use new style class for Annealer to support dependency injection; fixes

#3

README.md

@@ -118,6 +118,20 @@ tsp.set_schedule(auto_schedule)
 itinerary, miles = tsp.anneal()
 ```
 
+## Extra data dependencies
+
+You might have noticed that the `energy` function above requires a `cities` dict 
+that is presumably defined in the enclosing scope. This is not necessarily a good
+design pattern. The dependency on additional data can be made explicit by passing 
+them into the constructor like so
+
+    # pass extra data (the distance matrix) into the constructor
+    def __init__(self, state, distance_matrix):
+        self.distance_matrix = distance_matrix
+        super(TravellingSalesmanProblem, self).__init__(state)  # important!
+
+The last line (calling init on the super class) is critical. 
+
 ## Implementation Details
 
 The simulated annealing algorithm requires that we track state (current, previous, best) and thus means we need to copy the `self.state` frequently.

examples/salesman.py

@@ -17,6 +17,10 @@ class TravellingSalesmanProblem(Annealer):
 
     """Test annealer with a travelling salesman problem.
     """
+
+    def __init__(self, state, distance_matrix):
+        self.distance_matrix = distance_matrix
+        super(TravellingSalesmanProblem, self).__init__(state)  # important! 
 
     def move(self):
         """Swaps two cities in the route."""
@@ -28,8 +32,7 @@ def energy(self):
         """Calculates the length of the route."""
         e = 0
         for i in range(len(self.state)):
-            e += distance(cities[self.state[i - 1]],
-                               cities[self.state[i]])
+            e += self.distance_matrix[self.state[i-1]][self.state[i]]
         return e
 
 
@@ -60,9 +63,23 @@ def energy(self):
         'Baltimore': (39.28, 76.62)
     }
 
+    # initial state, a randomly-ordered itinerary
     init_state = list(cities.keys())
+    random.shuffle(init_state)
 
-    tsp = TravellingSalesmanProblem(init_state)
+    # create a distance matrix
+    distance_matrix = {}
+    for ka, va in cities.items():
+        distance_matrix[ka] = {}
+        for kb, vb in cities.items():
+            if kb == ka:
+                distance_matrix[ka][kb] = 0.0
+            else:
+                distance_matrix[ka][kb] = distance(va, vb)
+
+    tsp = TravellingSalesmanProblem(init_state, distance_matrix)
+    # since our state is just a list, slice is the fastest way to copy
+    tsp.copy_strategy = "slice"  
     state, e = tsp.anneal()
 
     while state[0] != 'New York City':

setup.py

@@ -18,7 +18,7 @@
 """
 
 setup(name='simanneal',
-      version='0.1',
+      version='0.1.1',
       description='Simulated Annealing in Python',
       license='BSD',
       author='Matthew Perry',

simanneal/anneal.py

@@ -22,7 +22,7 @@ def time_string(seconds):
     return '%4i:%02i:%02i' % (h, m, s)
 
 
-class Annealer:
+class Annealer(object):
 
     """Performs simulated annealing by calling functions to calculate
     energy and make moves on a state.  The temperature schedule for