Source code for openmdao.lib.doegenerators.optlh

# This implementation is based on a matlab implementation that has the
# following license:
# Copyright 2007 A Sobester
# This program is free software: you can redistribute it and/or modify  it
# under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or any
# later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# General Public License for more details.
# You should have received a copy of the GNU General Public License and GNU
# Lesser General Public License along with this program. If not, see
# <>.

from random import randint

# pylint: disable-msg=E0611,F0401
from numpy import array, size, sum, floor
from numpy.linalg import norm

from enthought.traits.api import HasTraits

from openmdao.lib.datatypes.api import implements, Int, Enum, Float

from openmdao.util.mdo import rand_latin_hypercube
from openmdao.main.interfaces import IDOEgenerator

[docs]class LatinHypercube(object): def __init__(self, doe, q=2, p=1): self.q = q self.p = p self.doe = doe self.phi = None # Morris-Mitchell sampling criterion @property
[docs] def shape(self): """Size of the LatinHypercube doe (rows,cols).""" return self.doe.shape
[docs] def mmphi(self): """Returns the Morris-Mitchell sampling criterion for this Latin hypercube.""" if self.phi is None: n,m = self.doe.shape distdict = {} #calculate the norm between each pair of points in the DOE # TODO: This norm takes up the majority of the computation time. It # should be converted to C or ShedSkin. arr = self.doe for i in range(n): for j in range(i+1, n): nrm = norm(arr[i]-arr[j], ord=self.p) distdict[nrm] = distdict.get(nrm, 0) + 1 distinct_d = array(distdict.keys()) #mutltiplicity array with a count of how many pairs of points have a given distance J = array(distdict.values()) self.phi = sum(J*(distinct_d**(-self.q)))**(1.0/self.q) return self.phi
[docs] def perturb(self, mutation_count): """ Interchanges pairs of randomly chosen elements within randomly chosen columns of a doe a number of times. The result of this operation will also be a Latin hypercube. """ new_doe = self.doe.copy() n,k = self.doe.shape for count in range(mutation_count): col = randint(0, k-1) #choosing two distinct random points el1 = randint(0, n-1) el2 = randint(0, n-1) while el1==el2: el2 = randint(0, n-1) new_doe[el1, col] = self.doe[el2, col] new_doe[el2, col] = self.doe[el1, col] return LatinHypercube(new_doe, self.q, self.p)
def __iter__(self): return self._get_rows() def _get_rows(self): for row in self.doe: yield row def __repr__(self): return repr(self.doe) def __str__(self): return str(self.doe) def __getitem__(self,*args): return self.doe.__getitem__(*args)
_norm_map = {"1-norm":1,"2-norm":2}
[docs]class OptLatinHypercube(HasTraits): """IDOEgenerator which provides a Latin hypercube DOE sample set. The Morris-Mitchell sampling criterion of the DOE is optimzied using an evolutionary algorithm. """ implements(IDOEgenerator) num_sample_points = Int(20, desc="Number of sample points in the DOE sample set.") num_parameters = Int(2, desc="Number of parameters, or dimensions, for the DOE.") population = Int(20, desc="Size of the population used in the evolutionary optimization.") generations = Int(2, desc="Number of generations the optimization will evolve over.") norm_method = Enum(["1-norm","2-norm"], desc="Vector norm calculation method. '1-norm' is faster, but less accurate.") def __init__(self, num_samples=None, population=None,generations=None): super(OptLatinHypercube,self).__init__() self.qs = [1,2,5,10,20,50,100] #list of qs to try for Phi_q optimization if num_samples is not None: self.num_sample_points = num_samples if population is not None: self.population = population if generations is not None: self.generations = generations def __iter__(self): """Return an iterator over our sets of input values.""" return self._get_input_values() def _get_input_values(self): rand_doe = rand_latin_hypercube(self.num_sample_points, self.num_parameters) best_lhc = LatinHypercube(rand_doe, q=1, p=_norm_map[self.norm_method]) for q in self.qs: lh = LatinHypercube(rand_doe, q, _norm_map[self.norm_method]) lh_opt = _mmlhs(lh, self.population, self.generations) if lh_opt.mmphi() < best_lhc.mmphi(): best_lhc = lh_opt for row in best_lhc: yield row
def _mmlhs(x_start, population, generations): """Evolutionary search for most space filling Latin-Hypercube. Returns a new LatinHypercube instance with an optimized set of points. """ x_best = x_start phi_best = x_start.mmphi() n = x_start.shape[1] level_off = floor(0.85*generations) for it in range(generations): if it < level_off and level_off > 1.: mutations = int(round(1+(0.5*n-1)*(level_off-it)/(level_off-1))) else: mutations = 1 x_improved = x_best phi_improved = phi_best for offspring in range(population): x_try = x_best.perturb(mutations) phi_try = x_try.mmphi() if phi_try < phi_improved: x_improved = x_try phi_improved = phi_try if phi_improved < phi_best: phi_best = phi_improved x_best = x_improved return x_best if __name__== "__main__": # pragma no cover import sys try: from matplotlib import pyplot except ImportError: print "Couldn't find matplotlib" test = """ x = rand_latin_hypercube(80,2) lh = LatinHypercube(x,2,1) print lh.mmphi() lh_opt = _mmlhs(lh,20,20) print lh_opt.mmphi() """ if '--profile' in sys.argv: import cProfile import pstats,"") p = pstats.Stats("") p.sort_stats('cumulative').print_stats(10) else: exec(test) if 'pyplot' in globals(): pyplot.figure(1) pyplot.scatter(lh[:,0],lh[:,1]) pyplot.scatter(lh_opt[:,0],lh_opt[:,1],c='r')
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