本文整理汇总了Python中stats.Stats.process_text方法的典型用法代码示例。如果您正苦于以下问题：Python Stats.process_text方法的具体用法？Python Stats.process_text怎么用？Python Stats.process_text使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类stats.Stats的用法示例。
在下文中一共展示了Stats.process_text方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: signal_handler
# 需要导入模块: from stats import Stats [as 别名]
# 或者: from stats.Stats import process_text [as 别名]
class Pool:
def signal_handler(self, signal, frame):
self.end = True
print 'You pressed Ctrl+C!'
def __init__(self, ciphertext, stats_filenames, dict_file, max_pop = 10, params = default_params, alphabet = default_alphabet, stat_file = None, naive_key = True):
self.params = params
self.alphabet = alphabet
self.single_stats_filename, self.double_stats_filename = stats_filenames
self.ciphertext = ciphertext
self.max_pop = max_pop
self.exp_stats = Stats(self.alphabet)
self.exp_stats.load_singlef_from_file(self.single_stats_filename)
self.exp_stats.load_doublef_from_file(self.double_stats_filename)
self.ciph_stats = Stats(self.alphabet)
self.ciph_stats.process_text(self.ciphertext)
self.initialize(naive_key)
self.stat_file = None
if stat_file != None:
self.stat_file = open(stat_file, "w")
self.stat_file.write("gen\tworst\tavg\tbest\tderiv\n")
self.sliding_table = []
self.end = False
self.dictionary = Dictionary(dict_file)
self.temp_stats = Stats(self.alphabet)
signal.signal(signal.SIGINT, self.signal_handler)
def compute_sliding_deriv(self, current_value, maxlen):
self.sliding_table.append(current_value)
slen = len(self.sliding_table)
if slen > maxlen:
self.sliding_table = self.sliding_table[slen - maxlen:]
slen = len(self.sliding_table)
return (self.sliding_table[slen - 1] - self.sliding_table[0]) / float(slen)
''' the smaller the better '''
def get_fitness(self, key):
deciphered = key.encdec(self.ciphertext)
self.temp_stats.process_text(deciphered)
#print deciphered.encode("utf-8")
corr = self.exp_stats.compute_difference(self.temp_stats)
return corr
#return self.dictionary.count(deciphered) / 100.0 + corr * 0.1
def initialize(self, naive):
self.keys = []
naive_key = Key(self.alphabet)
naive_key.compute_naive(self.exp_stats, self.ciph_stats)
naive_key.reverse()
for i in xrange(0, self.max_pop):
if naive:
new_key = naive_key.clone()
new_key.shuffle(3)
else:
new_key = Key(self.alphabet)
new_key.randomize()
self.keys.append(new_key)
def process_mutations(self):
count = int((self.params["mutations_percent"] * len(self.keys)) / 100)
maxp = len(self.keys) - 1
for i in xrange(0, count):
p = random.randint(0, maxp)
n = self.keys[p].clone()
n.shuffle(self.params["mutation_swap_count"])
self.keys.append(n)
def make_step(self):
crossovers = int(self.params["crossover_percent"] * self.max_pop / 100)
#.........这里部分代码省略.........