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

%

% FUNCTION:

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

%

% DESCRIPTION:

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

% boosting 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] = gradient_boosting_predict (model, X, y)

    num_iterations = length(model.weights);

    no_objects = length(y);

    pred_prediction = zeros([no_objects num_iterations]);

    for alg = 1 : num_iterations

        value = zeros([no_objects 1]) + model.b_0;

        for i = 1 : alg

            if strcmp(model.algorithm, 'epsilon_svr')

                value = value + svmpredict(y, X, model.models{i}) * model.weights(i);

            elseif strcmp(model.algorithm, 'regression_tree')

                value = value + predict(model.models{i}, X) * model.weights(i);

            end

        end

        pred_prediction(:,alg) = value;

    end

    prediction = pred_prediction(:,end);

    err = zeros([num_iterations 1]);

    if strcmp(model.loss, 'absolute')

        temp = (bsxfun(@minus, pred_prediction, y));

        err = abs(sum(temp)) / no_objects;

    elseif strcmp(model.loss, 'logistic')

        prediction = sign(prediction);

        temp = (bsxfun(@eq, sign(pred_prediction), y));

        err = sum(temp == 0) / no_objects;

    end

    if size(err, 1) == 1

        err = err';

    end

end