Merge.

--HG-- branch : dev
falconzyx · Jun 19, 2013 · 12645bb · 12645bb
2 parents d9ea368 + 8edc235
commit 12645bb
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diff --git a/deap/algorithms.py b/deap/algorithms.py
@@ -35,15 +35,15 @@ def varAnd(population, toolbox, cxpb, mutpb):
     fitness invalidated. The individuals are cloned so returned population is
     independent of the input population.
     
-    :param population: A list of individuals to variate.
+    :param population: A list of individuals to vary.
     :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                     operators.
     :param cxpb: The probability of mating two individuals.
     :param mutpb: The probability of mutating an individual.
     :returns: A list of varied individuals that are independent of their
               parents.
     
-    The variator goes as follow. First, the parental population
+    The variation goes as follow. First, the parental population
     :math:`P_\mathrm{p}` is duplicated using the :meth:`toolbox.clone` method
     and the result is put into the offspring population :math:`P_\mathrm{o}`.
     A first loop over :math:`P_\mathrm{o}` is executed to mate consecutive
@@ -147,7 +147,7 @@ def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None,
         # Select the next generation individuals
         offspring = toolbox.select(population, len(population))
 
-        # Variate the pool of individuals
+        # Vary the pool of individuals
         offspring = varAnd(offspring, toolbox, cxpb, mutpb)
 
         # Evaluate the individuals with an invalid fitness
@@ -177,7 +177,7 @@ def varOr(population, toolbox, lambda_, cxpb, mutpb):
     their fitness invalidated. The individuals are cloned so returned
     population is independent of the input population.
     
-    :param population: A list of individuals to variate.
+    :param population: A list of individuals to vary.
     :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                     operators.
     :param lambda\_: The number of children to produce
@@ -186,7 +186,7 @@ def varOr(population, toolbox, lambda_, cxpb, mutpb):
     :returns: A list of varied individuals that are independent of their
               parents.
     
-    The variator goes as follow. On each of the *lambda_* iteration, it
+    The variation goes as follow. On each of the *lambda_* iteration, it
     selects one of the three operations; crossover, mutation or reproduction.
     In the case of a crossover, two individuals are selected at random from
     the parental population :math:`P_\mathrm{p}`, those individuals are cloned
@@ -283,7 +283,7 @@ def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
 
     # Begin the generational process
     for gen in range(1, ngen+1):
-        # Variate the population
+        # Vary the population
         offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
 
         # Evaluate the individuals with an invalid fitness
@@ -366,7 +366,7 @@ def eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
 
     # Begin the generational process
     for gen in range(1, ngen+1):
-        # Variate the population
+        # Vary the population
         offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
 
         # Evaluate the individuals with an invalid fitness

diff --git a/deap/tools/structure.py b/deap/tools/structure.py
@@ -151,108 +151,6 @@ def genealogy(index, depth):
         genealogy(individual.history_index, 0)
         return gtree
 
-
-class Checkpoint(object):
-    """A checkpoint is a file containing the state of any object that has been
-    hooked. While initializing a checkpoint, add the objects that you want to
-    be dumped by appending keyword arguments to the initializer or using the 
-    :meth:`add`. 
-
-    In order to use efficiently this module, you must understand properly the
-    assignment principles in Python. This module uses the *pointers* you passed
-    to dump the object, for example the following won't work as desired ::
-
-        >>> my_object = [1, 2, 3]
-        >>> cp = Checkpoint()
-        >>> cp.add("my_object", my_object)
-        >>> my_object = [3, 5, 6]
-        >>> cp.dump(open("example.ecp", "w"))
-        >>> cp.load(open("example.ecp", "r"))
-        >>> cp["my_object"]
-        [1, 2, 3]
-
-    In order to dump the new value of ``my_object`` it is needed to change its
-    internal values directly and not touch the *label*, as in the following ::
-
-        >>> my_object = [1, 2, 3]
-        >>> cp = Checkpoint()
-        >>> cp.add("my_object", my_object)
-        >>> my_object[:] = [3, 5, 6]
-        >>> cp.dump(open("example.ecp", "w"))
-        >>> cp.load(open("example.ecp", "r"))
-        >>> cp["my_object"]
-        [3, 5, 6]
-
-    """
-    def __init__(self):
-        self.objects = {}
-        self.keys = {}
-        self.values = {}
-
-    def add(self, name, object, key=identity):
-        """Add an object to the list of objects to be dumped. The object is
-        added under the name specified by the argument *name*, the object
-        added is *object*, and the *key* argument allow to specify a subpart
-        of the object that should be dumped (*key* defaults to an identity key
-        that dumps the entire object).
-        
-        :param name: The name under which the object will be dumped.
-        :param object: The object to register for dumping.
-        :param key: A function access the subcomponent of the object to dump,
-                    optional.
-        
-        The following illustrates how to use the key.
-        ::
-
-            >>> from operator import itemgetter
-            >>> my_object = [1, 2, 3]
-            >>> cp = Checkpoint()
-            >>> cp.add("item0", my_object, key=itemgetter(0))
-            >>> cp.dump(open("example.ecp", "w"))
-            >>> cp.load(open("example.ecp", "r"))
-            >>> cp["item0"]
-            1
-
-        """
-        self.objects[name] = object
-        self.keys[name] = partial(key, object)
-
-    def remove(self, *args):
-        """Remove objects with the specified name from the list of objects to
-        be dumped.
-        
-        :param name: The name of one or more object to remove from dumping.
-        
-        """
-        for element in args:
-            del self.objects[element]
-            del self.keys[element]
-            del self.values[element]
-
-    def __getitem__(self, value):
-        return self.values.get(value)
-
-    def dump(self, file):
-        """Dump the current registered object values in the provided
-        *filestream*.
-        
-        :param filestream: A stream in which write the data.
-        """
-        self.values = dict.fromkeys(self.objects.iterkeys())
-        for name, key in self.keys.iteritems():
-            self.values[name] = key()
-        pickle.dump(self.values, file)
-
-    def load(self, file):
-        """Load a checkpoint from the provided *filestream* retrieving the
-        dumped object values, it is not safe to load a checkpoint file in a
-        checkpoint object that contains references as all conflicting names
-        will be updated with the new values.
-        
-        :param filestream: A stream from which to read a checkpoint.
-        """
-        self.values.update(pickle.load(file))
-
 class Statistics(object):
     """Object that compiles statistics on a list of arbitrary objects. 
     When created the statistics object receives a *key* argument that 
@@ -653,7 +551,7 @@ def update(self, population):
             if not is_dominated and not has_twin:
                 self.insert(ind)
 
-__all__ = ['HallOfFame', 'ParetoFront', 'History', 'Statistics', 'MultiStatistics', 'Logbook', 'Checkpoint']
+__all__ = ['HallOfFame', 'ParetoFront', 'History', 'Statistics', 'MultiStatistics', 'Logbook']
 
 if __name__ == "__main__":
     doctest.run_docstring_examples(Statistics.register, globals())

diff --git a/doc/api/tools.rst b/doc/api/tools.rst
@@ -187,18 +187,6 @@ Hall-Of-Fame
    
 ..   .. autoattribute:: deap.tools.EvolutionLogger.output
 
-Checkpoint
-----------
-.. autoclass:: deap.tools.Checkpoint()
-
-   .. automethod:: deap.tools.Checkpoint.dump(filestream)
-
-   .. automethod:: deap.tools.Checkpoint.load(filestream)
-
-   .. automethod:: deap.tools.Checkpoint.add(name, object[, key])
-
-   .. automethod:: deap.tools.Checkpoint.remove(name[, ...])
-
 History
 -------
 .. autoclass:: deap.tools.History

diff --git a/doc/tutorials/advanced/benchmarking.rst b/doc/tutorials/advanced/benchmarking.rst
@@ -55,7 +55,7 @@ to the :func:`main` function). The single line that is related to the provided
 algorithm in the call to the :func:`main` function.
 
 .. literalinclude:: /code/examples/bbob.py
-   :lines: 26,27,28,89-135
+   :lines: 26,27,28,90-137
 
 Once these experiments are done, the data contained in the :file:`ouput`
 directory can be used to build the results document. See the `BBOB

diff --git a/doc/tutorials/basic/part1.rst b/doc/tutorials/basic/part1.rst
@@ -24,7 +24,7 @@ objective and single objective fitnesses can be treated the same way. A
    :lines: 9
 
 rendering a fitness that minimize the first objective and maximize the
-second one. The weights can also be used to variate the importance of each
+second one. The weights can also be used to vary the importance of each
 objective one against another. This means that the weights can be any real
 number and only the sign is used to determine if a maximization or
 minimization is done. An example of where the weights can be useful is in the

diff --git a/examples/bbob.py b/examples/bbob.py
@@ -87,7 +87,7 @@ def main(func, dim, maxfuncevals, ftarget=None):
             return best
 
     return best
-    
+
 if __name__ == "__main__":
     # Maximum number of restart for an algorithm that detects stagnation
     maxrestarts = 1000

diff --git a/examples/coev/coop_adapt.py b/examples/coev/coop_adapt.py
@@ -76,7 +76,7 @@ def main(extended=True, verbose=True):
         # Initialize a container for the next generation representatives
         next_repr = [None] * len(species)
         for i, s in enumerate(species):
-            # Variate the species individuals
+            # Vary the species individuals
             s = algorithms.varAnd(s, toolbox, 0.6, 1.0)
 
             r = representatives[:i] + representatives[i+1:]

diff --git a/examples/coev/coop_evol.py b/examples/coev/coop_evol.py
@@ -84,7 +84,7 @@ def main(extended=True, verbose=True):
         # Initialize a container for the next generation representatives
         next_repr = [None] * len(species)
         for (i, s), j in zip(enumerate(species), species_index):
-            # Variate the species individuals
+            # Vary the species individuals
             s = algorithms.varAnd(s, toolbox, 0.6, 1.0)
 
             # Get the representatives excluding the current species

diff --git a/examples/coev/coop_gen.py b/examples/coev/coop_gen.py
@@ -76,7 +76,7 @@ def main(extended=True, verbose=True):
         # Initialize a container for the next generation representatives
         next_repr = [None] * len(species)
         for i, s in enumerate(species):
-            # Variate the species individuals
+            # Vary the species individuals
             s = algorithms.varAnd(s, toolbox, 0.6, 1.0)
 
             # Get the representatives excluding the current species

diff --git a/examples/coev/coop_niche.py b/examples/coev/coop_niche.py
@@ -69,7 +69,7 @@ def main(extended=True, verbose=True):
         # Initialize a container for the next generation representatives
         next_repr = [None] * len(species)
         for i, s in enumerate(species):
-            # Variate the species individuals
+            # Vary the species individuals
             s = algorithms.varAnd(s, toolbox, 0.6, 1.0)
 
             # Get the representatives excluding the current species

diff --git a/examples/ga/nsga2.py b/examples/ga/nsga2.py
@@ -87,7 +87,7 @@ def main(seed=None):
 
     # Begin the generational process
     for gen in range(1, NGEN):
-        # Variate the population
+        # Vary the population
         offspring = tools.selTournamentDCD(pop, len(pop))
         offspring = [toolbox.clone(ind) for ind in offspring]