43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
import math
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import numpy as np
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from scipy import integrate
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#历史数据处理
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def handle_data(all_marks=[569,
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570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,
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595,596,597,598,599
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]):#以列表形式传入省份所有考生的成绩[1,2,3]
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a = np.mean(all_marks)#平均值
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sigma = np.std(all_marks)#方差
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print('a=',a,'sigma=',sigma)
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return a,sigma
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#等效分计算
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def mark(a=584.0,sigma=8.94427190999916,per_mark=600):#a和sigma由上一个函数得出，per_mark为需要转化成等效成绩的高考分数
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re_mark = 100 * ((per_mark - a) / sigma) + 500
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if re_mark > 900:
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re_mark = 900
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elif re_mark < 100:
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re_mark = 100
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return re_mark
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#录取概率
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def P_get(uni_marks=[580,581,582,583,
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584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599],per_mark=589): #以列表形式学校录取的所有考生的成绩
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per_mark = mark(per_mark=per_mark)
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re_uni_mark = [] #学校录取学生的等效分
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for i in uni_marks:
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re_uni_mark.append(mark(per_mark=i))
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E = np.mean(re_uni_mark) #学校录取考生的等效分平均值即期望
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S = np.std(re_uni_mark)
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D = S ** 2 #样本方差
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def p(x): #录取分数的概率密度函数
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return (1/(D*(2*math.pi)**0.5))*((math.e)**(-((x-E)**2)/(2*D)))
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P,err = integrate.quad(p,100,per_mark)
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P = P*100
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print('录取概率为',P)
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return P
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# num = 570
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# while num < 600:
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# print('分数为',num,'时','录取概率为',P_get(per_mark=num))
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# num += 1 |