Merge remote-tracking branch 'origin/tastyness'

Conflicts:
	strategy.py

Todesmerge aus der Hölle.

1 files changed, 60 insertions, 3 deletions

diff --git a/strategy.py b/strategy.py
index 258bef0..5b8ca5a 100644
--- a/strategy.py
+++ b/strategy.py
@@ -33,7 +33,7 @@ class Strategy:
             relpos = ((cell.pos[0]-self.c.player.center[0]),(cell.pos[1]-self.c.player.center[1]))
             dist = math.sqrt(relpos[0]**2+relpos[1]**2)
 
-            if (not cell.is_virus and dist < 500+2*cell.size and  cell.mass > 1.25 * my_smallest) or (cell.is_virus and dist < my_largest and cell.mass < my_largest):
+            if (not cell.is_virus and dist < ((500+2*cell.size) if cell.mass > 1.25*my_smallest*2 else (300+cell.size)) and  cell.mass > 1.25 * my_smallest) or (cell.is_virus and dist < my_largest and cell.mass < my_largest):
                 angle = math.atan2(relpos[1],relpos[0])
                 corridor_halfwidth = math.asin(cell.size / dist)
                 forbidden_intervals += canonicalize_angle_interval((angle-corridor_halfwidth, angle+corridor_halfwidth))
@@ -73,7 +73,21 @@ 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: 
+        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
+            
             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):
@@ -86,7 +100,50 @@ class Strategy:
             
             if not self.has_target:
                 food = list(filter(self.edible, self.c.world.cells.values()))
-                food = sorted(food, key = self.dist)
+                
+                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 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)
                 
                 if len(food) > 0:
                     self.target = (food[0].pos[0], food[0].pos[1])