[#1] - Feature/adding generate s curve function

README.md

@@ -29,7 +29,7 @@ Dataset         | Title
 ----------------|------------------------------------------------------------------------|--------------------------------------------------
 make_blobs      | Generate isotropic Gaussian blobs for clustering.                      | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html)
 make_moons      | Make two interleaving half circles                                     | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html)
-
+make_s_curve    | Generate an S curve dataset.                                           | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_s_curve.html)
 
 [travis-img]: https://travis-ci.com/ATISLabs/SyntheticDatasets.jl.svg?branch=master
 [travis-url]: https://travis-ci.com/ATISLabs/SyntheticDatasets.jl

src/SyntheticDatasets.jl

@@ -75,7 +75,29 @@ function generate_blobs(;n_samples::Union{Int, Array{Int, 1}} = 100,
     return convert(features, labels)
 end
 
-function convert(features::Array{T, 2}, labels::Array{Int, 1})::DataFrame where T <: Number
+"""
+    generate_s_curve(;  n_samples::Int = 100, 
+                        noise = nothing, 
+                        random_state = nothing)::DataFrame
+Generate an S curve dataset. Sklearn interface to make_s_curve. 
+# Arguments
+- `n_samples::Int = 100`: The number of sample points on the S curve.
+- `noise::Union{Nothing, Float64} = nothing`: Standard deviation of Gaussian noise added to the data.
+- `random_state::Union{Int, Nothing} = nothing`: Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls.
+Reference: [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_s_curve.html)
+"""
+function generate_s_curve(; n_samples::Int = 100,
+                            noise::Float64 = 0.0, 
+                            random_state::Union{Int, Nothing} = nothing)::DataFrame
+
+    (features, labels) = datasets.make_s_curve(   n_samples = n_samples,
+                                                noise = noise, 
+                                                random_state = random_state)
+
+    return convert(features, labels)
+end
+
+function convert(features::Array{T, 2}, labels::Array{D, 1})::DataFrame where {T <: Number, D <: Number}
     df = DataFrame()
 
     for i = 1:size(features)[2]

test/runtests.jl

@@ -17,4 +17,12 @@ using Test
 
     @test size(data)[1] == samples
     @test size(data)[2] == 3
+
+    data = SyntheticDatasets.generate_s_curve(n_samples = samples,
+                                              noise = 2.2,
+                                              random_state = 5)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 4
+
 end