0.36.1

Author: bartzbeielstein

notebooks/spot_aquisition_random_rosenbrock_2d.ipynb

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.36.0"
+version = "0.36.1"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

pyproject.toml

@@ -2,7 +2,7 @@
 from sklearn.gaussian_process.kernels import Kernel, NormalizedKernelMixin, Hyperparameter
 
 
-def _correlation(kernel, D, kernel_params=None):
+def _spot_correlation(kernel, D, kernel_params=None):
     """
     Dispatches to the selected kernel function.
     Args:
@@ -49,7 +49,7 @@ def _correlation(kernel, D, kernel_params=None):
         raise ValueError(f"Unknown kernel: {kernel}")
 
 
-# Example: Custom sklearn-compatible RBF kernel using _correlation
+# Example: Custom sklearn-compatible RBF kernel using _spot_correlation
 class CustomRBF(NormalizedKernelMixin, Kernel):
     def __init__(self, length_scale=1.0, length_scale_bounds=(1e-5, 1e5)):
         self.length_scale = length_scale
@@ -61,7 +61,7 @@ def hyperparameter_length_scale(self):
 
     def __call__(self, D, eval_gradient=False):
         # D is assumed to be the squared distance matrix
-        K = _correlation("gauss", D)
+        K = _spot_correlation("gauss", D)
         if eval_gradient:
             # Gradient not implemented
             return K, np.empty((K.shape[0], K.shape[1], 0))
@@ -85,7 +85,7 @@ def hyperparameter_length_scale(self):
         return Hyperparameter("length_scale", "numeric", self.length_scale_bounds)
 
     def __call__(self, D, eval_gradient=False):
-        K = _correlation("matern", D, {"nu": self.nu})
+        K = _spot_correlation("matern", D, {"nu": self.nu})
         if eval_gradient:
             # Gradient not implemented
             return K, np.empty((K.shape[0], K.shape[1], 0))

src/spotpython/surrogate/kernels.py

@@ -8,7 +8,7 @@
 from numpy import linspace, append
 from scipy.spatial.distance import cdist, pdist, squareform
 from spotpython.surrogate.plot import plotkd
-from spotpython.surrogate.kernels import _correlation  # Only import _correlation
+from spotpython.surrogate.kernels import _spot_correlation
 
 
 # --- The New Kriging Class with Nyström Approximation as introduced in v0.34.0 ---
@@ -222,7 +222,7 @@ def _correlation(self, D):
         Returns:
             np.ndarray: Correlation matrix.
         """
-        return _correlation(self.kernel, D, self.kernel_params)
+        return _spot_correlation(self.kernel, D, self.kernel_params)
 
     # -------- Basis correlation construction (identical to kriging.py) --------