Nothing
# skip_if(debug_mode)
set.seed(1)
T <- 50
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)
}
# Initil weights should be uniform on default:
model <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
trace = FALSE
)
expect_true(all(model$weights[1, , , ] == 0.5))
# Weights should be populated for all quantiles if not specified individually
init_weights <- matrix(c(0.3, 0.7), byrow = T, ncol = N, nrow = P)
init_weights <- array(init_weights, dim = c(1, P, N))
model <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
init = list(init_weights = init_weights),
trace = FALSE
)
expect_true(all(model$weights[1, , , ] == init_weights[1, , ]))
# Weights can be specified for each quantile individually:
init_weights <- matrix(nrow = P, ncol = N)
init_weights[, 1] <- 1:9 / 10
init_weights[, 2] <- 9:1 / 10
init_weights <- array(init_weights, dim = c(1, P, N))
model <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
init = list(init_weights = init_weights),
trace = FALSE
)
expect_true(all(model$weights[1, , , ] == init_weights[1, , ]))
# Weights should allways sum to 1:
init_weights <- matrix(nrow = P, ncol = N)
init_weights[, 1] <- 1:9 / 10
init_weights[, 2] <- 9:1 / 10
init_weights[1:5, ] <- init_weights[1:5, ] * 2
init_weights[6:9, ] <- init_weights[6:9, ] / 3
init_weights <- array(init_weights, dim = c(1, P, N))
model <- online(
y = matrix(y),
experts = experts,
tau = prob_grid,
init = list(init_weights = init_weights),
trace = FALSE
)
expect_true(all(rowSums(model$weights[1, , , ]) == 1))
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