Merge pull request #20 from ATISLabs/feature/generate_swiss_roll

Feature/generate_swiss_roll

Author: filipebraida

src/sklearn.jl

@@ -238,4 +238,26 @@ function generate_classification(;  n_samples::Int = 100,
                                                         random_state = random_state)
 
     return convert(features, labels)
-end
+end
+
+"""
+function generate_swiss_roll(;  n_samples::Int = 100,
+                               noise::Float64 = 0.0,
+                               random_state::Union{Int,Nothing} = nothing)
+Generate a swiss roll dataset.
+#Arguments
+- `n_samples::Int = 100`: The number of samples.
+- `noise::Float64 = 0.0 : 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. See Glossary.
+Reference: [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_swiss_roll.htmll)
+"""
+function generate_swiss_roll(;  n_samples::Int = 100,
+                               noise::Float64 = 0.0,
+                               random_state::Union{Int,Nothing} = nothing)
+
+   (features, labels) = datasets.make_swiss_roll(  n_samples = n_samples,
+                                                   noise = noise,
+                                                   random_state = random_state)
+
+   return convert(features, labels)
+end

test/runtests.jl

@@ -44,8 +44,13 @@ using Test
                                                     n_features = features,
                                                     n_classes = 1)
 
-                 
     @test size(data)[1] == samples
     @test size(data)[2] == features + 1
 
-end
+    data = SyntheticDatasets.generate_swiss_roll(n_samples =samples,
+                                                 noise = 2.2,
+                                                 random_state = 5)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 4
+end