Nothing
skip_if(debug_mode)
set.seed(1)
T <- 4
N <- 2
P <- 9
prob_grid <- 1:P / (P + 1)
mean_y <- 0
sd_y <- 5
# Realized observations
y <- rnorm(n = T)
# Expert predictions
experts <- array(dim = c(T, P, N))
for (t in 1:T) {
experts[t, , 1] <- qnorm(prob_grid, mean = -5, sd = 2)
experts[t, , 2] <- qnorm(prob_grid, mean = 5, sd = 2)
}
model_lg_true <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
loss_gradient = TRUE,
save_past_performance = TRUE,
trace = FALSE
)
model_lg_false <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
loss_gradient = FALSE,
save_past_performance = TRUE,
trace = FALSE
)
regret <- sweep(
x = -model_lg_false$experts_loss,
MARGIN = 1:3,
FUN = "+",
model_lg_false$forecaster_loss
)
model2 <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
regret = regret,
trace = FALSE
)
expect_true(
identical(model_lg_false$weights, model2$weights)
)
expect_false(
identical(model_lg_true$weights, model2$weights)
)
model3 <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
regret = list(regret = regret, share = 1),
trace = FALSE
)
expect_true(
identical(model2$weights, model3$weights)
)
model4 <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
regret = list(regret = regret, share = 0),
trace = FALSE
)
expect_true(
identical(model_lg_true$weights, model4$weights)
)
model5 <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
regret = list(regret = regret, share = c(0, 0.5, 1)),
save_past_performance = TRUE,
trace = FALSE
)
expect_true(all(model5$past_performance[, , , 1] ==
model_lg_true$past_performance[, , , 1]))
expect_true(
all(model5$past_performance[, , , 3]
== model_lg_false$past_performance[, , , 1])
)
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