sim_data_multi {sparselink} | R Documentation |
Data simulation for related problems
Description
Simulates data for multi-task learning and transfer learning.
Usage
sim_data_multi(
prob.common = 0.05,
prob.separate = 0.05,
q = 3,
n0 = 100,
n1 = 10000,
p = 200,
rho = 0.5,
family = "gaussian"
)
sim_data_trans(
prob.common = 0.05,
prob.separate = 0.05,
q = 3,
n0 = c(50, 100, 200),
n1 = 10000,
p = 200,
rho = 0.5,
family = "gaussian"
)
Arguments
prob.common |
probability of common effect (number between 0 and 1) |
prob.separate |
probability of separate effect (number between 0 and 1) |
q |
number of datasets: integer |
n0 |
number of training samples: integer vector of length |
n1 |
number of testing samples for all datasets: integer |
p |
number of features: integer |
rho |
correlation (for decreasing structure) |
family |
character |
Value
Multi-task learning: Returns a list with slots
y_train
(n_0 \times q
matrix),X_train
(n_0 \times p
matrix),y_test
(n_1 \times q
matrix),X_test
(n_1 \times p
matrix), andbeta
(p \times q
matrix).Transfer learning: Returns a list with slots
y_train
(q
vectors) andX_train
(q
matrices withp
columns) for training data, andy_test
(vectors
) andX_test
(q
matrices withp
columns) for testing data, andbeta
for effects (p \times q
matrix).
Examples
#--- multi-task learning ---
data <- sim_data_multi()
sapply(X=data,FUN=dim)
#--- transfer learning ---
data <- sim_data_trans()
sapply(X=data$y_train,FUN=length)
sapply(X=data$X_train,FUN=dim)
sapply(X=data$y_test,FUN=length)
sapply(X=data$X_test,FUN=dim)
dim(data$beta)