# 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser
# 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
# <http://www.gnu.org/licenses/>.
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
cProfile.run(test,"test.prof")
p = pstats.Stats("test.prof")
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')
pyplot.show()
