Merge pull request #37 from JuliaAI/dev

For a 0.3.1 release

Author: ablaom

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJMultivariateStatsInterface"
 uuid = "1b6a4a23-ba22-4f51-9698-8599985d3728"
 authors = ["Anthony D. Blaom <[email protected]>", "Thibaut Lienart <[email protected]>", "Okon Samuel <[email protected]>"]
-version = "0.3.0"
+version = "0.3.1"
 
 [deps]
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"

src/models/decomposition_models.jl

@@ -168,7 +168,7 @@ end
 function MMI.fit(model::ICA, verbosity::Int, X)
     icagfun(fname::Symbol, ::Type{T} = Float64) where T<:Real=
     fname == :tanh ? MS.Tanh{T}(1.0) :
-    fname == :gaus ? MS.Gaus{T}() :
+    fname == :gaus ? MS.Gaus() :
     error("Unknown gfun $(fname)")
 
     Xarray = MMI.matrix(X)

test/models/decomposition_models.jl

@@ -49,6 +49,32 @@ end
         winit=randn(rng, eltype(X_array), size(X_array, 2), k))
     test_composition_model(ica_ms, ica_mlj, X, X_array, test_inverse=false)
 end
+@testset "ICA2" begin
+    X_array = matrix(X)
+    k = 5
+    tolerance = 5.0
+    # MultivariateStats ICA
+    rng = StableRNG(1234) # winit gets randomly initialised
+    #Random.seed!(1234) # winit gets randomly initialised
+    ica_ms = MultivariateStats.fit(
+        MultivariateStats.ICA,
+        permutedims(X_array),
+        k;
+        tol=tolerance,
+        fun=MultivariateStats.Gaus(),
+        winit = randn(rng, eltype(X_array), size(X_array, 2), k)
+    )
+    # MLJ ICA
+    rng = StableRNG(1234) # winit gets randomly initialised
+    #Random.seed!(1234) # winit gets randomly initialised
+    ica_mlj = ICA(
+        k=k,
+        tol=tolerance,
+        fun=:gaus,
+        winit=randn(rng, eltype(X_array), size(X_array, 2), k))
+    test_composition_model(ica_ms, ica_mlj, X, X_array, test_inverse=false)
+end
+
 
 @testset "PPCA" begin
     X_array = matrix(X)