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meanTransferMDM.m
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meanTransferMDM.m
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close all;
clear;
clc;
%%
% subjects = [1];
% sessions = [1];
table_to_show = [];
% 1,2 - worst
% 5,6 - best
% 5,3 - suppose to be good
% 11,4 - other mean
% 5,8 - mean vs pca
% 7,8 - pca vs pca
% 5,10 - mean vs our pca
% 9,10 - our pca vs our pca
table_to_show = [...
"name" ,...
"worst" ,...
"best" ,...
"suppose to be good " ,...
"other mean " ,...
"mean vs pca " ,...
"pca vs pca " ,...
"mean vs our pca " ,...
"our pca vs our pca " ,...
];
for subject1 = 1 : 7
for sess1 = 1 : 8
for subject2 = subject1 : 7
for sess2 = 1 : 8
if subject1 == subject2 & sess1 == sess2
continue
end
%% get events 1
epsilon = 0.01;
max_iter = 100;
[Events1, vClass1] = getERPEvents(subject1, sess1);
cov1 = covFromCellArrayOfEvents(Events1);
mean_of_cov1 = riemannianMean(cov1, epsilon, max_iter);
%% get events 2
[Events2, vClass2] = getERPEvents(subject2, sess2);
cov2 = covFromCellArrayOfEvents(Events2);
mean_of_cov2 = riemannianMean(cov2, epsilon, max_iter);
%% transorm events 1 to events 2
T = ( mean_of_cov2 * ( (mean_of_cov1)^(-1) ) ) ^ (1 / 2);
Events1_transformed = {};
for ii = 1 : length(Events1)
Events1_transformed{ii} = (T * Events1{ii}')';
end
cov_transformed = covFromCellArrayOfEvents(Events1_transformed);
% %% sanity check - show data before and after transformation
% % before
% tmp_cov = cat(3, cov1, cov2);
% flattened_cov = prepareForClassification(tmp_cov);
% tsne_points = tsne(flattened_cov');
% vClass_one = zeros(size(flattened_cov, 2), 1);
% vClass_one(1:size(cov1,3)) = 1;
% class_1 = 1;
% class_2 = 2;
%
% figure();
% scatter( tsne_points(vClass_one == 1, 1), tsne_points(vClass_one == 1, 2), 30, 'r', 'filled', 'MarkerEdgeColor', 'k' );
% hold on;
% scatter( tsne_points(vClass_one ~= 1, 1), tsne_points(vClass_one ~= 1, 2), 30, 'b', 'filled', 'MarkerEdgeColor', 'k' );
%
% %after
% tmp_cov = cat(3, cov_transformed, cov2);
% flattened_cov = prepareForClassification(tmp_cov);
% tsne_points = tsne(flattened_cov');
% vClass_one = zeros(size(flattened_cov, 2), 1);
% vClass_one(1:size(cov2,3)) = 1;
% class_1 = 1;
% class_2 = 2;
%
% figure();
% scatter( tsne_points(vClass_one == 1, 1), tsne_points(vClass_one == 1, 2), 30, 'r', 'filled', 'MarkerEdgeColor', 'k' );
% hold on;
% scatter( tsne_points(vClass_one ~= 1, 1), tsne_points(vClass_one ~= 1, 2), 30, 'b', 'filled', 'MarkerEdgeColor', 'k' );
%
%% -see average of target
% figure();
% subplot(3, 1, 1);
% plot( getMean(Events1 , vClass1) );
% title('mean of events1 data');
% subplot(3, 1, 2);
% plot( getMean(Events2 , vClass2) );
% title('mean of events2 data');
% subplot(3, 1, 3);
% plot( getMean(Events1_transformed, vClass1) );
% title('mean of events1 on events 2 damain');
%% Original Data
%V 1,2 - worst Events1_transformed, Events2
%V 4,5 - best TEvents1_with_mean , Events2_with_mean
%V 4,3 - suppose to be good TEvents1_with_mean, Events2_with_transformed_mean
% 4,7 - mean vs pca TEvents1_with_mean, Events2_with_pca
% 6,7 - pca vs pca TEvents1_with_pca, Events2_with_pca
% 4,9 - mean vs pca TEvents1_with_mean, Events2_with_our_pca
% 8,9 - pca vs pca TEvents1_with_our_pca, Events2_with_our_pca
Events1_with_mean = addAverageSub(Events1, vClass1);
TEvents1_with_mean = addAverageSub(Events1_transformed, vClass1);
Events2_with_mean = addAverageSub(Events2 , vClass2);
[~,Events2_with_transformed_mean] = addDiffrenceAverageOldDataTest( Events1_transformed,...
Events2 ,...
vClass1);
[~,Events2_with_oter_mean] = addDiffrenceAverageOldDataTest( Events1,...
Events2 ,...
vClass1);
[TEvents1_with_pca,...
Events2_with_pca] = addPCAAverageTest(Events1_transformed, Events2);
[TEvents1_with_our_pca, ~] = addPCAAverageOldDataTest(Events1_transformed, [], vClass1);
[Events2_with_our_pca, ~] = addPCAAverageOldDataTest(Events2, [], vClass2);
%%
c_data_for_classifier = {};
c_description_for_data = {};
funcs = { @covFromCellArrayOfEvents};
funcs_names = { "Covariance"};
tmp_description = "subject " + num2str(subject1) + " session " + num2str(sess1);
events_names = {
"transformed" + tmp_description,...
"new session" + tmp_description,...
"new session transformed mean" + tmp_description,...
"new session other mean" + tmp_description,...
"transformed session with mean" + tmp_description,...
"new session with real mean" + tmp_description,...
"transformed session PCA" + tmp_description,...
"new session PCA " + tmp_description,...
"transformed session our PCA" + tmp_description,...
"new session our PCA " + tmp_description,...
"original with mean" + tmp_description,...
};
events_cell = {
Events1_transformed ,...
Events2 ,...
Events2_with_transformed_mean ,...
Events2_with_oter_mean ,...
TEvents1_with_mean ,...
Events2_with_mean ,...
TEvents1_with_pca ,...
Events2_with_pca ,...
TEvents1_with_our_pca ,...
Events2_with_our_pca ,...
Events1_with_mean ,...
};
c_data_for_classifier = {};
for ii = 1:length(events_cell)
c_data_for_classifier{ii} = covFromCellArrayOfEvents( events_cell{ii} );
end
%% - show TSNE
% figure();
% subplot(3, 3, 1);
% showTSNElocal(c_data_for_classifier{2}, vClass1);
% title('TSNE original');
%
% subplot(3, 3, 2);
% showTSNElocal(c_data_for_classifier{4}, vClass1);
% title('TSNE with mean');
%%
% 1,2 - worst
% 5,6 - best
% 5,3 - suppose to be good
% 11,4 - other mean
% 5,8 - mean vs pca
% 7,8 - pca vs pca
% 5,10 - mean vs our pca
% 9,10 - our pca vs our pca
NUM_OF_RES = 8;
precision = zeros(NUM_OF_RES, 1);
tmp_idx = 1;
[pre1, ~] = calPrecision(...
c_data_for_classifier{1},...
c_data_for_classifier{2},...
vClass1 ,...
vClass2 ...
);
precision(tmp_idx) = precision(tmp_idx) + pre1;
tmp_idx = tmp_idx + 1;
[pre1, ~] = calPrecision(...
c_data_for_classifier{5},...
c_data_for_classifier{6},...
vClass1 ,...
vClass2 ...
);
precision(tmp_idx) = precision(tmp_idx) + pre1;
tmp_idx = tmp_idx + 1;
[pre1, ~] = calPrecision( ...
c_data_for_classifier{5},...
c_data_for_classifier{3},...
vClass1 ,...
vClass2 ...
);
precision(tmp_idx) = precision(tmp_idx) + pre1;
tmp_idx = tmp_idx + 1;
[pre1, ~] = calPrecision( ...
c_data_for_classifier{11},...
c_data_for_classifier{4},...
vClass1 ,...
vClass2 ...
);
precision(tmp_idx) = precision(tmp_idx) + pre1;
tmp_idx = tmp_idx + 1;
[pre1, ~] = calPrecision( ...
c_data_for_classifier{5},...
c_data_for_classifier{8},...
vClass1 ,...
vClass2 ...
);
precision(tmp_idx) = precision(tmp_idx) + pre1;
tmp_idx = tmp_idx + 1;
[pre1, ~] = calPrecision( ...
c_data_for_classifier{7},...
c_data_for_classifier{8},...
vClass1 ,...
vClass2 ...
);
precision(tmp_idx) = precision(tmp_idx) + pre1;
tmp_idx = tmp_idx + 1;
[pre1, ~] = calPrecision( ...
c_data_for_classifier{5},...
c_data_for_classifier{10},...
vClass1 ,...
vClass2 ...
);
precision(tmp_idx) = precision(tmp_idx) + pre1;
tmp_idx = tmp_idx + 1;
[pre1, ~] = calPrecision( ...
c_data_for_classifier{9},...
c_data_for_classifier{10},...
vClass1 ,...
vClass2 ...
);
precision(tmp_idx) = precision(tmp_idx) + pre1;
tmp_table_to_show = ["subject: " + num2str(subject1) + " session: " + num2str(sess1) + " to " + "subject: " + num2str(subject2) + " session: " + num2str(sess2)];
table_to_show = [table_to_show ;
[tmp_table_to_show ,...
num2str( precision(1) ),...
num2str( precision(2) ),...
num2str( precision(3) ),...
num2str( precision(4) ),...
num2str( precision(5) ),...
num2str( precision(6) ),...
num2str( precision(7) ),...
num2str( precision(8) )]
];
disp(table_to_show);
end
end
end
end
%%
function [mean_1] = getMean(cInput, vClass)
%ADDAVERAGE Summary of this function goes here
% Detailed explanation goes here
%calc mean of vClass == 1
mat = cat(3, cInput{:});
mean_1 = mean(mat(:, :, vClass==2 ), 3);
end
function [] = showTSNElocal(flattened_cov, vClass)
tsne_points = tsne(flattened_cov');
% firsst session
class_1 = 1;
class_2 = 2;
scatter( tsne_points(vClass == class_1, 1), tsne_points(vClass == class_1, 2), 30, 'r', 'filled', 'MarkerEdgeColor', 'k' );
hold on;
scatter( tsne_points(vClass == class_2, 1), tsne_points(vClass == class_2, 2), 30, 'b', 'filled', 'MarkerEdgeColor', 'k' );
hold on;
% legend('not target', 'target');
end
function [] = showPCA(flattened_cov, vClass)
eigen_vectors = pca(flattened_cov');
two_components = (flattened_cov' * eigen_vectors(:, 1:2));
% firsst session
class_1 = 1;
class_2 = 2;
scatter( two_components(vClass == class_1, 1), two_components(vClass == class_1, 2), 30, 'r', 'filled', 'MarkerEdgeColor', 'k' );
hold on;
scatter( two_components(vClass == class_2, 1), two_components(vClass == class_2, 2), 30, 'b', 'filled', 'MarkerEdgeColor', 'k' );
hold on;
% legend('not target', 'target');
end
function [pre1, pre2] = calPrecision(X, X_test,y, y_test, data_name)
class1 = 1;
class2 = 2;
class1_cov = X(:,:,vClass == class1);
class2_cov = X(:,:,vClass == class2);
epsilon = 1e-6;
max_iter = 100;
mean1 = riemannianMean(class1_cov, epsilon, max_iter);
mean2 = riemannianMean(class2_cov, epsilon, max_iter);
dist1 = calcDistanceBetweenCovMat(mean1, X_test);
dist2 = calcDistanceBetweenCovMat(mean2, X_test);
predicted_label = (dist1 > dist2) + 1;
% calc precision
tp = sum((y_test == 2) & (predicted_label == 2));
fp = sum((y_test == 1) & (predicted_label == 2));
if tp + fp ~= 0
pre1 = tp / (tp + fp);
else
pre1 = 0;
end
%calc recal
tn = sum((y_test == 1) & (predicted_label == 1));
fn = sum((y_test == 2) & (predicted_label == 1));
if tn + fn ~= 0
pre2 = tn / (tn + fn);
else
pre2 = 0;
end
end