KalmanFilterRegression

class KalmanFilterRegression(C=1)

Class for the Kalman Filter Decoder

Parameters:

• float (C -)

• optional

• 1 (default)

• in (This parameter scales the noise matrix associated with the transition)

• new (kinematic states. It effectively allows changing the weight of the)

• update. (neural evidence in the current)

• on (Our implementation of the Kalman filter for neural decoding is based)

• (https (that of Wu et al 2003)

• the (-cursor-motion-using-a-kalman-filter.pdf) with the exception of)

• previously (addition of the parameter C. The original implementation has)

• Morris (been coded in Matlab by Dan)

• (http (//dmorris.net/projects/neural_decoding.html#code))

fit(X_kf_train, y_train)

Train Kalman Filter Decoder

Parameters:

• X_kf_train (numpy 2d array of shape [n_samples(i.e. timebins) ,)

• n_neurons] – This is the neural data in Kalman filter format. See example file for an example of how to format the neural data correctly

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

predict(X_kf_test, y_test)

Predict outcomes using trained Kalman Filter Decoder

Parameters:

• X_kf_test (numpy 2d array of shape [n_samples(i.e. timebins) ,)

• n_neurons] – This is the neural data in Kalman filter format.

• y_test (numpy 2d array of shape [n_samples(i.e. timebins),n_outputs]) – The actual outputs This parameter is necesary for the Kalman filter (unlike other decoders) because the first value is nececessary for initialization

Returns:

• y_test_predicted (numpy 2d array of shape [n_samples(i.e. timebins),)

• n_outputs] – The predicted outputs