Thanks for the interesting suggestion.
I will have to experiment with that.
FWIW, this is the python code I am trying to “replicate”:
class DiscreteRV(object):
"""
Generates an array of draws from a discrete random variable with vector of
probabilities given by q.
"""
def __init__(self, q):
self._q = q
self.Q = cumsum(q)
def get_q(self):
return self._q
def set_q(self, val):
self._q = val
self.Q = cumsum(val)
q = property(get_q, set_q)
On Thursday, July 10, 2014 2:35:45 PM UTC-4, Tim Holy wrote:
If you're in the consenting-adults camp, overloading setindex! would be
> fine.
> Modulo bounds, error-checking, etc, it's as simple as
>
> function setindex!(rv::DiscreteRv, val, indx)
> rv.q[indx] = val
> for j = indx:length(rv.Q)
> rv.Q[j] = rv.Q[j-1]+rv.q[j]
> end
> rv
> end
>
> If you want to make it "impossible" to change q without updating Q, then
> you've got a much harder problem on your hands. If a language allows you
> to
> take a pointer to an object (which Julia does, for C interop), then
> there's
> almost nothing you can guarantee.
>
> --Tim
>
> On Thursday, July 10, 2014 10:38:40 AM Spencer Lyon wrote:
> > So I read through issue 1974
> > <https://github.com/JuliaLang/julia/issues/1974>. It seems like what I
> am
> > trying to do is not possible before that gets implemented. Is that
> correct?
> >
> > On Thursday, July 10, 2014 1:15:14 AM UTC-4, Spencer Lyon wrote:
> >
> > I have a very simple composite type:
> > > type DiscreteRv{T <: Real}
> > >
> > > q::Vector{T}
> > > Q::Vector{T}
> > >
> > > end
> > >
> > > DiscreteRv{T <: Real}(x::Vector{T}) = DiscreteRv(x, cumsum(x))
> > >
> > > Here q represents a probability vector for a discrete random variable.
> Q
> > > is the associated cdf.
> > >
> > > I want to make sure that the relationship Q = cumsum(q) is preserved
> > > whenver q is updated.
> > >
> > > I presume I need to overload Base.setindex!, but I haven’t been able
> to
> > > figure it out. Any suggestions?
> > > ------------------------------
> > >
> > > In the end I would like this example to work:
> > >
> > > julia> d = DiscreteRv([.1, .9])
> > > DiscreteRv{Float64}([0.1,0.9],[0.1,1.0])
> > >
> > > julia> d.q = [.3, .7]; d
> > > DiscreteRv{Float64}([0.3,0.7],[0.3,1.0])
> > >
> > >
> >
> >
>
>
