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Viewing changes to doc/code/tutorials/part_3/stats.py

  • Committer: Package Import Robot
  • Author(s): Daniel Stender, Miriam Ruiz, Daniel Stender, Jakub Wilk
  • Date: 2014-07-06 00:03:41 UTC
  • mfrom: (1.1.2)
  • Revision ID: package-import@ubuntu.com-20140706000341-s7gij1ki3d8xz6t9
Tags: 1.0.1-1
http://bugs.debian.org/675200
[ Miriam Ruiz ]
* New Upstream Release. Closes: #675200
* Upgraded Standards-Version from 3.9.2 to 3.9.5
* Switched to dh_python2
* Upgraded debian/compat to 9
* Added build-arch and build-indep targets to debian/rules
* Using awk to remove connection from the documentation to google analytics
* Using jquery.js and underscore.js from libjs-jquery and libjs-underscore
* Added patches/doc.patch

[ Daniel Stender ]
* deb/control:
  + Added myself to Uploaders.
  + Added version to b-p on python-all.
  + Updated Homepage.
  + Wrapped and sorted.
* deb/copyright:
  + Changed copyright file towards DEP-5.
  + Updated Source URI (project source moved to Github).
  + Added email addresses for copyright holders.
  + Dropped license for eap/toolbox.py (not included anymore).
  + Extended copyrights to 2014.
  + Added myself to copyrights for deb/*.
  + Specified license location.
* Added watch file [initial version by Jackson Doak].

[ Jakub Wilk ]
* Use canonical URIs for Vcs-* fields.

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doc/code/tutorials/part_3/stats.py
 
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import random
 
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import numpy
 
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from deap import algorithms
 
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from deap import base
 
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from deap import creator
 
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from deap import tools
 
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random.seed(0)
 
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stats = tools.Statistics(key=lambda ind: ind.fitness.values)
 
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stats.register("avg", numpy.mean)
 
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stats.register("std", numpy.std)
 
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stats.register("min", numpy.min)
 
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stats.register("max", numpy.max)
 
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def evalOneMax(individual):
 
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    return sum(individual),
 
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creator.create("FitnessMax", base.Fitness, weights=(1.0,))
 
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creator.create("Individual", list, fitness=creator.FitnessMax)
 
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toolbox = base.Toolbox()
 
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toolbox.register("attr_bool", random.randint, 0, 1)
 
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toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100)
 
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
 
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toolbox.register("evaluate", evalOneMax)
 
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pop = toolbox.population(n=100)
 
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# Evaluate the individuals
 
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fitnesses = map(toolbox.evaluate, pop)
 
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for ind, fit in zip(pop, fitnesses):
 
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    ind.fitness.values = fit
 
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record = stats.compile(pop)
 
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print(record)
 
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stats = tools.Statistics(key=lambda ind: ind.fitness.values)
 
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stats.register("avg", numpy.mean, axis=0)
 
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stats.register("std", numpy.std, axis=0)
 
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stats.register("min", numpy.min, axis=0)
 
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stats.register("max", numpy.max, axis=0)
 
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record = stats.compile(pop)
 
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print(record)
 
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pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=0, 
 
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                                   stats=stats, verbose=True)
 
 
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