v0.5.21

documentation

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.5.20"
+version = "0.5.21"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/budget/ocba.py

@@ -8,10 +8,13 @@
 from numpy import zeros, square, sqrt, full, around
 
 
-def get_ocba(means, vars, delta):
+def get_ocba(means, vars, delta) -> int32:
     """
     Optimal Computer Budget Allocation (OCBA)
 
+    This function calculates the budget recommendations for a given set of means,
+    variances, and incremental budget using the OCBA algorithm.
+
     References:
         Chun-Hung Chen and Loo Hay Lee:
         Stochastic Simulation Optimization: An Optimal Computer Budget Allocation,
@@ -24,6 +27,17 @@ def get_ocba(means, vars, delta):
         and
         https://github.com/TomMonks/sim-tools
 
+    Args:
+        means (numpy.array): An array of means.
+        vars (numpy.array): An array of variances.
+        delta (int): The incremental budget.
+
+    Returns:
+        numpy.array: An array of budget recommendations.
+
+    Note:
+        The implementation is based on the pseudo-code in the Chen et al. book (p. 49).
+
     Examples:
 
         From the Chen et al. book (p. 49):
@@ -32,18 +46,6 @@ def get_ocba(means, vars, delta):
         get_ocba(mean_y, var_y, 50)
 
         [11  9 19  9  2]
-
-        Args:
-        means (numpy.array):
-            means
-        vars (numpy.array):
-            variances
-        delta (int):
-            incremental budget
-
-        Returns:
-        (numpy.array):
-            budget recommendations. `(n,)` numpy.array
     """
     n_designs = means.shape[0]
     allocations = zeros(n_designs, int32)
@@ -79,6 +81,19 @@ def get_ocba(means, vars, delta):
     return add_budget - allocations
 
 
-def get_ocba_X(X, means, vars, delta):
+def get_ocba_X(X, means, vars, delta) -> float64:
+    """
+    This function calculates the OCBA allocation and repeats the input array X along the specified axis.
+
+    Args:
+        X (numpy.ndarray): Input array to be repeated.
+        means (list): List of means for each alternative.
+        vars (list): List of variances for each alternative.
+        delta (float): Indifference zone parameter.
+
+    Returns:
+        numpy.ndarray: Repeated array of X along the specified axis based on the OCBA allocation.
+
+    """
     o = get_ocba(means=means, vars=vars, delta=delta)
     return repeat(X, o, axis=0)

src/spotPython/build/kriging.py

@@ -215,17 +215,20 @@ def __init__(
         logger.setLevel(self.log_level)
         logger.info(f"Starting the logger at level {self.log_level} for module {__name__}:")
 
-    def exp_imp(self, y0, s0):
+    def exp_imp(self, y0: float, s0: float) -> float:
         """
-        Returns the expected improvement for y0 and error s0 (in coded units).
+        Calculates the expected improvement for a given function value and error in coded units.
+
         Args:
-            y0 (float):
-                function value (in coded units)
-            s0 (float):
-                error
+            y0 (float): The function value in coded units.
+            s0 (float): The error value.
+
         Returns:
-            (float):
-                The expected improvement value.
+            float: The expected improvement value.
+
+        Example:
+            >>> exp_imp(1.0, 2.0)
+            0.1977966
         """
         # y_min = min(self.cod_y)
         y_min = min(self.mean_cod_y)