gognjanovski

namespace Com.Example.Controllers
{
    [Authorize]
    public class HomeController : Controller
    {
        public HomeController()
        {
        }
        
        //Use Refresh Token to generate new Access Token

gognjanovski

(function () {
    'use strict';
    angular.module('token.service')
        .factory('AuthorizationTokenService', AuthorizationTokenService);
    AuthorizationTokenService.$inject = ['$q', '$injector'];

    function AuthorizationTokenService($q, $injector) {
        //Local storage for token
        var tokenVM = {
            accessToken: null,

gognjanovski

(function () {
    'use strict';

    var core = angular.module('config');
    core.config(configure);
    configure.$inject = ['$httpProvider'];

    /* @ngInject */
    function configure($httpProvider) {

gognjanovski

allwords = "";

% - Load and concatenate all the emails in one string
Files=dir('data/*/*.txt');
for k=1:length(Files)
    FileNames = Files(k).name;
    dr = Files(k).folder;
    file = fileread(strcat(dr, '\', FileNames));
    file = regexprep(file, '\<\w{1,2}\>', "");
    allwords = strcat(allwords, ' ', file);

gognjanovski

dictionaryList = get_dictionary_list();


#Train Features
#Train Labels

docId = 1;
featureTrain = zeros(1,3);
labelTrain = zeros(1,1);

gognjanovski

numTrainDocs = 100;
numTokens = 2500;

% - Load train emails features
M = dlmread('train-features.txt', ' ');
spmatrix = sparse(M(:, 1), M(:, 2), M(:, 3), numTrainDocs, numTokens);
train_matrix = full(spmatrix);

% - Load train emails labels
train_labels = dlmread('train-labels.txt', ' ');

gognjanovski

N = dlmread('test-features.txt', ' ');
test_matrix = sparse(N(:, 1), N(:, 2), N(:, 3));

numTestDocs = size(test_matrix, 1);
numTestTokens = size(test_matrix, 2);

output = zeros(numTestDocs, 1);

% - Probability that one email is spam, number of spam emails divided by number of all emails
% - Because the ration of spam - nonspam emails in the training set is 50:50 this probability will be 0.5 (50%)

gognjanovski

function data_reshape = getCsvData(filename)
%GETCSVDATA load the csv data into cell array

    fileID = fopen(filename,'r');
    % Start row is 1 since the first line is the heading (name of the columns)
    startRow = 1;
    data = textscan(fileID, '%d %d\n', 'Delimiter', ';',...
        'HeaderLines', startRow,'ReturnOnError', false);
    fclose(fileID);

gognjanovski

Year;Population
2007;9182927
2008;9256347
2009;9340682
2010;9415570
2011;9482855
2012;9555893
2013;9644864
2014;9747355
2015;9851017

gognjanovski

% Load Population Data
statistics_data = getCsvData('swe_pop_2007_2017.csv');

% Convert cell to matrix
X = cell2mat(statistics_data(:,1));
y = cell2mat(statistics_data(:,2));

% Convert matrix values to double
X = double(X);
y = double(y);