CODEWITHSUNDEEP
pythonscipy
user3263704
user3263704

Reputation: 71

Use of fmin in python

I'm trying to use fmin to minize my function:

def minim(self,x_r,x_i):
    self.a=complex(3,4)*(3*np.exp(1j*self.L_ch))
    x = x_r + x_i
    self.T=np.array([[0.0,2.0*self.a],[(0.00645+(x_r)^2), 4.3*x_i^2]])
    return self.T

part_real=0.532
part_imag=1.2
R_0 = fmin(A.minim,part_real,part_imag)

but i got this error:

  File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/optimize/optimize.py", line 268, in function_wrapper
    return function(x, *args)
TypeError: minim() argument after * must be a sequence, not float

I tried to use something else like minimize but the same error appear. Thank you.

Upvotes: 0

Views: 1299

Answers (1)

CT Zhu
CT Zhu

Reputation: 54340

You are not using fmin correctly.

scipy.optimize.fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0, callback=None). If you want to optimize for both x_r and x_i, you should pass them together as x0. The way you are doing it now passes part_imag as args, which should be a sequence, not a scalar. That's why your get an exception

Without an reproducible example, I guess you need to change your code to:

def minim(self,p):
    x_r=p[0]
    x_i=p[1]
    self.a=complex(3,4)*(3*np.exp(1j*self.L_ch))
    x = x_r + x_i
    self.T=np.array([[0.0,2.0*self.a],[(0.00645+(x_r)^2), 4.3*x_i^2]])
    return self.T

part_real=0.532
part_imag=1.2
R_0 = fmin(A.minim,[part_real,part_imag])

And see if it works.

Also your x seems never get used.

Upvotes: 2

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