if (FALSE) {
library(h2o)
h2o.init()
# Import the USArrests dataset into H2O:
arrests <- h2o.importFile(
"https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv"
)
# Split the dataset into a train and valid set:
arrests_splits <- h2o.splitFrame(data = arrests, ratios = 0.8, seed = 1234)
train <- arrests_splits[[1]]
valid <- arrests_splits[[2]]
# Build and train the model:
glrm_model = h2o.glrm(training_frame = train,
k = 4,
loss = "Quadratic",
gamma_x = 0.5,
gamma_y = 0.5,
max_iterations = 700,
recover_svd = TRUE,
init = "SVD",
transform = "STANDARDIZE")
# Eval performance:
arrests_perf <- h2o.performance(glrm_model)
# Generate predictions on a validation set (if necessary):
arrests_pred <- h2o.predict(glrm_model, newdata = valid)
# Transform the data using the dataset "valid" to retrieve the new coefficients:
glrm_transform <- h2o.transform_frame(glrm_model, valid)
}
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