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
46
47
48
49
50
51
|
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2025 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/pathos/blob/master/LICENSE
def fmin_powell(cost, x0, full=1, disp=1, monitor=0):
""" change default behavior for selected optimizers """
from mystic.solvers import fmin_powell as solver
from mystic.monitors import Monitor, VerboseMonitor
if monitor: mon = VerboseMonitor(10)
else: mon = Monitor()
npop = 10*len(x0)
solved = solver(cost, x0, npop=npop, full_output=full,
disp=disp, itermon=mon, handler=0)
# return: solution, energy, generations, fevals
return solved[0], solved[1], solved[2], solved[3]
def diffev(cost, x0, full=1, disp=1, monitor=0):
""" change default behavior for selected optimizers """
from mystic.solvers import diffev as solver
from mystic.monitors import Monitor, VerboseMonitor
if monitor: mon = VerboseMonitor(10)
else: mon = Monitor()
npop = 10*len(x0)
solved = solver(cost, x0, npop=npop, full_output=full,
disp=disp, itermon=mon, handler=0)
# return: solution, energy, generations, fevals
return solved[0], solved[1], solved[2], solved[3]
def best_results(results):
""" get the results with the lowest energy """
results = list(results) # in case we used an iterator
best = list(results[0][0]), results[0][1]
bestpath = results[0][2]
besteval = results[0][3]
func_evals = besteval
for result in results[1:]:
func_evals += result[3] # add function evaluations
if result[1] < best[1]: # compare energy
best = list(result[0]), result[1]
bestpath = result[2]
besteval = result[3]
# return best: solution, energy, generations, fevals
return best[0], best[1], bestpath, besteval, func_evals
# EOF
|
