public CandidateClassifier(string name, int tp, int sl, char dealType, int hour, CandidateParameter cp)
{
this.Name = name;
this.Tp = tp;
this.Sl = sl;
this.DealType = dealType;
this.Hour = hour;
this.Deals = new DealsInfo(cp.DealInfoLastMinutes);
this.WekaData = new WekaData(dealType, tp, sl, cp);
}
public string BuildPricePatternDeals(bool withPrice = false)
{
WekaUtils.Instance.WriteLog("Now BuildPricePatternDeals");
var cp = TestParameters2.CandidateParameter;
string resultFile = TestParameters.GetBaseFilePath(string.Format("IncrementTest_{0}_{1}_{2}.txt",
cp.MainSymbol, "Price", cp.MainPeriod));
if (File.Exists(resultFile))
return string.Empty;
TestParameters.EnablePerhourTrain = false;
TestParameters.UseFilter = false;
cp.DeleteUnusedIndicators();
var dataDates = TestManager.GetDataDateRange();
SetTrainTime(dataDates[0], dataDates[1]);
WekaData wekaData = new WekaData(cp);
string arffFileName = TestParameters.GetBaseFilePath(string.Format("{0}_{1}_{2}.arff",
cp.MainSymbol, "Price", cp.MainPeriod));
weka.core.Instances allInstances;
if (!System.IO.File.Exists(arffFileName))
{
WekaData.GenerateArffTemplate(true, false, cp);
wekaData.UseNullHp = true;
wekaData.GenerateData(true, false);
allInstances = wekaData.CurrentTrainInstances;
var hps = HpData.Instance.GetHpSum(cp.MainSymbol, cp.MainPeriod);
DateTime nextHpDate = DateTime.MinValue;
weka.core.Instances newAllInstances = new weka.core.Instances(allInstances, 0);
//java.util.LinkedList deleteList = new java.util.LinkedList();
for (int i = 0; i < allInstances.numInstances(); ++i)
{
DateTime nowDate = WekaUtils.GetDateValueFromInstances(allInstances, 0, i);
if (TestParameters2.RealTimeMode && i == allInstances.numInstances() - 1)
{
allInstances.instance(i).setClassValue(0);
allInstances.instance(i).setValue(1, WekaUtils.GetTimeFromDate(Parameters.MaxDate) * 1000);
newAllInstances.add(allInstances.instance(i));
}
else if (hps.ContainsKey(nowDate))
{
int selectedDeal = hps[nowDate].Item1;
// 此时还不知道hp，不能滤
//if (TxtTest.Filter(nowDate, selectedDeal))
//{
// deleteList.Add(allInstances.instance(i));
//}
//else
{
allInstances.instance(i).setClassValue(hps[nowDate].Item1);
allInstances.instance(i).setValue(1, hps[nowDate].Item2 * 1000);
}
//if (nowDate < nextHpDate)
//{
// deleteList.Add(allInstances.instance(i));
//}
//else
//{
// DateTime hpDate = WekaUtils.GetDateTimeValueFromInstances(allInstances, 1, i);
// nextHpDate = hpDate;
//}
newAllInstances.add(allInstances.instance(i));
}
else
{
//deleteList.Add(allInstances.instance(i));
}
}
//allInstances.removeAll(deleteList);
allInstances = newAllInstances;
WekaUtils.SaveInstances(allInstances, arffFileName);
}
else
{
allInstances = WekaUtils.LoadInstances(arffFileName);
}
//TestParameters.ClassifierType = typeof(weka.classifiers.lazy.IBk);
//var cls = WekaUtils.CreateClassifier(0, 0);
//TestParameters.ClassifierType = typeof(weka.classifiers.functions.LibSVM);
//cls = WekaUtils.CreateClassifier(typeof(ProbClassifier));
//GenerateHpDateSpan(allInstances);
//return;
int n = (int)(24 / TestParameters2.MainPeriodOfHour);
n = TestParameters2.nPeriod;
if (!withPrice)
{
return TestManager.IncrementTest(allInstances, () =>
{
return WekaUtils.CreateClassifier(typeof(MyLibLinear), 0, 0, "-S 1 -C 1.0 -E 0.01 -B 1.0");
//return WekaUtils.CreateClassifier(typeof(weka.classifiers.lazy.IBk));
//return WekaUtils.CreateClassifier(typeof(ProbClassifier));
//return WekaUtils.CreateClassifier(typeof(InstancesProb));
},
"1,2,3,4,5,6,7,8", resultFile, n);
}
else
{
return TestManager.IncrementTest(allInstances, () =>
{
return WekaUtils.CreateClassifier(typeof(MyLibLinear), 0, 0, "-S 1 -C 1.0 -E 0.01 -B 1.0");
},
"1,2,3,5,6,7,8", resultFile, n);
}
}
public void BatchGenerateData()
{
WekaData data = new WekaData('B', 20, 20, null);
BatchAction(() =>
{
data.GenerateData();
});
}