[deap] 3 new revisions pushed by felix.antoine.fortin on 2014-04-06 21:03 GMT

[de...@googlecode.com]

3 new revisions:

Revision: 4de7df29dd0f
Branch: default
Author: felix.antoine.fortin
Date: Sun Apr 6 21:02:24 2014 UTC
Log: Fix changeset 5b918b6ffd44....
http://code.google.com/p/deap/source/detail?r=4de7df29dd0f

Revision: 75db5c311f05
Branch: dev
Author: felix.antoine.fortin
Date: Sun Apr 6 21:02:24 2014 UTC
Log: Fix changeset 5b918b6ffd44....
http://code.google.com/p/deap/source/detail?r=75db5c311f05

Revision: ad805687abe2
Branch: dev
Author: felix.antoine.fortin
Date: Sun Apr 6 21:02:59 2014 UTC
Log: Merge.
http://code.google.com/p/deap/source/detail?r=ad805687abe2

==============================================================================
Revision: 4de7df29dd0f
Branch: default
Author: felix.antoine.fortin
Date: Sun Apr 6 21:02:24 2014 UTC
Log: Fix changeset 5b918b6ffd44.

sortLogNondominated was computing but not returning the asked
front following the addition of some parameters required to
be compatible to sortNondominated.
http://code.google.com/p/deap/source/detail?r=4de7df29dd0f

Modified:
/deap/tools/emo.py

=======================================
--- /deap/tools/emo.py Thu Mar 20 18:07:51 2014 UTC
+++ /deap/tools/emo.py Sun Apr 6 21:02:24 2014 UTC
@@ -251,7 +251,7 @@
for i, front in enumerate(pareto_fronts):
count += len(front)
if count >= k:
- return pareto_fronts[:i]
+ return pareto_fronts[:i+1]
return pareto_fronts
else:
return pareto_fronts[0]

==============================================================================
Revision: 75db5c311f05
Branch: dev
Author: felix.antoine.fortin
Date: Sun Apr 6 21:02:24 2014 UTC
Log: Fix changeset 5b918b6ffd44.

sortLogNondominated was computing but not returning the asked
front following the addition of some parameters required to
be compatible to sortNondominated.
http://code.google.com/p/deap/source/detail?r=75db5c311f05

Modified:
/deap/tools/emo.py

=======================================
--- /deap/tools/emo.py Sat Feb 22 14:41:46 2014 UTC
+++ /deap/tools/emo.py Sun Apr 6 21:02:24 2014 UTC
@@ -259,7 +259,7 @@
for i, front in enumerate(pareto_fronts):
count += len(front)
if count >= k:
- return pareto_fronts[:i]
+ return pareto_fronts[:i+1]
return pareto_fronts
else:
return pareto_fronts[0]

==============================================================================
Revision: ad805687abe2
Branch: dev
Author: felix.antoine.fortin
Date: Sun Apr 6 21:02:59 2014 UTC
Log: Merge.
http://code.google.com/p/deap/source/detail?r=ad805687abe2

Deleted:
/examples/eda/fctmin.py

=======================================
--- /examples/eda/fctmin.py Mon Jun 10 14:28:32 2013 UTC
+++ /dev/null
@@ -1,85 +0,0 @@
-# This file is part of DEAP.
-#
-# DEAP 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 (at your option) any later version.
-#
-# DEAP 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 Lesser General Public
-# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
-
-import random
-
-import numpy
-
-from operator import attrgetter
-
-from deap import algorithms
-from deap import base
-from deap import benchmarks
-from deap import creator
-from deap import tools
-
-creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
-creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMin)
-
-class EDA(object):
- def __init__(self, centroid, sigma, mu, lambda_):
- self.dim = len(centroid)
- self.loc = numpy.array(centroid)
- self.sigma = numpy.array(sigma)
- self.lambda_ = lambda_
- self.mu = mu
-
- def generate(self, ind_init):
- # Generate lambda_ individuals and put them into the provided class
- arz = self.sigma * numpy.random.randn(self.lambda_, self.dim) +
self.loc
- return list(map(ind_init, arz))
-
- def update(self, population):
- # Sort individuals so the best is first
- sorted_pop = sorted(population, key=attrgetter("fitness"),
reverse=True)
-
- # Compute the average of the mu best individuals
- z = sorted_pop[:self.mu] - self.loc
- avg = numpy.mean(z, axis=0)
-
- # Adjust variances of the distribution
- self.sigma = numpy.sqrt(numpy.sum((z - avg)**2, axis=0) / (self.mu
- 1.0))
- self.loc = self.loc + avg
-
-def main():
- N, LAMBDA = 30, 1000
- MU = int(LAMBDA/4)
- strategy = EDA(centroid=[5.0]*N, sigma=[5.0]*N, mu=MU, lambda_=LAMBDA)
-
- toolbox = base.Toolbox()
- toolbox.register("evaluate", benchmarks.rastrigin)
- toolbox.register("generate", strategy.generate, creator.Individual)
- toolbox.register("update", strategy.update)
-
- # Numpy equality function (operators.eq) between two arrays returns the
- # equality element wise, which raises an exception in the if similar()
- # check of the hall of fame. Using a different equality function like
- # numpy.array_equal or numpy.allclose solve this issue.
- hof = tools.HallOfFame(1, similar=numpy.array_equal)
- stats = tools.Statistics(lambda ind: ind.fitness.values)
- stats.register("avg", numpy.mean)
- stats.register("std", numpy.std)
- stats.register("min", numpy.min)
- stats.register("max", numpy.max)
-
- algorithms.eaGenerateUpdate(toolbox, ngen=150, stats=stats,
halloffame=hof)
-
- return hof[0].fitness.values[0]
-
-if __name__ == "__main__":
- main()
-
-
-