def calc_smooth():
sample = []
with open('./data/obj_tests.csv', newline='') as csvfile, \
open('./data/paperObjective.json') as configFile:
config = json.load(configFile)
reader = csv.reader(csvfile)
next(reader)
for row in reader:
ml_inst = mergelife.new_ml_instance(HEIGHT, WIDTH, row[0])
obj = mergelife.objective_function(ml_inst, config['config']['evalCycles'], config['objective'])
base_score = float(obj['score'])
print("** {} {}".format(row[0],base_score))
moves = expand_rule(row[0])
sum = 0
for move in moves:
ml_inst = mergelife.new_ml_instance(HEIGHT, WIDTH, move)
obj = mergelife.objective_function(ml_inst, config['config']['evalCycles'], config['objective'])
move_score = float(obj['score'])
diff = abs(base_score - move_score)
# print("{}:{}".format(diff,move_score))
if diff>0.9999:
sum += 1
a = float(sum)/float(len(moves))
sample.append(a)
print("Above: {}".format(a))
print("Avg: {}".format(statistics.mean(sample)))
def score(config, ruleText):
width = config['config']['cols']
height = config['config']['rows']
ml_inst = mergelife.new_ml_instance(height, width, ruleText)
result = mergelife.objective_function(ml_inst,
config['config']['evalCycles'],
config['objective'], True)
print("Final result: {}".format(result['score']))
def produceLatexTable():
with open('./data/obj_tests.csv', newline='') as csvfile, \
open('./data/paperObjective.json') as configFile:
config = json.load(configFile)
reader = csv.reader(csvfile)
next(reader)
for row in reader:
ml_inst = mergelife.new_ml_instance(HEIGHT, WIDTH, row[0])
obj = mergelife.objective_function(ml_inst, config['config']['evalCycles'], config['objective'])
row += [str(obj)]
print("\\texttt{{{}}} & {} & {} \\\\".format(row[0],row[1],obj['score']))
def render(config, ruleText):
width = config['config']['cols']
height = config['config']['rows']
steps = config['config']['renderSteps']
ml_inst = mergelife.new_ml_instance(height, width, ruleText)
for i in range(steps):
mergelife.update_step(ml_inst)
filename = ruleText + ".png"
mergelife.save_image(ml_inst, filename)
print("Saved {}".format(filename))
def subprocessScore(inputQueue, outputQueue, config):
while True:
genome = inputQueue.get()
rule_str = genome['rule']
width = config['config']['cols']
height = config['config']['rows']
ml_inst = mergelife.new_ml_instance(height, width, rule_str)
result = mergelife.objective_function(ml_inst,
config['config']['evalCycles'],
config['objective'])
outputQueue.put({
'rule': rule_str,
'score': result['score'],
'run': genome['run']
})
def calcStdDev():
devs = []
with open('./data/obj_tests.csv', newline='') as csvfile, \
open('./data/paperObjective.json') as configFile:
config = json.load(configFile)
reader = csv.reader(csvfile)
next(reader)
for row in reader:
sample = []
for i in range(10):
ml_inst = mergelife.new_ml_instance(HEIGHT, WIDTH, row[0])
obj = mergelife.objective_function(ml_inst, config['config']['evalCycles'], config['objective'])
sample.append(float(obj['score']))
print(sample)
d = statistics.stdev(sample)
devs.append(d)
print("{} - {}".format(row[0],d))
print("Average deviation: {}".format(statistics.mean(devs)))
parser.add_argument('--zoom',
nargs=1,
type=int,
help="the pixel size for rendering")
parser.add_argument('rule',
metavar='rule',
type=str,
help='the MergeLife rule to animate')
args = parser.parse_args()
if args.rows is not None:
rows = args.rows[0]
else:
rows = 100
if args.cols is not None:
cols = args.cols[0]
else:
cols = 100
time_step = 0
fig = plt.figure()
data = np.random.randint(0, 256, size=(rows, cols, 3), dtype=np.uint8)
im = plt.imshow(data, animated=True)
ml_inst = mergelife.new_ml_instance(rows, cols, args.rule)
go_animate()