Esempio n. 1
0
        //multi-task learning
        public static void train_multi_mtl(List <dataSet> XXList, toolbox tb)
        {
            Global.reinitGlobal();

            for (double iter = 0; iter < Global.ttlIter; iter++)
            {
                Global.glbIter++;
                Stopwatch timer = new Stopwatch();
                timer.Start();

                double error = tb.train_multi();

                timer.Stop();
                double time = timer.ElapsedMilliseconds / 1000.0;
                Global.swLog.WriteLine("Training used time (second): " + time.ToString());

                //evaluate
                List <double> scoreList = tb.test_multi_mtl(XXList, iter, Global.swOutputList);
                for (int i = 0; i < Global.nTask; i++)
                {
                    Global.scoreTaskList_multi[i].Add(scoreList[i]);
                }
                Global.timeList_multi[Global.glbIter - 1]  += time;
                Global.errorList_multi[Global.glbIter - 1] += error;

                if (iter > 30 && Global.diff > 0 && Global.diff < Global.convergeTol)
                {
                    break;
                }
            }

            //save model
            if (Global.save == 1)
            {
                for (int i = 0; i < Global.nTask; i++)
                {
                    tb.ModelList[i].save(Global.modelDir + i.ToString() + Global.fModel);
                }
            }
        }
Esempio n. 2
0
        static void multiTask()
        {
            //train
            if (Global.runMode.Contains("train"))
            {
                //load data
                List <dataSet> XList  = new List <dataSet>();
                List <dataSet> XXList = new List <dataSet>();
                dataSet        X      = new dataSet();
                loadData_multi(XList, X, XXList);

                toolbox toolbox;

                //single-task training in multi-task framework: each task has its own independent train & test data
                if (Global.mt_singleTrain)
                {
                    foreach (double r in Global.regList)//experiments for each different regularizer value
                    {
                        Global.swResRaw.WriteLine("\n%single-task! r: {0}", r);
                        Console.WriteLine("\nsingle-task! r: {0}", r);

                        for (int i = 0; i < Global.nTask; i++)
                        {
                            Global.swLog.WriteLine("\nsingle-task! #task, r: " + (i + 1).ToString() + "," + r.ToString());
                            Console.WriteLine("\nsingle-task! #task, r: " + (i + 1).ToString() + "," + r.ToString());
                            Global.reg = r;
                            dataSet Xi = XList[i];
                            toolbox = new toolbox(Xi);
                            train_multi_single(XXList, toolbox, i);
                        }
                        resProcess.write_multi();
                    }
                    Global.swResRaw.WriteLine();
                }

                //merged training in multi-task framework: merge all training data to train a unified model
                if (Global.mt_mergeTrain)
                {
                    foreach (double r in Global.regList)//experiments for each different regularizer value
                    {
                        Global.reg = r;
                        Global.swLog.WriteLine("\nmerged-task! r: " + r.ToString());
                        Console.WriteLine("\nmerged-task! r: " + r.ToString());
                        Global.swResRaw.WriteLine("\n%merged-task! r: " + r.ToString());
                        toolbox = new toolbox(X);
                        train_multi_merge(XXList, toolbox);

                        resProcess.write_multi();
                    }
                    Global.swResRaw.WriteLine();
                }

                //multi-task learning
                if (Global.mt_mtTrain)
                {
                    foreach (double r in Global.regList)//experiments for each different regularizer value
                    {
                        Global.reg = r;
                        foreach (double cFactor in Global.cFactors)//experiments for each different C value (see Eq. 18 & 19 of [Sun+ TKDE 2013] for the definition of C)
                        {
                            Global.C = cFactor;
                            Global.swLog.WriteLine("\n%multi-task! reg, rate0, C, kernel: {0},{1},{2},{3}", Global.reg, Global.rate0, Global.C, Global.simiMode);
                            Global.swSimi.WriteLine("\n%multi-task! reg, rate0, C, kernel: {0},{1},{2},{3}", Global.reg, Global.rate0, Global.C, Global.simiMode);
                            Global.swResRaw.WriteLine("\n%multi-task! reg, rate0, C, kernel: {0},{1},{2},{3}", Global.reg, Global.rate0, Global.C, Global.simiMode);
                            Console.WriteLine("\nmulti-task! reg, rate0, C, kernel: {0},{1},{2},{3}", Global.reg, Global.rate0, Global.C, Global.simiMode);
                            toolbox = new toolbox(X, XList);
                            train_multi_mtl(XXList, toolbox);

                            resProcess.write_multi();
                        }
                    }
                    Global.swResRaw.WriteLine();
                }
            }
            else if (Global.runMode.Contains("test1"))//normal test
            {
                //load data
                List <dataSet> XList  = new List <dataSet>();
                List <dataSet> XXList = new List <dataSet>();
                dataSet        X      = new dataSet();
                loadData_multi(XList, X, XXList);
                //load model etc.
                toolbox tb = new toolbox(X, XList, false);

                if (Global.mt_mergeTrain)//multi_merge
                {
                    List <double> scoreList = tb.test_multi_merge(XXList, 0, Global.swOutputList);
                    for (int i = 0; i < Global.nTask; i++)
                    {
                        Global.scoreTaskList_multi[i].Add(scoreList[i]);
                    }
                    resProcess.write_multi();
                }
                else//multi_single or multi_mtl: they have the same testing schema
                {
                    List <double> scoreList = tb.test_multi_mtl(XXList, 0, Global.swOutputList);
                    for (int i = 0; i < Global.nTask; i++)
                    {
                        Global.scoreTaskList_multi[i].Add(scoreList[i]);
                    }
                    resProcess.write_multi();
                }
            }
            else if (Global.runMode.Contains("test2"))//for multi_mtl: test a new task via choosing the most similar model
            {
                //load data
                List <dataSet> XList  = new List <dataSet>();
                List <dataSet> XXList = new List <dataSet>();
                dataSet        X      = new dataSet();
                loadData_multi(XList, X, XXList);
                //get vectors
                List <List <double> > vecList = new List <List <double> >();
                foreach (dataSet Xi in XList)
                {
                    List <double> vec = getVecFromX(Xi);
                    vecList.Add(vec);
                }
                //load model & test
                toolbox       tb        = new toolbox(X, XList, false);
                List <double> scoreList = tb.test2_multi_mtl(vecList, XXList, 0, Global.swOutputList);
                for (int i = 0; i < Global.nTask; i++)
                {
                    Global.scoreTaskList_multi[i].Add(scoreList[i]);
                }
                resProcess.write_multi();
            }
            else if (Global.runMode.Contains("test3"))//for multi_mtl: test a new task via voted-test based on all models, i.e., the OMT-SBD method described in Section 4.4 of [Sun+ TKDE 2013]
            {
                //load data
                List <dataSet> XList  = new List <dataSet>();
                List <dataSet> XXList = new List <dataSet>();
                dataSet        X      = new dataSet();
                loadData_multi(XList, X, XXList);
                //get vectors
                List <List <double> > vecList = new List <List <double> >();
                foreach (dataSet Xi in XList)
                {
                    List <double> vec = getVecFromX(Xi);
                    vecList.Add(vec);
                }
                //load model & test
                toolbox       tb        = new toolbox(X, XList, false);
                List <double> scoreList = tb.test3_multi_mtl(vecList, XXList, 0, Global.swOutputList);
                for (int i = 0; i < Global.nTask; i++)
                {
                    Global.scoreTaskList_multi[i].Add(scoreList[i]);
                }
                resProcess.write_multi();
            }
            else
            {
                throw new Exception("error");
            }
        }