`MLPKriging::logLikelihood`

Description

Return the log-likelihood evaluated at the fitted hyper-parameters.

Usage

Python

# mk = MLPKriging(...)
mk.logLikelihood()

R

# mk <- MLPKriging(...)
mk$logLikelihood()

Julia

# mk = MLPKriging(...)
ll = logLikelihood(mk)

Arguments

Argument	Description
None	`logLikelihood()` reads the current fitted objective value.

Details

The reported value corresponds to the fitted deep-kernel model after jointly optimising the MLP parameters and GP hyper-parameters.

Value

A numeric scalar — the log-likelihood at the fitted parameters.

Examples

f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
X <- as.matrix(seq(0.05, 0.95, length.out = 10))
y <- f(X)

mk <- MLPKriging(
  y, X,
  hidden_dims = c(4L),
  d_out = 1L,
  activation = "tanh",
  kernel = "gauss",
  parameters = list(max_iter_adam = "20", max_iter_bfgs = "10")
)
print(mk)
print(mk$logLikelihood())

Results

* MLPKriging
* data: 10x[0.05,0.95] -> 10x[0.163421,0.976851]
* trend constant (est.): -745.4
* variance (est.): 3.13668e+08
* covariance:
  * kernel: gauss
  * range (est.): 2.88992
  * warpings:
      joint: "mlp_joint(4,1,tanh)"  →  MLPJoint(1 -> 4 -> 1, 13 params)
  * total warp params: 13
  * fit:
    * objective: LL
    * optim: BFGS+Adam
[1] -25.26018

MLPKriging::logLikelihood