I have defined the following function:
def GMM(s1, s2, s3, s4, s5, a):
"""The GMM objective function.
Arguments
---------
si: float
standard deviations of preference distribution
a: float
marginal utility of residutal income
Paramters
---------
Px: array (1,ns)
projector onto nonprice characteristic space
xk, z: arrays (J, 5) and (J, 12)
nonprice char. and instruments
invW: array (12, 12)
GMM weight matrix
Returns
-------
float."""
delta = invert(s1, s2, s3, s4, s5, a, delta0) # Invert market shares to get mean utility
bmean = np.dot(Px, delta) # Project delta onto charancteristic space
xihat = delta - np.dot(xk, bmean) # Compute implied unobservable prod. quality
temp1 = np.dot(xihat.T, z)
if np.any(np.isnan(delta)) == True:
value = 1e+10
else:
value = np.dot(np.dot(temp1, invW), temp1.T)
return np.sqrt(value)
My question pertains to the variable delta
bound inside of the function. Outside of the function I will set the initial value of delta0
. Now, ultimately I will minimize this function. What I would like to have happen is that each time the function GMM
evaluates, delta
from the previous evaluation is used as the new delta0
. I tried defining delta0
as a global variable, but it did not seem to work... likely this was my error though. Although, I have read here that generally this is a bad approach. Any suggestions?
There are multiple ways to achieve what you want. delta is saved across function calls in the following examples.
1- Class
class Example:
def __init__(self, value):
self.delta = value
def gmm(self):
self.delta += 1
return self.delta
e = Example(0)
print e.gmm()
2- Generator
def gmm():
delta = 0
while True:
delta += 1
yield delta
for v in gmm():
print v
3- Function attribute
def gmm():
gmm.delta += 1
return delta
gmm.delta = 0
4- Global variable (discouraged as you said):
delta = 0
def gmm():
global delta
delta += 1
return delta
etc...
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