예제 #1
0
        private static void TrainLambda(List <Session> sess, out List <double> trainErrors, out List <double> cvErrors)
        {
            trainErrors = new List <double>();
            cvErrors    = new List <double>();
            for (double alpha = 0.1; alpha < 2; alpha += 0.1)
            {
                var trainSize = (int)(0.7 * sess.Count);
                var cvSize    = sess.Count - trainSize;
                var train     = sess.GetRange(0, trainSize);
                var cv        = sess.GetRange(trainSize, cvSize);

                NeuroNet      nn = new NeuroNet();
                List <double> tErr, cvErr;
                nn.Train(train, cv, out tErr, out cvErr, new SigmoidFunction(alpha));
                trainErrors.Add(tErr.Last());
                cvErrors.Add(cvErr.Last());
                Console.WriteLine("Trained alpha: {0}", alpha);
            }
        }
예제 #2
0
        private static void Train(List <Session> sess, out List <double> trainErrors, out List <double> cvErrors)
        {
            trainErrors = new List <double>();
            cvErrors    = new List <double>();
            for (int size = sess.Count % 1000; size < sess.Count; size += 1000)
            {
                var trainSize = (int)(0.8 * size);
                var cvSize    = size - trainSize;
                var train     = sess.GetRange(0, trainSize);
                var cv        = sess.GetRange(trainSize, cvSize);

                var detectedTrain = train.Count(p => p.Switch != Session.SwitchType.No);
                var detectedCV    = cv.Count(p => p.Switch != Session.SwitchType.No);

                NeuroNet      nn = new NeuroNet();
                List <double> tErr, cvErr;
                nn.Train(train, cv, out tErr, out cvErr);
                trainErrors.Add(tErr.Last());
                cvErrors.Add(cvErr.Last());
                Console.WriteLine("Trained size: {0}/{1}", size, sess.Count);
            }
        }
예제 #3
0
        static void Main(string[] args)
        {
            string path = Environment.CurrentDirectory + @"\..\..\..\dataset\train";

            Dictionary <string, User>    users    = new Dictionary <string, User>();
            Dictionary <string, Session> sessions = new Dictionary <string, Session>();

            int linesReaded = 0;

            using (var fs = new FileStream(path, FileMode.Open))
                using (var sr = new StreamReader(fs))
                {
                    string line = null;
                    while (linesReaded < MAX_LINES)
                    //while ((line = sr.ReadLine()) != null)
                    {
                        line = sr.ReadLine();
                        string[] tokens = line.Split('\t');

                        if (tokens[2].Equals("M"))
                        {
                            User user;
                            if (users.ContainsKey(tokens[3])) // userid
                            {
                                user = users[tokens[3]];
                            }
                            else
                            {
                                user = new User()
                                {
                                    UserID = UInt64.Parse(tokens[3])
                                };
                                users.Add(tokens[3], user);
                            }

                            var session = Session.Create(user, tokens);
                            user.Sessions.Add(session);

                            if (!sessions.ContainsKey(tokens[0]))
                            {
                                sessions.Add(tokens[0], session);
                            }
                            else
                            {
                                Debug.Fail("unexpected");
                            }
                        }
                        if (tokens[2].Equals("Q"))
                        {
                            if (sessions.ContainsKey(tokens[0]))
                            {
                                var query = Query.Create(tokens);
                                sessions[tokens[0]].AddQuery(query);
                            }
                            else
                            {
                                Debug.Fail("unexpected");
                            }
                        }
                        if (tokens[2].Equals("C"))
                        {
                            if (sessions.ContainsKey(tokens[0]))
                            {
                                var click = Click.Create(tokens);
                                sessions[tokens[0]].AddClick(click);
                            }
                            else
                            {
                                Debug.Fail("unexpected");
                            }
                        }
                        if (tokens[2].Equals("S"))
                        {
                            if (sessions.ContainsKey(tokens[0]))
                            {
                                sessions[tokens[0]].AddSwitch(Switch.Create(tokens));
                            }
                            else
                            {
                                Debug.Fail("unexpected");
                            }
                        }
                        linesReaded++;
                        if (linesReaded % 1000 == 0)
                        {
                            Console.WriteLine(linesReaded);
                        }
                    }
                }

            var           sess = sessions.Values.ToList <Session>(); //.FindAll(p => p.Switch != Session.SwitchType.No);
            List <double> trainErrors;
            List <double> cvErrors;
            //Train(sess, out trainErrors, out cvErrors);

            //Visualize(trainErrors, cvErrors);

            //TrainLambda(sess, out trainErrors, out cvErrors);
            //Visualize(trainErrors, cvErrors);

            var trainSize = (int)(0.7 * sess.Count);
            var cvSize    = sess.Count - trainSize;
            var train     = sess.GetRange(0, trainSize);

            train = train.FindAll(p => p.User.Sessions.Count > 5);
            var cv = sess.GetRange(trainSize, cvSize);

            Console.WriteLine("Start neuro");
            NeuroNet      nn = new NeuroNet();
            List <double> tErr, cvErr;

            nn.Train(train, cv, out tErr, out cvErr);

            //Visualize(tErr, cvErr);

            Console.WriteLine();
            Console.WriteLine("Train data metrics");
            //EstimateMetrics(train, nn);
            Console.WriteLine();
            Console.WriteLine("CV data metrics");
            EstimateMetrics(cv, nn);

            Console.ReadKey();
        }
예제 #4
0
        private static void Train(List<Session> sess, out List<double> trainErrors, out List<double> cvErrors)
        {
            trainErrors = new List<double>();
            cvErrors = new List<double>();
            for (int size = sess.Count % 1000; size < sess.Count; size += 1000)
            {
                var trainSize = (int)(0.8 * size);
                var cvSize = size - trainSize;
                var train = sess.GetRange(0, trainSize);
                var cv = sess.GetRange(trainSize, cvSize);

                var detectedTrain = train.Count(p => p.Switch != Session.SwitchType.No);
                var detectedCV = cv.Count(p => p.Switch != Session.SwitchType.No);

                NeuroNet nn = new NeuroNet();
                List<double> tErr, cvErr;
                nn.Train(train, cv, out tErr, out cvErr);
                trainErrors.Add(tErr.Last());
                cvErrors.Add(cvErr.Last());
                Console.WriteLine("Trained size: {0}/{1}", size, sess.Count);
            }
        }
예제 #5
0
        static void Main(string[] args)
        {
            string path = Environment.CurrentDirectory + @"\..\..\..\dataset\train";

            Dictionary<string, User> users = new Dictionary<string, User>();
            Dictionary<string, Session> sessions = new Dictionary<string, Session>();

            int linesReaded = 0;
            using (var fs = new FileStream(path, FileMode.Open))
            using (var sr = new StreamReader(fs))
            {
                string line = null;
                while (linesReaded < MAX_LINES)
                //while ((line = sr.ReadLine()) != null)
                {
                    line = sr.ReadLine();
                    string[] tokens = line.Split('\t');

                    if (tokens[2].Equals("M"))
                    {
                        User user;
                        if (users.ContainsKey(tokens[3])) // userid                        
                            user = users[tokens[3]];
                        else
                        {
                            user = new User() { UserID = UInt64.Parse(tokens[3]) };
                            users.Add(tokens[3], user);
                        }
                        
                        var session = Session.Create(user, tokens);
                        user.Sessions.Add(session);

                        if (!sessions.ContainsKey(tokens[0]))
                            sessions.Add(tokens[0], session);
                        else
                            Debug.Fail("unexpected");
                    }
                    if (tokens[2].Equals("Q"))
                    {
                        if (sessions.ContainsKey(tokens[0]))
                        {
                            var query = Query.Create(tokens);
                            sessions[tokens[0]].AddQuery(query);
                        }
                        else
                            Debug.Fail("unexpected");
                    }
                    if (tokens[2].Equals("C"))
                    {
                        if (sessions.ContainsKey(tokens[0]))
                        {
                            var click = Click.Create(tokens);
                            sessions[tokens[0]].AddClick(click);
                        }
                        else
                            Debug.Fail("unexpected");
                    }
                    if (tokens[2].Equals("S"))
                    {
                        if (sessions.ContainsKey(tokens[0]))
                            sessions[tokens[0]].AddSwitch(Switch.Create(tokens));
                        else
                            Debug.Fail("unexpected");
                    }
                    linesReaded++;
                    if (linesReaded % 1000 == 0)
                        Console.WriteLine(linesReaded);
                }
            }

            var sess = sessions.Values.ToList<Session>(); //.FindAll(p => p.Switch != Session.SwitchType.No);            
            List<double> trainErrors;
            List<double> cvErrors;
            //Train(sess, out trainErrors, out cvErrors);

            //Visualize(trainErrors, cvErrors);

            //TrainLambda(sess, out trainErrors, out cvErrors);
            //Visualize(trainErrors, cvErrors);

            var trainSize = (int)(0.7 * sess.Count);
            var cvSize = sess.Count - trainSize;
            var train = sess.GetRange(0, trainSize);
            train = train.FindAll(p => p.User.Sessions.Count > 5);
            var cv = sess.GetRange(trainSize, cvSize);

            Console.WriteLine("Start neuro");
            NeuroNet nn = new NeuroNet();
            List<double> tErr, cvErr;
            nn.Train(train, cv, out tErr, out cvErr);

            //Visualize(tErr, cvErr);

            Console.WriteLine();
            Console.WriteLine("Train data metrics");
            //EstimateMetrics(train, nn);
            Console.WriteLine();
            Console.WriteLine("CV data metrics");
            EstimateMetrics(cv, nn);

            Console.ReadKey();
        }
예제 #6
0
 private static void TrainLambda(List<Session> sess, out List<double> trainErrors, out List<double> cvErrors)
 {
     trainErrors = new List<double>();
     cvErrors = new List<double>();
     for (double alpha = 0.1; alpha < 2; alpha += 0.1)
     {
         var trainSize = (int)(0.7 * sess.Count);
         var cvSize = sess.Count - trainSize;
         var train = sess.GetRange(0, trainSize);
         var cv = sess.GetRange(trainSize, cvSize);
         
         NeuroNet nn = new NeuroNet();
         List<double> tErr, cvErr;
         nn.Train(train, cv, out tErr, out cvErr, new SigmoidFunction(alpha));
         trainErrors.Add(tErr.Last());
         cvErrors.Add(cvErr.Last());
         Console.WriteLine("Trained alpha: {0}", alpha);
     }
 }