472 linhas
40 KiB
MQL5
472 linhas
40 KiB
MQL5
//+------------------------------------------------------------------+
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//| Study.mq5 |
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//| Copyright DNG® |
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//| https://www.mql5.com/ru/users/dng |
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//+------------------------------------------------------------------+
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#property copyright "Copyright DNG®"
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#property link "https://www.mql5.com/ru/users/dng"
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#property version "1.00"
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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#define Study
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#include "Trajectory.mqh"
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//+------------------------------------------------------------------+
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//| Input parameters |
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//+------------------------------------------------------------------+
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input int Iterations = 100000;
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input float Tau = 0.001f;
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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STrajectory Buffer[];
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CNet Encoder;
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CNet TargetEncoder;
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CNet Actor;
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CNet Critic1;
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CNet Critic2;
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CNet Convolution;
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CNet Descriminator;
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CNet SkillProject;
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//---
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float dError;
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datetime dtStudied;
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//---
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CBufferFloat State;
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CBufferFloat Account;
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CBufferFloat TargetState;
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CBufferFloat TargetAccount;
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CBufferFloat Actions;
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CBufferFloat Gradient;
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CBufferFloat Skills;
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CBufferFloat *Result;
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vector<float> check;
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//---
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COpenCLMy *OpenCL;
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//+------------------------------------------------------------------+
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//| Expert initialization function |
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//+------------------------------------------------------------------+
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int OnInit()
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{
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//---
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ResetLastError();
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if(!LoadTotalBase())
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{
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PrintFormat("Error of load study data: %d", GetLastError());
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return INIT_FAILED;
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}
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//--- load models
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float temp;
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if(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
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!Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
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!Critic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
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!Critic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true) ||
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!Descriminator.Load(FileName + "Des.nnw", temp, temp, temp, dtStudied, true) ||
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!SkillProject.Load(FileName + "Skp.nnw", temp, temp, temp, dtStudied, true) ||
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!Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true) ||
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!TargetEncoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true))
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{
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CArrayObj *encoder = new CArrayObj();
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CArrayObj *actor = new CArrayObj();
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CArrayObj *critic = new CArrayObj();
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CArrayObj *descrim = new CArrayObj();
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CArrayObj *convolution = new CArrayObj();
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CArrayObj *skill_poject = new CArrayObj();
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if(!CreateDescriptions(encoder,actor, critic, convolution,descrim,skill_poject))
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{
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delete encoder;
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delete actor;
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delete critic;
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delete descrim;
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delete convolution;
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delete skill_poject;
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return INIT_FAILED;
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}
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if(!Encoder.Create(encoder) || !Actor.Create(actor) ||
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!Critic1.Create(critic) || !Critic2.Create(critic) ||
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!Descriminator.Create(descrim) || !SkillProject.Create(skill_poject) ||
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!Convolution.Create(convolution))
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{
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delete encoder;
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delete actor;
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delete critic;
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delete descrim;
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delete convolution;
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delete skill_poject;
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return INIT_FAILED;
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}
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if(!TargetEncoder.Create(encoder))
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{
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delete encoder;
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delete actor;
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delete critic;
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delete descrim;
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delete convolution;
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delete skill_poject;
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return INIT_FAILED;
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}
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delete encoder;
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delete actor;
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delete critic;
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delete descrim;
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delete convolution;
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delete skill_poject;
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//---
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TargetEncoder.WeightsUpdate(GetPointer(Encoder), 1.0f);
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}
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//---
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OpenCL = Actor.GetOpenCL();
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Encoder.SetOpenCL(OpenCL);
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Critic1.SetOpenCL(OpenCL);
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Critic2.SetOpenCL(OpenCL);
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TargetEncoder.SetOpenCL(OpenCL);
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Descriminator.SetOpenCL(OpenCL);
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SkillProject.SetOpenCL(OpenCL);
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Convolution.SetOpenCL(OpenCL);
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//---
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vector<float> ActorResult;
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Actor.getResults(ActorResult);
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if(ActorResult.Size() != NActions)
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{
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PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
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return INIT_FAILED;
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}
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//---
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Encoder.GetLayerOutput(0, Result);
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if(Result.Total() != (HistoryBars * BarDescr))
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{
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PrintFormat("Input size of State Encoder doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
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return INIT_FAILED;
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}
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//---
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vector<float> EncoderResults;
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Actor.GetLayerOutput(0,Result);
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Encoder.getResults(EncoderResults);
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if(Result.Total() != int(EncoderResults.Size()))
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{
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PrintFormat("Input size of Actor doesn't match Encoder outputs (%d <> %d)", Result.Total(), EncoderResults.Size());
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return INIT_FAILED;
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}
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//---
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Actor.GetLayerOutput(LatentLayer, Result);
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int latent_state = Result.Total();
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Critic1.GetLayerOutput(0, Result);
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if(Result.Total() != latent_state)
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{
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PrintFormat("Input size of Critic doesn't match latent state Actor (%d <> %d)", Result.Total(), latent_state);
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return INIT_FAILED;
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}
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//---
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Gradient.BufferInit(MathMax(AccountDescr,NSkills), 0);
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//---
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if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
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{
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PrintFormat("Error of create study event: %d", GetLastError());
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return INIT_FAILED;
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}
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//---
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return(INIT_SUCCEEDED);
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}
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//+------------------------------------------------------------------+
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//| Expert deinitialization function |
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//+------------------------------------------------------------------+
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void OnDeinit(const int reason)
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{
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//---
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TargetEncoder.WeightsUpdate(GetPointer(Encoder), Tau);
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Actor.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
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TargetEncoder.Save(FileName + "Enc.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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Critic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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Critic2.Save(FileName + "Crt2.nnw", Critic2.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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Convolution.Save(FileName + "CNN.nnw", 0, 0, 0, TimeCurrent(), true);
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Descriminator.Save(FileName + "Des.nnw", 0, 0, 0, TimeCurrent(), true);
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SkillProject.Save(FileName + "Skp.nnw", 0, 0, 0, TimeCurrent(), true);
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delete Result;
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}
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//+------------------------------------------------------------------+
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//| ChartEvent function |
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//+------------------------------------------------------------------+
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void OnChartEvent(const int id,
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const long &lparam,
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const double &dparam,
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const string &sparam)
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{
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//---
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if(id == 1001)
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Train();
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}
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//+------------------------------------------------------------------+
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//| Train function |
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//+------------------------------------------------------------------+
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void Train(void)
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{
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int total_tr = ArraySize(Buffer);
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uint ticks = GetTickCount();
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//---
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int total_states = Buffer[0].Total - 1;
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for(int i = 1; i < total_tr; i++)
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total_states += Buffer[i].Total - 1;
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vector<float> temp;
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Convolution.getResults(temp);
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matrix<float> state_embedding = matrix<float>::Zeros(total_states,temp.Size());
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int state = 0;
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for(int tr = 0; tr < total_tr; tr++)
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{
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for(int st = 0; st < Buffer[tr].Total - 1; st++)
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{
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State.AssignArray(Buffer[tr].States[st].state);
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float PrevBalance = Buffer[tr].States[MathMax(st,0)].account[0];
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float PrevEquity = Buffer[tr].States[MathMax(st,0)].account[1];
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State.Add((Buffer[tr].States[st].account[0] - PrevBalance) / PrevBalance);
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State.Add(Buffer[tr].States[st].account[1] / PrevBalance);
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State.Add((Buffer[tr].States[st].account[1] - PrevEquity) / PrevEquity);
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State.Add(Buffer[tr].States[st].account[2]);
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State.Add(Buffer[tr].States[st].account[3]);
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State.Add(Buffer[tr].States[st].account[4] / PrevBalance);
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State.Add(Buffer[tr].States[st].account[5] / PrevBalance);
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State.Add(Buffer[tr].States[st].account[6] / PrevBalance);
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double x = (double)Buffer[tr].States[st].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_W1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_D1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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//---
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State.AddArray(Buffer[tr].States[st + 1].state);
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State.Add((Buffer[tr].States[st + 1].account[0] - PrevBalance) / PrevBalance);
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State.Add(Buffer[tr].States[st + 1].account[1] / PrevBalance);
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State.Add((Buffer[tr].States[st + 1].account[1] - PrevEquity) / PrevEquity);
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State.Add(Buffer[tr].States[st + 1].account[2]);
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State.Add(Buffer[tr].States[st + 1].account[3]);
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State.Add(Buffer[tr].States[st + 1].account[4] / PrevBalance);
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State.Add(Buffer[tr].States[st + 1].account[5] / PrevBalance);
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State.Add(Buffer[tr].States[st + 1].account[6] / PrevBalance);
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x = (double)Buffer[tr].States[st + 1].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_W1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_D1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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if(!Convolution.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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ExpertRemove();
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return;
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}
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Convolution.getResults(temp);
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state_embedding.Row(temp,state);
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state++;
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if(GetTickCount() - ticks > 500)
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{
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string str = StringFormat("%-15s %6.2f%%", "Embedding ", state * 100.0 / (double)(total_states));
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Comment(str);
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ticks = GetTickCount();
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}
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}
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}
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if(state != total_states)
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{
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state_embedding.Reshape(state,state_embedding.Cols());
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total_states = state;
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}
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//---
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vector<float> reward = vector<float>::Zeros(NRewards);
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vector<float> rewards1 = reward, rewards2 = reward;
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int bar = (HistoryBars-1) * BarDescr;
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for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
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{
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int tr = (int)((MathRand() / 32767.0) * (total_tr - 1));
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int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
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if(i < 0)
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{
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iter--;
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continue;
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}
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//--- State
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State.AssignArray(Buffer[tr].States[i].state);
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float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
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float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
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Account.Clear();
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Account.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
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Account.Add(Buffer[tr].States[i].account[1] / PrevBalance);
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Account.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
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Account.Add(Buffer[tr].States[i].account[2]);
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Account.Add(Buffer[tr].States[i].account[3]);
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Account.Add(Buffer[tr].States[i].account[4] / PrevBalance);
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Account.Add(Buffer[tr].States[i].account[5] / PrevBalance);
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Account.Add(Buffer[tr].States[i].account[6] / PrevBalance);
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double x = (double)Buffer[tr].States[i].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_W1);
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_D1);
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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if(Account.GetIndex() >= 0)
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Account.BufferWrite();
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//--- Skills
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vector<float> skills = vector<float>::Zeros(NSkills);
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//for(int sk = 0; sk < NSkills; sk++)
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// skills[sk] = (float)((double)MathRand() / 32767.0);
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//skills.Activation(skills,AF_SOFTMAX);
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skills[int((double)MathRand() / 32768.0 * NSkills)] = 1;
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Skills.AssignArray(skills);
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if(Skills.GetIndex() >= 0 && !Skills.BufferWrite())
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//--- Encoder State
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if(!Encoder.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//--- Actor
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if(!Actor.feedForward(GetPointer(Encoder), -1, GetPointer(Skills)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//--- Next State
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TargetState.AssignArray(Buffer[tr].States[i + 1].state);
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double cl_op = Buffer[tr].States[i + 1].state[bar];
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double prof_1l = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_PROFIT) * cl_op /
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SymbolInfoDouble(_Symbol, SYMBOL_POINT);
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Actor.getResults(Result);
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vector<float> forecast = ForecastAccount(Buffer[tr].States[i].account,Result,prof_1l,Buffer[tr].States[i + 1].account[7]);
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TargetAccount.AssignArray(forecast);
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if(TargetAccount.GetIndex() >= 0 && !TargetAccount.BufferWrite())
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(!TargetEncoder.feedForward(GetPointer(TargetState), 1, false, GetPointer(TargetAccount)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//--- Descriminator
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if(!Descriminator.feedForward(GetPointer(Encoder),-1,GetPointer(TargetEncoder),-1) ||
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!SkillProject.feedForward(GetPointer(Skills),1,false,(CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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Descriminator.getResults(rewards1);
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SkillProject.getResults(rewards2);
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float norm1 = rewards1.Norm(VECTOR_NORM_P,2);
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float norm2 = rewards2.Norm(VECTOR_NORM_P,2);
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reward[0] = 0;//(rewards1 / norm1).Dot(rewards2 / norm2);
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Result.AssignArray(rewards2);
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if(!Descriminator.backProp(Result,GetPointer(TargetEncoder)) ||
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!Encoder.backPropGradient(GetPointer(Account),GetPointer(Gradient)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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Result.AssignArray(rewards1);
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if(!SkillProject.backProp(Result,(CNet *)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//---
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if(forecast[3] == 0.0f && forecast[4] == 0.f)
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reward[0] -= Buffer[tr].States[i + 1].state[bar + 6] / PrevBalance;
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//---
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State.AddArray(GetPointer(Account));
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State.AddArray(GetPointer(TargetState));
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State.AddArray(GetPointer(TargetAccount));
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if(!Convolution.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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Convolution.getResults(rewards1);
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reward[0] += KNNReward(7,rewards1,state_embedding);
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Result.AssignArray(reward);
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//---
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if(!Critic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor),-1) ||
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!Critic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor),-1))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(Critic1.getRecentAverageError() <= Critic2.getRecentAverageError())
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{
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if(!Critic1.backProp(Result, GetPointer(Actor)) ||
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!Actor.backPropGradient(GetPointer(Skills), GetPointer(Gradient), -1) ||
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!Critic2.backProp(Result, GetPointer(Actor)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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}
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else
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{
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if(!Critic2.backProp(Result, GetPointer(Actor)) ||
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!Actor.backPropGradient(GetPointer(Skills), GetPointer(Gradient), -1) ||
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!Critic1.backProp(Result, GetPointer(Actor)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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}
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//--- Update Target Nets
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TargetEncoder.WeightsUpdate(GetPointer(Encoder), Tau);
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//---
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if(GetTickCount() - ticks > 500)
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{
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string str = StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Critic1", iter * 100.0 / (double)(Iterations), Critic1.getRecentAverageError());
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str += StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Critic2", iter * 100.0 / (double)(Iterations), Critic2.getRecentAverageError());
|
|
str += StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Descriminator", iter * 100.0 / (double)(Iterations), Descriminator.getRecentAverageError());
|
|
Comment(str);
|
|
ticks = GetTickCount();
|
|
}
|
|
}
|
|
Comment("");
|
|
//---
|
|
PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Critic1", Critic1.getRecentAverageError());
|
|
PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Critic2", Critic2.getRecentAverageError());
|
|
ExpertRemove();
|
|
//---
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
float KNNReward(ulong k, vector<float> &embedding, matrix<float> &state_embedding)
|
|
{
|
|
if(embedding.Size() != state_embedding.Cols())
|
|
{
|
|
PrintFormat("%s -> %d Inconsistent embedding size", __FUNCTION__, __LINE__);
|
|
return (0);
|
|
}
|
|
//---
|
|
ulong size = embedding.Size();
|
|
ulong states = state_embedding.Rows();
|
|
matrix<float> temp = matrix<float>::Zeros(states,size);
|
|
//---
|
|
for(ulong i = 0; i < size; i++)
|
|
temp.Col(MathPow(state_embedding.Col(i) - embedding[i],2.0f),i);
|
|
vector<float> dist = MathSqrt(temp.Sum(1));
|
|
vector<float> min_dist = vector<float>::Zeros(k);
|
|
for(ulong i = 0; i < k; i++)
|
|
{
|
|
ulong pos = dist.ArgMin();
|
|
min_dist[i] = dist[pos];
|
|
dist[pos] = FLT_MAX;
|
|
}
|
|
//---
|
|
vector<float> ri = MathLog(min_dist + 1.0f);
|
|
//---
|
|
float result = ri.Mean();
|
|
//---
|
|
return (result);
|
|
}
|
|
//+------------------------------------------------------------------+
|