h2o.glrm  R Documentation 
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,
representation_name = NULL,
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 

representation_name 
Frame key to save resulting X 
loading_name 
[Deprecated] Use representation_name instead. 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. 
an object of class H2ODimReductionModel.
M. Udell, C. Horn, R. Zadeh, S. Boyd (2014). Generalized Low Rank Models[https://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[https://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217288, June 2011.
h2o.kmeans, h2o.svd
, h2o.prcomp
## 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)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.