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.
3.3.3. fmralign.alignment_methods.ScaledOrthogonalAlignment¶
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class
fmralign.alignment_methods.ScaledOrthogonalAlignment(scaling=True)[source]¶ Compute a orthogonal mixing matrix R and a scaling sc such that Frobenius norm ||sc RX - Y||^2 is minimized.
- Parameters
scaling : boolean, optional
Determines whether a scaling parameter is applied to improve transform.
Attributes
R
(ndarray (n_features, n_features)) Optimal orthogonal transform
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fit(X, Y)[source]¶ Fit orthogonal R s.t. ||sc XR - Y||^2
- 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.
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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