Datasources

Datasource objects (DS) take binned data and labels as inputs and generate population vectors (which are used by a cross-validator object to train and test a classifier).

The Neural Decoding Toolbox comes with following datasource objects:

1. basic_DS
2. generalization_DS

Methods that must be implemented

Datasources must implement a get_data method that returns the training and test data splits. The get_data method must have the following structure:

[all_XTr, all_YTr, all_XTe, all_YTe, ADDITIONAL_INFO] = ds.get_data

The outputs of this methods are:

1.all_XTr{iTime}{iCV} = [num_features x num_training_points]

A cell array that has the training data for all times and cross-validation splits

2.all_YTr = [num_training_point x 1]

The training labels

3.all_XTe{iTime}{iCV} = [num_features x num_test_points]

A cell array that has the test data for all times and cross-validation splits

4.all_YTe = [num_test_point x 1]

The test labels