3.2*2 is 9.6 ... or maybe it isn't?
dickinsm at gmail.com
Thu Jun 25 21:31:05 CEST 2009
On Jun 25, 7:41 pm, Michael Torrie <torr... at gmail.com> wrote:
> I guess PHP artificially rounds the results or something to make it seem
> like it's doing accurate calculations, which is a bit surprising to me.
After a bit of experimentation on my machine, it *looks* as though PHP
is using the usual hardware floats internally (no big surprise there),
but implicit conversions to string use 14 significant digits. If
Python's repr used '%.14g' internally instead of '%.17g' then we'd see
pretty much the same thing in Python.
> We all know that IEEE floating point is a horribly inaccurate
> representation [...]
That's a bit extreme! Care to elaborate?
, but I guess I'd rather have my language not hide that
> fact from me. Maybe PHP is using BCD or something under the hood (slow
> but accurate).
> If you want accurate math, check out other types like what is in the
> decimal module:
As Robert Kern already said, there really isn't any sense in which
floating-point is any more accurate than binary floating-point, except
that---somewhat tautologically---it's better at representing decimal
The converse isn't true, though, from a numerical perspective: there
are some interesting examples of bad things that can happen with
decimal floating-point but not with binary. For example, given any
two Python floats a and b, and assuming IEEE 754 arithmetic with
default rounding, it's always true that a <= (a+b)/2 <= b, provided
that a+b doesn't overflow. Not so for decimal floating-point:
>>> import decimal
>>> decimal.getcontext().prec = 6 # set working precision to 6 sig figs
>>> (decimal.Decimal('7.12346') + decimal.Decimal('7.12348'))/2
Similarly, sqrt(x*x) == x is always true for a positive IEEE 754
double x (again
assuming the default roundTiesToEven rounding mode, and assuming that
x*x neither overflows nor underflows). But this property fails for
IEEE 754-compliant decimal floating-point.
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