v0.8.15

Author: jbytecode

CHANGELOG.md

@@ -1,6 +1,10 @@
-# v0.8.15 (Upcoming release)
+# v0.8.16 (Upcoming Release)
+
+
+# v0.8.15 
 - Modularize dataimage()
 - Grouped tests
+- x === nothing style decisions replaces by isnothing(x)
 
 
 # v0.8.14

Project.toml

@@ -1,7 +1,7 @@
 name = "LinRegOutliers"
 uuid = "6d4de0fb-32d9-4c65-aac1-cc9ed8b94b1a"
 authors = ["Mehmet Hakan Satman <mhsatman@gmail.com>", "Shreesh Adiga <16567adigashreesh@gmail.com>", "Guillermo Angeris <angeris@stanford.edu>", "Emre Akadal <emre.akadal@istanbul.edu.tr>"]
-version = "0.8.14"
+version = "0.8.15"
 
 [deps]
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"

src/atkinson94.jl

@@ -69,7 +69,7 @@ function atkinson94(
     # the median index
     h = Int(floor((n + p + 1) / 2.0))
 
-    if iters === nothing
+    if isnothing(iters)
         iters = minimum([500 * p, 3000])
     end
 

src/diagnostics.jl

@@ -55,7 +55,7 @@ end
 function mahalanobisSquaredBetweenPairs(pairs::Matrix; covmatrix = nothing)
     n, _ = size(pairs)
     newmat = zeros(Float64, n, n)
-    if covmatrix === nothing
+    if isnothing(covmatrix) 
         covmatrix = cov(pairs)
     end
     try
@@ -526,10 +526,10 @@ function mahalanobisSquaredMatrix(
     meanvector = nothing,
     covmatrix = nothing,
 )::Union{Nothing,Array{Float64,2}}
-    if meanvector === nothing
+    if isnothing(meanvector)
         meanvector = applyColumns(mean, datamat)
     end
-    if covmatrix === nothing
+    if isnothing(covmatrix)
         covmatrix = cov(datamat)
     end
     try

src/hs93.jl

@@ -183,7 +183,7 @@ function hs93(
     alpha = 0.05,
     basicsubsetindices = nothing,
 )
-    if basicsubsetindices === nothing
+    if isnothing(basicsubsetindices)
         initialsetindices = hs93initialset(X, y)
         basicsubsetindices = hs93basicsubset(X, y, initialsetindices)
     end

src/lms.jl

@@ -65,7 +65,7 @@ end
 function lms(X::Array{Float64,2}, y::Array{Float64,1}; iters = nothing, crit = 2.5)
     n, p = size(X)
     h = Int(floor((n + 1.0) / 2.0))
-    if iters === nothing
+    if isnothing(iters)
         iters = minimum([500 * p, 3000])
     end
     bestobjective = Inf

src/lts.jl

@@ -147,7 +147,7 @@ end
 function lts(X::Array{Float64,2}, y::Array{Float64,1}; iters = nothing, crit = 2.5)
     n, p = size(X)
     h = Int(floor((n + p + 1.0) / 2.0))
-    if iters === nothing
+    if isnothing(iters)
         iters = minimum([500 * p, 3000])
     end
     allindices = collect(1:n)

src/satman2013.jl

@@ -88,7 +88,7 @@ function satman2013(X::Array{Float64,2}, y::Array{Float64,1})
     sorted_indices = sortperm(md)
     best_h_indices = sorted_indices[1:h]
 
-    crit, bestset = iterateCSteps(X, y, best_h_indices, h)
+    _, bestset = iterateCSteps(X, y, best_h_indices, h)
 
     olsreg = ols(X[bestset, :], y[bestset])
     betas = coef(olsreg)

src/satman2015.jl

@@ -58,8 +58,9 @@ Sort multidimensional data usin non-dominated sorting algorithm.
 
 
 # References
-Satman, Mehmet Hakan. "A new algorithm for detecting outliers in linear regression." 
-International Journal of statistics and Probability 2.3 (2013): 101.
+Satman, Mehmet Hakan. "Fast online detection of outliers using least-trimmed squares 
+regression with non-dominated sorting based initial subsets." 
+International Journal of Advanced Statistics and Probability 3.1 (2015): 53.
 
 Deb, Kalyanmoy, et al. "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II." 
 International conference on parallel problem solving from nature. Springer, Berlin, Heidelberg, 2000.
@@ -140,8 +141,9 @@ julia> ndsranks(datamat)
 ```
 
 # References
-Satman, Mehmet Hakan. "A new algorithm for detecting outliers in linear regression." 
-International Journal of statistics and Probability 2.3 (2013): 101.
+Satman, Mehmet Hakan. "Fast online detection of outliers using least-trimmed squares 
+regression with non-dominated sorting based initial subsets." 
+International Journal of Advanced Statistics and Probability 3.1 (2015): 53.
 
 Deb, Kalyanmoy, et al. "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II." 
 International conference on parallel problem solving from nature. Springer, Berlin, Heidelberg, 2000.
@@ -192,8 +194,9 @@ julia> midlist(10,3)
 ```
 
 # References
-Satman, Mehmet Hakan. "A new algorithm for detecting outliers in linear regression." 
-International Journal of statistics and Probability 2.3 (2013): 101.
+Satman, Mehmet Hakan. "Fast online detection of outliers using least-trimmed squares 
+regression with non-dominated sorting based initial subsets." 
+International Journal of Advanced Statistics and Probability 3.1 (2015): 53.
 """
 function midlist(n::Int, p::Int)::Array{Int,1}
     midlist = []
@@ -238,8 +241,9 @@ Dict{Any,Any} with 1 entry:
 ```
 
 # References
-Satman, Mehmet Hakan. "A new algorithm for detecting outliers in linear regression." 
-International Journal of statistics and Probability 2.3 (2013): 101.
+Satman, Mehmet Hakan. "Fast online detection of outliers using least-trimmed squares
+ regression with non-dominated sorting based initial subsets." 
+International Journal of Advanced Statistics and Probability 3.1 (2015): 53.
 """
 function satman2015(setting::RegressionSetting)
     X, y = @extractRegressionSetting setting
@@ -271,7 +275,7 @@ function satman2015(X::Array{Float64,2}, y::Array{Float64,1})
     sorted_indices = sortperm(md)
     best_h_indices = sorted_indices[1:h]
 
-    crit, bestset = iterateCSteps(X, y, best_h_indices, h)
+    _, bestset = iterateCSteps(X, y, best_h_indices, h)
 
     olsreg = ols(X[bestset, :], y[bestset])
     betas = coef(olsreg)