public bool insertRecommenderJob(DataResult avgs)
{
try
{
IElasticSvc svc = (IElasticSvc)this.getService(typeof(IElasticSvc).Name);
return svc.insertRecommenderJob(avgs);
}
catch (ServiceLoadException ex)
{
return false;
}
}
public bool insertRecommenderJob(DataResult avgs)
{
try
{
bool flag = false;
RecommendedJobManager mgr = new RecommendedJobManager();
for (int n = 0; n < avgs.Number_top_jobs; n++)
{
RecommendedJob job = new RecommendedJob();
job.RecruiteeId = new Guid(avgs.User_profile.UserID);
job.JobId = new Guid(avgs.TopJobNames[n]);
job.PredictedRankingValue = (decimal)avgs.Mylist.ElementAt(n).PredRecJob;
flag = mgr.insertRecommendedJob(job);
}
return flag;
}
catch (Exception ex)
{
return false;
}
}
//just write information in the difficult file
public bool writeDifficultyToFile(StreamWriter writeText, DataResult avgs)
{
try
{
writeText.WriteLine(avgs.Avg_topJobDiff + "\t" + avgs.Avg_similarJobsDifficulty + "\t" + avgs.User_profile.UserRating + "\t" + avgs.Rating_total_avg);
return true;
}
catch (Exception ex)
{
return false;
}
}
//For each one of the users, writes averages analysis to a file. Everything has 2 decimal places (ToString("N2")).
public bool writeAveragesToFile(DataResult result, StreamWriter writeText, UserProfile user)
{
try
{
writeText.WriteLine("USER: "******" with rating:\t" + user.UserRating);
for (int k = 0; k < result.Number_top_jobs; k++)
{
writeText.WriteLine("Job " + result.TopJobNames[k] + "\t" + result.TopJobDiff[k].ToString("N2") + "\t" +
result.Mylist.ElementAt(k).PredRecJob.ToString("N2") + "\t" + result.Rating_average[k].ToString("N2") + "\t" +
result.Percentage_average[k].ToString("N2") + "\t" + result.SimilarJobsDifficulty[k].ToString("N2"));
}
writeText.WriteLine("RATING AVGS TOTAL\t" + result.Rating_total_avg.ToString("N2"));
writeText.WriteLine("PERCENTAGE AVGS TOTAL\t" + result.Percentage_total_avg.ToString("N2"));
writeText.WriteLine("JOB DIFFICULTY AVGS TOTAL\t" + result.Avg_topJobDiff.ToString("N2"));
writeText.WriteLine("SIMILAR JOBS DIFFICULTY AVGS TOTAL\t" + result.Avg_similarJobsDifficulty.ToString("N2"));
writeText.WriteLine("SELF INACCURACY\t" + result.Self_inaccuracy.ToString("N2") + "\n");
return true;
}
catch (Exception ex)
{
return false;
}
}
public bool writeDifficultyToFile(StreamWriter writeText, DataResult avgs)
{
try
{
IFileSystemSvc svc = (IFileSystemSvc)this.getService(typeof(IFileSystemSvc).Name);
return svc.writeDifficultyToFile(writeText, avgs);
}
catch (ServiceLoadException ex)
{
return false;
}
}
public bool writeAveragesToFile(DataResult result, StreamWriter writeText, UserProfile user)
{
try
{
IFileSystemSvc svc = (IFileSystemSvc)this.getService(typeof(IFileSystemSvc).Name);
return svc.writeAveragesToFile(result, writeText, user);
}
catch (ServiceLoadException ex)
{
return false;
}
}
public bool ExecuteMainRoutine()
{
try
{
ElasticManager elasticMgr = new ElasticManager();
//Object to Hold Task Parameters
TaskDimensions task = new TaskDimensions();
//User number to start proccessing
int user_number = 1;
//Load File System Service
FileSystemManager fileSystemMgr = new FileSystemManager();
//Method call to get the number of jobs and users from the file Y
fileSystemMgr.detectSizeOfJobsColumns(task, path_fix + "files/new_Y_53.txt");
//Method call to get the number of features from the file X, and allocating the X matrix
double[,] X = fileSystemMgr.getNumberOfFeaturesX(path_fix + "files/X.txt", task);
fileSystemMgr.readFeaturesX(X, path_fix + "files/X.txt", task);
//Method call to get the jobs names
String[] job_list = fileSystemMgr.readJobNames(path_fix + "files/expressions.txt", task);
//method that return the users profile
UserProfile[] users_profile = fileSystemMgr.readUserProfile(path_fix + "files/new_user_table_53.txt", task);
//Creating a variable to write in a File the job recommendations and comparisons
//Load File Writer
StreamWriter writeTextResult = fileSystemMgr.getResultStreamWriter();
StreamWriter writeTextAverages = fileSystemMgr.getAverageStreamWriter();
StreamWriter writeText = fileSystemMgr.getIdandAvgStreamWriter();
StreamWriter writeTextDiff = fileSystemMgr.getDifficultyStreamWriter();
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;
//Creating a MatLab reference to execute the recommended job script
MatlabManager matlabMgr = new MatlabManager();
while (user_number <= task.num_users_init)
{
// job 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 = fileSystemMgr.readTrainingY(path_fix + "files/new_Y_53.txt", task, my_ratings, user_number);
double[,] R = fileSystemMgr.readTrainingR(path_fix + "files/new_R_53.txt", task, user_number);
object[] res = matlabMgr.executeFilter(task, job_list, path_fix + "files", my_ratings, Y, R, X);
//Each time creates a to be used to write the recommended jobs in a file
List<TopJobData> mylist = fileSystemMgr.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();
fileSystemMgr.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);
users_calculated_raitings[user_number - 1] = avgs.Rating_total_avg;
//writing in the difficulty file
fileSystemMgr.writeDifficultyToFile(writeTextDiff, avgs);
new Thread(() =>
{
Thread.CurrentThread.IsBackground = true;
// ////used to insert recommended jobs for a user in the database
bool result = elasticMgr.insertRecommenderJob(avgs);
}).Start();
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
fileSystemMgr.writeGlobalAveragesInformation(total_rating_avg_system, total_similarity_avg_system, total_inaccuracy_system,
task, writeTextAverages, users_profile, users_calculated_raitings);
//closing the three files
writeText.Close();
writeTextResult.Close();
writeTextAverages.Close();
writeTextDiff.Close();
return true;
/*
* Used to insert rating for task performed by workers. (User interface need to be built)
*
double[,] full_Y = svc.readFullY(Directory.GetCurrentDirectory() + "/files/Y.txt", task);
IElasticSvc e = new ElasticSvcImpl();
e.InsertRatings(job_list, users_profile, full_Y);
*/
}
catch (Exception ex)
{
return false;
}
}
public bool ExecuteMainRoutine()
{
try
{
RecommendedJobManager recJobMgr = new RecommendedJobManager();
new Thread(() =>
{
Thread.CurrentThread.IsBackground = true;
bool result_delete = recJobMgr.deleteAllRecommendedJob();
}).Start();
JobManager jobMgr = new JobManager();
String[] job_list = jobMgr.selectExpressionNames(); //job_names
double[] X = jobMgr.selectExpressionDifficulty(); //X
double[,] new_X = new double[X.Length, 1];
for (int i = 0; i < X.Length; i++)
{
new_X[i, 0] = X[i];
}
//////User Profile (just the user ID, I still need the user self rating)
//////RecruiteeSvcImpl r = new RecruiteeSvcImpl();
RecruiteeManager recMgr = new RecruiteeManager();
String[] recruitee_names = recMgr.selectRecruiteeNames();
double[] recruitee_skill = recMgr.selectRecruiteeSkills();
UserProfile[] users_profile = new UserProfile[recruitee_skill.Length];
for (int i = 0; i < recruitee_skill.Length; i++)
{
users_profile[i] = new UserProfile("", 0);
users_profile[i].UserID = recruitee_names[i];
users_profile[i].UserRating = recruitee_skill[i];
}
//////new_Y
ElasticManager elasticMgr = new ElasticManager();
double[,] Y = elasticMgr.SelectRatings(job_list, users_profile);
///////////// WRITING VARIABLES IN FILE ////////////
//FromWebToFileManager file = new FromWebToFileManager();
//file.writeFiles(job_list, new_X, users_profile, Y);
//////Object to Hold Task Parameters
TaskDimensions task = new TaskDimensions();
task.num_features = new_X.GetLength(1); //1 is the number of columns
task.num_jobs_init = job_list.Length;
task.num_users_init = recruitee_names.Length;
//////User number to start proccessing
int user_number = 1;
//////Load File System Service
FileSystemManager fileSystemMgr = new FileSystemManager();
//////Creating a variable to write in a File the job recommendations and comparisons
//////Load File Writer
StreamWriter writeTextResult = fileSystemMgr.getResultStreamWriter();
StreamWriter writeTextAverages = fileSystemMgr.getAverageStreamWriter();
StreamWriter writeText = fileSystemMgr.getIdandAvgStreamWriter();
StreamWriter writeTextDiff = fileSystemMgr.getDifficultyStreamWriter();
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;
MatlabManager matlabMgr = new MatlabManager();
while (user_number <= task.num_users_init)
{
double[,] my_ratings = new double[task.num_jobs_init, 1];
double[,] new_Y = new double[task.num_jobs_init, task.num_users_init - 1];
double[,] R = new double[task.num_jobs_init, task.num_users_init - 1];
for (int i = 0; i < job_list.Length; i++)
{
int k = 0;
for (int n = 0; n < users_profile.Length; n++)
{
if (n != (user_number - 1))
{
new_Y[i, k] = Y[i, n];
if (Y[i, n] != 0)
R[i, k] = 1;
else
R[i, k] = 0;
k++;
}
else
my_ratings[i, 0] = Y[i, n];
}
}
object[] res = matlabMgr.executeFilter(task, job_list, path_fix + "files", my_ratings, new_Y, R, new_X);
// ////Each time creates a to be used to write the recommended jobs in a file
List<TopJobData> mylist = fileSystemMgr.writeValuesToFile(writeTextResult, res, job_list, user_number, new_X);
// ////Calculate Averages for Jobs for a User
DataResult avgs = new DataResult(mylist, mylist.Count, users_profile[user_number - 1]);
avgs.AverageForEachJob();
fileSystemMgr.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);
users_calculated_raitings[user_number - 1] = avgs.Rating_total_avg;
// ////writing in the difficulty file
fileSystemMgr.writeDifficultyToFile(writeTextDiff, avgs);
new Thread(() =>
{
Thread.CurrentThread.IsBackground = true;
////used to insert recommended jobs for a user in the database
bool result = elasticMgr.insertRecommenderJob(avgs);
}).Start();
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
fileSystemMgr.writeGlobalAveragesInformation(total_rating_avg_system, total_similarity_avg_system, total_inaccuracy_system,
task, writeTextAverages, users_profile, users_calculated_raitings);
//////closing the four files
writeText.Close();
writeTextResult.Close();
writeTextAverages.Close();
writeTextDiff.Close();
return true;
}
catch (Exception ex)
{
return false;
}
}