Builds a generalized low rank decomposition of an H2O data frame
h2o.glrm(training_frame, cols = NULL, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, loading_name = NULL, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), k = 1, loss = c("Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic"), loss_by_col = c("Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic", "Categorical", "Ordinal"), loss_by_col_idx = NULL, multi_loss = c("Categorical", "Ordinal"), period = 1, regularization_x = c("None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex"), regularization_y = c("None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex"), gamma_x = 0, gamma_y = 0, max_iterations = 1000, max_updates = 2000, init_step_size = 1, min_step_size = 1e04, seed = 1, init = c("Random", "SVD", "PlusPlus", "User"), svd_method = c("GramSVD", "Power", "Randomized"), user_y = NULL, user_x = NULL, expand_user_y = TRUE, impute_original = FALSE, recover_svd = FALSE, max_runtime_secs = 0, export_checkpoints_dir = NULL)
training_frame  Id of the training data frame. 

cols  (Optional) A vector containing the data columns on which kmeans operates. 
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 

loading_name  Frame key to save resulting X 
transform  Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE. 
k  Rank of matrix approximation Defaults to 1. 
loss  Numeric loss function Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic". Defaults to Quadratic. 
loss_by_col  Loss function by column (override) Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic", "Categorical", "Ordinal". 
loss_by_col_idx  Loss function by column index (override) 
multi_loss  Categorical loss function Must be one of: "Categorical", "Ordinal". Defaults to Categorical. 
period  Length of period (only used with periodic loss function) Defaults to 1. 
regularization_x  Regularization function for X matrix Must be one of: "None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None. 
regularization_y  Regularization function for Y matrix Must be one of: "None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None. 
gamma_x  Regularization weight on X matrix Defaults to 0. 
gamma_y  Regularization weight on Y matrix Defaults to 0. 
max_iterations  Maximum number of iterations Defaults to 1000. 
max_updates  Maximum number of updates, defaults to 2*max_iterations Defaults to 2000. 
init_step_size  Initial step size Defaults to 1. 
min_step_size  Minimum step size Defaults to 0.0001. 
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). 
init  Initialization mode Must be one of: "Random", "SVD", "PlusPlus", "User". Defaults to PlusPlus. 
svd_method  Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults to Randomized. 
user_y  Userspecified initial Y 
user_x  Userspecified initial X 
expand_user_y 

impute_original 

recover_svd 

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.
M. Udell, C. Horn, R. Zadeh, S. Boyd (2014). Generalized Low Rank Models[http://arxiv.org/abs/1410.0342]. Unpublished manuscript, Stanford Electrical Engineering Department 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.glrm(training_frame = australia, k = 5, loss = "Quadratic", regularization_x = "L1", gamma_x = 0.5, gamma_y = 0, max_iterations = 1000) # }