pyriemann.clustering.Kmeans

class pyriemann.clustering.Kmeans(n_clusters=2, max_iter=100, metric='riemann', random_state=None, init='random', n_init=10, n_jobs=1, tol=0.0001)

Clustering by k-means with SPD matrices as inputs.

Find clusters that minimize the sum of squared distance to their centroids. This is a direct implementation of the k-means algorithm with a Riemannian metric.

Parameters

n_clusterint, default=2

Number of clusters.

max_iterint, default=100

The maximum number of iteration to reach convergence.

metricstring, default=’riemann’

The type of metric used for centroid and distance estimation.

random_stateinteger or np.RandomState, optional

The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.

init‘random’ or ndarray, shape (n_clusters, n_channels, n_channels), default=’random’

Method for initialization of centers. ‘random’: choose k observations (rows) at random from data for the initial centroids. If an ndarray is passed, it should be of shape (n_clusters, n_channels, n_channels) and gives the initial centers.

n_initint, default=10

Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.

n_jobsint, default=1

The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

tolfloat, default=1e-4

The stopping criterion to stop convergence, representing the minimum amount of change in labels between two iterations.

See also

Kmeans

MDM

Notes

New in version 0.2.2.

Attributes

mdm_MDM instance.

MDM instance containing the centroids.

labels_

Labels of each point.

inertia_float

Sum of distances of samples to their closest cluster center.

__init__(n_clusters=2, max_iter=100, metric='riemann', random_state=None, init='random', n_init=10, n_jobs=1, tol=0.0001)

Init.

centroids()

Helper for fast access to the centroid.

Returns

centroidslist of SPD matrices, len (n_cluster)

Return a list containing the centroid of each cluster.

fit(X, y=None)

Fit (estimates) the clusters.

Parameters

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

yndarray, shape (n_matrices,) | None, default=None

Not used, here for compatibility with sklearn API.

Returns

selfKmeans instance

The Kmeans instance.

fit_predict(X, y=None)

Perform clustering on X and returns cluster labels.

Parameters

Xarray-like of shape (n_samples, n_features)

Input data.

yIgnored

Not used, present for API consistency by convention.

Returns

labelsndarray of shape (n_samples,), dtype=np.int64

Cluster labels.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

predict(X)

Get the predictions.

Parameters

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns

predndarray of int, shape (n_matrices,)

Prediction for each matrix according to the closest centroid.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

transform(X)

Get the distance to each centroid.

Parameters

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns

distndarray, shape (n_matrices, n_cluster)

The distance to each centroid according to the metric.