/// <summary>
/// 计算峰值
/// </summary>
/// <param name="N">回览次数</param>
/// <param name="K">成功次数</param>
/// <param name="p">正常概率</param>
public override List <ChanceClass> getChances(BaseCollection sc, ExpectData ed)
{
List <ChanceClass> ret = new List <ChanceClass>();
MLDataFactory pkdls = new MLDataFactory();// this.LastUseData());
//Dictionary<int, Dictionary<int, int>> res = pkdls.getAllShiftAndColMaxProbList(this.ReviewExpectCnt- this.InputMinTimes-1, this.InputMinTimes,true);
List <MLFeature <int> > features = pkdls.getAllSpecRowRoundFeatures(this.LastUseData().Count - 1, this.InputMinTimes, 0);
Dictionary <string, int> AllCodes = new Dictionary <string, int>();
for (int i = 0; i < features.Count; i++)
{
MLFeature <int> feature = features[i];
MaxEnt me = new MaxEnt();
if (MaxEnt.FeatureSummary == null)
{
me.OnLoadLocalFile = LoadLocalTrainData;
me.LoadSummary();
}
me.FillStructBySummary(i);
MLInstance <int, int> instance = new MLInstance <int, int>(feature);
int label = me.Classify(instance);
if (label < 0)
{
continue;
}
string strAllCode = string.Format("{0}/{1}", (i + 1) % 10, label);
AllCodes.Add(strAllCode, 1);
}
////foreach (int key in res.Keys)
//// AllCodes.Add(string.Format("{0}/{1}",key,string.Join("",res[key].Keys.ToArray())),1);
if (true)
{
ChanceClass cc = new ChanceClass();
cc.SignExpectNo = ed.Expect;
cc.ChanceType = 2;
cc.InputTimes = 1;
cc.strInputTimes = string.Join("_", new string[2] {
"1", "1"
});
//cc.AllowMaxHoldTimeCnt = this.AllowMaxHoldTimeCnt;
cc.InputExpect = ed;
cc.StragId = this.GUID;
//cc.MinWinRate = this.CommSetting.GetGlobalSetting().DefaultHoldAmtSerials.MaxRates[cc.ChipCount - 1];
cc.IsTracer = 0;
cc.HoldTimeCnt = 1;
cc.Odds = this.CommSetting.GetGlobalSetting().Odds;
cc.ExpectCode = ed.Expect;
cc.ChanceCode = string.Join("+", AllCodes.Keys.ToArray());
cc.ChipCount = ChanceClass.getChipsByCode(cc.ChanceCode);
cc.CreateTime = DateTime.Now;
cc.UpdateTime = DateTime.Now;
cc.AllowMaxHoldTimeCnt = 1;
cc.Closed = false;
ret.Add(cc);
}
return(ret);
}
public Dictionary <int, double> getMaxProb(int col, int shiftCol, int TestLength, int LastTimes, bool LastisSerial)
{
Dictionary <int, double> ret = new Dictionary <int, double>();
List <Instance> TrainSet = OccurrTestInstances(col, shiftCol, TestLength, LastTimes, LastisSerial);
List <Instance> TestSet = getLastFeatures(col, shiftCol, Data.Count - 1, LastTimes, LastisSerial);
ret = MaxEnt.getLabels(TrainSet, TestSet);
return(ret);
}
