0.28.5

mo functions

Author: bartzbeielstein

notebooks/00_spotPython_tests_files/figure-html/cell-458-output-2.png

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.28.4"
+version = "0.28.5"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

notebooks/00_spotPython_tests_files/figure-html/cell-459-output-1.png

@@ -0,0 +1,96 @@
+import numpy as np
+
+
+def conversion_pred(X) -> np.ndarray:
+    """
+    Compute conversion predictions for each row in the input array.
+
+    Args:
+        X (np.ndarray): 2D array where each row is a configuration.
+
+    Returns:
+        np.ndarray: 1D array of conversion predictions.
+
+    Examples:
+        >>> import numpy as np
+        >>> from spotpython.mo.functions import conversion_pred
+        >>> # Example input data
+        >>> X = np.array([[1, 2, 3], [4, 5, 6]])
+        >>> conversion_pred(X)
+        array([  3.5,  19.5])
+
+    """
+    return (
+        81.09
+        + 1.0284 * X[:, 0]
+        + 4.043 * X[:, 1]
+        + 6.2037 * X[:, 2]
+        - 1.8366 * X[:, 0] ** 2
+        + 2.9382 * X[:, 1] ** 2
+        - 5.1915 * X[:, 2] ** 2
+        + 2.2150 * X[:, 0] * X[:, 1]
+        + 11.375 * X[:, 0] * X[:, 2]
+        - 3.875 * X[:, 1] * X[:, 2]
+    )
+
+
+def activity_pred(X)-> np.ndarray:
+    """
+    Compute activity predictions for each row in the input array.
+
+    Args:
+        X (np.ndarray): 2D array where each row is a configuration.
+
+    Returns:
+        np.ndarray: 1D array of activity predictions.
+
+    Examples:
+        >>> import numpy as np
+        >>> from spotpython.mo.functions import activity_pred
+        >>> # Example input data
+        >>> X = np.array([[1, 2, 3], [4, 5, 6]])
+        >>> activity_pred(X)
+        array([  1.5,  10.5])
+    """
+    return (
+        59.85
+        + 3.583 * X[:, 0]
+        + 0.2546 * X[:, 1]
+        + 2.2298 * X[:, 2]
+        + 0.83479 * X[:, 0] ** 2
+        + 0.07484 * X[:, 1] ** 2
+        + 0.05716 * X[:, 2] ** 2
+        - 0.3875 * X[:, 0] * X[:, 1]
+        - 0.375 * X[:, 0] * X[:, 2]
+        + 0.3125 * X[:, 1] * X[:, 2]
+    )
+
+
+def fun_myer16a(X, fun_control=None)-> np.ndarray:
+    """
+    Compute both conversion and activity predictions for each row in the input array.
+
+    Notes:
+        Implements a response surface experiment described by Myers, Montgomery, and Anderson-Cook (2016). The function computes two objectives: conversion and activity.
+
+    References:
+        - Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M. Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons, 2016.
+        - Kuhn, M. desirability: Function optimization and ranking via desirability functions. Tech. rep., 9 2016.
+
+    Args:
+        X (np.ndarray): 2D array where each row is a configuration.
+        fun_control (dict, optional): Additional control parameters (not used here).
+
+    Returns:
+        np.ndarray: 2D array where each row contains [conversion_pred, activity_pred].
+
+    Examples:
+        >>> import numpy as np
+        >>> from spotpython.mo.functions import fun_myer16a
+        >>> # Example input data
+        >>> X = np.array([[1, 2, 3], [4, 5, 6]])
+        >>> fun_myer16a(X)
+        array([[  3.5,   1.5],
+               [ 19.5,  10.5]])
+    """
+    return np.column_stack((conversion_pred(X), activity_pred(X)))