Charles R Harris wrote:
On Thu, Apr 1, 2010 at 12:04 AM, Anne Archibald
wrote: On 1 April 2010 01:59, Charles R Harris
wrote: On Wed, Mar 31, 2010 at 11:46 PM, Anne Archibald <
peridot.faceted@gmail.com>
wrote:
On 1 April 2010 01:40, Charles R Harris
wrote: On Wed, Mar 31, 2010 at 11:25 PM,
wrote: On Thu, Apr 1, 2010 at 1:22 AM,
wrote: > On Thu, Apr 1, 2010 at 1:17 AM, Charles R Harris > wrote: >> >> On Wed, Mar 31, 2010 at 6:08 PM, wrote: >>> On Wed, Mar 31, 2010 at 7:37 PM, Warren Weckesser >>> wrote: >>>> T J wrote: >>>>> On Wed, Mar 31, 2010 at 1:21 PM, Charles R Harris >>>>> wrote: >>>>> >>>>>> Looks like roundoff error. >>>>>> >>>>>> >>>>> So this is "expected" behavior? >>>>> >>>>> In [1]: np.logaddexp2(-1.5849625007211563, -53.584962500721154)
>>>>> Out[1]: -1.5849625007211561 >>>>> >>>>> In [2]: np.logaddexp2(-0.5849625007211563, -53.584962500721154) >>>>> Out[2]: nan >>>>> >>>> Is any able to reproduce this? I don't get 'nan' in either 1.4.0 >>>> or >>>> 2.0.0.dev8313 (32 bit Mac OSX). In an earlier email T J reported >>>> using >>>> 1.5.0.dev8106. >>> >>> >>>>>> np.logaddexp2(-0.5849625007211563, -53.584962500721154) >>> nan >>>>>> np.logaddexp2(-1.5849625007211563, -53.584962500721154) >>> -1.5849625007211561 >>> >>>>>> np.version.version >>> '1.4.0' >>> >>> WindowsXP 32 >>> >> What compiler? Mingw? > yes, mingw 3.4.5. , official binaries release 1.4.0 by David sse2 Pentium M
Can you try the exp2/log2 functions with the problem data and see if something goes wrong? Works fine for me.
If it helps clarify things, the difference between the two problem input values is exactly 53 (and that's what logaddexp2 does an exp2 of); so I can provide a simpler example:
In [23]: np.logaddexp2(0, -53) Out[23]: nan
Of course, for me it fails in both orders.
Ah, that's progress then ;) The effective number of bits in a double is 53 (52 + implicit bit). That shouldn't cause problems but it sure looks suspicious. Indeed, that's what led me to the totally wrong suspicion that denormals have something to do with the problem. More data points:
In [38]: np.logaddexp2(63.999, 0) Out[38]: nan
In [39]: np.logaddexp2(64, 0) Out[39]: 64.0
In [42]: np.logaddexp2(52.999, 0) Out[42]: 52.999000000000002
In [43]: np.logaddexp2(53, 0) Out[43]: nan
It looks to me like perhaps the NaNs are appearing when the smaller term affects only the "extra" bits provided by the FPU's internal larger-than-double representation. Some such issue would explain why the problem seems to be hardware- and compiler-dependent.
Hmm, that 63.999 is kinda strange. Here is a bit of code that might confuse a compiler working with different size mantissas:
@type@ npy_log2_1p@c@(@type@ x) { @type@ u = 1 + x; if (u == 1) { return LOG2E*x; } else { return npy_log2@c@(u) * x / (u - 1); } }
It might be that u != 1 does not imply u-1 != 0.
I don't think that's true for floating point, since the spacing at 1 and at 0 are quite different, and that's what defines whether two floating point numbers are close or not. But I think you're right about the problem. I played a bit on my netbook where I have the same problem (Ubuntu 10.04 beta, gc 4.4.3, 32 bits). The problem is in npy_log2_1p (for example, try npy_log2_1p(1e-18) vs npy_log2_p1(1e-15)). If I compile with -ffloat-store, the problem also disappears. I think the fix is to do : @type@ npy_log2_1p@c@(@type@ x) { @type@ u = 1 + x; if ((u-1) == 0) { return LOG2E*x; } else { return npy_log2@c@(u) * x / (u - 1); } } this works for a separate C program, but for some reason, once I try this in numpy, it does not work, and I have no battery anymore, cheers, David