genetics code (untested)

genetics.py

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+import numpy as np
+import random
+from brain import Neural_Network
+
+def genetic_selection(brains):
+    # tot_fitness = sum ([int(b.fitness) for b in brains])
+
+    # does not seem very optimized... TBR
+    # constitute a list where every brain is represented
+    # proportionnally to its relative fitness
+    wheel = []
+    for b in brains :
+        wheel += [b] * b.brains
+
+    
+    tot_fitness = len(wheel)
+    half_pop = int(len(brains)/2)
+
+    # selection of pool/2 pair of parents to reproduce  
+    parents_pool = []
+    for _ in range(half_pop):
+        parents_pool.append([round(random.random()*tot_fitness), round(random.random()*tot_fitness)])
+    
+
+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 :
+            choice = p1_gene
+        else :
+            choice = p2_gene
+        # Mutation
+        if random.random() < 0.005 :
+            choice[random.randint(0, len(choice - 1))] = random.random()
+            print("Mutation !")
+        child.append(np.array(choice))
+    return child
+
+
+def genetic_reproduction(parents_pool):
+    # every pair of parents will produce a mixed child
+    new_pop = []
+    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)
+        c_brain2 = Neural_Network(W1=W1_kid, W2=W2_kid)
+        new_pop.append(c_brain1)
+        new_pop.append(c_brain2)
+    return new_pop
+
+
+
+