function [Y_compute, Y_prob] = PerceptronKernel(para, X_train, Y_train, X_test, Y_test, num_class)

global temp_model_file preprocess;

Y_compute = zeros(size(Y_test)); Y_prob = zeros(size(Y_test));
if (num_class > 2)
    error('PerceptronKernel: The class number is larger than 2!');
end;

class_set = GetClassSet(Y_train);
p = str2num(char(ParseParameter(para, {'-Kernel';'-KernelParam'; '-CostFactor'; '-MaxIter'}, {'0';'0.05';'1';'100'}, 1)));
KernelType = p(1);
KernelPara = p(2); 
CostFactor = p(3);
MaxIter = p(4);

X_train_ext = [X_train ones(size(X_train, 1), 1)];
X_train_ext = X_train_ext(1:size(X_train_ext, 1), :);
Y_train  = Y_train(1:size(Y_train, 1), :);
X_test_ext = [X_test ones(size(X_test, 1), 1)];
X_ext = X_train_ext;

Logit_Y_prob = zeros(size(X_test, 1), 1);
beta = []; 
if (~isempty(X_train)),
    % Convert the binary labels into +/-1
    Y_train = (Y_train == class_set(1)) - (Y_train ~= class_set(1));
    beta = LearnPerceptKernel(Y_train, X_train_ext, KernelType, KernelPara, CostFactor, MaxIter);
    fid = fopen(temp_model_file, 'w');
    if (fid > 0),
        fprintf('Writing to %s .... \n', temp_model_file);  
        fprintf(fid, 'File: %s\n', preprocess.input_file); 
        fprintf(fid, 'N: %d\n', size(Y_train, 1)); 
        fprintf(fid, '%d ', class_set); fprintf(fid, '\n');     
        format_str = ''; 
        for i = 1:size(X_ext,2)+1, format_str = strcat(format_str, '%f,'); end;
        format_str = strcat(format_str, '\n');
        fprintf(fid, format_str, [beta X_ext]');
        fclose(fid);    
    end;
else
    fid = fopen(temp_model_file, 'r');
    if (fid > 0),
        fgets(fid);
        line = fgetl(fid); num = sscanf(line, 'N: %d');
        line = fgetl(fid); class_set = sscanf(line, '%d');      
        input = fscanf(fid, '%f,');
        input = reshape(input, [], num)';
        beta = input(:, 1); X_ext = input(:, 2:size(input, 2));
        fclose(fid);    
        preprocess.ClassSet = class_set;
    end;    
end;

Logit_Y_prob = PredictPerceptKernel(beta, X_ext, X_test_ext, KernelType, KernelPara);

% Y_prob = (exp(Logit_Y_prob) ./ (1 + exp(Logit_Y_prob))) .* (Logit_Y_prob >= 0) + (1 ./ (1 + exp(Logit_Y_prob))) .* (Logit_Y_prob < 0);
Y_prob = exp(Logit_Y_prob) ./ (1 + exp(Logit_Y_prob));
Y_compute = class_set(1) * (Logit_Y_prob >= 0) + class_set(2) * (Logit_Y_prob < 0);

% Learning 

function beta = LearnPerceptKernel(Y_train, X_train_ext, KernelType, KernelPara, CostFactor, MaxIter)   

extx = X_train_ext;
mextx = size(extx, 1);
% Build the kernel matrix
switch (KernelType) 
    case 0 
        kernel = extx * extx'; 
    case 1
        kernel = (1 + extx * extx') .^ KernelPara;
    case 2
        kernel = eye(mextx);
        for i = 1:mextx
            k = repmat(extx(i, :), size(extx, 1), 1) - extx;
            kernel(:, i) = sum(k .* k, 2);
        end;
        % kernel = exp(-KernelPara * kernel);
        kernel = exp(- kernel / (2 * KernelPara ^2));
end;

[num_data, num_feature] = size(X_train_ext);
beta = zeros(num_data, 1);
for t = 1:MaxIter,
    all_correct = true;
    for i = 1:num_data,
       if (Y_train(i) .* (kernel(i, :) * beta)) <= 0, 
            beta(i) = beta(i) + Y_train(i);
            all_correct = false;
       end;
    end;
    if (all_correct), break; end;
    % fprintf('%d', t);
end;

% Prediction 
function [L_output, kernel] = PredictPerceptKernel(beta, D_train, D_test, KernelType, KernelPara)

if nargin<4, kerneltype = 0; end;
if nargin<5, kernelpara = 0; end;

switch (KernelType) 
    case 0 
        kernel = D_test * D_train'; 
    case 1
        kernel = (1 + D_test * D_train') .^ KernelPara;
    case 2
        % RBFftr = 0.01;
        num_test = size(D_test, 1);
        num_train = size(D_train, 1);        
        kernel = zeros(num_test, num_train);
        for i = 1:num_test
            for j = 1:num_train
                kernel(i, j) = (D_test(i, :) - D_train(j, :)) * (D_test(i, :) - D_train(j, :))';
            end;
        end;
%        kernel = exp(-KernelPara * kernel);
        kernel = exp(- kernel / (2 * KernelPara ^2));
end;
L_output = kernel * beta;