reduce memory allocations in hadi92 and hadi94

Author: jbytecode

CHANGELOG.md

@@ -2,8 +2,8 @@
 
 - More explicit return types, drop `Dict` with `Dict{String, Any}` or `Dict{String, Vector}`
 - Add `Julia v.1.10` to GitHub actions
-- Reduce memory allocation in `lts()` and `lms()`
-
+- Reduce memory allocations in `lts()` and `lms()`
+- Reduce memory allocations in `hadi92()` and `hadi94()`
 
 # v0.11.3 
 

src/hadi1992.jl

@@ -109,30 +109,37 @@ function hadi1992(multivariateData::AbstractMatrix{Float64}; alpha = 0.05)
     basic_subset = []
     sorted_mah1 = []
 
+    Cb = zeros(Float64, p)
+    Sb = zeros(Float64, p, p)
+    newSb = zeros(Float64, p, p)
+    
+    msm3 = zeros(Float64, n, n)
+    msm4 = zeros(Float64, n, n)
+
     while r < n
         cnpr = 1 + (r / (n - p))^2.0
         basic_subset_indices = ordering_indices_mah1[1:r]
         basic_subset = multivariateData[basic_subset_indices, :]
-        Cb = applyColumns(mean, basic_subset)
-        Sb = cov(basic_subset)
+        Cb .= applyColumns(mean, basic_subset)
+        Sb .= cov(basic_subset)
 
         r += 1
         cfactor = cnpr * sqrt(sort(mah1)[h]) / chi_50_quantile
         if det(cfactor * Sb) == 0
             @info "singular Sb case"
-            newSb = hadi1992_handle_singularity(cfactor * Sb)
+            newSb .= hadi1992_handle_singularity(cfactor * Sb)
 
-            msm3 = mahalanobisSquaredMatrix(multivariateData, meanvector = Cb, covmatrix = newSb,)
+            msm3 .= mahalanobisSquaredMatrix(multivariateData, meanvector = Cb, covmatrix = newSb,)
             @assert !isnothing(msm3)
 
-            mah1 = diag(msm3)
+            mah1 .= diag(msm3)
 
             ordering_indices_mah1 = sortperm(mah1)
             basic_subset_indices = ordering_indices_mah1[1:r]
         else
-            msm4 = mahalanobisSquaredMatrix(multivariateData, meanvector = Cb, covmatrix = (cfactor * Sb))
+            msm4 .= mahalanobisSquaredMatrix(multivariateData, meanvector = Cb, covmatrix = (cfactor * Sb))
             @assert !isnothing(msm4)
-            mah1 = diag(msm4)
+            mah1 .= diag(msm4)
             ordering_indices_mah1 = sortperm(mah1)
             basic_subset_indices = ordering_indices_mah1[1:r]
         end

src/hadi1994.jl

@@ -76,11 +76,18 @@ function hadi1994(multivariateData::AbstractMatrix{Float64}; alpha = 0.05)
     ordering_indices_mah1 = sortperm(mah1)
 
     r = p + 1
-    basic_subset_indices = []
+
+    basic_subset_indices = Int[]
+    
     basic_subset = []
+
     sorted_mah1 = []
-    Cb = []
-    Sb = []
+    
+    Cb = zeros(Float64, p)
+    Sb = zeros(Float64, p, p)
+    msm3 = zeros(Float64, n, n)
+    sorted_mah1 = zeros(Float64, n)
+
     cfactor = 0
     isFullRank = false
 
@@ -91,8 +98,8 @@ function hadi1994(multivariateData::AbstractMatrix{Float64}; alpha = 0.05)
         while !isFullRank
             basic_subset_indices = ordering_indices_mah1[1:r]
             basic_subset = multivariateData[basic_subset_indices, :]
-            Cb = applyColumns(mean, basic_subset)
-            Sb = cov(basic_subset)
+            Cb .= applyColumns(mean, basic_subset)
+            Sb .= cov(basic_subset)
             cfactor = cnp * sqrt(sort(mah1)[h]) / chi_50_quantile
             r += 1
 
@@ -103,19 +110,21 @@ function hadi1994(multivariateData::AbstractMatrix{Float64}; alpha = 0.05)
             end
         end
 
-        msm3 = mahalanobisSquaredMatrix(
+        msm3 .= mahalanobisSquaredMatrix(
             multivariateData,
             meanvector = Cb,
             covmatrix = (cfactor * Sb),
         )
+
         @assert !isnothing(msm3)
 
         mah1 = diag(msm3)
 
         ordering_indices_mah1 = sortperm(mah1)
         basic_subset_indices = ordering_indices_mah1[1:r]
 
-        sorted_mah1 = sort(mah1)
+        sorted_mah1 .= sort(mah1)
+
         if sorted_mah1[r] >= critical_quantile
             break
         end