`ChowLiuTrees.MST`

— MethodCompute the Minimum Spanning Tree (MST) of graph g with weights `weights`

, with constraints such that `included_edges`

should be included while `excluded_edges`

should be excluded.

`ChowLiuTrees.learn_chow_liu_trees`

— MethodLearn Chow Liu Tree(s). It will run on CPU/GPU based on where `train_x`

is.

Arguments:

`train_x`

: training data. If want gpu, move to gpu before calling this`CuArray(train_x)`

.

Keyword arguments:

`num_trees=1`

: number of trees you want to learned.`dropout_prob=0.0`

: drop edges with probability`dropout_prob`

when learning maximum spanning tree.`weights=nothing`

: weights of samples. Weights are all 1 if`nothing`

.`pseudocount=0.0`

: add a total of pseudo count spread out overall all categories.`Float=Float64`

: precision.`Float32`

is faster if`train_x`

a large.

`ChowLiuTrees.topk_MST`

— MethodTop k minimum spanning trees for a complete graph with `weights`

as weights http://www.nda.ac.jp/~yamada/paper/enum-mst.pdf