37a57f5ad43f86dd13e1d4f35e8e1b6421e73657,examples/fcma/classification.py,,,#,24

Before Change

    mask_file = sys.argv[3]
    epoch_file = sys.argv[4]
    raw_data, labels = prepare_fcma_data(data_dir, extension, mask_file, epoch_file)
    epochs_per_subj = int(sys.argv[5])
    // no shrinking, set C=1
    use_clf = svm.SVC(kernel="precomputed", shrinking=False, C=1)
    //use_clf = LogisticRegression()

After Change

    epoch_list = np.load(epoch_file)
    num_subjects = len(epoch_list)
    num_epochs_per_subj = epoch_list[0].shape[1]

    raw_data, labels = prepare_fcma_data(data_dir, extension, mask_file, epoch_file)

    // no shrinking, set C=1
    use_clf = svm.SVC(kernel="precomputed", shrinking=False, C=1)
    //use_clf = LogisticRegression()
    clf = Classifier(use_clf, epochs_per_subj=num_epochs_per_subj)

    // doing leave-one-subject-out cross validation
    for i in range(num_subjects):
        leave_start = i * num_epochs_per_subj
        leave_end = (i+1) * num_epochs_per_subj
        training_data = raw_data[0:leave_start] + raw_data[leave_end:]
        test_data = raw_data[leave_start:leave_end]
        training_labels = labels[0:leave_start] + labels[leave_end:]
        test_labels = labels[leave_start:leave_end]
        clf.fit(training_data, training_labels)
        // joblib can be used for saving and loading models
        //joblib.dump(clf, "model/logistic.pkl")
        //clf = joblib.load("model/svm.pkl")
        print(clf.predict(test_data))

Instances