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src/SyntheticDatasets.jl

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@@ -40,25 +40,25 @@ end
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generate_blobs(; n_samples::Union{Int, Array{Int, 1}} = 100,
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n_features::Int = 2,
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centers::Union{Int, Union{Nothing, Array{Float64, 2}}} = nothing,
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cluster_std = 1.0,
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center_box::Tuple{Float64, Float64} = (-10.0, 10.0),
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cluster_std::Union{Float64, Array{Float64, 1}} = 1.0,
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center_box = (-10.0, 10.0),
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shuffle::Bool = true,
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random_state::Union{Int, Nothing} = nothing)::DataFrame
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Make two interleaving half circles. Sklearn interface to make_moons.
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# Arguments
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- `n_samples = 100`: If int, it is the total number of points equally divided among clusters. If array-like, each element of the sequence indicates the number of samples per cluster.
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- `n_features = 2`: The number of features for each sample.
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- `centers::Union{Int, Union{Nothing, Array{Float64, 2}}} = nothing`: The number of centers to generate, or the fixed center locations. If n_samples is an int and centers is None, 3 centers are generated. If n_samples is array-like, centers must be either None or an array of length equal to the length of n_samples.
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- `cluster_std = 1.0`:
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- `center_box::Tuple{Float64, Float64} = (-10.0, 10.0)`:
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- `cluster_std::Union{Float64, Array{Float64, 1}} = 1.0`: The standard deviation of the clusters.
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- `center_box::Tuple{Float64, Float64} = (-10.0, 10.0)`: The bounding box for each cluster center when centers are generated at random.
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- `shuffle::Bool = true`: Shuffle the samples.
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- `random_state::Union{Int, Nothing} = nothing`: Determines random number generation for dataset shuffling and noise.
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Reference: [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html)
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"""
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function generate_blobs(;n_samples::Union{Int, Array{Int, 1}} = 100,
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n_features::Int = 2,
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centers::Union{Int, Union{Nothing, Array{Float64, 2}}} = nothing,
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cluster_std = 1.0,
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cluster_std::Union{Float64, Array{Float64, 1}} = 1.0,
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center_box::Tuple{Float64, Float64} = (-10.0, 10.0),
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shuffle::Bool = true,
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random_state::Union{Int, Nothing} = nothing)::DataFrame

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