2o.prcomp(training_frame, x, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), pca_method = c("GramSVD", "Power", "Randomized", "GLRM"), pca_impl = c("MTJ_EVD_DENSEMATRIX", "MTJ_EVD_SYMMMATRIX", "MTJ_SVD_DENSEMATRIX", "JAMA"), k = 1, max_iterations = 1000, use_all_factor_levels = FALSE, compute_metrics = TRUE, impute_missing = FALSE, seed = 1, max_runtime_secs = 0, export_checkpoints_dir = NULL)
training_frame  Id of the training data frame. 

x  A vector containing the 
model_id  Destination id for this model; autogenerated if not specified. 
validation_frame  Id of the validation data frame. 
ignore_const_cols 

score_each_iteration 

transform  Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE. 
pca_method  Specify the algorithm to use for computing the principal components: GramSVD  uses a distributed computation of the Gram matrix, followed by a local SVD; Power  computes the SVD using the power iteration method (experimental); Randomized  uses randomized subspace iteration method; GLRM  fits a generalized lowrank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental) Must be one of: "GramSVD", "Power", "Randomized", "GLRM". Defaults to GramSVD. 
pca_impl  Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX  eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX  eigenvalue decompositions for symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX  singularvalue decompositions for dense matrix using MTJ; JAMA  eigenvalue decompositions for dense matrix using JAMA. References: JAMA  http://math.nist.gov/javanumerics/jama/; MTJ  https://github.com/fommil/matrixtoolkitsjava/ Must be one of: "MTJ_EVD_DENSEMATRIX", "MTJ_EVD_SYMMMATRIX", "MTJ_SVD_DENSEMATRIX", "JAMA". 
k  Rank of matrix approximation Defaults to 1. 
max_iterations  Maximum training iterations Defaults to 1000. 
use_all_factor_levels 

compute_metrics 

impute_missing 

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 (timebased random number). 
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. 
Returns an object of class H2ODimReductionModel.
N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions[http://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217288, June 2011.
# NOT RUN { library(h2o) h2o.init() australia_path < system.file("extdata", "australia.csv", package = "h2o") australia < h2o.uploadFile(path = australia_path) h2o.prcomp(training_frame = australia, k = 8, transform = "STANDARDIZE") # }