Consider the attached zip archive containing two csv files. I assume you change
the path of where the file is downloaded too. The archive contains:
1. A distance/dissimilarity matrix
2. 100 two-dimensional points from which the dissimilarity matrix was computed
from
I wish to compute the **euclidean minimum spanning tree** of said matrix, built
from said points.
I've attached a reproducible PDF showing several MST implementation results
using the traditional MST algorithms from R, printing out the sum of the
minimum edge weights for each edge from the resulting MST.
I also attached a reproducible PDF of the python implementation I'm trying to
match, which is actually the only package using the Dual Tree Boruvka approach
like MLPACK.
Finally, if I attempt to do this with MLPACK using the following code, I get a
number slightly above the other MST edge weight estimates:
```
#include <iostream>
using namespace std;
#include <mlpack/core.hpp>
#include <mlpack/methods/emst/dtb.hpp>
using namespace mlpack;
int main()
{
arma::mat dm;
data::Load("/Users/mpiekenbrock/Downloads/dissimilarity_matrix.csv",
dm, false);
// Make the dual tree with defaults
mlpack::emst::DualTreeBoruvka<> dtb(dm);
// Run the DTB algorithm.
arma::mat results;
dtb.ComputeMST(results);
// Get distances of MST manually, add them up
double cum_edge_weight = 0;
for (int i = 0; i < results.n_cols; ++i){
cum_edge_weight += dm(results(0, i), results(1, i));
}
std::cout << cum_edge_weight << std::endl;
}
```
This outputs 34.1134, slightly above all of the previous MST results of
33.50638.
[mst_test_case.pdf](https://github.com/mlpack/mlpack/files/623515/mst_test_case.pdf)
[test_data.zip](https://github.com/mlpack/mlpack/files/623342/test_data.zip)
[MST_example.pdf](https://github.com/mlpack/mlpack/files/623522/MST_example.pdf)
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