fix for b2b

Project.toml

@@ -5,10 +5,13 @@ version = "0.1.0"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+MLBase = "f0e99cf1-93fa-52ec-9ecc-5026115318e0"
 MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Unfold = "181c99d8-e21b-4ff3-b70b-c233eddec679"
 
 [compat]

src/UnfoldDecode.jl

@@ -6,7 +6,10 @@ import Unfold.fit
 using MLJ
 using MultivariateStats
 import MLJBase
+using MLBase
 using DataFrames
+using ProgressMeter
+using LinearAlgebra
 using Logging # to deactivate some MLJ output
 
 # Write your package code here.

src/b2b.jl

@@ -2,48 +2,50 @@
 # 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, :]
+function solver_b2b(X,data::AbstractArray{T,3};kwargs...) where {T<:Union{Missing,<:Number}}
+    X, data = drop_missing_epochs(X, data)
+    solver_b2b(X,data; kwargs...)
+end
 
+function solver_b2b(
+        X,
+        data::AbstractArray{T,3};
+        cross_val_reps = 10,
+        multithreading = true,
+        show_progress=true,
+    ) where {T<:Number}
+
+    E = zeros(T,size(data, 2), size(X, 2), size(X, 2))
+    W = Array{T}(undef, size(data, 2), size(X, 2), size(data, 1))
+
+    prog = Progress(size(data, 2) * cross_val_reps;dt=0.1,enabled=show_progress)
+    Unfold.@maybe_threads multithreading for t = 1:size(data, 2)
+
+             for m = 1:cross_val_reps
+                k_ix = collect(Kfold(size(data, 3), 2))
+                X1 = @view X[k_ix[1], :] # view(X,k_ix[1],：)
+                X2 = @view X[k_ix[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)
+    return Unfold.LinearModelFit(beta, modelinfo)
 end