Fixed documentation error on pypi

The example code included in the README breaks the formatting of the pypi index
page. Fixed now.

EXAMPLES.rst

@@ -0,0 +1,67 @@
+Examples
+---------
+
+* ``one_dimensional_knapsack.py``
+
+  This example solves the one-dimensional knapsack problem used as the example
+  on the Wikipedia page for the `Knapsack problem <http://en.wikipedia.org/wiki/Knapsack_problem>`_. Here is the `problem statement <http://git.io/fa25nw>`_. ::
+
+    from pyeasyga import pyeasyga
+
+    # setup data
+    data = [{'name': 'box1', 'value': 4, 'weight': 12},
+            {'name': 'box2', 'value': 2, 'weight': 1},
+            {'name': 'box3', 'value': 10, 'weight': 4},
+            {'name': 'box4', 'value': 1, 'weight': 1},
+            {'name': 'box5', 'value': 2, 'weight': 2}]
+
+    ga = pyeasyga.GeneticAlgorithm(data)        # initialise the GA with data
+
+    # define a fitness function
+    def fitness(individual, data):
+        values, weights = 0, 0
+        for selected, box in zip(individual, data):
+            if selected:
+                values += box.get('value')
+                weights += box.get('weight')
+        if weights > 15:
+            values = 0
+        return values
+
+    ga.fitness_function = fitness               # set the GA's fitness function
+    ga.run()                                    # run the GA
+    print ga.best_individual()                  # print the GA's best solution
+
+
+* ``multi_dimensional_knapsack.py``
+
+  This solves the multidimensional knapsack problem (MKP) seen `here <http://git.io/Wz4jBQ>`_. It is a well-known NP-hard combinatorial optimisation problem. ::
+
+    from pyeasyga import pyeasyga
+
+    # setup data
+    data = [(821, 0.8, 118), (1144, 1, 322), (634, 0.7, 166), (701, 0.9, 195),
+            (291, 0.9, 100), (1702, 0.8, 142), (1633, 0.7, 100), (1086, 0.6, 145),
+            (124, 0.6, 100), (718, 0.9, 208), (976, 0.6, 100), (1438, 0.7, 312),
+            (910, 1, 198), (148, 0.7, 171), (1636, 0.9, 117), (237, 0.6, 100),
+            (771, 0.9, 329), (604, 0.6, 391), (1078, 0.6, 100), (640, 0.8, 120),
+            (1510, 1, 188), (741, 0.6, 271), (1358, 0.9, 334), (1682, 0.7, 153),
+            (993, 0.7, 130), (99, 0.7, 100), (1068, 0.8, 154), (1669, 1, 289)]
+
+    ga = pyeasyga.GeneticAlgorithm(data)        # initialise the GA with data
+
+    # define a fitness function
+    def fitness(individual, data):
+        weight, volume, price = 0, 0, 0
+        for (selected, item) in zip(individual, data):
+            if selected:
+                weight += item[0]
+                volume += item[1]
+                price += item[2]
+        if weight > 12210 or volume > 12:
+            price = 0
+        return price
+
+    ga.fitness_function = fitness               # set the GA's fitness function
+    ga.run()                                    # run the GA
+    print ga.best_individual()                  # print the GA's best solution

README.rst

@@ -24,7 +24,6 @@ A simple and easy-to-use Genetic Algorithm implementation library in Python.
 * Free software: BSD license
 * Documentation: http://pyeasyga.readthedocs.org.
 
-.. include:: ../examples/README.rst
 
 Note
 --------

docs/usage.rst

@@ -5,3 +5,5 @@ Usage
 To use pyeasyga in a project::
 
 	from pyeasyga import pyeasyga
+
+.. include:: ../EXAMPLES.rst

examples/README.rst

@@ -1,67 +1 @@
-Examples
----------
-
-* ``one_dimensional_knapsack.py``
-
-  This example solves the one-dimensional knapsack problem used as the example
-  on the Wikipedia page for the `Knapsack problem <http://en.wikipedia.org/wiki/Knapsack_problem>`_. Here is the `problem statement <http://git.io/fa25nw>`_. ::
-
-    from pyeasyga import pyeasyga
-
-    # setup data
-    data = [{'name': 'box1', 'value': 4, 'weight': 12},
-            {'name': 'box2', 'value': 2, 'weight': 1},
-            {'name': 'box3', 'value': 10, 'weight': 4},
-            {'name': 'box4', 'value': 1, 'weight': 1},
-            {'name': 'box5', 'value': 2, 'weight': 2}]
-
-    ga = pyeasyga.GeneticAlgorithm(data)        # initialise the GA with data
-
-    # define a fitness function
-    def fitness(individual, data):
-        values, weights = 0, 0
-        for selected, box in zip(individual, data):
-            if selected:
-                values += box.get('value')
-                weights += box.get('weight')
-        if weights > 15:
-            values = 0
-        return values
-
-    ga.fitness_function = fitness               # set the GA's fitness function
-    ga.run()                                    # run the GA
-    print ga.best_individual()                  # print the GA's best solution
-
-
-* ``multi_dimensional_knapsack.py``
-
-  This solves the multidimensional knapsack problem (MKP) seen `here <http://git.io/Wz4jBQ>`_. It is a well-known NP-hard combinatorial optimisation problem. ::
-
-    from pyeasyga import pyeasyga
-
-    # setup data
-    data = [(821, 0.8, 118), (1144, 1, 322), (634, 0.7, 166), (701, 0.9, 195),
-            (291, 0.9, 100), (1702, 0.8, 142), (1633, 0.7, 100), (1086, 0.6, 145),
-            (124, 0.6, 100), (718, 0.9, 208), (976, 0.6, 100), (1438, 0.7, 312),
-            (910, 1, 198), (148, 0.7, 171), (1636, 0.9, 117), (237, 0.6, 100),
-            (771, 0.9, 329), (604, 0.6, 391), (1078, 0.6, 100), (640, 0.8, 120),
-            (1510, 1, 188), (741, 0.6, 271), (1358, 0.9, 334), (1682, 0.7, 153),
-            (993, 0.7, 130), (99, 0.7, 100), (1068, 0.8, 154), (1669, 1, 289)]
-
-    ga = pyeasyga.GeneticAlgorithm(data)        # initialise the GA with data
-
-    # define a fitness function
-    def fitness(individual, data):
-        weight, volume, price = 0, 0, 0
-        for (selected, item) in zip(individual, data):
-            if selected:
-                weight += item[0]
-                volume += item[1]
-                price += item[2]
-        if weight > 12210 or volume > 12:
-            price = 0
-        return price
-
-    ga.fitness_function = fitness               # set the GA's fitness function
-    ga.run()                                    # run the GA
-    print ga.best_individual()                  # print the GA's best solution
+.. include:: ../EXAMPLES.rst