trappmartin / sumproductnetworks.jl Goto Github PK
View Code? Open in Web Editor NEWSum-product networks in Julia.
License: MIT License
Sum-product networks in Julia.
License: MIT License
Hey Martin, I might be missing something obvious, but is there not yet a way to compute the log marginalized likelihood of the SPN?
i.e. in this mixture of bivariate gaussians, I can't find a way to work with the Normal(-1, 1) + Normal(1,1) mixture.
root = FiniteSumNode{Float32}();
add!(root, FiniteProductNode(), Float32(log(0.5))); # Weight 0.5
add!(root, FiniteProductNode(), Float32(log(0.5))); # Weight 0.5
add!(children(root)[1], UnivariateNode(Normal(-1, 1), 1))
add!(children(root)[2], UnivariateNode(Normal(1, 1), 1))
add!(children(root)[1], UnivariateNode(Normal(-1, 10), 2))
add!(children(root)[2], UnivariateNode(Normal(1, 10), 2))
The tag name "v.0.1.1" is not of the appropriate SemVer form (vX.Y.Z).
cc: @trappmartin
Hey, I was trying to run the example code in the readme. First line calls FiniteSumNode(). It looks like it'll need FiniteSumNode{Float64}() or some other concrete float type. Is this unintended and a bug, or should the readme be updated to reflect that? Thanks!
Hi Martin,
I was delighted to see an SPN implementation in Julia just as I was starting to dig into them. As part of my exploration, I was wondering if you had a timeline for filling out the generate_spn function, either in this package or another. I'm curious about replacing other function approximators with SPNs in some reinforcement learning projects I have going on; with the right structure, SPNs offer a lot over neural nets for these problems.
Thanks for opening your work!
Kendall
Todo:
I should make types immutable by default and add additional mutable types, e.g. MutableSumNode
.
Further, I should sub-type Distributions.Distribution
instead of my own SPNNode
type. This way the code would be compatible with Turing.
Distributions
instead of SPNNode
fit
overload for EM fitting of an SPN.Make SumProductNetworks ready for julia 1.0
I should add region graphs, similar to the implementation for BayesianSPNs, at some point to this package.
Add the implementation of safe semi-supervised learning of SPNs
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