/*
* Create mining model
*/
private void CreateMiningModel(Microsoft.AnalysisServices.MiningStructure objStructure, string sName, string sAlgorithm,
List <string> lsAtrPredict, List <string> lsMeasurePredict, List <bool> lbPredictItems, int parOne, int parTwo)
{
Microsoft.AnalysisServices.MiningModel myMiningModel = objStructure.CreateMiningModel(true, sName);
/* Notes:
* Each mining column must have its' input and predict columns
* Input and key columns are added automatically when they are created in the mining structure
* Predict columns can be added in the mining model
* An input column can be also a predict column
*/
myMiningModel.Algorithm = sAlgorithm;
switch (sAlgorithm)
{
case MiningModelAlgorithms.MicrosoftClustering:
myMiningModel.AlgorithmParameters.Add("CLUSTERING_METHOD", parOne);
if (parTwo > 0)
{
myMiningModel.AlgorithmParameters.Add("CLUSTER_COUNT", parTwo);
}
break;
//case MiningModelAlgorithms.MicrosoftTimeSeries:
// myMiningModel.AlgorithmParameters.Add("PERIODICITY_HINT", "{12}"); // {12} represents the number of months for prediction
// break;
case MiningModelAlgorithms.MicrosoftNaiveBayes:
break;
case MiningModelAlgorithms.MicrosoftDecisionTrees:
myMiningModel.AlgorithmParameters.Add("SCORE_METHOD", parOne);
myMiningModel.AlgorithmParameters.Add("SPLIT_METHOD", parTwo);
break;
}
/***************** Predict columns *****************/
// add optional predict columns
if (lsAtrPredict.Count != 0)
{
// predict columns
for (int i = 0; i < lsAtrPredict.Count; i++)
{
Microsoft.AnalysisServices.MiningModelColumn modelColumn = myMiningModel.Columns.GetByName(lsAtrPredict[i]);
modelColumn.SourceColumnID = lsAtrPredict[i];
if (lbPredictItems[i] == true)
{
modelColumn.Usage = MiningModelColumnUsages.PredictOnly;
}
else
{
modelColumn.Usage = MiningModelColumnUsages.Predict;
}
}
}
myMiningModel.Update();
}
/*---------------------------------
* Description: Update Mining Model and process StockPredict DB with new MAX, MIN when MAX,MIN are changed by Technology Analysis (IdentifyTrend)
* ----------------------------------- */
public static bool UpdateMMbyAnalysis(string sStockCode)
{
Server svr = ConnectServer(str_Con_Svr);
Database db = svr.Databases.GetByName("StockPredict");
Microsoft.AnalysisServices.MiningStructure ms = db.MiningStructures.FindByName(sStockCode);
Microsoft.AnalysisServices.MiningModel mm = ms.MiningModels.FindByName(sStockCode);
mm.AlgorithmParameters.Remove("MAXIMUM_SERIES_VALUE");
mm.AlgorithmParameters.Remove("MINIMUM_SERIES_VALUE");
// Max, Min Time Series
mm.AlgorithmParameters.Add("MAXIMUM_SERIES_VALUE", MAXIMUM_SERIES_VALUE);
mm.AlgorithmParameters.Add("MINIMUM_SERIES_VALUE", MINIMUM_SERIES_VALUE);
mm.Update();
mm.Process(ProcessType.ProcessFull);
// Update parameters into StockForecastModel
try
{
SqlConnection conn = new SqlConnection(str_Con_SQL);
conn.Open();
SqlCommand cmdUpdate = conn.CreateCommand();
cmdUpdate.CommandText = "Update StockForecastModel Set MAXIMUM_SERIES_VALUE=" + MAXIMUM_SERIES_VALUE.ToString().Replace(',', '.')
+ ", MINIMUM_SERIES_VALUE=" + MINIMUM_SERIES_VALUE.ToString().Replace(',', '.')
+ " Where StockCode='" + sStockCode + "'";
cmdUpdate.ExecuteNonQuery();
cmdUpdate.Dispose();
conn.Close();
}
catch (Exception ex) { MessageBox.Show("Không thể cập nhật StockForecastModel!"); return(false); }
return(true);
}
public static bool UpdateMMTest(Microsoft.AnalysisServices.MiningModel mm, string strStockCode, DateTime dtTo)
{
mm.AlgorithmParameters.Clear();
// Default:10; 10:5 -> from 0 to (n-10)/5
//mm.AlgorithmParameters.Add("MINIMUM_SUPPORT", 10);
// 0.1:0.05 -> from 0 to (1-0.1)/0.05=18
mm.AlgorithmParameters.Add("COMPLEXITY_PENALTY", COMPLEXITY_PENALTY);
// {5,20,60}, 0:0.1 -> from 0 to (1-0.1)/0.05=18
mm.AlgorithmParameters.Add("PERIODICITY_HINT", "{5,20,60}");
mm.AlgorithmParameters.Add("AUTO_DETECT_PERIODICITY", AUTO_DETECT_PERIODICITY);
// Defeult: 1, 10
mm.AlgorithmParameters.Add("HISTORIC_MODEL_COUNT", HISTORIC_MODEL_COUNT);
mm.AlgorithmParameters.Add("HISTORIC_MODEL_GAP", HISTORIC_MODEL_GAP);
// Max, Min Time Series
mm.AlgorithmParameters.Add("MAXIMUM_SERIES_VALUE", MAXIMUM_SERIES_VALUE);
mm.AlgorithmParameters.Add("MINIMUM_SERIES_VALUE", MINIMUM_SERIES_VALUE);
mm.Update();
mm.Process(ProcessType.ProcessFull);
return(ADOMDLib.CheckResultTest(strStockCode, dtTo));
}
/*---------------------------------
* Description: Update data for Analysis DB and reprocess it
* ----------------------------------- */
public static void UpdateTrainDB(Microsoft.AnalysisServices.Server svr, string sStockCode, bool bAll, DateTime dtFrom, DateTime dtTo, bool bMulti)
{
Database db = svr.Databases.GetByName("Stock");
CreateDataAccessObjects(db, sStockCode, bAll, dtFrom, dtTo, bMulti, false);
Microsoft.AnalysisServices.MiningModel mm = db.MiningStructures.GetByName(sStockCode).MiningModels.GetByName(sStockCode);
// Max, Min Time Series
mm.AlgorithmParameters.Remove("MAXIMUM_SERIES_VALUE");
mm.AlgorithmParameters.Remove("MINIMUM_SERIES_VALUE");
mm.AlgorithmParameters.Add("MAXIMUM_SERIES_VALUE", MAXIMUM_SERIES_VALUE);
mm.AlgorithmParameters.Add("MINIMUM_SERIES_VALUE", MINIMUM_SERIES_VALUE);
mm.Update();
//db.MiningStructures.GetByName(sStockCode).Process(ProcessType.ProcessFull);
//mm.Process(ProcessType.ProcessFull);
db.Process(ProcessType.ProcessFull);
}
/*---------------------------------
* Description: Create Mining Model
* ----------------------------------- */
public static void CreateMM(Microsoft.AnalysisServices.MiningStructure ms, string strStockCode, bool bMulti)
{
if (ms.MiningModels.ContainsName(strStockCode))
{
ms.MiningModels[strStockCode].Drop();
}
Microsoft.AnalysisServices.MiningModel mm = ms.CreateMiningModel(true, strStockCode);
mm.Algorithm = MiningModelAlgorithms.MicrosoftTimeSeries;
InitialParameters(strStockCode);
// 0.1:0.05 -> from 0 to (1-0.1)/0.05=18
mm.AlgorithmParameters.Add("COMPLEXITY_PENALTY", COMPLEXITY_PENALTY);
// {5,20,60}, 0:0.1 -> from 0 to (1-0.1)/0.05=18
mm.AlgorithmParameters.Add("PERIODICITY_HINT", "{5,20,60}");
mm.AlgorithmParameters.Add("AUTO_DETECT_PERIODICITY", AUTO_DETECT_PERIODICITY);
// Defeult: 1, 10
mm.AlgorithmParameters.Add("HISTORIC_MODEL_COUNT", HISTORIC_MODEL_COUNT);
mm.AlgorithmParameters.Add("HISTORIC_MODEL_GAP", HISTORIC_MODEL_GAP);
// Max, Min Time Series
mm.AlgorithmParameters.Add("MAXIMUM_SERIES_VALUE", MAXIMUM_SERIES_VALUE);
mm.AlgorithmParameters.Add("MINIMUM_SERIES_VALUE", MINIMUM_SERIES_VALUE);
mm.AllowDrillThrough = true;
mm.Columns["ID"].Usage = MiningModelColumnUsages.Key;
mm.Columns["ClosePrice"].Usage = MiningModelColumnUsages.Predict;
if (strStockCode.ToUpper() != "VNINDEX" && !bMulti)
{
mm.Columns["OpenPrice"].Usage = MiningModelColumnUsages.Input;
mm.Columns["HighPrice"].Usage = MiningModelColumnUsages.Input;
mm.Columns["LowPrice"].Usage = MiningModelColumnUsages.Input;
//mm.Columns["Volume"].Usage = MiningModelColumnUsages.Input;
}
mm.Update();
// Update parameters into StockForecastModel
UpdateForecastModel(strStockCode);
}
/*
* Create mining model with custom fields and algorithm
*/
private void CreateCustomModel(MiningStructure objStructure, string sAlgorithm, string sModelName, string sKeyColumn, List <string> lPredictColumns, List <bool> lbPredictColumns, int parOne, int parTwo)
{
// drop existing model
if (objStructure.MiningModels.ContainsName(sModelName))
{
objStructure.MiningModels[sModelName].Drop();
}
// Detailed description of the model algorithms is here:
// http://msdn.microsoft.com/en-us/library/ms175595.aspx
// More customisation for these algorithms can be found here:
// http://msdn.microsoft.com/en-us/library/cc280427.aspx
// Also a model example can be found here:
// http://msdn.microsoft.com/en-us/library/ms345087(v=SQL.100).aspx
Microsoft.AnalysisServices.MiningModel myMiningModel = objStructure.CreateMiningModel(true, sModelName);
myMiningModel.Algorithm = sAlgorithm;
switch (sAlgorithm)
{
case MiningModelAlgorithms.MicrosoftClustering:
myMiningModel.AlgorithmParameters.Add("CLUSTERING_METHOD", parOne);
myMiningModel.AlgorithmParameters.Add("CLUSTER_COUNT", parTwo);
break;
//case MiningModelAlgorithms.MicrosoftTimeSeries:
// myMiningModel.AlgorithmParameters.Add("PERIODICITY_HINT", "{12}"); // {12} represents the number of months for prediction
// break;
case MiningModelAlgorithms.MicrosoftNaiveBayes:
break;
case MiningModelAlgorithms.MicrosoftDecisionTrees:
myMiningModel.AlgorithmParameters.Add("SCORE_METHOD", parOne);
myMiningModel.AlgorithmParameters.Add("SPLIT_METHOD", parTwo);
break;
}
/***************** Predict columns *****************/
// add optional predict columns
if (lPredictColumns.Count != 0)
{
// predict columns
for (int i = 0; i < lPredictColumns.Count; i++)
{
Microsoft.AnalysisServices.MiningModelColumn modelColumn = myMiningModel.Columns.GetByName(lPredictColumns[i]);
modelColumn.SourceColumnID = lPredictColumns[i];
if (lbPredictColumns[i] == true)
{
modelColumn.Usage = MiningModelColumnUsages.PredictOnly;
}
else
{
modelColumn.Usage = MiningModelColumnUsages.Predict;
}
}
}
myMiningModel.Update();
}