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.
3.3.2. fmralign.alignment_methods.DiagonalAlignment¶
-
class
fmralign.alignment_methods.DiagonalAlignment(n_jobs=1, parallel_backend='threading')[source]¶ Compute the voxelwise projection factor between X and Y.
- Parameters
n_jobs: integer, optional (default = 1)
The number of CPUs to use to do the computation. -1 means ‘all CPUs’, -2 ‘all CPUs but one’, and so on.
parallel_backend: str, ParallelBackendBase instance, None (default: ‘threading’)
Specify the parallelization backend implementation. For more informations see joblib.Parallel documentation
Attributes
R
(scipy.sparse.diags) Scaling matrix containing the optimal shrinking factor for every voxel
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__init__(n_jobs=1, parallel_backend='threading')[source]¶ Initialize self. See help(type(self)) for accurate signature.
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fit(X, Y)[source]¶ - Parameters
X: (n_samples, n_features) nd array
source data
Y: (n_samples, n_features) nd array
target data
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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
X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
- Returns
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
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get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params : mapping of string to any
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 pipelines). The latter have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.- Returns
self