CODEWITHSUNDEEP
pythonpython-3.xscipy-optimize-minimizelevenberg-marquardt
Selcuk Yalcinkaya
Selcuk Yalcinkaya

Reputation: 9

How to minimize a value of a function by changing function parameters?

import numpy as np
x = np.array([1,2,3,4,5,6,7])
f1= np.array([1,2,3,4,5,6,7])
f2= np.array([1,2,3,4,5,6,7])

def func(w1,w2,x,f1,f2):
    w1=1-w2
    return np.std(x/(w1*f1+w2*f2))

i need my code to minimize func(w1,w2,x,f1,f2) by changing w1 and w2 then give me w1 and w2 values. w1 + w2 should be equal to 1.

Upvotes: -1

Views: 210

Answers (1)

Stuart
Stuart

Reputation: 9868

Something like this might be what you need:

x = np.random.randint(1, 10, 7)
f1 = np.random.randint(1, 10, 7)
f2 = np.random.randint(1, 10, 7)

def func(w, x, f1, f2):  # no need to pass w1 and w2 separately
    return np.std(x / (w[0] * f1 + (1 - w[0]) * f2))

res = scipy.optimize.minimize(func, x0=[0.5], args=(x, f1, f2), bounds=[(0, 1)])
w1 = res.x[0]
w2 = 1 - w1
print("Optimal weights are", w1, w2)

Upvotes: 0

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