array precision
Barry Drake
bldrake1 at yahoo.com
Thu Feb 7 16:19:30 CET 2002
Michael Hudson <mwh at python.net> wrote in message news:<ug04da4k1.fsf at python.net>...
> bldrake1 at yahoo.com (Barry Drake) writes:
>
> > Maybe I'm missing something, but here is what I get:
>
> Well, I'm missing your point.
>
> > Python 2.1.1 (#20, Jul 26 2001, 11:38:51) [MSC 32 bit (Intel)] on
> > win32
> > Type "copyright", "credits" or "license" for more information.
> > IDLE 0.8 -- press F1 for help
> > >>> import math
> > >>> n = 10
> > >>> nf = 10.0
> > >>> a = 1.0
> > >>> b = 0.5
> > >>> a/n
> 0.10000000000000001
> > >>> b + a/n
> 0.59999999999999998
> > >>> a/nf
> 0.10000000000000001
> > >>> b + a/nf
> 0.59999999999999998
> > >>>
> >
> > Python does double precision floats only (see Python Essential
> > Reference 2nd ed. by David Beazley, p.23).
>
> But you can have arrays (as in, objects of type array.ArrayType) that
> store C single floats. Which was Jason's point.
>
> > Python implements IEEE 754: 17 digits of precision with exponent in
> > -308 to 308.
>
> Oh No It Doesn't. Python uses (for the objects of type
> types.FloatType) whatever the C compiler that compiled it called
> "double". This may be IEEE 754 doubles on the platforms you've used,
> but there's certainly no guarantee of that (e.g. Crays, IBM boxes (I
> think)).
>
> > float double
> > python 64 bits 64 bits
> > C 32 bits 64 bits
>
> "python double" is meaningless.
>
> > BTW, you might want to check out the Numeric module in the NumPy
> > distribution at http://numpy.sourceforge.net.
>
> That lets you store floats or doubles too.
>
> Cheers,
> M.
Michael,
Your message refers to my first post, which was missing the last line
of the output. That being the actual point of my message I will
include it here:
Python 2.1.1 (#20, Jul 26 2001, 11:38:51) [MSC 32 bit (Intel)] on
win32
Type "copyright", "credits" or "license" for more information.
IDLE 0.8 -- press F1 for help
>>> import math
>>> n = 10
>>> nf = 10.0
>>> a = 1.0
>>> b = 0.5
>>> a/n
0.10000000000000001
>>> b + a/n
0.59999999999999998
>>> a/nf
0.10000000000000001
>>> b + a/nf
0.59999999999999998
>>> b
0.5
>>> 1.0 + (0.6 - (b + a/n))
1.0
This shows that the computer "sees" Jason's output as 0.6 within the
numerical precision of the machine. Perhaps I was answering the wrong
question, which I understood to be that Jason was seeing ("suposing
that n=10 we get: myArray[0] = 0.600000023 ("something like this")")
different results than he thought he should ("but if i do: myArray[i]
+ (1.0/n) it give's me 0.6"). If I was wrong, I stand corrected.
Also, regarding the double vs float (C types), I was talking about the
default behavior of Python, which I thought Jason was using. You are
correct, and I learned something new, that NumPy has a variety of
Floatnn types, which is very cool. They also say in the paragraph
following the discussion of Floatnn that this is an add-on to the
default in Python which is Float64.
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