View source: R/Create_simulated_data.R
Create_simulated_data | R Documentation |
Create an example dataset which contains 1), training datasets (X: feature matrices, Y: response vectors); 2), test datasets
(tX: feature matrices, tY: response vectors); 3), the ground truth model (W: coefficient matrix) and 4), extra
information for some algorithms (i.e. a matrix for encoding the network information is necessary for calling the MTL method with network
structure(Regularization=Graph
)
Create_simulated_data( t = 5, p = 50, n = 20, type = "Regression", Regularization = "L21" )
t |
Number of tasks |
p |
Number of features |
n |
Number of samples of each task. For simplicity, all tasks contain the same number of samples. |
type |
The type of problem, must be "Regression" or "Classification" |
Regularization |
The type of MTL algorithm (cross-task regularizer). The value must be
one of { |
The example dataset.
data<-Create_simulated_data(t=5,p=50, n=20, type="Regression", Regularization="L21") str(data)
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