cleanup

Author: CarloLucibello

GraphNeuralNetworks/src/layers/temporalconv_old.jl

@@ -167,99 +167,3 @@ end
 function Base.show(io::IO, a3tgcn::A3TGCN)
     print(io, "A3TGCN($(a3tgcn.in) => $(a3tgcn.out))")
 end
-
-
-"""
-    EvolveGCNO(ch; bias = true, init = glorot_uniform, init_state = Flux.zeros32)
-
-Evolving Graph Convolutional Network (EvolveGCNO) layer from the paper [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://arxiv.org/pdf/1902.10191).
-
-Perfoms a Graph Convolutional layer with parameters derived from a Long Short-Term Memory (LSTM) layer across the snapshots of the temporal graph.
-
-
-# Arguments
-
-- `in`: Number of input features.
-- `out`: Number of output features.
-- `bias`: Add learnable bias. Default `true`.
-- `init`: Weights' initializer. Default `glorot_uniform`.
-- `init_state`: Initial state of the hidden stat of the LSTM layer. Default `zeros32`.
-
-# Examples
-
-```jldoctest
-julia> tg = TemporalSnapshotsGNNGraph([rand_graph(10,20; ndata = rand(4,10)), rand_graph(10,14; ndata = rand(4,10)), rand_graph(10,22; ndata = rand(4,10))])
-TemporalSnapshotsGNNGraph:
-  num_nodes: [10, 10, 10]
-  num_edges: [20, 14, 22]
-  num_snapshots: 3
-
-julia> ev = EvolveGCNO(4 => 5)
-EvolveGCNO(4 => 5)
-
-julia> size(ev(tg, tg.ndata.x))
-(3,)
-
-julia> size(ev(tg, tg.ndata.x)[1])
-(5, 10)
-```
-"""
-struct EvolveGCNO
-    conv
-    W_init
-    init_state
-    in::Int
-    out::Int
-    Wf
-    Uf
-    Bf
-    Wi
-    Ui
-    Bi
-    Wo
-    Uo
-    Bo
-    Wc
-    Uc
-    Bc
-end
- 
-function EvolveGCNO(ch; bias = true, init = glorot_uniform, init_state = Flux.zeros32)
-    in, out = ch
-    W = init(out, in)
-    conv = GCNConv(ch; bias = bias, init = init)
-    Wf = init(out, in)
-    Uf = init(out, in)
-    Bf = bias ? init(out, in) : nothing
-    Wi = init(out, in)
-    Ui = init(out, in)
-    Bi = bias ? init(out, in) : nothing
-    Wo = init(out, in)
-    Uo = init(out, in)
-    Bo = bias ? init(out, in) : nothing
-    Wc = init(out, in)
-    Uc = init(out, in)
-    Bc = bias ? init(out, in) : nothing
-    return EvolveGCNO(conv, W, init_state, in, out, Wf, Uf, Bf, Wi, Ui, Bi, Wo, Uo, Bo, Wc, Uc, Bc)
-end
-
-function (egcno::EvolveGCNO)(tg::TemporalSnapshotsGNNGraph, x)
-    H = egcno.init_state(egcno.out, egcno.in)
-    C = egcno.init_state(egcno.out, egcno.in)
-    W = egcno.W_init
-    X = map(1:tg.num_snapshots) do i
-        F = Flux.sigmoid_fast.(egcno.Wf .* W + egcno.Uf .* H + egcno.Bf)
-        I = Flux.sigmoid_fast.(egcno.Wi .* W + egcno.Ui .* H + egcno.Bi)
-        O = Flux.sigmoid_fast.(egcno.Wo .* W + egcno.Uo .* H + egcno.Bo)
-        C̃ = Flux.tanh_fast.(egcno.Wc .* W + egcno.Uc .* H + egcno.Bc)
-        C = F .* C + I .* C̃
-        H = O .* tanh_fast.(C)
-        W = H
-        egcno.conv(tg.snapshots[i], x[i]; conv_weight = H)
-    end
-    return X
-end
- 
-function Base.show(io::IO, egcno::EvolveGCNO)
-    print(io, "EvolveGCNO($(egcno.in) => $(egcno.out))")
-end