test_that("train_esn function works correctly", {
# Create test data
n_ahead <- 18
n_obs <- length(AirPassengers)
n_train <- n_obs - n_ahead
ytrain <- AirPassengers[(1:n_train)]
ytest <- AirPassengers[((n_train+1):n_obs)]
# Test the function call
esn_model <- train_esn(ytrain)
# Test if the function output is of class "esn"
expect_true(class(esn_model) == "esn")
# Test if the residuals vector has the same length as the input vector
expect_equal(length(esn_model$actual), length(ytrain))
# Test if the residuals vector has the same length as the input vector
expect_equal(length(esn_model$fitted), length(ytrain))
# Test if the residuals vector has the same length as the input vector
expect_equal(length(esn_model$resid), length(ytrain))
# Test if the actual and fitted vectors have the same length
expect_equal(length(esn_model$actual), length(esn_model$fitted))
# Test if the actual and fitted vectors have the same length
expect_equal(length(esn_model$resid), length(esn_model$fitted))
# Test if the model_weights list contains the expected elements
expect_equal(names(esn_model$method$model_weights), c("win", "wres", "wout"))
# Test if n_states is as expected
expect_equal(esn_model$method$model_layers$n_states, 50)
# Test of n_models is as expected
expect_equal(esn_model$method$model_meta$n_models, 100)
# Add more tests as needed...
})
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