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I'm trying to minimize a function w.r.t. a list x0 of shape (30), but I got the error:

"invalid index to scalar variable"

In particular my code is like this:

def func(data, x0):
    s_i=np.zeros(data.shape[0])
    for i in range(data.shape[0]):
          x=(data[i][0]+(data[i][1:]*x0).sum())
          s_i[i]=x
    return (s_i*s_i).sum()-(s_i.sum())**2
x0=np.ones(30)
x0=list(x0)
out=scipy.optimize.minimize(func, x0, args=(data), method='Nelder-Mead', options={'maxiter':100000, 'disp': True})

in which data is a numpy array of shape (N,31) and data[i][1:]*x0 is the part which raises the error. How can I solve this?

emm gi
  • 45
  • 6

1 Answers1

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Solved. I re-defined the function by splitting my data before:

c=data[:,1:]
d=data[:,0]
def func(c,d, x0):
s_i=np.zeros(c.shape[0])
for i in range(c.shape[0]):
      x=(d[i]+(c[i]*x0).sum())
      s_i[i]=x
return (s_i*s_i).sum()-(s_i.sum())**2
emm gi
  • 45
  • 6