% Practicum, Task #3, 'Compositions of algorithms'.

%

% FUNCTION:

% [prediction, err] = bagging_predict (model, X, y)

%

% DESCRIPTION:

% This function use the composition of algorithms, trained with bagging 

% method, for prediction.

%

% 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.

% model --- trained composition.

%

% OUTPUT:

% prediction --- vector of predicted answers, N x 1 double vector.

% error --- the ratio of number of correct answers to number of objects on

%           each iteration, num_iterations x 1 vector

%

% AUTHOR: 

% Murat Apishev (great-mel@yandex.ru)

%

function [prediction, err] = bagging_predict (model, X, y)

    num_iterations = length(model.models);

    no_objects = length(y);

    pred_prediction = zeros([no_objects num_iterations]);

    err = zeros([num_iterations 1]);

    if strcmp(model.algorithm, 'svm')

        for alg = 1 : num_iterations

            pred_prediction(:,alg) = svmpredict(y, X, model.models{alg});

            func = @(i) find_max(pred_prediction(i,:));

            prediction = arrayfun(func, 1 : no_objects)';

            err(alg) = sum(prediction ~= y) / no_objects;

        end

    elseif strcmp(model.algorithm, 'classification_tree')

        for alg = 1 : num_iterations

            pred_prediction(:,alg) = predict(model.models{alg}, X);

            func = @(i) find_max(pred_prediction(i,:));

            prediction = arrayfun(func, 1 : no_objects)';

            err(alg) = sum(prediction ~= y) / no_objects;            

        end

    else

        error('Incorrect type of algorithm!');

    end    

end

function [result] = find_max (vector)

    if sum(vector == -1) > sum(vector == +1)

        result = -1;

    else

        result = +1;

    end

end