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// Ver 1.1
// 更新时间:2014/7/22 18:36
//更改了错误参数，对有规律的AI胜率较大
//最差情况一般胜率不低于33.3%

namespace RPS
{
enum state { rock = 0, paper = 1, scissors = 2, blank = 3 };
struct player
{

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#include<vector>
#include<iostream>
#include<string>
#include <algorithm>

using namespace std;
namespace RPS
{
enum state { rock = 0, paper = 1, scissors = 2, blank = 3 };
struct player

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#-*- coding:utf-8 -*-
import urllib2

def GetHtml(word):
    address = 'http://cn.bing.com/dict/search?&q=' + word;
    try:
        html = urllib2.urlopen(address).read().decode('utf-8')
        return html
    except:
        return ''

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#coding=utf-8
from pydub import AudioSegment
from pydub.silence import split_on_silence
import numpy as np
import matplotlib.pyplot as plt
import pyaudio
import os

file_folder = 'D:/Mirai/English/3L英语第二册/'

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from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
import numpy as np
try:
    import Queue
except:
    import queue as Queue

# total time
T = 3

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import mxnet as mx
from mxnet import nd
import numpy as np
import time

np.random.seed(1234)

feature_stride = 16 
scales = (8, 16, 32)
ratios = (0.5, 1, 2)

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import mxnet as mx
from mxnet import nd
import numpy as np

feature_stride = 16 
scales = (8, 16, 32)
ratios = (0.5, 1, 2)
rpn_pre_nms_top_n = 12000 
rpn_post_nms_top_n = 2000
threshold = 0.7

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import mxnet as mx
import time
from distutils.util import strtobool

BT = time.time()

class CountTimeOP(mx.operator.CustomOp):
    def __init__(self, first, cname):
        super(CountTimeOP, self).__init__()
        self.first = first

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import mxnet as mx
import count_time
mnist = mx.test_utils.get_mnist()

# Fix the seed
mx.random.seed(42)

# Set the compute context, GPU is available otherwise CPU
ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()

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import mobula_op
import mxnet as mx
import numpy as np
from mobula_op.test_utils import assert_almost_equal
import time

def check_conv(data, weight, bias, kernel, stride, dilate, pad, num_filter, no_bias, conv):
    data_mx = data.copy()
    weight_mx = weight.copy()
    bias_mx = bias.copy()