//private void ConvertNorminalToString(string fileName)
//{
// List<string> list = new List<string>();
// using (System.IO.StreamReader sr = new System.IO.StreamReader(fileName))
// {
// while (true)
// {
// if (sr.EndOfStream)
// break;
// string s = sr.ReadLine();
// if (string.IsNullOrEmpty(s))
// continue;
// int idx = s.IndexOf(' ');
// string c = idx == -1 ? s : s.Substring(0, idx);
// if (Convert.ToDouble(c) == 0)
// {
// list.Add("-1.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else if (Convert.ToDouble(c) == 1)
// {
// list.Add("0.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else if (Convert.ToDouble(c) == 2)
// {
// list.Add("+1.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else
// {
// list.Add(s);
// }
// }
// }
// using (System.IO.StreamWriter sw = new System.IO.StreamWriter(fileName))
// {
// foreach (string s in list)
// {
// sw.WriteLine(s);
// }
// }
//}
//private Random m_randomGenerator;
private void AddInstancesAccordWeight(Instances instances)
{
// 0, 2
double[] weights = MincostLiblinearClassifier.GetCount(instances);
if (weights == null)
return;
double c = m_tp / m_sl;
if (c == 1 && weights[0] == weights[1])
return;
int n = 0;
int toCopyClass = 0;
if (c >= 1)
{
int shouldWeight1 = (int)(c * weights[1]);
n = (int)(shouldWeight1 - weights[1]);
toCopyClass = 2;
}
else
{
int shouldShouldWeight0 = (int)(1 / c * weights[0]);
n = (int)(weights[1] - weights[0]);
toCopyClass = 0;
}
//m_randomGenerator = new Random((int)System.DateTime.Now.Ticks);
List<Instance> copyInstances = new List<Instance>();
for (int i = 0; i < instances.numInstances(); ++i)
{
if (instances.instance(i).classValue() == toCopyClass)
{
copyInstances.Add(instances.instance(i));
}
}
int nAll = n / copyInstances.Count;
for (int j = 0; j < nAll; ++j)
{
for (int i = 0; i < copyInstances.Count; ++i)
{
Instance newInstance = new weka.core.DenseInstance(copyInstances[i]);
instances.add(newInstance);
newInstance.setDataset(instances);
}
}
//for (int j = 0; j < n - nAll * copyInstances.Count; ++j)
//{
// int idx = (int)(m_randomGenerator.NextDouble() * copyInstances.Count);
// idx = Math.Min(idx, copyInstances.Count - 1);
// Instance newInstance = new weka.core.DenseInstance(copyInstances[idx]);
// instances.add(newInstance);
// newInstance.setDataset(instances);
//}
if (n - nAll * copyInstances.Count > 0)
{
Instance avgInstance = new weka.core.DenseInstance(instances.numAttributes());
for (int i = 0; i < avgInstance.numAttributes(); ++i)
{
double sum = 0;
for (int j = 0; j < copyInstances.Count; ++j)
{
sum += copyInstances[j].value(i);
}
avgInstance.setValue(i, sum / copyInstances.Count);
}
for (int j = 0; j < n - nAll * copyInstances.Count; ++j)
{
Instance newInstance = new weka.core.DenseInstance(avgInstance);
instances.add(newInstance);
}
}
}
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);
}
}
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances thisData = new weka.core.Instances(buffReader); //source.getDataSet();
if (thisData.classIndex() == -1)
{
thisData.setClassIndex(thisData.numAttributes() - 1);
}
weka.core.Instances thisUniqueData = new weka.core.Instances(thisData);
if (thisUniqueData.classIndex() == -1)
{
thisUniqueData.setClassIndex(thisUniqueData.numAttributes() - 1);
}
thisUniqueData.delete();
if (allUniqueData == null)
{
allUniqueData = new weka.core.Instances(thisData);
if (allUniqueData.classIndex() == -1)
{
allUniqueData.setClassIndex(allUniqueData.numAttributes() - 1);
}
allUniqueData.delete();
}
weka.core.InstanceComparator com = new weka.core.InstanceComparator(false);
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < allUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i), allUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
{
allUniqueData.add(thisData.instance(i));
}
else
{
dupInstances++;
}
}
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < thisUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i), thisUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
{
thisUniqueData.add(thisData.instance(i));
}
else
{
dupInstancesSamePlayer++;
}
}
//Debug.Log("All Data Instance Count = " + thisData.numInstances());
//Debug.Log("Unique Data Instance Count = " + thisUniqueData.numInstances());
//Debug.Log("Done!");
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
