[deap] 2 new revisions pushed by felix.antoine.fortin on 2014-04-04 20:04 GMT

[de...@googlecode.com]

2 new revisions:

Revision: 79a5d4316b4c
Branch: dev
Author: felix.antoine.fortin
Date: Fri Apr 4 20:04:11 2014 UTC
Log: Renamed fctmin.py by emna.py.
http://code.google.com/p/deap/source/detail?r=79a5d4316b4c

Revision: 9cf676f69d0d
Branch: dev
Author: felix.antoine.fortin
Date: Fri Apr 4 20:04:25 2014 UTC
Log: Merge.
http://code.google.com/p/deap/source/detail?r=9cf676f69d0d

==============================================================================
Revision: 79a5d4316b4c
Branch: dev
Author: felix.antoine.fortin
Date: Fri Apr 4 20:04:11 2014 UTC
Log: Renamed fctmin.py by emna.py.
http://code.google.com/p/deap/source/detail?r=79a5d4316b4c

Added:
/examples/eda/emna.py
Deleted:
/examples/eda/fctmin.py

=======================================
--- /dev/null
+++ /examples/eda/emna.py Fri Apr 4 20:04:11 2014 UTC
@@ -0,0 +1,94 @@
+# 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 EMNA(object):
+ """Estimation of Multivariate Normal Algorithm (EMNA) as described
+ by Algorithm 1 in:
+
+ Fabien Teytaud and Olivier Teytaud. 2009.
+ Why one must use reweighting in estimation of distribution algorithms.
+ In Proceedings of the 11th Annual conference on Genetic and
+ evolutionary computation (GECCO '09). ACM, New York, NY, USA, 453-460.
+ """
+ def __init__(self, centroid, sigma, mu, lambda_):
+ self.dim = len(centroid)
+ self.centroid = 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.centroid + self.sigma *
numpy.random.randn(self.lambda_, self.dim)
+ 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.centroid
+ avg = numpy.mean(z, axis=0)
+
+ # Adjust variance of the distribution
+ self.sigma = numpy.sqrt(numpy.sum(numpy.sum((z - avg)**2, axis=1))
/ (self.mu*self.dim))
+ self.centroid = self.centroid + avg
+
+
+def main():
+ N, LAMBDA = 30, 1000
+ MU = int(LAMBDA/4)
+ strategy = EMNA(centroid=[5.0]*N, sigma=5.0, mu=MU, lambda_=LAMBDA)
+
+ toolbox = base.Toolbox()
+ toolbox.register("evaluate", benchmarks.sphere)
+ 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()
+
+
+
=======================================
--- /examples/eda/fctmin.py Fri Apr 4 14:25:20 2014 UTC
+++ /dev/null
@@ -1,93 +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 EMNA(object):
- """Estimation of Multivariate Normal Algorithm (EMNA) as described
- by Algorithm 1 in:
-
- Fabien Teytaud and Olivier Teytaud. 2009.
- Why one must use reweighting in estimation of distribution algorithms.
- In Proceedings of the 11th Annual conference on Genetic and
- evolutionary computation (GECCO '09). ACM, New York, NY, USA, 453-460.
- """
- def __init__(self, centroid, sigma, mu, lambda_):
- self.dim = len(centroid)
- self.centroid = 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.centroid + self.sigma *
numpy.random.randn(self.lambda_, self.dim)
- 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.centroid
- avg = numpy.mean(z, axis=0)
-
- # Adjust variance of the distribution
- self.sigma = numpy.sqrt(numpy.sum(numpy.sum((z - avg)**2, axis=1))
/ (self.mu*self.dim))
- self.centroid = self.centroid + avg
-
-def main():
- N, LAMBDA = 30, 1000
- MU = int(LAMBDA/4)
- strategy = EMNA(centroid=[5.0]*N, sigma=5.0, 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()
-
-
-

==============================================================================
Revision: 9cf676f69d0d
Branch: dev
Author: felix.antoine.fortin
Date: Fri Apr 4 20:04:25 2014 UTC
Log: Merge.
http://code.google.com/p/deap/source/detail?r=9cf676f69d0d