public static void Main(string[] args)
{
//Object to Hold Task Parameters
TaskDimensions task = new TaskDimensions();
//Dictionary that contains a list of top ten jobs compared with other jobs for each user
Dictionary <int, List <TopJobData> > finalList = new Dictionary <int, List <TopJobData> >();
//User number to start proccessing
int user_number = 1;
IFileSystemSvc svc = new FileSystemSvcImpl();
//Method call to get the number of jobs and users from the file Y
svc.detectSizeOfJobsColumns(task, Directory.GetCurrentDirectory() + "/files/Y1.txt");
//Method call to get the number of features from the file X, and allocating the X matrix
double[,] X = svc.getNumberOfFeaturesX(Directory.GetCurrentDirectory() + "/files/X1.txt", task);
svc.readFeaturesX(X, Directory.GetCurrentDirectory() + "/files/X1.txt", task);
//Method call to get the jobs names
String[] job_list = svc.readJobNames(Directory.GetCurrentDirectory() + "/files/job_names1.txt", task);
//Creating a variable to write in a File the job recommendations and comparisons
StreamWriter writeText = svc.getResultStreamWriter();
while (user_number <= task.num_users_init)
{
// Movie rating file for a user
double[,] my_ratings = new double[task.num_jobs_init, 1];
//Now we read R and Y from theirs files (-1 because I will remove the chosen user from the matrixes)
double[,] Y = svc.readTrainingY(Directory.GetCurrentDirectory() + "/files/Y1.txt", task, my_ratings, user_number);
double[,] R = svc.readTrainingR(Directory.GetCurrentDirectory() + "/files/R1.txt", task, user_number);
//Creating a MatLab reference to execute the recommended job script
IMatlabSvc matSvc = new MatlabSvcImpl();
object[] res = matSvc.executeFilter(task, job_list, Directory.GetCurrentDirectory() + "/files", my_ratings, Y, R, X, user_number);
//Each time creates a to be used to write the recommended jobs in a file
List <TopJobData> mylist = svc.writeValuesToFile(writeText, res, job_list, user_number);
//adding the list at the Dictionary for each user
finalList.Add(user_number, mylist);
user_number++;
}
writeText.Close();
//creating a instance of DataResult to be used to write the averages in a file
DataResult avgs = new DataResult(finalList, 10);
avgs.calculateAverages();
Console.WriteLine("DONE");
// Wait until fisnih
Console.ReadLine();
}
public static void Main(string[] args)
{
//Object to Hold Task Parameters
TaskDimensions task = new TaskDimensions();
//User number to start proccessing
int user_number = 1;
//Load File System Service
IFileSystemSvc svc = new FileSystemSvcImpl();
//Method call to get the number of jobs and users from the file Y
svc.detectSizeOfJobsColumns(task, Directory.GetCurrentDirectory() + "/files/Y.txt");
//Method call to get the number of features from the file X, and allocating the X matrix
double[,] X = svc.getNumberOfFeaturesX(Directory.GetCurrentDirectory() + "/files/X.txt", task);
svc.readFeaturesX(X, Directory.GetCurrentDirectory() + "/files/X.txt", task);
//Method call to get the jobs names
String[] job_list = svc.readJobNames(Directory.GetCurrentDirectory() + "/files/expressions.txt", task);
//method that return the users profile
UserProfile[] users_profile = svc.readUserProfile(Directory.GetCurrentDirectory() + "/files/user_table.txt", task);
//Creating a variable to write in a File the job recommendations and comparisons
//Load File Writer
StreamWriter writeTextResult = svc.getResultStreamWriter();
StreamWriter writeTextAverages = svc.getAverageStreamWriter();
StreamWriter writeText = svc.getIdandAvgStreamWriter();
StreamWriter writeTextDiff = svc.getDifficultyStreamWriter();
int numUnderEstimated = 0, numOverEstimated = 0;
double[] users_calculated_raitings = new double[task.num_users_init];
double total_rating_avg_system = 0;
double total_similarity_avg_system = 0;
double total_inaccuracy_system = 0;
while (user_number <= task.num_users_init)
{
// Movie rating file for a user
double[,] my_ratings = new double[task.num_jobs_init, 1];
//Now we read R and Y from theirs files (-1 because I will remove the chosen user from the matrixes)
double[,] Y = svc.readTrainingY(Directory.GetCurrentDirectory() + "/files/Y.txt", task, my_ratings, user_number);
double[,] R = svc.readTrainingR(Directory.GetCurrentDirectory() + "/files/R.txt", task, user_number);
//Creating a MatLab reference to execute the recommended job script
IMatlabSvc matSvc = new MatlabSvcImpl();
object[] res = matSvc.executeFilter(task, job_list, Directory.GetCurrentDirectory() + "/files", my_ratings, Y, R, X, user_number);
//Each time creates a to be used to write the recommended jobs in a file
List <TopJobData> mylist = svc.writeValuesToFile(writeTextResult, res, job_list, user_number, X);
//Calculate Averages for Jobs for a User
DataResult avgs = new DataResult(mylist, mylist.Count, users_profile[user_number - 1]);
avgs.AverageForEachJob();
svc.writeAveragesToFile(avgs, writeTextAverages, users_profile[user_number - 1]);
total_rating_avg_system += avgs.Rating_total_avg;
total_similarity_avg_system += avgs.Percentage_total_avg;
total_inaccuracy_system += avgs.Self_inaccuracy;
//adding the list at the Dictionary for each user
//ID and AVGs file
writeText.WriteLine(users_profile[user_number - 1].UserID + "\t" + avgs.Rating_total_avg);
//people who under and overestimated themselves
if (avgs.Self_inaccuracy > 0)
{
numOverEstimated++;
}
else
{
numUnderEstimated++;
}
users_calculated_raitings[user_number - 1] = avgs.Rating_total_avg;
//writing in the difficulty file
svc.writeDifficultyToFile(writeTextDiff, avgs);
user_number++;
}
total_rating_avg_system /= task.num_users_init;
total_similarity_avg_system /= task.num_users_init;
total_inaccuracy_system /= task.num_users_init;
//writing some more global information
svc.writeGlobalAveragesInformation(total_rating_avg_system, total_similarity_avg_system, total_inaccuracy_system, numUnderEstimated,
numOverEstimated, task, writeTextAverages, users_profile, users_calculated_raitings);
//closing the three files
writeText.Close();
writeTextResult.Close();
writeTextAverages.Close();
writeTextDiff.Close();
//creating a instance of DataResult to be used to write the averages in a file
Console.WriteLine("DONE");
//Wait until fisnih
Console.ReadLine();
}
public static void Main(string[] args)
{
//Object to Hold Task Parameters
TaskDimensions task = new TaskDimensions();
//Dictionary that contains a list of top ten jobs compared with other jobs for each user
Dictionary <int, List <TopJobData> > finalList = new Dictionary <int, List <TopJobData> >();
//User number to start proccessing
int user_number = 1;
//Load File System Service
IFileSystemSvc svc = new FileSystemSvcImpl();
//Method call to get the number of jobs and users from the file Y
svc.detectSizeOfJobsColumns(task, Directory.GetCurrentDirectory() + "/files/Y1.txt");
//Method call to get the number of features from the file X, and allocating the X matrix
double[,] X = svc.getNumberOfFeaturesX(Directory.GetCurrentDirectory() + "/files/X1.txt", task);
svc.readFeaturesX(X, Directory.GetCurrentDirectory() + "/files/X1.txt", task);
//Method call to get the jobs names
String[] job_list = svc.readJobNames(Directory.GetCurrentDirectory() + "/files/job_names1.txt", task);
//method that return the users profile
UserProfile[] users_profile = svc.readUserProfile(Directory.GetCurrentDirectory() + "/files/user_table.txt", task);
//Creating a variable to write in a File the job recommendations and comparisons
//Load File Writer
StreamWriter writeTextResult = svc.getResultStreamWriter();
StreamWriter writeTextAverages = svc.getAverageStreamWriter();
double total_avg_system = 0;
double total_user_inaccuracy = 0;
while (user_number <= task.num_users_init)
{
// Movie rating file for a user
double[,] my_ratings = new double[task.num_jobs_init, 1];
//Now we read R and Y from theirs files (-1 because I will remove the chosen user from the matrixes)
double[,] Y = svc.readTrainingY(Directory.GetCurrentDirectory() + "/files/Y1.txt", task, my_ratings, user_number);
double[,] R = svc.readTrainingR(Directory.GetCurrentDirectory() + "/files/R1.txt", task, user_number);
//Creating a MatLab reference to execute the recommended job script
IMatlabSvc matSvc = new MatlabSvcImpl();
object[] res = matSvc.executeFilter(task, job_list, Directory.GetCurrentDirectory() + "/files", my_ratings, Y, R, X, user_number);
//Each time creates a to be used to write the recommended jobs in a file
List <TopJobData> mylist = svc.writeValuesToFile(writeTextResult, res, job_list, user_number);
//Calculate Averages for Jobs for a User
DataResult avgs = new DataResult(mylist, 10, users_profile[user_number - 1]);
avgs.AverageForEachJob();
svc.writeAveragesToFile(avgs, writeTextAverages, users_profile[user_number - 1]);
total_avg_system += avgs.Percentage_total_avg;
total_user_inaccuracy += avgs.Self_inaccuracy;
//adding the list at the Dictionary for each user
user_number++;
}
total_avg_system /= task.num_users_init;
writeTextAverages.WriteLine("AVGS TOTAL\t" + total_avg_system);
total_user_inaccuracy /= task.num_users_init;
writeTextAverages.WriteLine("COMMUNITY INACCURACY\t" + total_user_inaccuracy);
writeTextResult.Close();
writeTextAverages.Close();
//creating a instance of DataResult to be used to write the averages in a file
Console.WriteLine("DONE");
//Wait until fisnih
Console.ReadLine();
}