Merge branch 'rel-0.5.2'

README.rst

@@ -25,7 +25,7 @@ station at a least dish-washing dish washer competition.
 Solutions are found through an iterative process. In each iteration,
 several ants are allowed to find a solution that "visits" every node of
 the world. The amount of pheromone on each edge is updated according to
-its the length of solutions in which it was used. The ant that traveled the
+the length of the solutions in which it was used. The ant that traveled the
 least distance is considered to be the local best solution. If the local
 solution has a shorter distance than the best from any previous
 iteration, it then becomes the global best solution. The elite ant(s)
@@ -45,9 +45,9 @@ Installation via ``pip``
 
     $ pip3 install ACO-Pants
 
-------
-Useage
-------
+-----
+Usage
+-----
 
 Using **Pants** is simple. The example here uses Euclidean distance
 between 2D nodes with ``(x, y)`` coordinates, but there are no real
@@ -75,7 +75,7 @@ requirements for node data of any sort.
 
 
 3) Define your length function. This function must accept two nodes and
-   return the amonut of "work" between them. In this case, Euclidean 
+   return the amount of "work" between them. In this case, Euclidean 
    distance works well.
 
    .. code-block:: python

docs/conf.py

@@ -58,9 +58,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '0.5.1'
+version = '0.5.2'
 # The full version, including alpha/beta/rc tags.
-release = '0.5.1'
+release = '0.5.2'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

pants/__init__.py

@@ -17,7 +17,7 @@
 Solutions are found through an iterative process. In each iteration,
 several ants are allowed to find a solution that "visits" every node of
 the world. The amount of pheromone on each edge is updated according to
-its the length of solutions in which it was used. The ant that traveled the
+the length of the solutions in which it was used. The ant that traveled the
 least distance is considered to be the local best solution. If the local
 solution has a shorter distance than the best from any previous
 iteration, it then becomes the global best solution. The elite ant(s)

pants/ant.py

@@ -59,7 +59,7 @@ class Ant:
 
     Once an :class:`Ant` has found a solution (or at any time), the solution
     may be obtained and inspected by accessing its ``tour`` property, which
-    returns the nodes visited in order, or it's ``path`` property, which 
+    returns the nodes visited in order, or its ``path`` property, which 
     returns the edges visited in order. Also, the total distance of the 
     solution can be accessed through its ``distance`` property. :class:`Ant`\s
     are even sortable by their distance:

pants/world.py

@@ -67,7 +67,7 @@ def __init__(self, nodes, lfunc, **kwargs):
 
     @property
     def nodes(self):
-        """Nodes IDs."""
+        """Node IDs."""
         return list(range(len(self._nodes)))
 
     def create_edges(self):
@@ -133,15 +133,15 @@ class Edge:
     information, whereas *pheromone* level represents the dynamic, *a
     posteriori* information.
     
-    :param Node a: the node at the start of the :class:`Edge`
-    :param Node b: the node at the end of the :class:`Edge`
+    :param node start: the node at the start of the :class:`Edge`
+    :param node end: the node at the end of the :class:`Edge`
     :param float length: the length of the :class:`Edge` (default=1)
     :param float pheromone: the amount of pheromone on the :class:`Edge` 
                             (default=0.1)
     """
-    def __init__(self, a, b, length=None, pheromone=None):
-        self.start = a
-        self.end = b
+    def __init__(self, start, end, length=None, pheromone=None):
+        self.start = start
+        self.end = end
         self.length = 1 if length is None else length
         self.pheromone = 0.1 if pheromone is None else pheromone
 

setup.py

@@ -7,7 +7,7 @@
 
 setup(
     name="ACO-Pants",
-    version="0.5.1",
+    version="0.5.2",
     author="Robert Grant",
     author_email="rhgrant10@gmail.com",
     packages=["pants", "pants.test"],