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/HPD matrices as inputs.
The k-means is a clustering method used to find clusters that minimize the sum of squared distances between centroids and SPD/HPD matrices [1].
Then, for each new matrix, the class is affected according to the nearest centroid.
- Parameters:
- n_clustersint, default=2
Number of clusters.
- max_iterint, default=100
The maximum number of iteration to reach convergence.
- metricstring | dict, default=”riemann”
Metric used for mean estimation (for the list of supported metrics, see
pyriemann.utils.mean.mean_covariance()
) and for distance estimation (seepyriemann.utils.distance.distance()
). The metric can be a dict with two keys, “mean” and “distance” in order to pass different metrics.- random_stateNone | integer | np.RandomState, default=None
The generator used to initialize the centroids. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
- init“random” | ndarray, shape (n_clusters, n_channels, n_channels), default=”random”
Method for initialization of centroids. If “random”, it chooses k matrices at random for the initial centroids. If an ndarray is passed, it should be of shape (n_clusters, n_channels, n_channels) and gives the initial centroids.
- 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
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.
- Attributes:
- mdm_MDM instance
MDM instance containing the centroids.
- labels_ndarray, shape (n_matrices,)
Labels, ie centroid index, of each matrix of training set.
- inertia_float
Sum of distances of matrices to their closest cluster centroids.
See also
Kmeans
MDM
Notes
Added in version 0.2.2.
References
[1]Commande robuste d’un effecteur par une interface cerveau machine EEG asynchrone A. Barachant, Thesis, 2012
- __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 centroids.
- Returns:
- centroidsndarray, shape (n_clusters, n_channels, n_channels)
Centroids of each cluster.
- fit(X, y=None)¶
Fit (estimates) the clusters.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- yNone
Not used, here for compatibility with sklearn API.
- Returns:
- selfKmeans instance
The Kmeans instance.
- fit_predict(X, y=None)¶
Fit and predict in a single function.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- yNone
Not used, here for compatibility with sklearn API.
- Returns:
- predndarray of int, shape (n_matrices,)
Prediction for each matrix according to the closest centroid.
- fit_transform(X, y=None)¶
Fit and transform in a single function.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- yNone
Not used, here for compatibility with sklearn API.
- Returns:
- distndarray, shape (n_matrices, n_clusters)
Distance to each centroid according to the metric.
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- 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/HPD 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.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Test set of SPD matrices.
- yndarray, shape (n_matrices,)
True labels for each matrix.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix.
- Returns:
- scorefloat
Mean accuracy of clf.predict(X) wrt. y.
- set_output(*, transform=None)¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged
Added in version 1.4: “polars” option was added.
- Returns:
- selfestimator instance
Estimator instance.
- 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.
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') Kmeans ¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.
- transform(X)¶
Get the distance to each centroid.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- Returns:
- distndarray, shape (n_matrices, n_clusters)
Distance to each centroid according to the metric.
Examples using pyriemann.clustering.Kmeans
¶

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