NaiveBayesRegression

class NaiveBayesRegression(encoding_model='quadratic', res=100)

Class for the Naive Bayes Decoder

Parameters:

• encoding_model (string, default 'quadratic') – what encoding model is used

• res (int, default 100) – resolution of predicted values This is the number of bins to divide the outputs into (going from minimum to maximum) larger values will make decoding slower

fit(X_b_train, y_train)

Train Naive Bayes Decoder

Parameters:

• X_b_train (numpy 2d array of shape [n_samples,n_neurons]) – This is the neural training 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 (training data)

predict(X_b_test, y_test)

Predict outcomes using trained tuning curves

Parameters:

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

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

Returns:

y_test_predicted – The predicted outputs

Return type:

numpy 2d array of shape [n_samples,n_outputs]