--- On Fri, 7/1/11, Kenny Riddile wrote:

This isn't directly Boost-related, but this is still probably the best place to ask this question.  Just for fun, I was trying to write a compile-time approximation of cosine using the following Maclaurin series:

cos( x ) ~= 1 - ( x^2 / 2! ) + ( x^4 / 4! ) - ( x^6 / 6! ) + ...

I ended up at a point where I needed to assign a const floating point value to the result of an integer division, kind of like this:

static const float value = integerA / integerB;

My problem is of course that you can't cast to non integral/enum types in a constant expression, which means that I end up with the truncated result of integer division.  Suggestions?

If your code really needs to perform compile-time floating-point math, then I have two suggestions: 1. Use the recently accepted Boost.Ratio library (which should become part of the next release). 2. Try out the MPL.Math sublibrary (not yet accepted into Boost, currently located at <https://github.com/boost-vault/Template-Metaprogramming/blob/master/mpl_math.zip>) and browse the existing source code. Sorry, no real documentation yet. HTH, Cromwell D. Enage

...  Just for fun, I was trying to write a compile-time approximation of cosine using the following Maclaurin series: cos( x ) ~= 1 - ( x^2 / 2! ) + ( x^4 / 4! ) - ( x^6 / 6! ) + ... I ended up at a point where I needed to assign a const floating point value to the result of an integer division, kind of like this: static const float value = integerA / integerB; -------------------------------------------------------------------------------- Sometimes, I use Pade approximations for this kind of thing. But, as already mentioned, you also need to consider rational approximations to floats when metaprogramming. Consider the Pade approximation for sin(x) of Order(3) in the numerator and Order(4) in the denominator: sin(x) ~= (5880 x - 620 x^3)/(5880 + 360 x^2 + 11 x^4) I believe that it is easier to rationalize and metaprogram this thing than it is to work with a similarly truncated Taylor series. In fact, it's probably a viable alternative to your existing macros. I obtain Pade approximations using Mathematica. However, Wolfram's magnificent knowledge-based search machine "Alpha" will actually give you Pade approximations. For example, put this into Wolfram's Alpha:   PadeApproximant[Sin[x], {x, 0, {3, 4}}] Of course this stuff also works for sin, exp, log, etc. Maybe this will give you some new ideas or at least something to play around with. Sincerely, Chris.