great: that's exactly the case. the adjacency matrix of transitive
closure is the reachability matrix, so this is a good workarround.
On 14 Maio, 16:16, Jason Grout <[EMAIL PROTECTED]> wrote:
> Jason Grout wrote:
> > William Stein wrote:
> >> On Wed, May 14, 2008 at 6:57 AM, David Joyner <[EMAIL PROTECTED]> wrote:
> >>> On Wed, May 14, 2008 at 8:54 AM, Pedro Patricio <[EMAIL PROTECTED]> wrote:
> >>>> nope, booleans means 1+1=1.
> >>>> take + as OR and * as AND in the propositional calculus.
> >> So 1+1 = 1 and 1*1 = 1 and 1*0 = 0 and 1+0 = 1 and 0+0=0?
> >> That's *not* a ring, so you shouldn't make matrices over it in
> >> Sage, since in Sage all matrices are over rings.
>
> >> You could "fake things" by creating a new "the booleans" data
> >> type but then you are asking for trouble since they aren't a ring.
>
> >> You could also just make a very simple boolean type with
> >> the properties you want and make a numpy matrix with
> >> entries that type. What do you actually want to *do* with
> >> your boolean matrix?
>
> > From your post on sage-edu, it sounds like you just want the adjacency
> > matrix of the transitive closure of a digraph; is that right? If so,
> > you might use the transitive closure function. I can't remember if it
> > is defined for digraphs, but it should be easy to extend.
>
> Sorry, I wasn't thinking. Of course the transitive closure function is
> defined for digraphs: that's the only case where it's actually interesting.
>
> sage: g=DiGraph({0:[1,2], 1:[3], 2:[5,6]})
> sage: g.transitive_closure().adjacency_matrix()
> [0 1 1 1 1 1]
> [0 0 0 1 0 0]
> [0 0 0 0 1 1]
> [0 0 0 0 0 0]
> [0 0 0 0 0 0]
> [0 0 0 0 0 0]
>
> The transitive closure function isn't very smart right now. It ought to
> be sped up by looking at the strongly connected components (I don't know
> if we already have a function to do that, though).
>
> Jason
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