/usr/share/pyshared/openopt/solvers/Standalone/pswarm_oo.py is in python-openopt 0.34+svn1146-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | #from numpy import asfarray, argmax, sign, inf, log10
from openopt.kernel.baseSolver import baseSolver
from numpy import isfinite, asscalar, array, asfarray, ravel #asfarray, inf, atleast_1d
from pswarm_py import pswarm as PSWARM
from openopt.kernel.setDefaultIterFuncs import SMALL_DELTA_X, SMALL_DELTA_F
#from openopt.kernel.setDefaultIterFuncs import SMALL_DELTA_X, SMALL_DELTA_F
class pswarm(baseSolver):
__name__ = 'pswarm'
__license__ = "LGPL"
__authors__ = 'A. I. F. Vaz (http://www.norg.uminho.pt/aivaz), connected to OO by Dmitrey'
__alg__ = "A. I. F. Vaz and L. N. Vicente, A particle swarm pattern search method for bound constrained global optimization, Journal of Global Optimization, 39 (2007) 197-219. The algorithm combines pattern search and particle swarm. Basically, it applies a directional direct search in the poll step (coordinate search in the pure simple bounds case) and particle swarm in the search step."
iterfcnConnected = True
__homepage__ = 'http://www.norg.uminho.pt/aivaz/pswarm/'
__info__ = "parameters: social (default = 0.5), cognitial (0.5), fweight (0.4), iweight (0.9), size (42), tol (1e-5), ddelta (0.5), idelta (2.0). Can handle constraints lb <= x <= ub (values beyond 1e20 are treated as 1e20), A x <= b. Documentation says pswarm is capable of using parallel calculations (via MPI) but I don't know is it relevant to Python API."
__optionalDataThatCanBeHandled__ = ['lb', 'ub', 'A', 'b']
__isIterPointAlwaysFeasible__ = lambda self, p: p.__isNoMoreThanBoxBounded__()
social = 0.5
cognitial = 0.5
fweight = 0.4
iweight = 0.9
size = 42
tol = 1e-5
ddelta = 0.5
idelta = 2.0
def __init__(self):pass
def __solver__(self, p):
#if not p.__isFiniteBoxBounded__(): p.err('this solver requires finite lb, ub: lb <= x <= ub')
p.kernelIterFuncs.pop(SMALL_DELTA_X)
p.kernelIterFuncs.pop(SMALL_DELTA_F)
lb, ub = p.lb, p.ub
lb[lb < -1e20] = -1e20
ub[ub > 1e20] = 1e20
def f(x):
if len(x) == 1:
y = ravel(x)
else:
y = asfarray(x)
return array(p.f(y), 'float')
Problem = {
'Variables': p.n,
'objf': f,
'lb': lb.tolist(),
'ub': ub.tolist(),
}
if any(isfinite(p.b)):
Problem['A'] = p.A.tolist()
Problem['b'] = p.b.tolist()
if hasattr(p,'x0') and p.x0 is not None:
Problem['x0'] = p.x0.tolist()
def pswarm_iterfcn(it, leader, fx, x):
p.xk = array(x)
p.fk = array(fx)
#print x, fx
p.iterfcn()
if p.istop != 0:
p.debugmsg('istop:'+str(p.istop))
return -1.0
else: return 1.0
Options = {
'maxf': 2*p.maxFunEvals, # to provide correct istop triggered from OO kernel
'maxit': p.maxIter+15, # to provide correct istop triggered from OO kernel
'social': self.social,
'cognitial': self.cognitial,
'fweight': self.fweight,
'iweight': self.iweight,
'size': self.size,
'tol': self.tol,
'ddelta': self.ddelta,
'idelta': self.idelta,
'iprint': 1,
'outputfcn': pswarm_iterfcn,
'vectorized': 1
}
result = PSWARM(Problem,Options)
#print 'results:', xf, ff
p.xf, p.ff = result['x'], result['f']
if p.istop == 0: p.istop = 1000
#if p.istop == 0 : p.istop = 1000
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