nan result from np.linalg.lstsq()
I'm having some trouble using the linalg.lstsq() function with certain data and trying to understand why. It's always returning nans when I feed it this numpy array of float64 data: data = df.close.values #coming from a pandas dataframe type(data)
numpy.ndarray data.dtype dtype('float64') data array([ 1.31570348, 1.31565421, 1.3157375 , ..., 1.32175 ,
1.32180441, 1.321775 ]) xi = np.arange(0,len(data)) A = np.vstack([xi, np.ones(len(xi))]).T A
array([[ 0.00000000e+00, 1.00000000e+00],
[ 1.00000000e+00, 1.00000000e+00], [ 2.00000000e+00, 1.00000000e+00], ..., [ 2.87800000e+03, 1.00000000e+00], [ 2.87900000e+03, 1.00000000e+00], [ 2.88000000e+03, 1.00000000e+00]]) m, c = np.linalg.lstsq(A, data)[0] m, c
(nan, nan)
oy, do not understand. Does anyone else know why?
participants (3)
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eat
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Larry Paltrow
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Pauli Virtanen