Use size(x, 1) and size(x, 2) instead of n, _ = size(x) and _, p = size(x), respectively

Author: jbytecode

CHANGELOG.md

@@ -1,5 +1,7 @@
 # v0.11.7 (Upcoming Release)
 
+- Use `size(x, 1)` and `size(x, 2)` instead of `n, _  = size(x)` and `_, p = size(x)`, respectively.
+
 
 # v0.11.6 
 

src/bacon.jl

@@ -28,7 +28,7 @@ function initial_basic_subset_multivariate_data(
     m::Int;
     method::String = "mahalanobis",
 )
-    n, _ = size(X)
+    n = size(X, 1)
     if method == "mahalanobis"
         msm = mahalanobisSquaredMatrix(X)
 

src/dataimage.jl

@@ -69,7 +69,7 @@ function dataimage(
 
     end
     colours = 1.0 .- d / maximum(d)
-    n, _ = size(d)
+    n = size(d, 1)
     colormatrix = Array{RGBX{Float64},2}(undef, n, n)
     for i = 1:n
         for j = 1:n

src/diagnostics.jl

@@ -43,7 +43,7 @@ Marchette, David J., and Jeffrey L. Solka. "Using data images for outlier detect
 Computational Statistics & Data Analysis 43.4 (2003): 541-552.
 """
 function euclideanDistances(dataMatrix::AbstractMatrix{Float64})::AbstractMatrix{Float64}
-	n, _ = size(dataMatrix)
+	n = size(dataMatrix, 1)
 	d = zeros(Float64, n, n)
 	for i ∈ 1:n
 		for j ∈ i:n
@@ -59,7 +59,7 @@ end
 
 
 function mahalanobisSquaredBetweenPairs(pairs::AbstractMatrix{Float64}; covmatrix = nothing)::Union{Nothing, AbstractMatrix}
-	n, _ = size(pairs)
+	n = size(pairs, 1)
 	newmat = zeros(Float64, n, n)
 	if isnothing(covmatrix)
 		covmatrix = cov(pairs)
@@ -103,7 +103,7 @@ Computational Statistics & Data Analysis 43.4 (2003): 541-552.
 """
 function mahalanobisBetweenPairs(dataMatrix::AbstractMatrix{Float64})::Union{Nothing, Matrix}
 
-    n, _ = size(dataMatrix)
+    n = size(dataMatrix, 1)
 
     d = zeros(Float64, n, n)
 
@@ -153,7 +153,7 @@ julia> coordinatwisemedians(mat)
 ```
 """
 function coordinatwisemedians(datamat::AbstractMatrix{Float64})::AbstractVector{Float64}
-	_, p = size(datamat)
+	p = size(datamat, 2)
 	meds = map(i -> median(datamat[:, i]), 1:p)
 	return meds
 end
@@ -200,7 +200,7 @@ function dffit(setting::RegressionSetting, i::Int)::Float64
 end
 
 function dffit(X::AbstractMatrix{Float64}, y::AbstractVector{Float64}, i::Int)::Float64
-	n, _ = size(X)
+	n = size(X, 1)
 	indices = [j for j ∈ 1:n if i != j]
 	olsfull = ols(X, y)
 	Xsub = X[indices, :]
@@ -256,13 +256,13 @@ Belsley, David A., Edwin Kuh, and Roy E. Welsch. Regression diagnostics:
 Identifying influential data and sources of collinearity. Vol. 571. John Wiley & Sons, 2005.
 """
 function dffits(setting::RegressionSetting)::AbstractVector{Float64}
-	n, _ = size(setting.data)
+	n = size(setting.data, 1)
 	result = [dffit(setting, i) for i ∈ 1:n]
 	return result
 end
 
 function dffits(X::AbstractMatrix{Float64}, y::AbstractVector{Float64})::AbstractVector{Float64}
-	n, _ = size(X)
+	n = size(X, 1)
 	result = [dffit(X, y, i) for i ∈ 1:n]
 	return result
 end
@@ -398,7 +398,7 @@ end
 
 function adjustedResiduals(X::AbstractMatrix{Float64}, y::AbstractVector{Float64})::AbstractVector{Float64}
 	olsreg = ols(X, y)
-	n, _ = size(X)
+	n = size(X, 1)
 	e = residuals(olsreg)
 	hat = hatmatrix(X)
 	stde = [e[i] / (sqrt(1 - hat[i, i])) for i ∈ 1:n]

src/hadi1992.jl

@@ -29,7 +29,7 @@ Hadi, Ali S. "Identifying multiple outliers in multivariate data."
 Journal of the Royal Statistical Society: Series B (Methodological) 54.3 (1992): 761-771.
  """
 function hadi1992_handle_singularity(S::AbstractMatrix{Float64})::AbstractMatrix{Float64}
-    p, _ = size(S)
+    p = size(S, 2)
     eigen_structure = eigen(S)
     values = eigen_structure.values
     vectors = eigen_structure.vectors

src/hs93.jl

@@ -45,7 +45,7 @@ function hs93initialset(setting::RegressionSetting)::Array{Int,1}
 end
 
 function hs93initialset(X::AbstractMatrix{Float64}, y::AbstractVector{Float64})::Array{Int,1}
-    _, p = size(X)
+    p = size(X, 2)
     s = p + 1
     dfs = abs.(dffits(X, y))
     sortedindices = sortperm(dfs)

src/lts.jl

@@ -80,7 +80,7 @@ function iterateCSteps(
     initialBetas::AbstractVector{Float64},
     h::Int; eps::Float64 = 0.01, maxiter::Int = 10000
 )
-    _, p = size(X)
+    p = size(X, 2)
     res = y - X * initialBetas
     sortedresindices = sortperm(abs.(res))
     subsetindices = sortedresindices[1:p]

src/py95.jl

@@ -198,7 +198,7 @@ end
 
 
 function py95(X::AbstractMatrix{Float64}, y::AbstractVector{Float64})
-    n, _ = size(X)
+    n = size(X, 1)
     all_indices = collect(1:n)
     suspicious_sets = py95SuspectedObservations(X, y)
     outlierset = Set{Int}()

src/satman2015.jl

@@ -150,7 +150,7 @@ Deb, Kalyanmoy, et al. "A fast elitist non-dominated sorting genetic algorithm f
 International conference on parallel problem solving from nature. Springer, Berlin, Heidelberg, 2000.
 """
 function ndsranks(data::AbstractMatrix)::Array{Int}
-    n, _ = size(data)
+    n = size(data, 1)
     ranks = zeros(Int, n)
     mat = convert(Matrix, data)
     for i = 1:n

src/summary.jl

@@ -106,7 +106,7 @@ function detectOutliers(X::AbstractMatrix{Float64}, y::AbstractVector{Float64};
             end
         elseif method == "bacon"
             try
-                _, p = size(X)
+                p = size(X, 2)
                 result = bacon(X, y, m = p + 1)["outliers"]
             catch
                 result = Int[]