Find K cluster centroids via kmeans++ center initialization followed by Lloyds algorithm. Dataset must be a matrix (2d tensor).

• dataset - 2d matrix of numeric datatype.
• n-centroids - How many centroids to find.

Returns map of:

• :centroids - 2d tensor of double centroids
• :centroid-indexes - 1d integer vector of assigned center indexes.
• :iteration-scores - n-iters+1 length array of mean squared error scores container the scores from centroid assigned up to the score when the algorithm terminates.

Options:

• :minimal-improvement-threshold - defaults to 0.01 - algorithm terminates if (1.0 - error(n-1)/error(n-2)) < error-diff-threshold. When Zero means algorithm will always train to max-iters.
• :n-iters - defaults to 100 - Max number of iterations, algorithm terminates if `(>= iter-idx n-iters).
• :rand-seed - integer or implementation of java.util.Random.

Order the dataset and labels such that labels are monotonically increasing. returns tuple of [dataset labels]

Return both a probability distribution per row across each label and a 1d tensor of assigned label indexes.

Returns:

• :probability-distribution - each row sums to one, max prob is the index picked.
• :label-indexes - int32 assigned indexes for each row in the dataset.

Quantize an image using kmeans. Copies data into a new image and, if dest-path is provided, saves the image.

Returns:

• :centroids - result of the quantization.
• :result - resulting BufferedImage.
• :scores - Scores after each iteration including initialization.

Given a dataset along with per-row integer labels, train N per-label kmeans centroids returning a model which you can use can use with predict-per-label.