The Refactoring

moved tons of helper functions out of process_frame() into the class
scope.

1 files changed, 65 insertions, 62 deletions

diff --git a/strategy.py b/strategy.py
index 37dc340..5240fd3 100644
--- a/strategy.py
+++ b/strategy.py
@@ -11,20 +11,80 @@ class Strategy:
         self.color = (0,0,0)
         self.c = c
     
+    def get_my_smallest(self):
+        return sorted(self.c.player.own_cells, key = lambda x: x.mass)[0]
+    
     def dist(self, cell):
         return math.sqrt((cell.pos[0]-self.c.player.center[0])**2 + (cell.pos[1]-self.c.player.center[1])**2)
     
     def edible(self, cell):
-        return ((cell.is_food) or (cell.mass <= sorted(self.c.player.own_cells, key = lambda x: x.mass)[0].mass * 0.75)) and not (cell.is_virus)
+        return ((cell.is_food) or (cell.mass <= self.get_my_smallest().mass * 0.75)) and not (cell.is_virus)
     
     def threat(self, cell):
-        if cell.is_virus and (cell.mass <= sorted(self.c.player.own_cells, key = lambda x: x.mass)[0].mass * 0.75):
+        if cell.is_virus and (cell.mass <= self.get_my_smallest().mass * 0.75):
             return True
-        elif (cell.mass <= sorted(self.c.player.own_cells, key = lambda x: x.mass)[0].mass * 1.25):
+        elif (cell.mass <= self.get_my_smallest().mass * 1.25):
             return True
         else:
             return False
     
+    def rival(self, cell, food):
+        if cell.is_virus or cell.is_food: return False
+        if cell.cid in self.c.player.own_ids: return False
+
+        if cell.mass < 1.25*self.get_my_smallest().mass:
+            return food.is_food or cell.size > 1.25*food.size
+        else:
+            return False
+    
+    def splitkiller(self, cell):
+        return not cell.is_virus and not cell.is_food and cell.mass > 1.25*2*self.get_my_smallest().mass
+    
+    def nonsplitkiller(self, cell):
+        return not cell.is_virus and not cell.is_food and 1.20*self.get_my_smallest().mass < cell.mass and cell.mass < 1.25*2*self.get_my_smallest().mass
+    
+    def quality(self, cell):
+        dd_sq = max((cell.pos[0]-self.c.player.center[0])**2 + (cell.pos[1]-self.c.player.center[1])**2,0.001)
+        sigma = 500
+        dist_score = -math.exp(-dd_sq/(2*sigma**2))
+
+        rivals = filter(lambda r : self.rival(r,cell), self.c.world.cells.values())
+        splitkillers = filter(self.splitkiller, self.c.world.cells.values())
+        nonsplitkillers = filter(self.nonsplitkiller, self.c.world.cells.values())
+
+        rival_score = 0
+        for r in rivals:
+            dd_sq = max(0.001, (r.pos[0]-cell.pos[0])**2 + (r.pos[1]-cell.pos[1])**2)
+            sigma = r.size + 100
+            rival_score += math.exp(-dd_sq/(2*sigma**2))
+
+        splitkill_score = 0
+        for s in splitkillers:
+            dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2)
+            sigma = (500+2*s.size)
+            splitkill_score += math.exp(-dd_sq/(2*sigma**2))
+
+        nonsplitkill_score = 0
+        for s in nonsplitkillers:
+            dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2)
+            sigma = (300+s.size)
+            nonsplitkill_score += math.exp(-dd_sq/(2*sigma**2))
+
+        density_score = 0
+        sigma = 300
+        for f in filter(lambda c : c.is_food and c!=cell, self.c.world.cells.values()):
+            dd_sq = (f.pos[0]-cell.pos[0])**2 + (f.pos[1]-cell.pos[1])**2
+            density_score -= math.exp(-dd_sq/(2*sigma**2))
+
+        wall_score = 0
+        wall_dist = min( cell.pos[0]-self.c.world.top_left[1], self.c.world.bottom_right[1]-cell.pos[0], cell.pos[1]-self.c.world.top_left[0], self.c.world.bottom_right[0]-cell.pos[1] )
+        sigma = 100
+        wall_score = math.exp(-wall_dist**2/(2*sigma**2))
+
+        return 2.5*dist_score + 0.2*rival_score + nonsplitkill_score + 5*splitkill_score + 0.1*density_score + 5*wall_score
+        ##print (density_score)
+        #return density_score
+    
     def weight_cell(self, cell):
         df = (1/self.dist(cell))
         if self.edible(cell):
@@ -93,21 +153,7 @@ class Strategy:
                 gui.draw_arc(self.c.player.center, self.c.player.total_size+10, i, (255,0,255))
 
         # if however there's no enemy to avoid, chase food or jizz randomly around
-        else:
-            def rival(cell, food):
-                if cell.is_virus or cell.is_food: return False
-                if cell.cid in self.c.player.own_ids: return False
-
-                if cell.mass < 1.25*my_smallest:
-                    return food.is_food or cell.size > 1.25*food.size
-                else:
-                    return False
-            def splitkiller(cell):
-                return not cell.is_virus and not cell.is_food and cell.mass > 1.25*2*my_smallest
-            
-            def nonsplitkiller(cell):
-                return not cell.is_virus and not cell.is_food and 1.20*my_smallest < cell.mass and cell.mass < 1.25*2*my_smallest
-            
+        else:          
             if self.target_cell != None:
                 self.target = tuple(self.target_cell.pos)
                 if self.target_cell not in self.c.world.cells.values() or not self.edible(self.target_cell):
@@ -120,50 +166,7 @@ class Strategy:
             
             if not self.has_target:
                 food = list(filter(self.edible, self.c.world.cells.values()))
-                
-                def quality(cell):
-                    dd_sq = max((cell.pos[0]-self.c.player.center[0])**2 + (cell.pos[1]-self.c.player.center[1])**2,0.001)
-                    sigma = 500
-                    dist_score = -math.exp(-dd_sq/(2*sigma**2))
-
-                    rivals = filter(lambda r : rival(r,cell), self.c.world.cells.values())
-                    splitkillers = filter(splitkiller, self.c.world.cells.values())
-                    nonsplitkillers = filter(nonsplitkiller, self.c.world.cells.values())
-
-                    rival_score = 0
-                    for r in rivals:
-                        dd_sq = max(0.001, (r.pos[0]-cell.pos[0])**2 + (r.pos[1]-cell.pos[1])**2)
-                        sigma = r.size + 100
-                        rival_score += math.exp(-dd_sq/(2*sigma**2))
-
-                    splitkill_score = 0
-                    for s in splitkillers:
-                        dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2)
-                        sigma = (500+2*s.size)
-                        splitkill_score += math.exp(-dd_sq/(2*sigma**2))
-
-                    nonsplitkill_score = 0
-                    for s in nonsplitkillers:
-                        dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2)
-                        sigma = (300+s.size)
-                        nonsplitkill_score += math.exp(-dd_sq/(2*sigma**2))
-
-                    density_score = 0
-                    sigma = 300
-                    for f in filter(lambda c : c.is_food and c!=cell, self.c.world.cells.values()):
-                        dd_sq = (f.pos[0]-cell.pos[0])**2 + (f.pos[1]-cell.pos[1])**2
-                        density_score -= math.exp(-dd_sq/(2*sigma**2))
-
-                    wall_score = 0
-                    wall_dist = min( cell.pos[0]-self.c.world.top_left[1], self.c.world.bottom_right[1]-cell.pos[0], cell.pos[1]-self.c.world.top_left[0], self.c.world.bottom_right[0]-cell.pos[1] )
-                    sigma = 100
-                    wall_score = math.exp(-wall_dist**2/(2*sigma**2))
-
-                    return 2.5*dist_score + 0.2*rival_score + nonsplitkill_score + 5*splitkill_score + 0.1*density_score + 5*wall_score
-                    ##print (density_score)
-                    #return density_score
-
-                food = sorted(food, key = quality)
+                food = sorted(food, key = self.quality)
                 
                 if len(food) > 0:
                     self.target = (food[0].pos[0], food[0].pos[1])