Merge pull request #35 from alan-turing-institute/dev

For a 0.1.4 release

Author: ablaom

Project.toml

@@ -1,7 +1,7 @@
 name = "NearestNeighborModels"
 uuid = "636a865e-7cf4-491e-846c-de09b730eb36"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>", "Sebastian Vollmer <s.vollmer.4@warwick.ac.uk>", "Thibaut Lienart <thibaut.lienart@gmail.com>", "Okon Samuel <okonsamuel50@gmail.com>"]
-version = "0.1.3"
+version = "0.1.4"
 
 [deps]
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
@@ -15,8 +15,8 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
-Distances = "^0.9,^0.10"
-FillArrays = "^0.9"
+Distances = "^0.9, ^0.10"
+FillArrays = "^0.9, ^0.10, ^0.11 "
 MLJModelInterface = "^0.3.5, ^0.4"
 NearestNeighbors = "^0.4"
 StatsBase = "^0.33"

src/models.jl

@@ -229,16 +229,19 @@ end
 
 function _predict_knnreg(weights, y, idxs_matrix)
     @inbounds labels = @view(y[idxs_matrix])
-    preds = @view(_sum(labels .* weights, dims=2)[:]) ./ _sum(weights, dims=2)
+    preds = @inbounds(
+        @view(_sum(labels .* weights, dims=2)[:]) ./ @view(_sum(weights, dims=2)[:])
+    )
     return preds
 end
+
 function MMI.predict(m::KNNRegressor, fitresult, X)
     err_if_given_invalid_K(m.K)
     #Xmatrix = MMI.matrix(X, transpose=true) # NOTE: copies the data
     Xmatrix = transpose(MMI.matrix(X))
     check_onebased_indexing("prediction input", Xmatrix)
     predict_args = setup_predict_args(m, Xmatrix, fitresult)
-    preds = _predict_knnreg(predict_args...) |> vec
+    preds = _predict_knnreg(predict_args...)
     return preds
 end