Merge branch 'master' of github.com:DEAP/deap

deap/benchmarks/__init__.py

@@ -140,9 +140,11 @@ def h1(individual):
        * - Global optima
          - :math:`\mathbf{x} = (8.6998, 6.7665)`, :math:`f(\mathbf{x}) = 2`\n
        * - Function
-         - :math:`f(\mathbf{x}) = \frac{\sin(x_1 - \frac{x_2}{8})^2 + \
-            \sin(x_2 + \frac{x_1}{8})^2}{\sqrt{(x_1 - 8.6998)^2 + \
-            (x_2 - 6.7665)^2} + 1}`
+         - .. math::
+
+             f(\mathbf{x}) = \frac{\sin(x_1 - \frac{x_2}{8})^2 + \
+             \sin(x_2 + \frac{x_1}{8})^2}{\sqrt{(x_1 - 8.6998)^2 + \
+             (x_2 - 6.7665)^2} + 1}
 
     .. plot:: code/benchmarks/h1.py
        :width: 67 %
@@ -167,8 +169,10 @@ def ackley(individual):
        * - Global optima
          - :math:`x_i = 0, \forall i \in \lbrace 1 \ldots N\rbrace`, :math:`f(\mathbf{x}) = 0`
        * - Function
-         - :math:`f(\mathbf{x}) = 20 - 20\exp\left(-0.2\sqrt{\frac{1}{N} \
-            \sum_{i=1}^N x_i^2} \right) + e - \exp\left(\frac{1}{N}\sum_{i=1}^N \cos(2\pi x_i) \right)`
+         - .. math::
+
+              f(\mathbf{x}) = 20 - 20\exp\left(-0.2\sqrt{\frac{1}{N}
+              \sum_{i=1}^N x_i^2} \right) + e - \exp\left(\frac{1}{N}\sum_{i=1}^N \cos(2\pi x_i) \right)
 
     .. plot:: code/benchmarks/ackley.py
        :width: 67 %
@@ -192,8 +196,10 @@ def bohachevsky(individual):
        * - Global optima
          - :math:`x_i = 0, \forall i \in \lbrace 1 \ldots N\rbrace`, :math:`f(\mathbf{x}) = 0`
        * - Function
-         -  :math:`f(\mathbf{x}) = \sum_{i=1}^{N-1}(x_i^2 + 2x_{i+1}^2 - \
-                   0.3\cos(3\pi x_i) - 0.4\cos(4\pi x_{i+1}) + 0.7)`
+         -  .. math::
+
+               f(\mathbf{x}) = \sum_{i=1}^{N-1}(x_i^2 + 2x_{i+1}^2 -
+               0.3\cos(3\pi x_i) - 0.4\cos(4\pi x_{i+1}) + 0.7)
 
     .. plot:: code/benchmarks/bohachevsky.py
        :width: 67 %
@@ -216,8 +222,10 @@ def griewank(individual):
        * - Global optima
          - :math:`x_i = 0, \forall i \in \lbrace 1 \ldots N\rbrace`, :math:`f(\mathbf{x}) = 0`
        * - Function
-         - :math:`f(\mathbf{x}) = \frac{1}{4000}\sum_{i=1}^N\,x_i^2 - \
-                  \prod_{i=1}^N\cos\left(\frac{x_i}{\sqrt{i}}\right) + 1`
+         - .. math::
+
+              f(\mathbf{x}) = \frac{1}{4000}\sum_{i=1}^N\,x_i^2 -
+              \prod_{i=1}^N\cos\left(\frac{x_i}{\sqrt{i}}\right) + 1
 
     .. plot:: code/benchmarks/griewank.py
        :width: 67 %
@@ -252,9 +260,11 @@ def rastrigin(individual):
 def rastrigin_scaled(individual):
     r"""Scaled Rastrigin test objective function.
 
-    :math:`f_{\text{RastScaled}}(\mathbf{x}) = 10N + \sum_{i=1}^N \
-        \left(10^{\left(\frac{i-1}{N-1}\right)} x_i \right)^2 - \
-        10\cos\left(2\pi 10^{\left(\frac{i-1}{N-1}\right)} x_i \right)`
+    .. math::
+
+       f_{\text{RastScaled}}(\mathbf{x}) = 10N + \sum_{i=1}^N
+       \left(10^{\left(\frac{i-1}{N-1}\right)} x_i \right)^2 -
+       10\cos\left(2\pi 10^{\left(\frac{i-1}{N-1}\right)} x_i \right)`
     """
     N = len(individual)
     return 10 * N + sum((10 ** (i / (N - 1)) * x) ** 2 -
@@ -264,13 +274,18 @@ def rastrigin_scaled(individual):
 def rastrigin_skew(individual):
     r"""Skewed Rastrigin test objective function.
 
-     :math:`f_{\text{RastSkew}}(\mathbf{x}) = 10N + \sum_{i=1}^N \left(y_i^2 - 10 \cos(2\pi x_i)\right)`
+    .. math::
 
-     :math:`\text{with } y_i = \
-                            \begin{cases} \
-                                10\cdot x_i & \text{ if } x_i > 0,\\ \
-                                x_i & \text{ otherwise } \
-                            \end{cases}`
+       f_{\text{RastSkew}}(\mathbf{x}) =
+       10N + \sum_{i=1}^N \left(y_i^2 - 10 \cos(2\pi x_i)\right)
+
+    .. math::
+
+       \text{with } y_i =
+       \begin{cases}
+          10\cdot x_i & \text{ if } x_i > 0,\\ \\
+          x_i & \text{ otherwise }
+       \end{cases}
     """
     N = len(individual)
     return 10 * N + sum((10 * x if x > 0 else x) ** 2
@@ -291,9 +306,10 @@ def schaffer(individual):
        * - Global optima
          - :math:`x_i = 0, \forall i \in \lbrace 1 \ldots N\rbrace`, :math:`f(\mathbf{x}) = 0`
        * - Function
-         -  :math:`f(\mathbf{x}) = \sum_{i=1}^{N-1} (x_i^2+x_{i+1}^2)^{0.25} \cdot \
-                  \left[ \sin^2(50\cdot(x_i^2+x_{i+1}^2)^{0.10}) + 1.0 \
-                  \right]`
+         -  .. math::
+
+               f(\mathbf{x}) = \sum_{i=1}^{N-1} (x_i^2+x_{i+1}^2)^{0.25} \cdot
+               \left[ \sin^2(50\cdot(x_i^2+x_{i+1}^2)^{0.10}) + 1.0 \right]
 
     .. plot:: code/benchmarks/schaffer.py
         :width: 67 %
@@ -316,9 +332,9 @@ def schwefel(individual):
        * - Global optima
          - :math:`x_i = 420.96874636, \forall i \in \lbrace 1 \ldots N\rbrace`, :math:`f(\mathbf{x}) = 0`
        * - Function
-         - :math:`f(\mathbf{x}) = 418.9828872724339\cdot N - \
-            \sum_{i=1}^N\,x_i\sin\left(\sqrt{|x_i|}\right)`
-
+         - .. math::
+              f(\mathbf{x}) = 418.9828872724339\cdot N -
+              \sum_{i=1}^N\,x_i\sin\left(\sqrt{|x_i|}\right)
 
     .. plot:: code/benchmarks/schwefel.py
         :width: 67 %

deap/cma.py

@@ -228,7 +228,7 @@ class StrategyOnePlusLambda(object):
     | ``d``          | ``1.0 + N / (2.0 *        | Damping for step-size.     |
     |                | lambda_)``                |                            |
     +----------------+---------------------------+----------------------------+
-    | ``ptarg``      | ``1.0 / (5 + sqrt(lambda_)| Target success rate.        |
+    | ``ptarg``      | ``1.0 / (5 + sqrt(lambda_)| Target success rate.       |
     |                | / 2.0)``                  |                            |
     +----------------+---------------------------+----------------------------+
     | ``cp``         | ``ptarg * lambda_ / (2.0 +| Step size learning rate.   |
@@ -243,7 +243,7 @@ class StrategyOnePlusLambda(object):
     +----------------+---------------------------+----------------------------+
 
     .. [Igel2007] Igel, Hansen, Roth, 2007. Covariance matrix adaptation for
-    multi-objective optimization. *Evolutionary Computation* Spring;15(1):1-28
+       multi-objective optimization. *Evolutionary Computation* Spring;15(1):1-28
 
     """
     def __init__(self, parent, sigma, **kargs):
@@ -349,7 +349,7 @@ class StrategyMultiObjective(object):
     +================+===========================+============================+
     | ``d``          | ``1.0 + N / 2.0``         | Damping for step-size.     |
     +----------------+---------------------------+----------------------------+
-    | ``ptarg``      | ``1.0 / (5 + 1.0 / 2.0)`` | Target success rate.        |
+    | ``ptarg``      | ``1.0 / (5 + 1.0 / 2.0)`` | Target success rate.       |
     +----------------+---------------------------+----------------------------+
     | ``cp``         | ``ptarg / (2.0 + ptarg)`` | Step size learning rate.   |
     +----------------+---------------------------+----------------------------+

deap/creator.py

@@ -176,7 +176,7 @@ def __init__(self):
     .. warning::
 
        If your are inheriting from :class:`numpy.ndarray` see the
-       :doc:`tutorials/advanced/numpy` tutorial and the
+       :doc:`/tutorials/advanced/numpy` tutorial and the
        :doc:`/examples/ga_onemax_numpy` example.
 
     """

doc/api/gp.rst

@@ -14,7 +14,7 @@ Genetic Programming
 .. autoclass:: deap.gp.Terminal
 	:members:
 
-.. autoclass:: deap.gp.Ephemeral
+.. autoclass:: deap.gp.MetaEphemeral
 	:members:
 
 .. autofunction:: deap.gp.compile

doc/code/benchmarks/ackley.py

@@ -19,7 +19,7 @@ def ackley_arg0(sol):
 X = np.arange(-30, 30, 0.5)
 Y = np.arange(-30, 30, 0.5)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(ackley_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(ackley_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1,  norm=LogNorm(), cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/bohachevsky.py

@@ -19,7 +19,7 @@ def bohachevsky_arg0(sol):
 X = np.arange(-15, 15, 0.5)
 Y = np.arange(-15, 15, 0.5)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(bohachevsky_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(bohachevsky_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1,  norm=LogNorm(), cmap=cm.jet, linewidth=0.2)

doc/code/benchmarks/griewank.py

@@ -18,7 +18,7 @@ def griewank_arg0(sol):
 X = np.arange(-50, 50, 0.5)
 Y = np.arange(-50, 50, 0.5)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(griewank_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(griewank_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/h1.py

@@ -19,7 +19,7 @@ def h1_arg0(sol):
 X = np.arange(-25, 25, 0.5)
 Y = np.arange(-25, 25, 0.5)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(h1_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(h1_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1,  norm=LogNorm(), cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/himmelblau.py

@@ -18,7 +18,7 @@ def himmelblau_arg0(sol):
 X = np.arange(-6, 6, 0.1)
 Y = np.arange(-6, 6, 0.1)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(himmelblau_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(himmelblau_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/movingsc1.py

@@ -24,7 +24,7 @@
 X = np.arange(0, 100, 1.0)
 Y = np.arange(0, 100, 1.0)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(lambda x: mp(x)[0], zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(lambda x: mp(x)[0], zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/rastrigin.py

@@ -17,7 +17,7 @@ def rastrigin_arg0(sol):
 X = np.arange(-5, 5, 0.1)
 Y = np.arange(-5, 5, 0.1)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(rastrigin_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(rastrigin_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/rosenbrock.py

@@ -19,7 +19,7 @@ def rosenbrock_arg0(sol):
 X = np.arange(-2, 2, 0.1)
 Y = np.arange(-1, 3, 0.1)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(rosenbrock_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(rosenbrock_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1,  norm=LogNorm(), cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/schaffer.py

@@ -18,7 +18,7 @@ def schaffer_arg0(sol):
 X = np.arange(-25, 25, 0.25)
 Y = np.arange(-25, 25, 0.25)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(schaffer_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(schaffer_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/schwefel.py

@@ -18,7 +18,7 @@ def schwefel_arg0(sol):
 X = np.arange(-500, 500, 10)
 Y = np.arange(-500, 500, 10)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(schwefel_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(schwefel_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2)
 

doc/code/benchmarks/shekel.py

@@ -26,7 +26,7 @@ def shekel_arg0(sol):
 X = np.arange(0, 1, 0.01)
 Y = np.arange(0, 1, 0.01)
 X, Y = np.meshgrid(X, Y)
-Z = np.fromiter(map(shekel_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
+Z = np.fromiter(map(shekel_arg0, zip(X.flat,Y.flat)), dtype=float, count=X.shape[0]*X.shape[1]).reshape(X.shape)
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1,  norm=LogNorm(), cmap=cm.jet, linewidth=0.2)
 

doc/conf.py

@@ -11,7 +11,7 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-import sys, time
+import sys, time, os
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -35,8 +35,7 @@
 except ImportError:
     pass
 else:
-    extensions += ['matplotlib.sphinxext.only_directives',
-                   'matplotlib.sphinxext.plot_directive']
+    extensions += ['matplotlib.sphinxext.plot_directive']
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -110,6 +109,9 @@
 # Reload the cached values every 5 days
 intersphinx_cache_limit = 5
 
+# Align mathjax equations
+mathjax3_config = {'chtml': {'displayAlign': 'left'}}
+
 # -- Options for pyplot extension ----------------------------------------------
 
 # Default value for the include-source option
@@ -128,13 +130,15 @@
 
 # -- Options for extlinks extension ----------------------------------------------
 import subprocess
-try:
-    tree = subprocess.check_output(["git", "rev-parse", "HEAD"]).strip()
-except OSError:
-    import warnings
-    warnings.warn("Cannot link examples because we cannot retrieve the git version", Warning)
-else:
-    extlinks = {'example': ('https://github.com/DEAP/deap/blob/{tree}/examples/%s.py'.format(tree=tree), "examples/")}
+tree = os.getenv('GITHUB_COMMIT')
+if tree is None:
+    try:
+        tree = subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip()
+    except OSError:
+        import warnings
+        warnings.warn("Cannot link examples because we cannot retrieve the git version", Warning)
+if tree:
+    extlinks = {'example': ('https://github.com/DEAP/deap/blob/{tree}/examples/%s.py'.format(tree=tree), "examples/%s")}
 # -- Options for HTML output ---------------------------------------------------
 
 # Add any paths that contain custom themes here, relative to this directory.

doc/examples/eda.rst

@@ -15,13 +15,13 @@ The basic concept concept behind EDA is to sample :math:`\lambda` individuals
 with a certain distribution and estimate the problem distribution from the
 :math:`\mu` best individuals. This really simple concept adhere to the
 generate-update logic. The strategy contains a random number generator which
-is adapted from the population. The following :class:`EDA` class do just that.
+is adapted from the population. The following :class:`EMNA` class do just that.
 
-.. literalinclude:: /../examples/eda/fctmin.py
-   :pyobject: EDA
+.. literalinclude:: /../examples/eda/emna.py
+   :pyobject: EMNA
 
 A normal random number generator is initialized with a certain mean
-(*centroid*) and standard deviation (*sigma*) for each 
+(*centroid*) and standard deviation (*sigma*) for each
 dimension. The :meth:`generate` method uses numpy to generate *lambda_*
 sequences in *dim* dimensions, then the sequences are used to initialize
 individuals of class given in the *ind_init* argument. Finally, the
@@ -37,19 +37,19 @@ Two classes are needed, a minimization fitness and a individual that will
 combine the fitness and the real values. Moreover, we will use
 :class:`numpy.ndarray` as base class for our individuals.
 
-.. literalinclude:: /../examples/eda/fctmin.py
+.. literalinclude:: /../examples/eda/emna.py
    :lines: 28-29
 
 Operators
 =========
 
 The :func:`~deap.algorithms.eaGenerateUpdate` algorithm requires to set in a
 toolbox an evaluation function, an generation method and an update method.
-We will use the method of an initialized :class:`EDA`. For the generate
+We will use the method of an initialized :class:`EMNA`. For the generate
 method, we set the class that the individuals are transferred in to our
 :class:`Individual` class containing a fitness.
 
-.. literalinclude:: /../examples/eda/fctmin.py
+.. literalinclude:: /../examples/eda/emna.py
    :pyobject: main
 
-The complete :example:`eda/fctmin`. 
+The complete :example:`eda/fctmin`.

doc/examples/ga_onemax_short.rst

@@ -5,14 +5,14 @@ One Max Problem: Short Version
 ===============================
 
 The short One Max genetic algorithm example is very similar to the full one
-:ref:`-ga-onemax`. The only difference is that it makes use of the
+:ref:`ga-onemax`. The only difference is that it makes use of the
 :mod:`~deap.algorithms` module which implements some basic evolutionary
 algorithms. The initializations are almost the same. We only have to import
 some additional packages and modules.
 
 .. literalinclude:: /../examples/ga/onemax_short.py
    :lines: 16,19-21
-	   
+
 In order to use the evolution functions implemented in :mod:`~deap.algorithms`,
 we have to register some functions from the :mod:`~deap.tools` module:
 :func:`evaluate`, :func:`mate`, :func:`mutate`, and :func:`~deap.Toolbox.select`.
@@ -21,7 +21,7 @@ we have to register some functions from the :mod:`~deap.tools` module:
    :lines: 41-44
 
 The toolbox is then passed to the algorithm and via ``stats`` it uses the
-registered functions. 
+registered functions.
 
 .. literalinclude:: /../examples/ga/onemax_short.py
    :lines: 46, 49-58