import pandas as pd
from scipy.optimize import fmin
data = pd.DataFrame({'DIV': [1,2,3]*3,
'MONTH': ['May','May','May','June','June','Jun','Jul','Jul','Jul'],
'C':[8]*9,
'U':[3,2,1]*3,
'S':[9]*9})
data.to_csv(r'C:\Users\mbabski\Documents\Unit Plan Summer 2016\data_test.csv')
def return_array(x):
return x.values
def mape(c,u,s,r): #returns an array of line level Mean Absolute Percentage Errors
p = c + u * r
m = abs(1.0-(p/s))
return m
def e(c,u,s,r): #calculates average of the MAPEs
return np.mean(mape(c,u,s,r))
for d in range(1,4):
div_data = data[data.DIV==d]
c = return_array(div_data.C)
u = return_array(div_data.U)
s = return_array(div_data.S)
r0 = [[1.0]]
t = fmin(e,r0,args=(c,u,s))
print 'r:',t
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 29
Function evaluations: 58
r: [-69.]
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 29
Function evaluations: 58
r: [-70.]
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 29
Function evaluations: 58
r: [-71.]
为什么我得到r=-69、-70和-71? 我应该得到r=0.333,0.555,和0.999。你知道吗
scipy.optimize.fmin
将它试图最小化的值作为第一个参数传递给函数。如果你把函数重写为你得到了正确的结果
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