Feature/plot points

Project.toml

@@ -1,7 +1,7 @@
 name = "Fuzzy"
 uuid = "a9166f1b-85e5-4df0-9c26-e06b441f12e8"
 authors = ["Phelipe Wesley <phelipewesleydeoliveira@gmail.com> and contributors"]
-version = "0.3.0"
+version = "0.3.1"
 
 [compat]
 julia = "1.6"

README.md

@@ -1,12 +1,11 @@
 # Fuzzy
+
 =============
 
 Mamdani and Sugeno type Fuzzy Inference System in julia. This code is based in [Lepisma](https://github.com/lepisma/Fuzzy.jl).
 
-
 [![CI](https://github.com/phelipe/Fuzzy.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/phelipe/Fuzzy.jl/actions/workflows/ci.yml)
 
-
 ## Install
 
 ```julia
@@ -114,6 +113,18 @@ julia> eval_fis(fis, in_vals)
   - Einstein sum (E-SUM)
   - Hamacher sum (H-SUM)
 
+## Prepare to plot your Fuzzy sets
+
+- Create points to use in plot packages using the chart_prepare
+
+```julia
+julia> input_a = Dict("small" => TriangularMF(1, 2, 3), "large" => TriangularMF(5, 6, 7));
+julia> x = range(0, 8, length = 100);
+julia> data = chart_prepare(ipa, x)
+julia> using Plots
+julia> plot(x, data["values"], label = data["names"])
+```
+
 ## License
 
 MIT

src/DotsMF.jl

@@ -0,0 +1,31 @@
+get_dots(membership_function::MF, input_point_vector::Union{StepRange,StepRangeLen}) =
+    get_dots(membership_function, collect(input_point_vector))
+
+function get_dots(membership_function::MF, input_point_vector::Vector{T}) where {T<:Number}
+    return Real.(map(x -> Fuzzy.eval(membership_function, x), input_point_vector))
+end
+
+chart_prepare(sets_dict::Dict{String,N}, input_point_vector::Union{StepRange,StepRangeLen}) where {N<:MF} =
+    chart_prepare(sets_dict, collect(input_point_vector))
+
+
+"""
+    Create points
+
+    eval_fis(sets_dict, input_point_vector)
+
+    Parameters
+    ----------
+    `sets_dict` dictionary of membership functions
+    `input_point_vector` array of points
+
+"""
+function chart_prepare(sets_dict::Dict{String,N}, input_point_vector::Vector{T}) where {T<:Number,N<:MF}
+    names = String[]
+    outputs = Array{T,1}[]
+    for (name, membership_function) in sets_dict
+        push!(names, name)
+        push!(outputs, get_dots(membership_function, input_point_vector))
+    end
+    return Dict("names" => reshape(names, 1, :), "values" => outputs)
+end

src/Fuzzy.jl

@@ -1,31 +1,40 @@
 module Fuzzy
 
-    export TriangularMF, GaussianMF, BellMF, TrapezoidalMF, SigmoidMF
+export TriangularMF, GaussianMF, BellMF, TrapezoidalMF, SigmoidMF
 
-    export Rule, FISMamdani, FISSugeno
+export Rule, FISMamdani, FISSugeno
 
-    export mean_at, eval_fis
+export mean_at, eval_fis
 
-    export minimum_value, algebraic_product, bounded_difference, drastic_product, einstein_product, hamacher_product
+export chart_prepare
 
-    export maximum_value, algebraic_sum, bounded_sum, drastic_sum, einstein_sum, hamacher_sum
+export minimum_value, algebraic_product, bounded_difference, drastic_product, einstein_product, hamacher_product
 
-    # T-Norm
-    include("T-norm.jl")
+export maximum_value, algebraic_sum, bounded_sum, drastic_sum, einstein_sum, hamacher_sum
 
-    # S-Norm
-    include("S-norm.jl")
+export MFDict
 
-    # Membership functions
-    include("MF.jl")
+# T-Norm
+include("T-norm.jl")
 
-    # Membership functions evaluations
-    include("EvalMF.jl")
+# S-Norm
+include("S-norm.jl")
 
-    # FIS
-    include("FIS.jl")
+# Membership functions
+include("MF.jl")
 
-    # Evaluation functions
-    include("Eval.jl")
+# Membership functions evaluations
+include("EvalMF.jl")
+
+# FIS
+include("FIS.jl")
+
+# Evaluation functions
+include("Eval.jl")
+
+#Dots function
+include("DotsMF.jl")
+
+MFDict = Dict{String,MF}
 
 end

test/fis_mamdani.jl

@@ -27,18 +27,18 @@ ival2 = [5.7, 2.0]
 @assert eval_fis(fis, ival2) == 6.0
 
 
-service = Dict( "poor"      => TrapezoidalMF(0.0, 0.0, 2.0, 4.0), 
-                "good"      => TriangularMF(3., 5., 7.),
-                "excellent" => TrapezoidalMF(6., 8., 10., 10.))
+service = Dict("poor" => TrapezoidalMF(0.0, 0.0, 2.0, 4.0),
+    "good" => TriangularMF(3.0, 5.0, 7.0),
+    "excellent" => TrapezoidalMF(6.0, 8.0, 10.0, 10.0))
 
-food = Dict("rancid"    => TrapezoidalMF(0., 0., 3., 6.),
-            "delicious" => TrapezoidalMF(4., 7., 10., 10.))
+food = Dict("rancid" => TrapezoidalMF(0.0, 0.0, 3.0, 6.0),
+    "delicious" => TrapezoidalMF(4.0, 7.0, 10.0, 10.0))
 
 inputs = [service, food]
 
-tip  = Dict("cheap"     => TrapezoidalMF(10., 10., 20., 30.),
-            "average"   => TriangularMF(20., 30., 40.), 
-            "generous"  => TrapezoidalMF(30., 40, 50., 50.))
+tip = Dict("cheap" => TrapezoidalMF(10.0, 10.0, 20.0, 30.0),
+    "average" => TriangularMF(20.0, 30.0, 40.0),
+    "generous" => TrapezoidalMF(30.0, 40, 50.0, 50.0))
 
 rule1 = Rule(["poor", "rancid"], "cheap", "MAX")
 
@@ -50,6 +50,6 @@ rules = [rule1, rule2, rule3]
 
 fis_tips = FISMamdani(inputs, tip, rules)
 
-in_vals = [7., 8,]
+in_vals = [7.0, 8,]
 
 @assert eval_fis(fis_tips, in_vals) == 45.0

test/triangular_mf.jl

@@ -1,14 +1,14 @@
-l_vertex = 2.
-center = 5.
-r_vertex = 7.
+l_vertex = 2.0
+center = 5.0
+r_vertex = 7.0
 
 mf = TriangularMF(l_vertex, center, r_vertex)
 
 # Evaluation tests
 @assert Fuzzy.eval(mf, center) == 1
-@assert Fuzzy.eval(mf, l_vertex) == Fuzzy.eval(mf, r_vertex) == 0.
-@assert Fuzzy.eval(mf, (l_vertex + center) / 2.) == Fuzzy.eval(mf, (r_vertex + center) / 2.) == 0.5
+@assert Fuzzy.eval(mf, l_vertex) == Fuzzy.eval(mf, r_vertex) == 0.0
+@assert Fuzzy.eval(mf, (l_vertex + center) / 2.0) == Fuzzy.eval(mf, (r_vertex + center) / 2.0) == 0.5
 
 # Mean finding tests
-@assert Fuzzy.mean_at(mf, 1.) == center
-@assert Fuzzy.mean_at(mf, 0.) == (l_vertex + r_vertex) / 2.
+@assert Fuzzy.mean_at(mf, 1.0) == center
+@assert Fuzzy.mean_at(mf, 0.0) == (l_vertex + r_vertex) / 2.0