def __call__(self, orgs, prob=1.0):
if np.random.random_sample() > prob:
return orgs[0], orgs[1]
if isinstance(orgs[0], np.ndarray):
idx = np.random.random_integers(0, len(orgs[0]) - 1)
return (np.append(orgs[0][:idx], orgs[1][idx:], axis=0),
np.append(orgs[1][:idx], orgs[0][idx:], axis=0))
elif isinstance(orgs[0], TernaryString):
idx = np.random.random_integers(0, orgs[0].length - 1)
ret1 = TernaryString(orgs[0].base, orgs[0].known, orgs[0].length)
ret2 = TernaryString(orgs[1].base, orgs[1].known, orgs[1].length)
ret1[idx:] = orgs[1][idx:]
ret2[idx:] = orgs[0][idx:]
return ret1, ret2
else:
raise ValueError("Received unknown type in OnePointDualCrosser.")
def partition(self):
""" compute the partition function - only for small dimension !!! """
from pyec.util.TernaryString import TernaryString
total = 0
vsize = self.vsize
hsize = self.hsize
all = (1L << (vsize + hsize)) - 1L
for i in xrange(1 << (vsize + hsize)):
total += exp(self.__call__(TernaryString(long(i), all)))
return total
def mutate(self, x):
"""This method uses an iterative algorithm to flip bits in
a TernaryString with a given probability. The algorithm essentially
uses the binary representation of the bit-flipping probability in order
to convert random byte sampling (``p=.5``) to account for any probability,
with resolution of ``1e-16``.
"""
flipped = TernaryString.bernoulli(self.p(), self.config.space.dim)
base = x.base & ~flipped.base | ~x.base & flipped.base
known = x.known & flipped.known
return TernaryString(base, known, self.config.space.dim)
def __call__(self, network):
numBytes = int(ceil(len(network.variables) / 8.0))
numFull = len(network.variables) / 8
initial = ''
if numBytes != numFull:
extra = len(network.variables) % 8
initMask = 0
for i in xrange(extra):
initMask <<= 1
initMask |= 1
initial = struct.pack('B',initMask)
base = long(binascii.hexlify(random.bytes(numBytes)), 16)
known = long(binascii.hexlify(initial + '\xff'*numFull), 16)
return TernaryString(base, known)
def __call__(self, orgs, prob):
if np.random.random_sample() > prob:
return orgs[0]
if self.config.space.type == np.ndarray:
rnd = np.random.random_sample(len(orgs[0])).round()
return rnd * orgs[0] + (1 - rnd) * orgs[1]
elif self.config.space.type == TernaryString:
rnd = np.random.bytes(len(str(orgs[0])) * 8)
rnd = long(binascii.hexlify(rnd), 16)
base = rnd & orgs[0].base | ~rnd & orgs[1].base
known = rnd & orgs[0].known | ~rnd & orgs[1].known
return TernaryString(base, known, self.config.space.dim)
else:
err = "Unknown type for UniformCrossover: {0}"
raise NotImplementException(err.format(self.config.space.type))
def extent(self):
lower = 0L | (self.spec.known & self.spec.base)
upper = -1L & (self.spec.known & self.spec.base | ~self.spec.known)
return (TernaryString(lower, self.spec.known, self.spec.length),
TernaryString(upper, self.spec.known, self.spec.length))
def configurations(self):
cnt = 1L << (len(self.parents))
for i in xrange(cnt):
yield TernaryString(self.expand(long(i)), self.known)
raise StopIteration
def values(self):
mask = 1L << self.index
yield TernaryString(0L, mask)
yield TernaryString(mask, mask)
raise StopIteration
