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# coding: UTF8
# Copyright 2009 Thomas Jourdan
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) 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 General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
import pickle
import sys
import os.path
import random
import ka_debug
import ka_random
import model_protozoon
STATE_INIT = 'I'
STATE_RANDOMIZED = 'R'
STATE_EVOLVED = 'E'
class KandidModel(object):
"""
inv: self.size >= 2
inv: 1 <= self.fade_away <= self.size
#inv: 0.0 <= self._flurry_rate <= 9.0
inv: len(self.fitness) == self.size
inv: forall(self.fitness, lambda f: 0.0 <= f <= 9.0)
inv: len(self.protozoans) == self.size
inv: forall(self.protozoans, lambda p: p is not None)
# all protozoans must be distinct objects, maybe with equal content
inv: all_uniqe_reference(self.protozoans)
"""
def __init__(self, init_size):
self._state = STATE_INIT
self.size = init_size
self.fade_away = init_size / 2
#self._flurry_rate = 5.0
self.protozoans = [model_protozoon.Protozoon() for i in range(self.size)]
self.fitness = [4.0 for i in range(self.size)]
ka_debug.info('initializing model with population size %u' % init_size)
def get_flurry_rate(self):
return ka_random.get_flurry()
# return self._flurry_rate
def set_flurry_rate(self, value):
"""
pre: 0 <= value <= 9
"""
ka_random.set_flurry(value)
# self._flurry_rate = value
def is_overwrite_allowed(self):
"""Preserve an already evolved population from over writing."""
return not self._state == STATE_EVOLVED
def classify(self):
"""
# __return__[0] -> good
# __return__[1] -> moderate
# __return__[2] -> poor
post: len(__return__[0])+len(__return__[1])+len(__return__[2]) == self.size
post: len(__return__[0]) >= 1
post: len(__return__[2]) >= 1
# mutual exclusive
post: forall(__return__[0], lambda x: not contains_reference(x, __return__[1]))
post: forall(__return__[0], lambda x: not contains_reference(x, __return__[2]))
post: forall(__return__[2], lambda x: not contains_reference(x, __return__[0]))
post: forall(__return__[2], lambda x: not contains_reference(x, __return__[1]))
"""
good, moderate, poor = [], [], []
sorted_fitness = sorted(self.fitness)
poor_level = sorted_fitness[self.fade_away-1]
good_level = sorted_fitness[-1]
for protoz, fit in zip(self.protozoans, self.fitness):
if fit >= good_level and len(good) < 1:
good.append(protoz)
elif fit <= poor_level:
if len(poor) >= self.fade_away:
index = random.randint(0, len(poor)-1)
moderate.append(poor[index])
del poor[index]
poor.append(protoz)
else:
moderate.append(protoz)
return good, moderate, poor
def reduce_fitness(self, index):
"""
pre: 0 <= index < len(self.fitness)
post: self.fitness.count(0.0) == 1
"""
for raise_at, fit in enumerate(self.fitness):
if fit < 1.0:
self.fitness[raise_at] = 1.0
self.fitness[index] = 0.0
def raise_fitness(self, index):
"""
pre: 0 <= index < len(self.fitness)
post: self.fitness.count(9.0) == 1
"""
for lower_at, fit in enumerate(self.fitness):
if 5.0 < fit:
self.fitness[lower_at] = round(self.fitness[lower_at] - 1.0)
self.fitness[index] = 9.0
def randomize(self):
self._state = STATE_RANDOMIZED
for protoz in self.protozoans:
protoz.randomize()
def random(self):
"""
post: len(__return__) > 0
post: forall(__return__, lambda x: 0 <= x < self.size)
"""
new_indices = []
self._state = STATE_EVOLVED
good, moderate, poor = self.classify()
for new_at, protoz in enumerate(self.protozoans):
if protoz in poor:
self.protozoans[new_at].randomize()
self.fitness[new_at] = 4.0
new_indices.append(new_at)
print 'new_indices', new_indices
return new_indices
def breed(self):
"""
post: len(__return__) > 0
post: forall(__return__, lambda x: 0 <= x < self.size)
"""
new_indices = []
self._state = STATE_EVOLVED
good, moderate, poor = self.classify()
index = 0
for new_at, protoz in enumerate(self.protozoans):
if protoz in poor:
new_one = good[0].crossingover(moderate[index % len(moderate)])
new_one.shuffle()
new_one.mutate()
self.protozoans[new_at] = new_one
self.fitness[new_at] = 4.0
new_indices.append(new_at)
index += 1
print 'new_indices', new_indices
return new_indices
def replace(self, new_one):
"""Replace protozoon with lowest fitness.
pre: isinstance(new_one, model_protozoon.Protozoon)
"""
poor_level = 999999.9
for protoz, fit in zip(self.protozoans, self.fitness):
if fit < poor_level:
poor_level = fit
poor = protoz
for new_at, protoz in enumerate(self.protozoans):
if protoz is poor:
self.protozoans[new_at] = new_one
self.fitness[new_at] = 5.0
return new_at
return -1
flurry_rate = property(get_flurry_rate, set_flurry_rate)
def from_buffer(str_buffer):
ka_debug.info('read from_buffer')
obj = None
try:
obj = pickle.loads(str_buffer)
except:
ka_debug.err('failed reading buffer [%s] [%s]' % \
(sys.exc_info()[0], sys.exc_info()[1]))
ka_debug.info('[%s]' % str_buffer)
return obj
def to_buffer(obj):
ka_debug.info('write %s to_buffer' % type(obj))
try:
return pickle.dumps(obj)
except:
ka_debug.err('failed writing buffer [%s] [%s]' % \
(sys.exc_info()[0], sys.exc_info()[1]))
def read_file(file_path):
model = None
if os.path.isfile(file_path):
in_file = None
try:
in_file = open(file_path, 'r')
ka_debug.info('in_file [%s]' % in_file.name)
model = pickle.load(in_file)
except:
ka_debug.err('failed reading [%s] [%s] [%s]' % \
(in_file.name, sys.exc_info()[0], sys.exc_info()[1]))
finally:
if in_file:
in_file.close()
return model
def write_file(file_path, model):
out_file = None
try:
out_file = open(file_path, 'w')
ka_debug.info('write out_file [%s]' % out_file.name)
pickle.dump(model, out_file)
except:
ka_debug.err('failed writing [%s] [%s] [%s]' % \
(out_file.name, sys.exc_info()[0], sys.exc_info()[1]))
finally:
if out_file:
out_file.close()
def all_uniqe_reference(sequ):
# Brute force is all that's left.
unique = []
for elem in sequ:
if contains_reference(elem, unique):
return False
else:
unique.append(elem)
return len(unique) == len(sequ)
def contains_reference(find_elem, sequ):
for elem in sequ:
if id(find_elem) == id(elem):
return True
return False
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