本文整理匯總了Python中proxy.Proxy.make_train方法的典型用法代碼示例。如果您正苦於以下問題：Python Proxy.make_train方法的具體用法？Python Proxy.make_train怎麽用？Python Proxy.make_train使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類proxy.Proxy的用法示例。
在下文中一共展示了Proxy.make_train方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: len
# 需要導入模塊: from proxy import Proxy [as 別名]
# 或者: from proxy.Proxy import make_train [as 別名]
# points = res + points
# values = [v] * len(res) + values
print 'Added ', (x, v), 'to points'
def add_mismatched(x):
res = [(x, randint(0, LAST), randint(0, LAST))]
tmp = x
for i in xrange(8):
point = (x, tmp % 256, 0)
res.append(point)
tmp /= 256
return res
if __name__ == "__main__":
random.seed(0)
Config.TRAINING = True
# p = proxy.make_train(8, ["tfold"])
# p = proxy.make_train(15, ["tfold"])
p = proxy.make_train(12, ["tfold"])
inc_solve(p)
示例2: map
# 需要導入模塊: from proxy import Proxy [as 別名]
# 或者: from proxy.Proxy import make_train [as 別名]
if status == "error":
print 'Error!, proceeding to another guess'
h_gen.next()
if status == "mismatch":
x, y, my = map(from_hex, data["values"])
xs.append(x)
ys.append(y)
h_current, delta = find_first_good2(h_gen, xs, ys)
index += delta
if __name__ == "__main__":
seed(0)
Config.TRAINING = True
p = proxy.make_train(42)
solve2(p)
exit()
OPS = ["not", "and", "or", "xor", "shr1", "shr4", "shr16", "plus", "shl1", "if0"]
OPS = ["and","if0","or","plus","shr16","shr4"]
AR = tuple([randint(0, LAST) for i in xrange(1)])
g = gen_tree_values(12, OPS, AR, tuple(map(f, AR)))
print len(g[-1]), tuple(map(f, AR)) in g[-1]
h = find_all_trees_with_values(g, AR, tuple(map(f, AR)), OPS)
for a in h:
print a.dump()#, map(lambda x: a.getx(x), AR)
print len(h)
示例3: map
# 需要導入模塊: from proxy import Proxy [as 別名]
# 或者: from proxy.Proxy import make_train [as 別名]
if status == "error":
print 'Error!, proceeding to another guess'
h_gen.next()
if status == "mismatch":
x, y, my = map(from_hex, data["values"])
xs.append(x)
ys.append(y)
h_current, delta = find_first_good2(h_gen, xs, ys)
index += delta
if __name__ == "__main__":
seed(0)
Config.TRAINING = True
p = proxy.make_train(15)
solve2(p)
exit()
OPS = ["not", "and", "or", "xor", "shr1", "shr4", "shr16", "plus", "shl1", "if0"]
OPS = ["and","if0","or","plus","shr16","shr4"]
AR = tuple([randint(0, LAST) for i in xrange(1)])
g = gen_tree_values(12, OPS, AR, tuple(map(f, AR)))
print len(g[-1]), tuple(map(f, AR)) in g[-1]
h = find_all_trees_with_values(g, AR, tuple(map(f, AR)), OPS)
for a in h:
print a.dump()#, map(lambda x: a.getx(x), AR)
print len(h)
示例4: map
# 需要導入模塊: from proxy import Proxy [as 別名]
# 或者: from proxy.Proxy import make_train [as 別名]
h_gen.next()
if status == "mismatch":
x, y, my = map(from_hex, data["values"])
xs.append(x)
ys.append(y)
h_current, delta = find_first_good2(h_gen, xs, ys)
if h_current == None:
print ":("
return
index += delta
if __name__ == "__main__":
seed(0)
Config.TRAINING = True
p = proxy.make_train(137)
solve2(p)
exit()
OPS = ["not", "and", "or", "xor", "shr1", "shr4", "shr16", "plus", "shl1", "if0"]
OPS = ["and","if0","or","plus","shr16","shr4"]
AR = tuple([randint(0, LAST) for i in xrange(1)])
g = gen_tree_values(12, OPS, AR, tuple(map(f, AR)))
print len(g[-1]), tuple(map(f, AR)) in g[-1]
h = find_all_trees_with_values(g, AR, tuple(map(f, AR)), OPS)
for a in h:
print a.dump()#, map(lambda x: a.getx(x), AR)
print len(h)