Have been stuck on this error for a while, also tried all the online resources but can not find a working solution anywhere.
I would like to use scipy.minimize function to find a minimum of my two variable function f(alpha, z). I get the following error:
"missing 1 required positional argument: 'z'"
I tried everything I knew, I am new to python so it is not much but tried my best. Can anyone please help me?
def calculate_loss(alpha, z):
scaled_data = []
for ind, L in enumerate(LLs):
scaled_data.append(data[ind][:,1:45])
scaled_data[ind][0] = np.log10(scaled_data[ind][0]/(L**z))
scaled_data[ind][1] = np.log10(scaled_data[ind][1]/(L**alpha))
a = []
b = []
for ind, L in enumerate(LLs):
popt, pcov = curve_fit(f, scaled_data[ind][0], scaled_data[ind][1])
a.append(popt[0])
b.append(popt[1])
N = len(LLs)
loss = 0
for i in range(0,N):
for j in range(i 1,N):
#tu musim doplnit loss function
loss = np.sum((scaled_data[ind][1] - (a[j]*(scaled_data[ind][0]) b[j]))**2)
return loss
initial = [1, 2]
res = minimize(calculate_loss, initial)
print(res.x)
fitted = res.x
CodePudding user response:
The variables are passed on as a single array. You can unpack this inside your function:
def calculate_loss(x):
alpha = x[0]
z = x[1]
. . .