max () vmax = round ( vmax ) vmin = - vmax for ival_idx in range ( n_ivals ): ax = plt. shape n_ivals = len ( ivals ) for class_idx in range ( n_classes ): vmax = np.

jumping_means ( epo, ivals ) n_classes = epo.

count_nonzero ( a = b ) / len ( a ) print 'Accuracy: %.1f%% ' % ( accuracy * 100 ) acc += accuracy print print 'Overal accuracy: %.1f%% ' % ( 100 * acc / 2 )ĭef plot_scalps ( epo, ivals ): # ratio scalp to colorbar width scale = 10 dat = proc. upper () print 'True labels : %s ' % labels a = np. reshape ( 100, - 1 ) text = '' for i in range ( 100 ): row = rows - 6 col = cols letter = MATRIX text += letter print print 'Result for subject %d ' % ( subject + 1 ) print 'Constructed labels: %s ' % text. sum ( axis = 1 ) # lda_out_prob = lda_out_prob. reshape ( 100, 15, 12 ) lda_out_prob = lda_out_prob, static_idx, unscramble_idx ] #lda_out_prob = lda_out_prob # destil the result of the 15 runs #lda_out_prob = lda_out_prob.prod(axis=1) lda_out_prob = lda_out_prob. lda_apply ( fv_test, cfy ) # unscramble the order of stimuli unscramble_idx = fv_test. lda_train ( fv_train ) # load the testing set dat = load_bcicomp3_ds2 ( testing_set ) fv_test, _ = preprocessing ( dat, MARKER_DEF_TEST, jumping_means_ivals ) # predict lda_out_prob = proc. Epo = acc = 0 for subject in range ( 2 ): if subject = 0 : training_set = TRAIN_A testing_set = TEST_A labels = TRUE_LABELS_A jumping_means_ivals = JUMPING_MEANS_IVALS_A else : training_set = TRAIN_B testing_set = TEST_B labels = TRUE_LABELS_B jumping_means_ivals = JUMPING_MEANS_IVALS_B # load the training set dat = load_bcicomp3_ds2 ( training_set ) fv_train, epo = preprocessing ( dat, MARKER_DEF_TRAIN, jumping_means_ivals ) # train the lda cfy = proc.

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