[Date Index]
[Thread Index]
[Author Index]
Re: optimally picking one element from each list
*To*: mathgroup at smc.vnet.net
*Subject*: [mg48308] Re: [mg48297] optimally picking one element from each list
*From*: Andrzej Kozlowski <akoz at mimuw.edu.pl>
*Date*: Sun, 23 May 2004 06:15:35 -0400 (EDT)
*References*: <200405220704.DAA08848@smc.vnet.net>
*Sender*: owner-wri-mathgroup at wolfram.com
On 22 May 2004, at 16:04, Daniel Reeves wrote:
> Suppose you have a list of lists and you want to pick one element from
> each and put them in a new list so that the number of elements that are
> identical to their next neighbor is maximized.
> (in other words, for the resulting list l, minimize
> Length[Split[l]].)
> (in yet other words, we want the list with the fewest interruptions
> of
> identical contiguous elements.)
>
> EG, pick[{ {1,2,3}, {2,3}, {1}, {1,3,4}, {4,1} }]
> --> { 2, 2, 1, 1, 1 }
>
> Here's a preposterously brute force solution:
>
> pick[x_] := argMax[-Length[Split[#]]&, Distribute[x, List]]
>
> where argMax can be defined like so:
>
> (* argMax[f,domain] returns the element of domain for which f of
> that element is maximal -- breaks ties in favor of first
> occurrence.
> *)
> SetAttributes[argMax, HoldFirst];
> argMax[f_, dom_List] := Fold[If[f[#1] >= f[#2], #1, #2] &,
> First[dom], Rest[dom]]
>
> Below is an attempt at a search-based approach, which is also way too
> slow. So the gauntlet has been thrown down. Anyone want to give it a
> shot?
>
>
> (* Used by bestFirstSearch. *)
> treeSearch[states_List, goal_, successors_, combiner_] :=
> Which[states=={}, $Failed,
> goal[First[states]], First[states],
> True, treeSearch[
> combiner[successors[First[states]], Rest[states]],
> goal, successors, combiner]]
>
> (* Takes a start state, a function that tests whether a state is a goal
> state, a function that generates a list of successors for a state,
> and
> a function that gives the cost of a state. Finds a goal state that
> minimizes cost.
> *)
> bestFirstSearch[start_, goal_, successors_, costFn_] :=
> treeSearch[{start}, goal, successors,
> Sort[Join[#1,#2], costFn[#1] < costFn[#2] &]&]
>
> (* A goal state is one for which we've picked one element of every list
> in l.
> *)
> goal[l_][state_] := Length[state]==Length[l]
>
> (* If in state s we've picked one element from each list in l up to
> list
> i, then the successors are all the possible ways to extend s to pick
> elements thru list i+1.
> *)
> successors[l_][state_] := Append[state,#]& /@ l[[Length[state]+1]]
>
> (* Cost function g: higher cost for more neighbors different
> (Length[Split[state]]) and then breaks ties in favor of longer
> states to keep from unnecessarily expanding the search tree.
> *)
> g[l_][state_] :=
> Length[Split[state]]*(Length[l]+1)+Length[l]-Length[state]
>
> (* Pick one element from each of the lists in l so as to minimize the
> cardinality of Split, ie, maximize the number of elements that are
> the same as their neighbor.
> *)
> pick[l_] := bestFirstSearch[{}, goal[l], successors[l], g[l]]
>
>
> --
> http://ai.eecs.umich.edu/people/dreeves - - google://"Daniel Reeves"
>
> If you must choose between two evils,
> pick the one you've never tried before.
>
>
>
Here is an approach which, while not really fast, at least is much
faster than yours. It will correctly minimize Length[Split[l]], but
will not necessarily give the same answer as your approach because I
have not bothered to make sure that ties are settled in the way you
indicated. It should not be hard to modify it to do so but I did not
really wish to spend time on this matter.
The idea is to use the Backtrack function from
DiscreteMath`Combinatorica`.
Here is the program:
<<DiscreteMath`Combinatorica`
BacktrackPick[l_]:= Block[{n = Length[sp], c = Length[sp], w},
partialQ[l_List] := Which[Length[l] == n && (w = Length[Split[
l]]) <= c, c = w; True, Length[l] < n &&
Length[Split[l]] <= c, True, True, False];
solutionQ[l_List] := If[Length[l] == n && (w = Length[Split[l]]) <=
c,
c = w; True, False]; Last[Backtrack[sp, partialQ, solutionQ,
All]]]
To see how this compares with yours let's create a random test case:
sp=Table[Table[Random[Integer,{1,9}],{Random[Integer,{1,3}]}],{12}]
{{6},{2},{2},{7,8},{3,4,1},{2,8,2},{5,2},{7},{8,5,8},{2,1,7},{5},{5}}
Let's try your pick function:
Length[Split[sol1=pick[sp]]]//Timing
{15.33 Second,9}
Now BacktrackPick:
Length[Split[sol2=BacktrackPick[sp]]]//Timing
{0.34 Second,9}
Quite a difference. This time the answers happened to be the same:
sol1
{6,2,2,8,1,2,2,7,8,7,5,5}
sol2
{6,2,2,8,1,2,2,7,8,7,5,5}
But this will not be the case in general.
I am sure that one can write a customised backtracking solution that
will be much faster than mine. However, writing customised (that is
without using Backtrack) backtracking programs is a bit of an art that
requires patience and care and I have a short supply of both of these.
So I am cc-ing this message to a well known member of this list who is
a real master in this sort of thing ;-) so he might be interested in
taking up the challenge.
Andrzej Kozlowski
Prev by Date:
**Re: Uniform design**
Next by Date:
**Re: Re: Uniform design**
Previous by thread:
**optimally picking one element from each list**
Next by thread:
**Re: Re: optimally picking one element from each list**
| |