catch stan tests

tests/testthat/test-stan-guassian-process.R

@@ -50,19 +50,6 @@ test_that("diagSPD_Periodic returns correct dimensions and values", {
   expect_true(all(result > 0))  # Expect spectral density to be positive
 })
 
-test_that("diagSPD_Linear returns correct dimensions and values", {
-  alpha <- 1.0
-  L <- 1.0
-  M <- 5
-  result <- diagSPD_Linear(alpha, L, M)
-  expect_equal(length(result), M)
-  expect_true(all(result > 0))  # Expect spectral density to be positive
-  # Check specific values for known inputs
-  indices <- linspaced_vector(M, 1, M)
-  expected_result <- alpha * L^2 / (pi^2 * indices^2)
-  expect_equal(result, expected_result, tolerance = 1e-8)
-})
-
 test_that("PHI returns correct dimensions and values", {
   N <- 5
   M <- 3
@@ -153,46 +140,3 @@ test_that("update_gp returns correct dimensions and values", {
   expected_result <- PHI %*% (diagSPD * eta)
   expect_equal(matrix(result, ncol = 1), expected_result, tolerance = 1e-8)
 })
-
-test_that("update_gp with linear kernel returns correct dimensions and values", {
-  M <- 3
-  L <- 1.0
-  alpha <- 1.0
-  rho <- c(1.0)  # Not used for linear kernel
-  eta <- rep(1, M)
-  PHI <- matrix(runif(15), nrow = 5)  # 5 observations, 3 basis functions
-  type <- 3  # Linear kernel
-  nu <- 1.5  # Not used for linear kernel
-  result <- update_gp(PHI, M, L, alpha, rho, eta, type, nu)
-  expect_equal(length(result), nrow(PHI))  # Should match number of observations
-  # Check specific values for known inputs
-  diagSPD <- diagSPD_Linear(alpha, L, M)
-  expected_result <- PHI %*% (diagSPD * eta)
-  expect_equal(matrix(result, ncol = 1), expected_result, tolerance = 1e-8)
-})
-
-test_that("Linear kernel produces a linear GP", {
-  N <- 100
-  M <- 50
-  L <- 10.0
-  alpha <- 2.0
-  x <- seq(-L, L, length.out = N)
-
-  # Setup GP
-  PHI <- PHI(N, M, L, x)
-
-  # Generate random eta
-  set.seed(123)
-  eta <- rnorm(M)
-
-  # Compute GP
-  diagSPD <- diagSPD_Linear(alpha, L, M)
-  gp <- PHI %*% (diagSPD * eta)
-
-  # Fit a linear model
-  lm_fit <- lm(gp ~ x)
-
-  # Check if the GP is approximately linear
-  expect_gt(summary(lm_fit)$r.squared, 0.99)  # R-squared should be very close to 1
-  expect_lt(summary(lm_fit)$sigma, 0.1)  # Residual standard error should be small
-})