使用Python提取游戏中物品交易价格.pdf

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,同时从
样本量也可以得出服务器的活跃程度


,使用模块:
math:数学操作模块,附带开根等方法
numpy:科学计算模块,附带求标准差等等方法,该模块需额外安装,安装包见
附件

从游戏中提取物品价格是比较容易的事情,这里略过,当我们取到游戏中
一组数据后,
来的一个月的物品交易价格记录:
1.[1,1,1,1,1,1,1,70000,70000,15000,1,1,1,30000,30000,
55555,30000,30000,1,70000,50000,50000,50000,1000,10,
10,1,1,1,1,1,1,1,1,50000,50000,50000,50000,45000,
45000,45000,45000,45000,45000,100000,30000,30000,1,1,
1,80000,50000,50000,50000,50000,50000,50000,50000,
40000,40000,40000,40000,40000,40000,40000,40000,30000,
30000,30000,30000,30000,30000,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,1,50000,50000,50000,50000,
50000,30000,30000,30000,30000,30000,30000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,40000,40000,
40000,40000,40000,40000,40000,40000,40000,40000,35000,
35000,35000,1,1,1,30000,35000,35000,35000,35000,35000,
50000,50000,50000,50000,50000,50000,50000,1,1,1,1,
1,1,50000,50000,50000,50000,50000,50000,100000,100000,
100000,100000,75000,30000,30000,30000,30000,50000,
50000,50000,50000,50000,50000,50000,50000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,30000,30000,
50000,50000,50000,50000,50000,50000,50000,50000,50000,
55000,55000,55000,45000,50000,50000,50000,80000,50000,
80000,20000,20000,20000,20000,20000,20000,80000,80000,
50000,50000,50000,50000,50000,40000,35000,35000,35000,
35000,35000,35000,50000,50000,50000,50000,50000,50000,
50000,25000,25000,25000,25000,25000,25000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,50000,50000,
50000,50000,50000,50000,50000,30000,1,40000,40000,
40000,40000,40000,40000,50000,50000,50000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,50000,60000,
60000,70000,90000,50000,50000,50000,50000,50000,50000,
50000,50000,30000,30000,30000,30000,50000,45000,45000,
45000,30000,30000,45000,40000,40000,40000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,50000,50000,
50000,40000,50000,50000,50000,77777,50000,50000,50000,
50000,50000,50000,50000,35000,35000,30000,30000,30000,
55555,50000,50000,50000,55555,40000,40000,40000,40000,
40000,40000,35000,50000,50000,50000,50000,50000,50000,
50000,45000,45000,45000,60000,50000,50000,50000,50000,
50000,50000,50000,49999,50000,50000,40000,40000,40000,
60000,70000,100000,50000,50000,50000,50000,50000,50000,
50000,50000,66666,30000,30000,30000,50000,50000,50000,
35000,30000,30000,30000,30000,30000,30000,30000,55555,
30000,30000,30000,30000,15555,50000,50000,50000,50000,
35000,35000,1,35000,30000,30000,30000,30000,40000,
50000,50000,1,40000,40000,40000,40000,40000,50000,
50000,50000,50000,50000,20000,50000,1,1,1,1,1,20000,
20000,20000,1,1,30000,50000,30000,30000,30000,30000,
30000,1000,1,1,40000,40000,40000,40000,1,1,50000,
50000,50000,50000,50000,1,1,50000,50000,50000,50000,
50000,50000,50000,45000,50000,50000,50000,50000,50000,
50000,50000,45000,45000,45000,45000,45000,45000,50000,
50000,50000,100000,50000,20000,20000,20000,20000,50000,
10,35000,2,35000,35000,35200,50000,100000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,50000,50000,
50000,50000,50000,40000,40000,40000,40000,40000,40000,
50000,50000,40000,40000,40000,40000,40000,40000,100000,
40000,30000,30000,40000,40000,40000,50000,50000,50000,
40000,40000,40000,40000,40000,35000,35000,35000,35000,
35000,35000,35000,35000,30000,50000,35000,35000,35000,
35000,35000,35000,35000,35000,35000,35000,35000,35000,
35000,35000,40000,40000,40000,30000,30000,30000,30000,
30000,30000,1,1,25000,25000,25000,85000,85000,30000,
30000,40000,40000,40000,40000,40000,40000,40000,40000,
40000,40000,40000,40000,70000,50000,70000,70000,70000,
70000,70000,70000,50000,50000,65000,65000,65000,1,
40000,40000,40000,40000,40000,40000,40000,40000,50000,
50000,50000,50000,50000,50000,50000,30000,30000,30000,
30000,30000,30000,30000,30000,30000,30000,40000,40000,
40000,40000,40000,40000,40000,40000,40000,40000,40000,
40000,40000,85000,85000,85000,40000,40000,40000,40000,
40000,40000,40000,40000,40000,35000,35000,35000,35000,
35000,35000,50000,50000,30000,30000,25000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,65000,65000,
1,1,1,1,1,1,80000,80000,80000,80000,50000,50000,
50000,50000,50000,50000,50000,50000,50000,50000,50000,
50000,30000,30000,1,30000,30000,50000,50000,50000,
35000,35000,35000,35000,40000,40000,40000,40000,40000,
40000,40000,40000,40000,40000,60000,60000,75000,1,1,
1,1,1,1,50000,50000]
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由切比雪夫不等式不等式可知,与平均数的距离超过K倍标准差的数据所占的
比例之多是1/K**2;
这里使用数据集中与平均数距离超过2倍标准差的数据不超过25%作为我们判断
数据有效性的标准;
这里我们定义平均数与2倍标准差的偏离不得大于25%
假设平均数为A,标准差为V,数据集的数据长度为S,我们可以得到一个不等
式
即2V<=A*==>V<=A/8==>V/A<=
对于一个数据列表求标准差我们可以使用numpy模块中的求方差公式然后开根
=((moneylist))/(moneylist)
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其中方差=(moneylist)
平均值=(moneylist)
可以做出一个函数来说明这段数据的相关属性
,math
2.
(moneylist):
'数据集的标准差为%s'%((moneylist))
'数据集的平均值为%s'%(moneylist)
6.
=='__main__':
(moneylist)
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用以上数据我们可以得出结果


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此时V/A=,此时就需要对于数据做筛选我们可以
将里边偏离过大的数据清理掉
(moneylist):
()#将数据集进行正序排序
:

abs(each-(moneylist))>((moneyli
st))*2:#检测数据集中每一个数据是否偏离平均值2倍标准差以上
:
(each)#将数据从数据集中去除

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经过几次处理之后,按照这个去除标准,数据集中就再也去除不掉数据了,此
时可以认为该组数据为有效数据
1.#coding:gbk
,math
3.
(moneylist):

((moneylist))/(moneylist)<
:
((moneylist))
(moneylist)
'物品A的估价为%s'%(moneylist)
:
=accurate(moneylist)
=1
=0
!=secondlen:
=len(moneylist)#清理数据集前数据集长度
=accurate(moneylist)
=len(moneylist)#清理数据集后长度
17.
:
'物品A的估价为%s'%(moneylist)
20.
(moneylist):
()#将数据集进行正序排序
(moneylist)
:

abs(each-(moneylist))>((moneyli
st))*2:#检测数据集中每一个数据是否偏离平均值2倍方差以上
:
(each)

29.
=='__main__':
=[1,1,1,1,1,1,2150,860,1,1,1505,1,1,1,
1,1,1,1,1,1,1,1,1,215,1,1,1,1]#可以替换成上述提供数
据集来验证
(moneylist)
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如果需要将程序界面化,这时候是需要涉及GUI程序相关,所以暂时这里就不
讨论了
最终的界面如下图