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. |
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x | A vector containing the |
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. |
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 low-rank 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 - singular-value 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/matrix-toolkits-java/ 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 |
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compute_metrics |
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impute_missing |
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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). |
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[http://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.prcomp(training_frame = australia, k = 8, transform = "STANDARDIZE") # }