diff --git a/BayesValidRox/BayesInference/BayesInference.py b/BayesValidRox/BayesInference/BayesInference.py
index a1c2142268628dda713308d32697e4a4733da3ef..950e16ce9aa5014f0ed9a28ad2328993ca51492c 100644
--- a/BayesValidRox/BayesInference/BayesInference.py
+++ b/BayesValidRox/BayesInference/BayesInference.py
@@ -178,7 +178,7 @@ class BayesInference:
logL = multivariate_normal.logpdf(TotalOutputs, mean=Data, cov=covMatrix)
except:
logL = -np.inf
-
+
return logL
#--------------------------------------------------------------------------------------------------------
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index b1508bb70aa61ac0256c565abfa45ccc461f717d..10188743594894ebbf63190d3bf589ed2863e6b9 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -885,7 +885,7 @@ class Metamodel:
return pca, OutputMatrix
#--------------------------------------------------------------------------------------------------------
- def GaussianProcessEmulator(self, X, y, autoSelect=False, varIdx=None):
+ def GaussianProcessEmulator(self, X, y, autoSelect=True, varIdx=None):
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import (RBF, Matern, RationalQuadratic,
@@ -893,11 +893,10 @@ class Metamodel:
ConstantKernel)
- kernels = [1.0 * RBF(length_scale=1.0, length_scale_bounds=(1e-1, 10.0)),
- 1.0 * RationalQuadratic(length_scale=1.0, alpha=0.1),
- # ConstantKernel(0.1, (0.01, 10.0))
- # * (DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2),
- 1.0 * Matern(length_scale=1.0, length_scale_bounds=(1e-1, 10.0),
+ kernels = [1.0 * RBF(length_scale=1.0, length_scale_bounds=(1e-5, 1e5)),
+ 1.0 * RationalQuadratic(length_scale=0.2, alpha=0.1),
+ # 1.0 * ExpSineSquared(length_scale=1.0, length_scale_bounds=(1e-05, 1e05)),
+ 1.0 * Matern(length_scale=1.0, length_scale_bounds=(1e-5, 1e5),
nu=1.5)]
if autoSelect:# Automatic selection of the kernel
@@ -905,7 +904,7 @@ class Metamodel:
BME = []
for i, kernel in enumerate(kernels):
gp[i] = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=2,
- normalize_y=False)
+ normalize_y=True)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp[i].fit(X, y)
@@ -915,7 +914,8 @@ class Metamodel:
gp = gp[np.argmax(BME)]
else:
- gp = GaussianProcessRegressor(kernel=kernels[-1], n_restarts_optimizer=2)
+ gp = GaussianProcessRegressor(kernel=kernels[-1], n_restarts_optimizer=2,
+ normalize_y=True)
gp.fit(X, y)
# Compute score