Reduce line width of R docs.

panzelon · Jan 10, 2015 · 61f8164 · 61f8164
1 parent 842cde0
commit 61f8164
Show file tree

Hide file tree

Showing 4 changed files with 13 additions and 12 deletions.
diff --git a/R/h2o-package/man/h2o.SpeeDRF.Rd b/R/h2o-package/man/h2o.SpeeDRF.Rd
@@ -7,10 +7,10 @@ H2O: Single-Node Random Forest
 Performs single-node random forest classification on a data set.
 }
 \usage{
-h2o.SpeeDRF(x, y, data, key = "", classification = TRUE, nfolds = 0, validation, holdout.fraction = 0,
-  mtries = -1, ntree = 50, depth = 20, sample.rate = 2/3, oobee = TRUE, 
-  importance = FALSE, nbins = 1024, seed = -1, stat.type = "ENTROPY", 
-  balance.classes = FALSE, verbose = FALSE)
+h2o.SpeeDRF(x, y, data, key = "", classification = TRUE, nfolds = 0, validation,
+  holdout.fraction = 0, mtries = -1, ntree = 50, depth = 20, sample.rate = 2/3,
+  oobee = TRUE, importance = FALSE, nbins = 1024, seed = -1,
+  stat.type = "ENTROPY", balance.classes = FALSE, verbose = FALSE)
 }
 %- maybe also 'usage' for other objects documented here.
 \arguments{

diff --git a/R/h2o-package/man/h2o.deeplearning.Rd b/R/h2o-package/man/h2o.deeplearning.Rd
@@ -8,10 +8,10 @@ Performs Deep Learning neural networks on an \code{\linkS4class{H2OParsedData}}
 }
 \usage{
 h2o.deeplearning(x, y, data, key = "",override_with_best_model, classification = TRUE,
-    nfolds = 0, validation, holdout_fraction = 0, checkpoint = "", autoencoder, use_all_factor_levels,
-    activation, hidden, epochs, train_samples_per_iteration, seed, adaptive_rate,
-    rho, epsilon, rate, rate_annealing, rate_decay, momentum_start,
-    momentum_ramp, momentum_stable, nesterov_accelerated_gradient,
+    nfolds = 0, validation, holdout_fraction = 0, checkpoint = "", autoencoder,
+    use_all_factor_levels, activation, hidden, epochs, train_samples_per_iteration,
+    seed, adaptive_rate, rho, epsilon, rate, rate_annealing, rate_decay,
+    momentum_start, momentum_ramp, momentum_stable, nesterov_accelerated_gradient,
     input_dropout_ratio, hidden_dropout_ratios, l1, l2, max_w2,
     initial_weight_distribution, initial_weight_scale, loss,
     score_interval, score_training_samples, score_validation_samples,

diff --git a/R/h2o-package/man/h2o.gbm.Rd b/R/h2o-package/man/h2o.gbm.Rd
@@ -12,8 +12,8 @@ H2O: Gradient Boosted Machines
 \usage{
 h2o.gbm(x, y, distribution = "multinomial", data, key = "", n.trees = 10, 
   interaction.depth = 5, n.minobsinnode = 10, shrinkage = 0.1, n.bins = 20,
-  group_split = TRUE, importance = FALSE, nfolds = 0, validation, holdout.fraction = 0, balance.classes = FALSE,
-  max.after.balance.size = 5, class.sampling.factors = NULL)
+  group_split = TRUE, importance = FALSE, nfolds = 0, validation, holdout.fraction = 0,
+  balance.classes = FALSE, max.after.balance.size = 5, class.sampling.factors = NULL)
 }
 \arguments{
   \item{x}{

diff --git a/R/h2o-package/man/h2o.randomForest.Rd b/R/h2o-package/man/h2o.randomForest.Rd
@@ -10,8 +10,9 @@ Performs random forest classification on a data set.
 h2o.randomForest(x, y, data, key = "", classification = TRUE, ntree = 50, 
   depth = 20, mtries = -1, sample.rate = 2/3, nbins = 20, seed = -1,
   importance = FALSE, nfolds = 0, validation, holdout.fraction = 0, nodesize = 1,
-  balance.classes = FALSE, max.after.balance.size = 5, class.sampling.factors = NULL, doGrpSplit = TRUE,
-  verbose = FALSE, oobee = TRUE, stat.type = "ENTROPY", type = "fast")
+  balance.classes = FALSE, max.after.balance.size = 5, class.sampling.factors = NULL,
+  doGrpSplit = TRUE, verbose = FALSE, oobee = TRUE, stat.type = "ENTROPY",
+  type = "fast")
 }
 %- maybe also 'usage' for other objects documented here.
 \arguments{