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
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
|
#!/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
"""
Minimize the selected model with Powell's method.
Requires: development version of mystic, pathos, pyina
http://pypi.python.org/pypi/mystic
http://pypi.python.org/pypi/pathos
http://pypi.python.org/pypi/pyina
"""
def optimize(solver, mapper, nodes, target='rosen', **kwds):
if target == 'rosen': # 3d-rosenbrock
# Rosenbrock function
from dejong import rosen as the_model
ndim = 3
actual_coeffs = [1.0] * ndim
pprint = list
else: # 4th-order chebyshev
# Chebyshev cost function
from poly import chebyshev4cost as the_model
from poly import chebyshev4coeffs as actual_coeffs
ndim = len(actual_coeffs)
from mystic.math import poly1d as pprint
# number of trials
N = nodes
print("Number of trials: %s" % N)
print("===============")
# initial guess
import random
x0 = ([random.uniform(-100,100) for i in range(ndim)] for i in range(N))
model = (the_model for i in range(N))
# minimize the function
results = mapper(nodes).map(the_solver, model, x0)
# find the results with the lowest energy
from optimize_helper import best_results
solution = best_results(results)
print("===============")
print("Actual params:\n %s" % pprint(actual_coeffs))
print("Solved params:\n %s" % pprint(solution[0]))
print("Function value: %s" % solution[1])
print("Total function evals: %s" % solution[4])
return
# Powell's Directonal solver
from optimize_helper import fmin_powell as the_solver
# get the map functions
from pathos.serial import SerialPool as serial
from pathos.parallel import ParallelPool as pppool
from pathos.multiprocessing import ProcessPool as mppool
from pyina.launchers import Mpi as mpipool
if __name__ == '__main__':
target = 'rosen'
#target = 'cheby'
print("Function: %s" % target)
print("Solver: %s" % 'fmin_powell')
#NOTE: some of the below should fail, due to how objects are shipped in map
optimize(the_solver, serial, nodes=2, target=target)
#optimize(the_solver, mppool, nodes=2, target=target)
#optimize(the_solver, pppool, nodes=2, target=target)
#optimize(the_solver, mpipool, nodes=2, target=target) #XXX: Fails
# end of file
|
