Some observations about allclose
I have a test case where I would like to impose absolute
(logical_)and relative errors
(Pdb) pdf_r
array([ 2.97341655e-90, 3.68756271e-24, 2.01840159e-07,
3.98942280e-01, 4.83941449e-01, 1.07981933e-01])
(Pdb) pdf_st
array([ 3.09721604e-90, 3.69697466e-24, 2.01872148e-07,
3.98942181e-01, 4.83941207e-01, 1.07982122e-01])
(Pdb) pdf_st - pdf_r
array([ 1.23799491e-91, 9.41194691e-27, 3.19891525e-11,
-9.95821123e-08, -2.41764579e-07, 1.89015140e-07])
(Pdb) (pdf_st - pdf_r) / pdf_r
array([ 4.16354347e-02, 2.55234897e-03, 1.58487551e-04,
-2.49615338e-07, -4.99574028e-07, 1.75043301e-06])
allclose just uses the sum of abolute and relative errors, which I
think is binding in the wrong way, for what I want.
(Pdb) np.allclose(pdf_st, pdf_r, rtol=1e-4, atol=1e-8)
True
the relative and absolute errors are to tight on one side and too
loose on the other side
(Pdb) np.allclose(pdf_st, pdf_r, rtol=1e-2, atol=0)
False
(Pdb) np.allclose(pdf_st, pdf_r, rtol=0, atol=1e-8)
False
tighter bounds for both relative and absolute errors if I impose both separately
(Pdb) np.allclose(pdf_st, pdf_r, rtol=0, atol=1e-6)
True
(Pdb) np.allclose(pdf_st, pdf_r, rtol=1e-1, atol=0)
True
Are there any alternatives to this? (or am I too tired)
(test cases are for statistical distributions, where all values are
smaller than one, and some values can be very small)
Josef
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