Adjusted the cmaes_plotting example to the new logbook and adjusted t…

…he documentation accordingly. --HG-- branch : dev
falconzyx · Jun 29, 2013 · 9213a55 · 9213a55
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diff --git a/doc/examples/cmaes_plotting.rst b/doc/examples/cmaes_plotting.rst
@@ -19,35 +19,35 @@ The beginning of this example is exactly the same as the :ref:`CMA-ES
 :func:`~deap.algorithms.eaGenerateUpdate` is somewhat insufficient for our
 purpose. We need to gather the required data on each generation. So instead of
 using the  :func:`~deap.algorithms.eaGenerateUpdate` function, we'll develop
-it to get a grip on what is recorded. First, we'll created objects to record
+it to get a grip on what is recorded. First, we'll create objects to record
 our data. Here we want to plot, in addition to what the
-:class:`~deap.Statistics` and :class:`~deap.HallOfFame` objects contain, the
+:class:`~deap.tools.Logbook` and :class:`~deap.tools.HallOfFame` objects contain, the
 step size, the axis ratio and the major axis of the covariace matrix, the best
 value so far, the best coordinates so far and the standard deviation of the
 all coordinates at each generation.
 
 .. literalinclude:: /code/examples/es/cma_plotting.py
-   :lines: 56-61
+   :lines: 59-64
 
 Once the objects are created, the evolution loop, based on a generational
 stopping criterion, calls repeatedly the :meth:`generate`, :meth:`evaluate`
 and :meth:`update` methods registered in the toolbox.
 
 .. literalinclude:: /code/examples/es/cma_plotting.py
-   :lines: 63-72
+   :lines: 66-75
 
 Then, the previoulsy created objects start to play their role. The according
 data is recorded in each object on each generation.
 
 .. literalinclude:: /code/examples/es/cma_plotting.py
-   :lines: 77,84-89
+   :lines: 80,81,88-93
 
 Now that the data is recorded the only thing left to do is to plot it. We'll
 use `matplotlib <http://matplotlib.org>`_ to generate the graphics from the
 recorded data.
 
 .. literalinclude:: /code/examples/es/cma_plotting.py
-   :lines: 92-120
+   :lines: 95-124
 
 Which gives the following result.
 

diff --git a/examples/es/cma_plotting.py b/examples/es/cma_plotting.py
@@ -51,6 +51,9 @@ def main(verbose=True):
     stats.register("std", numpy.std)
     stats.register("min", numpy.min)
     stats.register("max", numpy.max)
+
+    logbook = tools.Logbook()
+    logbook.header = "gen", "evals", "std", "min", "avg", "max"
 
     # Objects that will compile the data
     sigma = numpy.ndarray((NGEN,1))
@@ -74,10 +77,11 @@ def main(verbose=True):
         # Update the hall of fame and the statistics with the
         # currently evaluated population
         halloffame.update(population)
-        stats.record(population, evals=len(population), gen=gen,)
+        record = stats.compile(population)
+        logbook.record(evals=len(population), gen=gen, **record)
 
         if verbose:
-            print(stats.stream)
+            print(logbook.stream)
 
         # Save more data along the evolution for latter plotting
         # diagD is sorted and sqrooted in the update method
@@ -90,7 +94,7 @@ def main(verbose=True):
 
     # The x-axis will be the number of evaluations
     x = list(range(0, strategy.lambda_ * NGEN, strategy.lambda_))
-    avg, max_, min_ = stats.select("avg", "max", "min")
+    avg, max_, min_ = logbook.select("avg", "max", "min")
     plt.figure()
     plt.subplot(2, 2, 1)
     plt.semilogy(x, avg, "--b")