mojolearn.ClassicalMDS#

class mojolearn.ClassicalMDS(n_components=2, *, metric='euclidean', metric_params=None, numeric_mode=None)#

Classical (Torgerson) multidimensional scaling.

Follows scikit-learn’s ClassicalMDS: double-centers -0.5 * D**2 for the pairwise distances D and embeds by its top eigenvectors scaled by the square roots of the eigenvalues.

Parameters:

  • n_components (int, default 2) – Embedding dimension.

  • metric (str, default “euclidean”) – "precomputed" (X is a distance matrix) or a distance metric: "euclidean", "l2", "minkowski" (p from metric_params), "manhattan" ("cityblock", "l1"), "chebyshev" ("infinity") or "cosine"; a callable or any other name raises NotImplementedError.

  • metric_params (dict or None, default None) – Only {"p": ...} is supported.

  • numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). In identical mode, mojolearn returns the same bits on Apple, NVIDIA and AMD GPUs. "fast" is available on Apple GPUs only.

Variables:

  • embedding_ (ndarray of shape (n_samples, n_components))

  • eigenvalues_ (ndarray of shape (n_components,))

  • dissimilarity_matrix_ (ndarray of shape (n_samples, n_samples))

  • n_features_in_ (int)

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((500, 8), dtype=np.float32)
>>> Y = mojolearn.ClassicalMDS(n_components=2).fit_transform(X)

See also

User guide: Decomposition and embeddings

Choosing an estimator

Methods

fit(X[, y])

Compute the embedding.

fit_transform(X[, y])

Compute the embedding and return embedding_.

get_params([deep])

Return this estimator's constructor parameters.

set_params(**params)

Set constructor parameters by keyword.
fit(X, y=None)#

Compute the embedding.

Parameters:

  • X (array-like of shape (n_samples, n_features) or (n_samples, n_samples)) – Data, or a distance matrix with metric="precomputed".

  • y (ignored)

Returns:

self

fit_transform(X, y=None)#

Compute the embedding and return embedding_.

Parameters:

  • X (array-like of shape (n_samples, n_features) or (n_samples, n_samples))

  • y (ignored)

Returns:

ndarray of shape (n_samples, n_components), float32

get_params(deep=True)#

Return this estimator’s constructor parameters.

Parameters:

deep (bool, default True) – Accepted for scikit-learn compatibility; it has no effect.

Returns:

dict – Parameter name to current value.

set_params(**params)#

Set constructor parameters by keyword.

Raises:

ValueError – If a keyword is not a parameter of this estimator.

Returns:

self