CODEWITHSUNDEEP
pythonnumpytypeerror
Mhmd Qadi
Mhmd Qadi

Reputation: 47

TypeError: 'numpy.float64' object is not callable in fmin numpy

i am trying to run the following script i have an error with (Fmin) i think. i have the following error:

TypeError: 'numpy.float64' object is not callable

thank you very much , i tried alot to solve it but i was not able ...Thanks again

   import numpy as n
   from scipy import optimize
        a=2
        b=3
        def f (ts):
            c= ts
            y= optimize.fmin(np.linalg.norm(a/c +b),x0=0.1)
            return y
        f2=np.vectorize(f)
        ts=np.linspace(1,50)

   print(f2(ts))

Upvotes: 0

Views: 754

Answers (2)

Iguananaut
Iguananaut

Reputation: 23356

Search for other TypeError: ... is not callable questions. This means you've made a programming error by passing a non-callable object somewhere that expects a callable object. In Python, "callable" generally means a function (or any object that accepts the obj() syntax. The scipy.optimize APIs typically take a function as their first argument, being the objective function you wish to optimize.

The expression np.linalg.norm(a/b + c) is not a function. You are simply evaluating the function on one argument and returning the result. It's not clear from your code which variable(s) you want to optimize for. Let's say it's c (with a and b fixed) (though you should really call it x--by convention it's common to use high letters in the Latin alphabet like w, x, y, z as independent variables, and low letters like a, b, c as constants--of course, this is highly context and domain specific). Then you might do

from np.linalg import norm
optimize.fmin(lambda x: norm(a/x + b), x0=0.01)

The lambda x: operator in this defines an in-line one argument function that implements your objective function on a single variable. Ｏf course, you can also optimize all of a, b, and c together as a vector. But you haven't specified what problem you're trying to solve, so I'll leave that as an exercise.

I should add, using np.vectorize the way you are is very slow. You can use fmin on an array.

Upvotes: 1

lorem_bacon
lorem_bacon

Reputation: 175

optimize.fmin()

takes in the first argument as a callable function but it is provided with a float64 object i.e. the return value of np.linalg.norm(a/c +b)

Upvotes: 0

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