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float64 / int comparison different from float / int comparison

Matthew Brett

31 Oct 2011 31 Oct '11

6:23 p.m.

Hi, I just ran into this confusing difference between np.float and np.float64: In [8]: np.float(2**63) == 2**63 Out[8]: True In [9]: np.float(2**63) > 2**63-1 Out[9]: True In [10]: np.float64(2**63) == 2**63 Out[10]: True In [11]: np.float64(2**63) > 2**63-1 Out[11]: False In [16]: np.float64(2**63-1) == np.float(2**63-1) Out[16]: True I believe values above 2*52 are all represented as integers in float64. http://matthew-brett.github.com/pydagogue/floating_point.html Is this this int64 issue that came up earlier in float128 comparison? Why the difference between np.float and np.float64? Thanks for any insight, Matthew

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Stéfan van der Walt

1 Nov 1 Nov

12:59 a.m.

New subject: float64 / int comparison different from float / int comparison

On Mon, Oct 31, 2011 at 11:23 AM, Matthew Brett wrote:

...

In [8]: np.float(2**63) == 2**63 Out[8]: True

In [9]: np.float(2**63) > 2**63-1 Out[9]: True

In [10]: np.float64(2**63) == 2**63 Out[10]: True

In [11]: np.float64(2**63) > 2**63-1 Out[11]: False

In [16]: np.float64(2**63-1) == np.float(2**63-1) Out[16]: True

Interesting. Turns out that np.float(x) returns a Python float object. If you change the experiment to only use numpy array scalars, things are more consistent: In [36]: np.array(2**63, dtype=np.float) > 2**63 - 1 Out[36]: False In [37]: np.array(2**63, dtype=np.float32) > 2**63 - 1 Out[37]: False In [38]: np.array(2**63, dtype=np.float64) > 2**63 - 1 Regards Stéfan

Matthew Brett

1:25 a.m.

New subject: float64 / int comparison different from float / int comparison

Hi, 2011/10/31 Stéfan van der Walt :

...

On Mon, Oct 31, 2011 at 11:23 AM, Matthew Brett wrote:

...
In [8]: np.float(2**63) == 2**63 Out[8]: True

In [9]: np.float(2**63) > 2**63-1 Out[9]: True

In [10]: np.float64(2**63) == 2**63 Out[10]: True

In [11]: np.float64(2**63) > 2**63-1 Out[11]: False

In [16]: np.float64(2**63-1) == np.float(2**63-1) Out[16]: True

Interesting. Turns out that np.float(x) returns a Python float object. If you change the experiment to only use numpy array scalars, things are more consistent:

In [36]: np.array(2**63, dtype=np.float) > 2**63 - 1 Out[36]: False

In [37]: np.array(2**63, dtype=np.float32) > 2**63 - 1 Out[37]: False

In [38]: np.array(2**63, dtype=np.float64) > 2**63 - 1

Oh, dear, I'm suffering now: In [11]: res = np.array((2**31,), dtype=np.float32) In [12]: res > 2**31-1 Out[12]: array([False], dtype=bool) OK - that's what I was expecting from the above, but now: In [13]: res[0] > 2**31-1 Out[13]: True In [14]: res[0].dtype Out[14]: dtype('float32') Sorry, maybe I'm not thinking straight, but I'm confused... See you, Matthew

Stéfan van der Walt

1:38 a.m.

New subject: float64 / int comparison different from float / int comparison

On Mon, Oct 31, 2011 at 6:25 PM, Matthew Brett wrote:

...

Oh, dear, I'm suffering now:

In [11]: res = np.array((2**31,), dtype=np.float32)

In [12]: res > 2**31-1 Out[12]: array([False], dtype=bool)

OK - that's what I was expecting from the above, but now:

In [13]: res[0] > 2**31-1 Out[13]: True

In [14]: res[0].dtype Out[14]: dtype('float32')

I'm seeing: In [7]: res = np.array((2**31,), dtype=np.float32) In [9]: res > (2**31-1) Out[9]: array([ True], dtype=bool) In [10]: res[0] Out[10]: 2.1474836e+09 In [11]: res[0] > (2**31-1) Out[11]: True Your result seems very strange, because the numpy scalars should perform exactly the same inside and outside an array. Stéfan

Chris.Barker

3:39 p.m.

New subject: float64 / int comparison different from float / int comparison

On 10/31/11 6:38 PM, Stéfan van der Walt wrote:

...

On Mon, Oct 31, 2011 at 6:25 PM, Matthew Brett wrote:

...
Oh, dear, I'm suffering now:

...

...
In [12]: res> 2**31-1 Out[12]: array([False], dtype=bool)

...

I'm seeing: ...

...

Your result seems very strange, because the numpy scalars should perform exactly the same inside and outside an array.

I get what Stéfan gets: In [32]: res = np.array((2**31,), dtype=np.float32) In [33]: res > 2**31-1 Out[33]: array([ True], dtype=bool) In [34]: res[0] > 2**31-1 Out[34]: True In [35]: res[0].dtype Out[35]: dtype('float32') In [36]: np.__version__ Out[36]: '1.6.1' (OS-X, Intel, Python2.7) Something is very odd with your build! -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov

Matthew Brett

2 Nov 2 Nov

12:55 a.m.

New subject: float64 / int comparison different from float / int comparison

Hi, On Tue, Nov 1, 2011 at 8:39 AM, Chris.Barker wrote:

...

On 10/31/11 6:38 PM, Stéfan van der Walt wrote:

...
On Mon, Oct 31, 2011 at 6:25 PM, Matthew Brett wrote:

...
Oh, dear, I'm suffering now:

...
...
In [12]: res> 2**31-1 Out[12]: array([False], dtype=bool)

...
I'm seeing: ...

...
Your result seems very strange, because the numpy scalars should perform exactly the same inside and outside an array.

I get what Stéfan gets:

In [32]: res = np.array((2**31,), dtype=np.float32)

In [33]: res > 2**31-1 Out[33]: array([ True], dtype=bool)

In [34]: res[0] > 2**31-1 Out[34]: True

In [35]: res[0].dtype Out[35]: dtype('float32')

In [36]: np.__version__ Out[36]: '1.6.1'

(OS-X, Intel, Python2.7)

Something is very odd with your build!

Well - numpy 1.4.1 on Debian squeeze. I get the same as you with current numpy trunk. Stefan and I explored the issue a bit further and concluded that, in numpy trunk, the current behavior is explicable by upcasting to float64 during the comparison: In [86]: np.array(2**63, dtype=np.float) > 2**63 - 1 Out[86]: False In [87]: np.array(2**31, dtype=np.float) > 2**31 - 1 Out[87]: True because 2**31 and 2**31-1 are both exactly representable in float64, but 2**31-1 is not exactly representable in float32. Maybe this: In [88]: np.promote_types('f4', int) Out[88]: dtype('float64') tells us this information. The command is not available for numpy 1.4.1. I suppose it's possible that the upcasting rules were different in 1.4.1 and that is the cause of the different behavior. Best, Matthew

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float64 / int comparison different from float / int comparison

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