metabci.brainda.algorithms.decomposition.SKLDA module

Shrinkage Linear Discriminant Analysis (SKLDA) algorithm, through the optimization of local features to achieve the purpose ofreducing the dimensionality of the data, can improve the small sample problem of the LDA algorithm to some extent.

author: OrionHan

email: jinhan9165@gmail.com

Created on: date (e.g.2022-02-15)

update log:

2023/12/08 by Yin ZiFan, promise010818@gmail.com, update code annotation

Refer: [1] Blankertz, et al. “Single-trial analysis and classification of ERP components—a tutorial.”

NeuroImage 56.2 (2011): 814-825.

Application:

class metabci.brainda.algorithms.decomposition.SKLDA.SKLDA[source]

Bases: BaseEstimator, TransformerMixin, ClassifierMixin

Shrinkage Linear discriminant analysis (SKLDA) for BCI.

avg_feats1

mean feature vector of class 1.

Type:

ndarray of shape (n_features,)

avg_feats2

mean feature vector of class 2.

Type:

ndarray of shape (n_features,)

sigma_c1

empirical covariance matrix of class 1.

Type:

ndarray of shape (n_features, n_features)

sigma_c2

empirical covariance matrix of class 2.

Type:

ndarray of shape (n_features, n_features)

D

the dimensionality of the feature space.

Type:

int, (=n_features)

nu_c1

for sigma penalty calculation in class 1.

Type:

float

nu_c2

for sigma penalty calculation in class 2.

Type:

float

classes_

Class labels.

Type:

ndarray

n_features

Number of features of the training data.

Type:

int

n_samples_c2

Number of samples in class 2.

Type:

int

n_samples_c1

Number of samples in class 1.

Type:

int

Tip

A example using SKLDA
 import numpy as np
 from metabci.brainda.algorithms.decomposition import SKLDA
 Xtrain = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
 y = np.array([1, 1, 1, 2, 2, 2])
 Xtest = np.array([[-0.8, -1], [-1.2, -1], [1.2, 1], [0.5, 2]])
 clf2 = SKLDA()
 clf2.fit(Xtrain, y)
 print(clf2.transform(Xtest))
fit(X: ndarray, y: ndarray)[source]

Train the model, Fit SKLDA.

Parameters:

  • X1 (ndarray of shape (n_samples, n_features)) – samples for class 1 (i.e. positive samples)

  • X2 (ndarray of shape (n_samples, n_features)) – samples for class 2 (i.e. negative samples)

  • X (array-like of shape (n_samples, n_features)) – Training data.

  • y (array-like of shape (n_samples,)) – Target values, {-1, 1} or {0, 1}.

Returns:

self – Some parameters (sigma_c1, sigma_c2, D) of SKLDA.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SKLDA

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • 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.

New 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

set_transform_request(*, Xtest: bool | None | str = '$UNCHANGED$') → SKLDA

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • 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.

New 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:

Xtest (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for Xtest parameter in transform.

Returns:

self – The updated object.

Return type:

object

transform(Xtest: ndarray)[source]

Project data and Get the decision values.

Parameters:

Xtest (ndarray of shape (n_samples, n_features).) – Input test data.

Returns:

proba – decision values of all test samples.

Return type:

ndarray of shape (n_samples,)