8000 BUG: Increase accuracy of log1p for small inputs by mfkasim1 · Pull Request #22611 · numpy/numpy · GitHub
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BUG: Increase accuracy of log1p for small inputs #22611

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BUG: Increase accuracy of log1p for small inputs #22611

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3c06dd4
BUG: Increase accuracy of log1p for small inputs
mfkasim1 Nov 17, 2022
c57b5a2
BUG: Handle overflow and underflow for log1p complex
mfkasim1 Nov 18, 2022
59ab5bd
STY: split lines that are too long for log1p complex test
mfkasim1 Nov 18, 2022
efd5c82
TST: Added infj test for log1p complex
mfkasim1 Nov 18, 2022
d559bca
TST: Added decimal arg in testing small numbers
mfkasim1 Nov 18, 2022
c1729cf
TST: Using assert_array_almost_equal_nulp for small numbers
mfkasim1 Nov 18, 2022
3ad9ec3
STY: Put a space after a comma
mfkasim1 Nov 18, 2022
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23 changes: 20 additions & 3 deletions numpy/core/src/umath/funcs.inc.src
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Original file line number Diff line number Diff line change
Expand Up @@ -328,9 +328,26 @@ nc_log@c@(@ctype@ *x, @ctype@ *r)
static void
nc_log1p@c@(@ctype@ *x, @ctype@ *r)
{
@ftype@ l = npy_hypot@c@(x->real + 1,x->imag);
r->imag = npy_atan2@c@(x->imag, x->real + 1);
r->real = npy_log@c@(l);
@ftype@ xx = x->real;
@ftype@ yy = x->imag;
r->imag = npy_atan2@c@(yy, xx + 1);

@ftype@ z0 = npy_hypot@c@(xx + 1, yy);

// special case for inputs close to 1
if (z0 < 1.5) {
@ftype@ z = xx * (2 + xx) + yy * yy;
if (z == 0) {
// handle underflow
r->real = xx;
}
else {
r->real = 0.5 * npy_log1p@c@(z);
}
}
else {
r->real = npy_log@c@(z0);
}
return;
}

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38 changes: 38 additions & 0 deletions numpy/core/tests/test_umath.py
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Expand Up @@ -1808,6 +1808,44 @@ def test_special(self):
assert_equal(ncu.log1p(-2.), np.nan)
assert_equal(ncu.log1p(-np.inf), np.nan)

class TestLog1pComplex:
def test_log1p_complex(self):
assert_almost_equal(ncu.log1p(0.2 + 0.3j), ncu.log(1.2 + 0.3j))
assert_almost_equal(ncu.log1p(1e-19 + 1e-18j), 1e-19 + 1e-18j,
decimal=23)
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I suspect that assert_almost_equal_nulp is the more convenient way of checking things here.

@WarrenWeckesser if you could want to dig into whether the implementation looks right, that would be great. I would still feel things would be much simpler if we can "steal" an implementation from elsewhere (that has a clearly BSD compatible license).

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Just to centralise ideas (pytorch/pytorch#89214 (comment)), I agree. Now, it's not clear where is this function implemented for complex numbers really...

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Done

# these numbers are obtained from wolfram alpha
assert_almost_equal(ncu.log1p(1e-18 + 0.1j), 0.00497517 + 0.0996687j)
assert_almost_equal(ncu.log1p(0.1 + 1e-18j).real, 0.0953102)
assert_almost_equal(ncu.log1p(0.1 + 1e-18j).imag, 9.0909091e-19,
decimal=23)
assert_almost_equal(ncu.log1p(0.5 + 0j), 0.40546510810816 + 0j)
assert_almost_equal(ncu.log1p(0.0 + 0.5j), 0.111571776 + 0.463647609j)

def test_extreme(self):
# these numbers are obtained from WarrenWeckesser
# and double checked with wolfram alpha
assert_almost_equal(ncu.log1p(-1 + 1e250j),
575.6462732485114 + 1.5707963267948966j)
assert_almost_equal(ncu.log1p(1e250 + 1j).real,
575.6462732485114)
assert_almost_equal(ncu.log1p(1e250 + 1j).imag,
1e-250, decimal=255)
assert_almost_equal(ncu.log1p(1e250 + 1e250j),
575.9928468387914 + 0.7853981633974483j)
assert_almost_equal(ncu.log1p(1e-250 + 1e250j),
575.6462732485114 + 1.5707963267948966j)
assert_almost_equal(ncu.log1p(1e-250 + 2e-250j),
1e-250 + 2e-250j, decimal=255)
assert_almost_equal(ncu.log1p(1e250 + 1e-250j),
575.6462732485114 + 0.0j)

def test_special(self):
with np.errstate(invalid="ignore", divide="ignore"):
assert_equal(ncu.log1p(np.nan + 1j), np.nan + np.nan * 1j)
assert_equal(ncu.log1p(np.inf + 1j), np.inf + 0j)
assert_equal(ncu.log1p(-np.inf + 1j), np.inf + np.pi * 1j)
assert_equal(ncu.log1p(complex(0, np.inf)), np.inf + np.pi * 0.5j)
assert_equal(ncu.log1p(-1 + 0j), -np.inf + 0j)

class TestExpm1:
def test_expm1(self):
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0