sparselink (version 1.0.0)

sim_data_multi: 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"
)

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), and beta (\(p \times q\) matrix).

• Transfer learning: Returns a list with slots y_train (\(q\) vectors) and X_train (\(q\) matrices with \(p\) columns) for training data, and y_test (\(vectors\)) and X_test (\(q\) matrices with \(p\) columns) for testing data, and beta for effects (\(p \times q\) matrix).

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 \(q\)

n1

number of testing samples for all datasets: integer

p

number of features: integer

rho

correlation (for decreasing structure)

family

character "gaussian" or "binomial"

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)