UMAP

#include <Skigen/Manifold>

template <typename Scalar = double>
class Skigen::UMAP(n_components=2, n_neighbors=15, min_dist=0.1, learning_rate=1, n_epochs=200, negative_sample_rate=5, random_state=std::nullopt)

Uniform Manifold Approximation and Projection (UMAP).

Non-linear dimensionality reduction that preserves both local and global structure. Constructs a weighted KNN graph in high-dimensional space, then optimises a low-dimensional layout via stochastic gradient descent on a cross-entropy objective.

Mirrors umap-learn.

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Parameters:

• n_components : int, default=2 Embedding dimension (default 2).

• n_neighbors : int, default=15 Local neighborhood size (default 15).

• min_dist : Scalar, default=0.1 Minimum distance in the embedding (default 0.1).

• learning_rate : Scalar, default=1 Initial SGD learning rate (default 1.0).

• n_epochs : int, default=200 Number of optimisation epochs (default 200).

• negative_sample_rate : int, default=5 Negative samples per positive edge (default 5).

• random_state : std::optional< uint64_t >, default=std::nullopt Optional RNG seed (default nullopt).

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Attributes:

• embedding : MatrixType Low-dimensional embedding (n_samples x n_components).

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Methods

SKIGEN_PARAMS()

Fit the UMAP model to training data X.

Builds the fuzzy KNN graph, computes membership strengths, then runs SGD to optimise the low-dimensional layout.

Parameters:

• X Training data of shape (n_samples, n_features).

Returns:

• result Reference to the fitted transformer (*this).

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transform()

Return the stored embedding for the training data.

Parameters:

• X Data matrix of shape (n_samples, n_features).

Returns:

• result : MatrixType Embedding of shape (n_samples, n_components).

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