function.mo.fun_myer16a
function.mo.fun_myer16a(X, fun_control=None)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.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| X | np.ndarray | 2D array where each row is a configuration. | required |
| fun_control | dict | Additional control parameters (not used here). | None |
Returns
| Name | Type | Description |
|---|---|---|
| np.ndarray | np.ndarray: 2D array where each row contains [conversion_pred, activity_pred]. |
Examples
>>> import numpy as np
>>> from spotoptim.function.mo 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]])