ML2

trainer.py

+import referee.__main__
+from referee.player import PlayerWrapper
+from referee.options import get_options
+from referee.log import StarLog
+import random
+import os
+
+NUM_GENERATIONS = 99
+
+MIN_WEIGHT = 0
+MID_WEIGHT = 5
+MAX_WEIGHT = 10
+weight_vals = [MIN_WEIGHT, MID_WEIGHT, MAX_WEIGHT]
+
+weights = [[w1, w2, w3, w4, w5, 0] for w1 in weight_vals for w2 in weight_vals
+for w3 in weight_vals for w4 in weight_vals for w5 in weight_vals]
+WEIGHT_ARR_SIZE = 81
+
+options = get_options()
+
+# Create a star-log for controlling the format of output from within this
+# program
+out = StarLog(level=options.verbosity, star="*")
+out.comment("all messages printed by the referee after this begin with a *")
+out.comment("(any other lines of output must be from your Player classes).")
+out.comment()
+
+def train(weight_array):
+
+	winners = []
+
+	for i in range(int(len(weight_array)/3)):
+
+		red_weight = random.choice(weight_array)
+		weight_array.remove(red_weight)
+		green_weight = random.choice(weight_array)
+		weight_array.remove(green_weight)
+		blue_weight = random.choice(weight_array)
+		weight_array.remove(blue_weight)
+
+		try:
+			os.remove('./Snek/player/weights')
+		except:
+			pass
+
+		with open('./Snek/player/weights','a+') as weight_file:
+			weight_file.write(str(red_weight))
+			weight_file.write("\n")
+			weight_file.write(str(green_weight))
+			weight_file.write("\n")
+			weight_file.write(str(blue_weight))
+
+		p_R = PlayerWrapper('red player',   options.playerR_loc, options, out)
+		p_G = PlayerWrapper('green player', options.playerG_loc, options, out)
+		p_B = PlayerWrapper('blue player',  options.playerB_loc, options, out)
+
+		winner = referee.__main__.play([p_R, p_G, p_B], options, out)
+		if "red" in winner:
+			p_R.init("red")
+			red_weight[-1] = calc_fitness(p_R)
+			winners.append(red_weight)
+
+		elif "green" in winner:
+			p_G.init("green")
+			green_weight[-1] = calc_fitness(p_G)
+			winners.append(green_weight)
+
+		elif "blue" in winner:
+			p_B.init("blue")
+			blue_weight[-1] = calc_fitness(p_B)
+			winners.append(blue_weight)
+
+	return winners
+		
+def calc_fitness(pl):
+	return pl.player.noMoves
+
+def next_generation_mutation(weight_array, num_to_gen):
+	next_gen_arr = []
+	for i in range(num_to_gen):
+		list1 = weight_array[random.randint(0, len(weight_array)-1)].copy()
+
+		ind1 = random.randint(0,len(list1)-2)
+		ind2 = random.randint(0,len(list1)-2)
+
+		mult1 = random.randint(80,120)/100
+		mult2 = random.randint(80,120)/100
+
+		list1[ind1] *= mult1
+		list1[ind2] *= mult2
+
+		next_gen_arr.append(list1)
+
+	for weight in weight_array:
+		next_gen_arr.append(weight)
+
+	return next_gen_arr
+
+def next_generation_combination(weight_array, num_to_gen):
+	next_gen_arr = []
+	for i in range(num_to_gen):
+		list1 = weight_array[random.randint(0, len(weight_array)-1)].copy()
+		list2 = weight_array[random.randint(0, len(weight_array)-1)].copy()
+		list3 = weight_array[random.randint(0, len(weight_array)-1)].copy()
+
+		A = list1[-1]
+		B = list2[-1]
+		C = list3[-1]
+
+		for j in range(len(list1)-1):
+			list_new.append((A*list1[j] + B*list2[j] + C*list3[j])/(A+B+C))
+		list_new.append(0)
+
+		next_gen_arr.append(list_new)
+
+	for weight in weight_array:
+		next_gen_arr.append(weight)
+
+	return next_gen_arr
+
+for i in range(NUM_GENERATIONS):
+	weights = train(weights)
+	weights = next_generation_mutation(weights, WEIGHT_ARR_SIZE - len(weights))
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