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pythonnumpymatrix-inverse

np.linalg.inv() leads to array full of np.nan

I am trying to calculate the inverse of a matrix via:


A = pd.read_csv("A_use.csv", header = None)

I = np.identity(len(A))

INV = np.linalg.inv(I - A)

However, the resulting array is full of np.nan.

enter image description here

I don't understand why that's the case. I've tried to replace all np.nan values in A (although there shouldn't be any) via A[np.isnan(A)] = 0 but the problem persists.

Any suggestions?

Solution

  • The reason is an np.inf value in at least column 293. The np.inf value can be replaced via A[np.isinf(A)] = 0. If np.inf is replaced with zero, there are no np.nan values in L.