WienerFilterClassification

class WienerFilterClassification(C=1)

Class for the Wiener Filter Decoder

There are no parameters to set.

This simply leverages the scikit-learn logistic regression.

fit(X_flat_train, y_train)

Train Wiener Filter Decoder

Parameters:

• X_flat_train (numpy 2d array of shape [n_samples,n_features]) – This is the neural data. See example file for an example of how to format the neural data correctly

• y_train (numpy 2d array of shape [n_samples, n_outputs]) – This is the outputs that are being predicted

predict(X_flat_test)

Predict outcomes using trained Wiener Cascade Decoder

Parameters:

X_flat_test (numpy 2d array of shape [n_samples,n_features]) – This is the neural data being used to predict outputs.

Returns:

y_test_predicted – The predicted outputs

Return type:

numpy 2d array of shape [n_samples,n_outputs]