Fix astar

Single character variable names are old school.

Author: cclauss

machine_learning/astar.py

@@ -1,5 +1,3 @@
-import numpy as np
-
 '''
 The A* algorithm combines features of uniform-cost search and pure
 heuristic search to efficiently compute optimal solutions.
@@ -12,6 +10,7 @@
 essentially planning ahead at each step so a more optimal decision
 is made.A* also known as the algorithm with brains
 '''
+import numpy as np
 
 
 class Cell(object):
@@ -32,6 +31,7 @@ def __init__(self):
         self.g = 0
         self.h = 0
         self.f = 0
+
     '''
     overrides equals method because otherwise cell assign will give
     wrong results
@@ -44,11 +44,10 @@ def showcell(self):
 
 
 class Gridworld(object):
-
     '''
     Gridworld class represents the  external world here a grid M*M
     matrix
-    w    : create a numpy array with the given world_size default is 5
+    world_size: create a numpy array with the given world_size default is 5
     '''
 
     def __init__(self, world_size=(5, 5)):
@@ -59,12 +58,10 @@ def __init__(self, world_size=(5, 5)):
     def show(self):
         print(self.w)
 
-    '''
-    get_neighbours
-    As the name suggests this function will return the neighbours of
-    the a particular cell
-    '''
     def get_neigbours(self, cell):
+        '''
+        Return the neighbours of cell
+        '''
         neughbour_cord = [
             (-1, -1), (-1, 0), (-1, 1), (0, -1),
             (0, 1), (1, -1), (1, 0), (1, 1)]
@@ -74,25 +71,21 @@ def get_neigbours(self, cell):
         for n in neughbour_cord:
             x = current_x + n[0]
             y = current_y + n[1]
-            if (
-               (x >= 0 and x < self.world_x_limit) and
-               (y >= 0 and y < self.world_y_limit)):
+            if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                 c = Cell()
                 c.position = (x, y)
                 c.parent = cell
                 neighbours.append(c)
         return neighbours
 
-'''
-Implementation of a start algorithm
-world : Object of the world object
-start : Object of the cell as  start position
-stop  : Object of the cell as goal position
-'''
-
 
 def astar(world, start, goal):
     '''
+    Implementation of a start algorithm
+    world : Object of the world object
+    start : Object of the cell as  start position
+    stop  : Object of the cell as goal position
+
     >>> p = Gridworld()
     >>> start = Cell()
     >>> start.position = (0,0)
@@ -130,23 +123,19 @@ def astar(world, start, goal):
         path.append(current.position)
         current = current.parent
     path.append(current.position)
-    path = path[::-1]
-    return path
+    return path[::-1]
+
 
 if __name__ == '__main__':
-    '''
-    sample run
-    '''
-#   object for the world
-    p = Gridworld()
+    world = Gridworld()
 #   stat position and Goal
     start = Cell()
     start.position = (0, 0)
     goal = Cell()
     goal.position = (4, 4)
-    print("path from {} to {} ".format(start.position, goal.position))
-    s = astar(p, start, goal)
-#   Just for visual Purpose
+    print(f"path from {start.position} to {goal.position}")
+    s = astar(world, start, goal)
+#   Just for visual reasons
     for i in s:
-        p.w[i] = 1
-    print(p.w)
+        world.w[i] = 1
+    print(world.w)