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MiniBatchKMeans

#include <Skigen/Cluster>

template <typename Scalar = double>
class Skigen::MiniBatchKMeans(n_clusters=8, batch_size=100, max_iter=100, random_state=42)

Mini-Batch K-Means clustering.

Alternative online implementation of KMeans that uses mini-batches to reduce the computation time, while still attempting to optimise the same objective function.

Mirrors sklearn.cluster.MiniBatchKMeans.

Parameters:

  • n_clusters : int, default=8 The number of clusters (int, default 8).

  • batch_size : int, default=100 Size of the mini batches (int, default 100).

  • max_iter : int, default=100 Maximum iterations (int, default 100).

  • random_state : unsigned int, default=42 RNG seed (unsigned int, default 42).

Attributes:

  • is_fitted : bool Whether the estimator has been fitted.

  • n_clusters : int The number of clusters.

  • cluster_centers : MatrixType Cluster centers (n_clusters × n_features).

  • labels : IndexVector Labels of each training point.

  • inertia : Scalar Sum of squared distances to closest cluster center.

Methods​

fit(X)​

Fit the MiniBatchKMeans model.

Uses k-means++ initialization on the first batch, then performs mini-batch stochastic updates to cluster centers.

Parameters:

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

Returns:

  • result : MiniBatchKMeans Reference to the fitted estimator (*this).

Throws:

  • std::invalid_argument — if n_samples < n_clusters.

predict(X)​

Predict the closest cluster each sample belongs to.

Parameters:

  • X : MatrixType New data of shape (n_samples, n_features).

Returns:

  • result : IndexVector Index of the closest cluster for each sample.

Throws:

  • std::runtime_error — if the model has not been fitted.

Example​

// MiniBatchKMeans — faster for large datasets
Skigen::MiniBatchKMeans<double> mbk(3, /*batch_size=*/30, /*max_iter=*/100, /*random_state=*/42);
mbk.fit(X);

std::cout << "=== MiniBatchKMeans (k=3, batch=30) ===\n";
std::cout << "Inertia:    " << mbk.inertia() << "\n";
std::cout << "Centers:\n" << mbk.cluster_centers() << "\n";