copy b2b

src/b2b

@@ -0,0 +1,49 @@
+# Code currently duplicated in Unfold.jl
+# https://github.com/unfoldtoolbox/Unfold.jl/edit/main/src/solver.jl
+
+# Basic implementation of https://doi.org/10.1016/j.neuroimage.2020.117028
+solver_b2b(X, data, cross_val_reps) = solver_b2b(X, data, cross_val_reps = cross_val_reps)
+function solver_b2b(
+    X,
+    data::AbstractArray{T,3};
+    cross_val_reps = 10,
+    multithreading = true,
+    showprogress=true,
+) where {T<:Union{Missing,<:Number}}
+
+    X, data = dropMissingEpochs(X, data)
+
+
+    E = zeros(size(data, 2), size(X, 2), size(X, 2))
+    W = Array{Float64}(undef, size(data, 2), size(X, 2), size(data, 1))
+
+    prog = Progress(size(data, 2) * cross_val_reps, 0.1;enabled=showprogress)
+    @maybe_threads multithreading for m = 1:cross_val_reps
+        k_ix = collect(Kfold(size(data, 3), 2))
+        X1 = @view X[k_ix[1], :]
+        X2 = @view X[k_ix[2], :]
+
+        for t = 1:size(data, 2)
+
+            Y1 = @view data[:, t, k_ix[1]]
+            Y2 = @view data[:, t, k_ix[2]]
+
+
+            G = (Y1' \ X1)
+            H = X2 \ (Y2' * G)
+
+            E[t, :, :] +=  Diagonal(H[diagind(H)])
+            ProgressMeter.next!(prog; showvalues = [(:time, t), (:cross_val_rep, m)])
+        end
+        E[t, :, :] = E[t, :, :] ./ cross_val_reps
+        W[t, :, :] = (X * E[t, :, :])' / data[:, t, :]
+
+    end
+
+    # extract diagonal
+    beta = mapslices(diag, E, dims = [2, 3])
+    # reshape to conform to ch x time x pred
+    beta = permutedims(beta, [3 1 2])
+    modelinfo = Dict("W" => W, "E" => E, "cross_val_reps" => cross_val_reps) # no history implemented (yet?)
+    return LinearModelFit(beta, modelinfo)
+end