Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.regions.ReNA#

class nilearn.regions.ReNA(mask_img, n_clusters=2, scaling=False, n_iter=10, threshold=1e-07, memory=None, memory_level=1, verbose=0)[source]#

Recursive Neighbor Agglomeration (ReNA): Recursively merges the pair of clusters according to 1-nearest neighbors criterion. See [1].

Parameters:
mask_imgNiimg-like object

Object used for masking the data.

n_clustersint, optional

The number of clusters to find. Default=2.

scalingbool, optional

If scaling is True, each cluster is scaled by the square root of its size, preserving the l2-norm of the image. Default=False.

n_iterint, optional

Number of iterations of the recursive neighbor agglomeration. Default=10.

thresholdfloat in the open interval (0., 1.), optional

Threshold used to handle eccentricities. Default=1e-7.

memoryinstance of joblib.Memory, str, or pathlib.Path

Used to cache the masking process. By default, no caching is done. If a str is given, it is the path to the caching directory.

memory_levelint, optional.

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching. Default=1.

verboseint, optional

Verbosity level (0 means no message). Default=0.

References

Attributes:
`labels_ `numpy.ndarray, shape = [n_features]

Cluster labels for each feature.

`n_clusters_`int

Number of clusters.

`sizes_`numpy.ndarray, shape = [n_features]

It contains the size of each cluster.

__init__(mask_img, n_clusters=2, scaling=False, n_iter=10, threshold=1e-07, memory=None, memory_level=1, verbose=0)[source]#
fit(X, y=None)[source]#

Compute clustering of the data.

Parameters:
Xnumpy.ndarray, shape = [n_samples, n_features]

Training data.

yIgnored
Returns:
selfReNA object
transform(X, y=None)[source]#

Apply clustering, reduce the dimensionality of the data.

Parameters:
Xnumpy.ndarray, shape = [n_samples, n_features]

Data to transform with the fitted clustering.

Returns:
X_rednumpy.ndarray, shape = [n_samples, n_clusters]

Data reduced with agglomerated signal for each cluster.

inverse_transform(X_red)[source]#

Send the reduced 2D data matrix back to the original feature space (voxels).

Parameters:
X_rednumpy.ndarray, shape = [n_samples, n_clusters]

Data reduced with agglomerated signal for each cluster.

Returns:
X_invnumpy.ndarray, shape = [n_samples, n_features]

Data reduced expanded to the original feature space.

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.

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.