blacked all

genetics.py

@@ -3,67 +3,60 @@ import random
 from brain import Neural_Network
 from params import MUTATION_RATE, SELECTION_ALG, KWAY_TOURNAMENT_PLAYERS
 
+
 def kway_selection(brains, exclude=None):
     tourn_pool = []
     best_play = None
-    if exclude :
+    if exclude:
         brains = [x for x in brains if x != exclude]
     for x in range(KWAY_TOURNAMENT_PLAYERS):
         new_play = random.choice(brains)
-        while new_play in tourn_pool :
+        while new_play in tourn_pool:
             new_play = random.choice(brains)
-        if not best_play or best_play.fitness < new_play.fitness :
+        if not best_play or best_play.fitness < new_play.fitness:
             best_play = new_play
     return best_play
 
+
 def genetic_selection(brains):
     parents_pool = []
-    half_pop = int(len(brains)/2)
+    half_pop = int(len(brains) / 2)
 
     if SELECTION_ALG == "kway":
-        for x in range(half_pop) :
+        for x in range(half_pop):
             p1 = kway_selection(brains)
             p2 = kway_selection(brains, exclude=p1)
-            parents_pool.append([
-                p1, 
-                p2
-            ])
+            parents_pool.append([p1, p2])
 
-
-
-    elif SELECTION_ALG == "roulette" :
+    elif SELECTION_ALG == "roulette":
         # does not seem very optimized... TBR
         # constitute a list where every brain is represented
         # proportionnally to its relative fitness
         wheel = []
-        for b in brains :
+        for b in brains:
             wheel += [b] * b.fitness
 
-        
         tot_fitness = len(wheel)
 
-        # selection of pool/2 pair of parents to reproduce  
+        # selection of pool/2 pair of parents to reproduce
         for _ in range(half_pop):
-            idx1 = round(random.random()*tot_fitness - 1)
-            idx2 = round(random.random()*tot_fitness - 1)
-            parents_pool.append([
-                wheel[idx1], 
-                wheel[idx2]
-                ])
+            idx1 = round(random.random() * tot_fitness - 1)
+            idx2 = round(random.random() * tot_fitness - 1)
+            parents_pool.append([wheel[idx1], wheel[idx2]])
     return parents_pool
-    
+
 
 def cross_mutate_genes(p1_gene, p2_gene):
     child = []
     p1_gene = list(p1_gene)
     p2_gene = list(p1_gene)
-    for idx,x in enumerate(p2_gene):
-        if random.random() > 0.5 :
+    for idx, x in enumerate(p2_gene):
+        if random.random() > 0.5:
             choice = p1_gene[idx]
-        else :
+        else:
             choice = p2_gene[idx]
         # Mutation
-        if random.random() < MUTATION_RATE :
+        if random.random() < MUTATION_RATE:
             choice[random.randint(0, len(choice) - 1)] = random.random()
             print("Mutation !")
         child.append(choice)
@@ -73,7 +66,7 @@ def cross_mutate_genes(p1_gene, p2_gene):
 def genetic_reproduction(parents_pool):
     # every pair of parents will produce a mixed child
     new_pop = []
-    for [p1,p2] in parents_pool:
+    for [p1, p2] in parents_pool:
         W1_kid = cross_mutate_genes(p1.W1, p2.W1)
         W2_kid = cross_mutate_genes(p1.W2, p2.W2)
         c_brain1 = Neural_Network(W1=W1_kid, W2=W2_kid)
@@ -81,7 +74,3 @@ def genetic_reproduction(parents_pool):
         new_pop.append(c_brain1)
         new_pop.append(c_brain2)
     return new_pop
-
-
-
-