EEGClassification.zip

% Practicum, Task #3, 'Compositions of algorithms'. % % FUNCTION: % [model] = gradient_boosting_train (X, y, num_iterations, base_algorithm, loss, ... % param_name1, param_value1, param_name2, param_value2, ... % param_name3, param_value3, param_name3, param_value3) % % DESCRIPTION: % This function train the composition of algorithms using gradient boosting method. % % INPUT: % X --- matrix of objects, N x K double matrix, N --- number of objects, % K --- number of features. % y --- vector of answers, N x 1 double vector, N --- number of objects. y % can have only two values --- +1 and -1 in case of classification % and all possible double values in case of regression. % num_iterations --- the number ob algorithms in composition, scalar. % base_algorithm --- the base algorithm, string. Can have one of two % values: 'regression_tree' or 'epsilon_svr'. % loss --- the loss function, string. Can have one of two values: % 'logistic' (for classification) or 'absolute' (for regression). % param_name1 --- learning rate, scalar. % param_name2 --- parameter of base_algorithm. For 'regression_tree' it % is a 'min_parent' --- min number of objects in the leaf of % regression tree. For 'epsilon_svr' it is 'epsilon' parameter. % param_name3 --- parameter, that exists only for 'epsilon_svr', it is a % 'gamma' parameter. % param_name4 --- parameter, that exists only for 'epsilon_svr', it is a % 'C' parameter. % param_value1, param_value2, param_value3, param_value4 --- values of % corresponding parametres, scalar. % OUTPUT: % model --- trained composition, structure with three fields % - b_0 --- the base of composition, scalar % - weights --- double array of weights, 1 x num_iterations % - models --- cell array with trained models, 1 x num_iterations % - algorithm --- string, 'epsilon_svr' or 'regression_tree' % - loss --- loss parameter (from INPUT). % % AUTHOR: % Murat Apishev (great-mel@yandex.ru) % function [model] = gradient_boosting_train (X, y, num_iterations, base_algorithm, loss, ... param_name1, param_value1, param_name2, param_value2, ... param_name3, param_value3, param_name4, param_value4) no_objects = size(X, 1); if ~strcmp(base_algorithm, 'epsilon_svr') && ~strcmp(base_algorithm, 'regression_tree') error('Incorrect type of algorithm!') end if strcmp(loss, 'logistic') loss_function = @(a, b) log(1 + exp(-a .* b)); grad_a_loss_function = @(a, b) -b .* exp(-a .* b) ./ ((1 + exp(-a .* b))); elseif strcmp(loss, 'absolute') loss_function = @(a, b) abs(a - b); grad_a_loss_function = @(a, b) -sign(b - a); else error('Incorrect type of loss function!'); end func = @(c) sum(loss_function(y, c)); b_0 = fminsearch(func, 0); model.b_0 = b_0; model.algorithm = base_algorithm; model.models = cell([1 num_iterations]); model.loss = loss; % the length of model is number of finite weights, not number of models! model.weights = repmat(+Inf, 1, num_iterations); z = zeros([no_objects 1]) + b_0; delta = zeros([no_objects 1]); for iter = 1 : num_iterations for obj = 1 : no_objects delta(obj) = -grad_a_loss_function(z(obj), y(obj)); end if strcmp(base_algorithm, 'epsilon_svr') model.models{iter} = svmtrain(delta, X, [' -s 3 -g ', num2str(param_value3), ... ' -c ', num2str(param_value4), ' -e ', num2str(param_value2)]); value = svmpredict(y, X, model.models{iter}); elseif strcmp(base_algorithm, 'regression_tree') model.models{iter} = RegressionTree.fit(X, delta, 'minparent', param_value2); value = predict(model.models{iter}, X); end func = @(g) sum(loss_function(z + g * value, y)); model.weights(iter) = fminsearch(func, 0); z = z + model.weights(iter) * value * param_value1; end end