59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
import numpy as np
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import json
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import matplotlib.pyplot as plt
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colorList = json.load(open('color/config.json','r'))["color"]
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psgRange=np.arange(1e6,5e6,1e5)
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prediction = 2433827
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taxShift=0.03
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torShift=1-0.054
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curTaxationRate=1.0
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temp1 = np.log(prediction/(torShift**(curTaxationRate/taxShift)))
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temp2 = np.log(torShift)
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def tax(x):
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return taxShift*(np.log(x)-temp1)/temp2
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def predictTotalPassengers(taxationRate):
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return (prediction/(torShift**(curTaxationRate/taxShift)))*(torShift**(taxationRate/taxShift))
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RNGk = 37.648854
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RNGb = 59397421.185785
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temp3=(curTaxationRate*(RNGk*(predictTotalPassengers(curTaxationRate))+RNGb))
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def f1(x):
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return 5*((tax(x))*(RNGk*x+RNGb) / temp3 -1)+1
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C2=1
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Cb2=1e8
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def f2(x):
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return 1 - C2*x/Cb2 - 0.2
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C3=1
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Cb3=4e8
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def f3(x):
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return 1 - C3*x/Cb3
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influenceFactor = np.array([0.21061,0.54848,0.24091])
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def f(x):
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return f1(x)*influenceFactor[0] + f2(x)*influenceFactor[1] + f3(x)*influenceFactor[2]
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from scipy.optimize import minimize_scalar
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result = minimize_scalar(lambda x: -f(x),bounds=(np.min(psgRange),np.max(psgRange)),method='bounded')
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print(result)
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plt.plot(psgRange,f(psgRange),label='result',color=colorList[0])
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plt.plot(psgRange,f1(psgRange),label='Ieco',color=colorList[1])
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plt.plot(psgRange,f2(psgRange),label='Ienv',color=colorList[2])
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plt.plot(psgRange,f3(psgRange),label='Isoc',color=colorList[3])
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plt.plot(result.x,f(result.x),'o',label='optimal',color=colorList[4])
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plt.xlabel('total passengers')
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plt.ylabel('Optimized objective function')
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plt.legend()
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plt.savefig('result/O.png')
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plt.show()
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print("Optimal total passengers:",result.x)
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print("Optimal taxation rate:",tax(result.x))
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print("Score",f(result.x)) |