Thanks for the reply Steven, that helped a lot
On Friday, May 8, 2015 at 10:34:24 PM UTC-4, Tim Holy wrote: > > In the context of Optim, I sketched an approach in > https://github.com/JuliaOpt/Optim.jl/issues/102#issuecomment-74658825 > that is now trivial with FastAnonymous if you're running 0.4: > > https://github.com/timholy/FastAnonymous.jl/blob/master/doc/README_0.4.md#changing-parameter-values > > > But someone needs to modify Optim so it's not so picky about the input > "function." > > --Tim > > On Friday, May 08, 2015 06:57:28 PM Tony Kelman wrote: > > This comes up pretty often with Optim as well, making a closure works > fine > > when you're only solving a single problem with a single set of > parameters, > > but it's a little cumbersome when you have a small set of parameter > values > > that you're trying to loop over many times. > > > > David, can you give a sketch of the funky workaround you were using > here? > > You wound up creating a one-element array that you were using to > transmit > > the index into the parameter data vector into the inner-loop function, > or > > something like that, right? > > > > On Friday, May 8, 2015 at 11:49:48 AM UTC-7, Steven G. Johnson wrote: > > > Here is the example from the SciPy manual, translated into working > Julia > > > code: > > > > > > @pyimport scipy.optimize as so > > > > > > using PyCall > > > args = (2, 3, 7, 8, 9, 10) > > > function fun(x, args...) > > > > > > u, v = x > > > a, b, c, d, e, f = args > > > return a*u^2 + b*u*v + c*v^2 + d*u + e*v + f > > > > > > end > > > function gradf(x, args...) > > > > > > u, v = x > > > a, b, c, d, e, f = args > > > gu = 2*a*u + b*v + d # u-component of the gradient > > > gv = b*u + 2*c*v + e # v-component of the gradient > > > return [gu, gv] > > > > > > end > > > x0 = [0, 0] > > > so.fmin_cg(fun, x0, fprime=gradf, args=args) > > > > > > > > > > > > I copied their style, but it would have been cleaner to write e.g. > > > > > > function fun(x, a, b, c, d, e, f) > > > > > > u, v = x > > > return a*u^2 + b*u*v + c*v^2 + d*u + e*v + f > > > > > > end > > > > > > > > > rather than using varargs. Of course, it would be cleaner to omit the > > > whole "args" nonsense to begin with, and just write: > > > > > > so.fmin_cg(x -> fun2(x, a, b, c, d, e, f), x0, fprime= x -> gradf(x, > a, b, > > > c, d, e, f)) > > > > > > > > > since, as I mentioned above, their "args" is just a clumsy workaround > for > > > lexical scoping, which both Python and Julia already have. > >
