first genetic algo bot

genetic.py

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+import supply
+import random
+import matrixbot
+import numpy as np
+from operator import itemgetter
+
+SOLD_BENEFIT = 2
+
+def run_generation(pop, i):
+    print("Generation %d" % i)
+    ranking = []
+    for x in pop:
+        score = evaluate(x)
+        ranking.append((score, x))
+    ranking.sort(key=itemgetter(0))
+    print("Best of generation: ", ranking[0])
+    newpop = [x[1] for x in ranking[:len(ranking)//2]]
+    newpop = breed(newpop, len(pop), i)
+    return newpop
+
+def evaluate(mem):
+    g = supply.variableRequestGenerator(supply.TURNS, 8, 14)
+    #chain = [StupidAI(12), PlayerChain("Manufacturer", 12), Base()]
+    chain = [matrixbot.MatrixBot(mem[:25], mem[25:], 12), supply.Base()]
+    total_sold = 0
+    history = []
+    for topreq in g:
+        requests = [x.request() for x in chain]
+        requests = requests[:-1]
+        requests.insert(0, topreq)
+        got = 0
+        for i, c in reversed(list(enumerate(chain))):
+            got = c.reconcile(requests[i], got)
+        #print("Sold ", got, " units.")
+        total_sold += got
+        history.append([x.cost() for x in chain])
+    total_cost = [sum([x[0] * supply.SUPPLY_COST + x[1] * supply.BACKLOG_COST for x in y]) for y in history]
+    cost = supply.integrate(total_cost)[-1] - (total_sold * SOLD_BENEFIT)
+    return cost
+
+
+def mate(a, b):
+    out = []
+    for x in range(len(a)):
+        if random.random() < 0.5:
+            out.append(a[x])
+        else:
+            out.append(b[x])
+    return np.array(out)
+
+def mutate(a):
+    for i in range(5):
+        x = random.randint(0, len(a) - 1)
+        amt = random.uniform(-0.5, 0.5)
+        a[x] = a[x] + amt
+
+WEIGHT = 0.9
+LATE_WEIGHT = 0.2
+GENS = 500
+
+def breed(pop, n, i):
+    global WEIGHT
+    mut_chance = (WEIGHT - LATE_WEIGHT) * (GENS - i / GENS) + LATE_WEIGHT
+    total = n - len(pop)
+    newbies = []
+    while total != 0:
+        a = random.choice(pop)
+        b = random.choice(pop)
+        c = mate(a, b)
+        if random.random() < mut_chance:
+            mutate(c)
+        newbies.append(c)
+        total -= 1
+    c = pop[:]
+    c.extend(newbies)
+    return c
+
+def main():
+    starting_pop = []
+    for i in range(300):
+        x = np.random.uniform(low=-2, high=2, size=30)
+        starting_pop.append(x)
+    pop = starting_pop
+    for gen in range(GENS):
+        pop = run_generation(pop, gen+1)
+
+if __name__ == "__main__":
+    main()