Hi Bert,
Good reference and David Urbina's example showed that a simple swap
was position dependent. The reason I pursued this is that it seems
more efficient to sequentially apply the precedence rules to the
arbitrarily sorted elements of the vector than to go through the
directed graph approach.
Thanks Bert. Excellent reference, I learned a lot from it!
Just a note: I did use search engines for at least 2 days before posting. BUT
as often happens, I did not use the right keywords. I tried several variants of
"Convert ordered pairs to sorted", "Sort vector on paired comparisons" and
If I understand correctly, the answer is a topological sort.
Here is an explanation
https://davidurbina.blog/on-partial-order-total-order-and-the-topological-sort/
This was found by a simple web search on
"Convert partial ordering to total ordering"
Btw. Please use search engines before
Hi Pedro,
This looks too simple to me, but it seems to work:
swap<-function(x,i1,i2) {
tmp<-x[i1]
x[i1]<-x[i2]
x[i2]<-tmp
return(x)
}
mpo<-function(x) {
L<-unique(as.vector(x))
for(i in 1:nrow(x)) {
i1<-which(L==x[i,1])
i2<-which(L==x[i,2])
if(i2 wrote:
>
> Dear All,
>
> This should
This is called topological sorting in some circles. The function below
will give you one ordering that is consistent with the contraints but not
all possible orderings. I couldn't find such a function in core R so I
wrote one a while back based on Kahn's algorithm, as described in Wikipedia.
>
Thanks for this.
Yes, this is checked before trying to process this.
Pedro
On 14/03/2019 14.09, Bert Gunter wrote:
This cannot be done unless transitivity is guaranteed. Is it?
S L
a b
b c
c a
Bert
On Thu, Mar 14, 2019, 4:30 AM Pedro Conte de Barros
mailto:pbar...@ualg.pt>> wrote:
This cannot be done unless transitivity is guaranteed. Is it?
S L
a b
b c
c a
Bert
On Thu, Mar 14, 2019, 4:30 AM Pedro Conte de Barros wrote:
> Dear All,
>
> This should be a quite established algorithm, but I have been searching
> for a couple days already without finding any
Try this. Anything that appears only in Smaller is candidate for smallest.
Among those, order is arbitrary.
Anything that appears only in Larger is a candidate for largest. Among
those order is arbitrary.
Remove rows of matComp containing the already classified items. Repeat
with the smaller
Dear All,
This should be a quite established algorithm, but I have been searching
for a couple days already without finding any satisfactory solution.
I have a matrix defining pairs of Smaller-Larger arbitrary character
values, like below
Smaller <- c("ASD", "DFE", "ASD", "SDR", "EDF", "ASD")
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