1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
|
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
"""
Example:
- Minimize Rosenbrock's Function with Powell's method.
- Dynamic print of parameter convergence to function minimum.
Demonstrates:
- standard models
- minimal solver interface
- parameter trajectories using callback
"""
# Powell's Directonal solver
from mystic.solvers import fmin_powell
# Rosenbrock function
from mystic.models import rosen
iter = 0
# plot the parameter trajectories
def print_params(params):
global iter
from numpy import asarray
print("Generation %d has best fit parameters: %s" % (iter,asarray(params)))
iter += 1
return
if __name__ == '__main__':
# initial guess
x0 = [0.8,1.2,0.7]
print_params(x0)
# use Powell's method to minimize the Rosenbrock function
solution = fmin_powell(rosen,x0,disp=1,callback=print_params,handler=True)
print(solution)
# end of file
|
