[WIP] Add code analyze and fix type inference

.github/workflows/Documentation.yml

@@ -15,7 +15,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v2
         with:
-          version: '1.9'
+          version: '1.10'
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy

analyze/Project.toml

@@ -0,0 +1,5 @@
+[deps]
+JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
+
+[compat]
+JET = "0.9"

analyze/analyze.bat

@@ -0,0 +1,5 @@
+@echo off
+
+SET BASEPATH=%~dp0
+
+CALL "%JULIA_1105%" --project=%BASEPATH% --load=%BASEPATH%\analyze.jl

analyze/analyze.jl

@@ -0,0 +1,20 @@
+import Pkg
+Pkg.instantiate()
+
+using JET
+
+Pkg.activate(dirname(@__DIR__))
+Pkg.instantiate()
+
+using UnsupervisedClustering
+
+package_path = dirname(@__DIR__)
+module_path = joinpath(package_path, "src/UnsupervisedClustering.jl")
+
+report = report_file(module_path; analyze_from_definitions = true)
+println("Report 1")
+@show report
+
+report = report_package(UnsupervisedClustering)
+println("Report 2")
+@show report

analyze/analyze.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+
+BASEPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+
+$JULIA_1105 --project=$BASEPATH $BASEPATH/analyze.jl

src/ensemble/chain.jl

@@ -6,10 +6,10 @@ ClusteringChain represents a chain of clustering algorithms that are executed se
 # Fields
 - `algorithms`: the vector of clustering algorithms that will be executed in sequence.
 """
-Base.@kwdef struct ClusteringChain <: AbstractAlgorithm
-    algorithms::AbstractVector{<:AbstractAlgorithm}
+Base.@kwdef struct ClusteringChain{T <: AbstractAlgorithm} <: AbstractAlgorithm
+    algorithms::Vector{T}
 
-    function ClusteringChain(algorithms::AbstractAlgorithm...)
+    function ClusteringChain(algorithms::T...) where T <: AbstractAlgorithm
         return new(collect(algorithms))
     end
 end

src/localsearch/gmm.jl

@@ -3,8 +3,8 @@
         estimator::CovarianceMatrixEstimator
         verbose::Bool = DEFAULT_VERBOSE
         rng::AbstractRNG = Random.GLOBAL_RNG
-        tolerance::Real = DEFAULT_TOLERANCE
-        max_iterations::Integer = DEFAULT_MAX_ITERATIONS
+        tolerance::Float64 = DEFAULT_TOLERANCE
+        max_iterations::Int = DEFAULT_MAX_ITERATIONS
         decompose_if_fails::Bool = true
     )
 
@@ -27,8 +27,8 @@ Base.@kwdef mutable struct GMM <: AbstractAlgorithm
     estimator::CovarianceMatrixEstimator
     verbose::Bool = DEFAULT_VERBOSE
     rng::AbstractRNG = Random.GLOBAL_RNG
-    tolerance::Real = DEFAULT_TOLERANCE
-    max_iterations::Integer = DEFAULT_MAX_ITERATIONS
+    tolerance::Float64 = DEFAULT_TOLERANCE
+    max_iterations::Int = DEFAULT_MAX_ITERATIONS
     decompose_if_fails::Bool = true
 end
 

src/localsearch/kmeans.jl

@@ -3,8 +3,8 @@
         metric::SemiMetric = SqEuclidean()
         verbose::Bool = DEFAULT_VERBOSE
         rng::AbstractRNG = Random.GLOBAL_RNG
-        tolerance::Real = DEFAULT_TOLERANCE
-        max_iterations::Integer = DEFAULT_MAX_ITERATIONS
+        tolerance::Float64 = DEFAULT_TOLERANCE
+        max_iterations::Int = DEFAULT_MAX_ITERATIONS
     )
 
 The k-means is a clustering algorithm that aims to partition data into clusters by minimizing the distances between data points and their cluster centroids.
@@ -28,8 +28,8 @@ Base.@kwdef mutable struct Kmeans <: AbstractAlgorithm
     metric::SemiMetric = SqEuclidean()
     verbose::Bool = DEFAULT_VERBOSE
     rng::AbstractRNG = Random.GLOBAL_RNG
-    tolerance::Real = DEFAULT_TOLERANCE
-    max_iterations::Integer = DEFAULT_MAX_ITERATIONS
+    tolerance::Float64 = DEFAULT_TOLERANCE
+    max_iterations::Int = DEFAULT_MAX_ITERATIONS
 end
 
 @doc """

src/localsearch/kmedoids.jl

@@ -2,7 +2,7 @@
     Kmedoids(
         verbose::Bool = DEFAULT_VERBOSE
         rng::AbstractRNG = Random.GLOBAL_RNG
-        tolerance::Real = DEFAULT_TOLERANCE
+        tolerance::Float64 = DEFAULT_TOLERANCE
         max_iterations::Integer = DEFAULT_MAX_ITERATIONS
     )
 
@@ -19,8 +19,8 @@ The k-medoids is a variation of k-means clustering algorithm that uses actual da
 Base.@kwdef mutable struct Kmedoids <: AbstractAlgorithm
     verbose::Bool = DEFAULT_VERBOSE
     rng::AbstractRNG = Random.GLOBAL_RNG
-    tolerance::Real = DEFAULT_TOLERANCE
-    max_iterations::Integer = DEFAULT_MAX_ITERATIONS
+    tolerance::Float64 = DEFAULT_TOLERANCE
+    max_iterations::Int = DEFAULT_MAX_ITERATIONS
 end
 
 @doc """

src/metaheuristic/generation.jl

@@ -1,6 +1,6 @@
-mutable struct Generation
-    population::AbstractVector{<:AbstractResult}
-    empty::Set{Integer}
+mutable struct Generation{T <: AbstractResult}
+    population::Vector{T}
+    empty::Set{Int}
 
     function Generation()
         return new(Vector{AbstractResult}(), Set{Int}())

src/metaheuristic/geneticalgorithm.jl

@@ -2,10 +2,10 @@
     GeneticAlgorithm(
         local_search::AbstractAlgorithm
         verbose::Bool = DEFAULT_VERBOSE
-        max_iterations::Integer = 200
-        max_iterations_without_improvement::Integer = 150
-        π_min::Integer = 40
-        π_max::Integer = 50
+        max_iterations::Int = 200
+        max_iterations_without_improvement::Int = 150
+        π_min::Int = 40
+        π_max::Int = 50
     )
 
 GeneticAlgorithm represents a clustering algorithm that utilizes a genetic algorithm approach to optimize cluster assignments. It combines evolutionary computation and local search elements to find high-quality clustering solutions.
@@ -23,10 +23,10 @@ GeneticAlgorithm represents a clustering algorithm that utilizes a genetic algor
 Base.@kwdef struct GeneticAlgorithm <: AbstractAlgorithm
     local_search::AbstractAlgorithm
     verbose::Bool = DEFAULT_VERBOSE
-    max_iterations::Integer = 200
-    max_iterations_without_improvement::Integer = 150
-    π_min::Integer = 40
-    π_max::Integer = 50
+    max_iterations::Int = 200
+    max_iterations_without_improvement::Int = 150
+    π_min::Int = 40
+    π_max::Int = 50
 end
 
 @doc """

src/metaheuristic/multistart.jl

@@ -2,7 +2,7 @@
     MultiStart(
         local_search::AbstractAlgorithm
         verbose::Bool = DEFAULT_VERBOSE
-        max_iterations::Integer = 200
+        max_iterations::Int = 200
     )
 
 The MultiStart approach repeatedly applies a clustering algorithm to generate multiple solutions with different initial points and selects the best solution.
@@ -15,7 +15,7 @@ The MultiStart approach repeatedly applies a clustering algorithm to generate mu
 Base.@kwdef struct MultiStart <: AbstractAlgorithm
     local_search::AbstractAlgorithm
     verbose::Bool = DEFAULT_VERBOSE
-    max_iterations::Integer = 200
+    max_iterations::Int = 200
 end
 
 @doc """

src/metaheuristic/randomswap.jl

@@ -2,8 +2,8 @@
     RandomSwap(
         local_search::AbstractAlgorithm
         verbose::Bool = DEFAULT_VERBOSE
-        max_iterations::Integer = 200
-        max_iterations_without_improvement::Integer = 150
+        max_iterations::Int = 200
+        max_iterations_without_improvement::Int = 150
     )
 
 RandomSwap is a meta-heuristic approach used for clustering problems. It follows an iterative process that combines local optimization with perturbation to explore the search space effectively. A local optimization algorithm is applied at each iteration to converge toward a local optimum. Then, a perturbation operator generates a new starting point and continues the search.
@@ -22,8 +22,8 @@ RandomSwap is a meta-heuristic approach used for clustering problems. It follows
 Base.@kwdef struct RandomSwap <: AbstractAlgorithm
     local_search::AbstractAlgorithm
     verbose::Bool = DEFAULT_VERBOSE
-    max_iterations::Integer = 200
-    max_iterations_without_improvement::Integer = 150
+    max_iterations::Int = 200
+    max_iterations_without_improvement::Int = 150
 end
 
 @doc """

test/gmmsk.jl

@@ -1,8 +1,8 @@
 Base.@kwdef mutable struct GMMSK <: UnsupervisedClustering.AbstractAlgorithm
     verbose::Bool = false
     rng::AbstractRNG = Random.GLOBAL_RNG
-    tolerance::Real = 1e-3
-    max_iterations::Integer = 1000
+    tolerance::Float64 = 1e-3
+    max_iterations::Int = 1000
 end
 
 function seed!(algorithm::GMMSK, seed::Integer)