forked from mnbvc188199/Warrior_EA
298 lines
22 KiB
MQL5
298 lines
22 KiB
MQL5
//+------------------------------------------------------------------+
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//| Study.mq5 |
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//| Copyright DNG® |
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//| https://www.mql5.com/en/users/dng |
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//+------------------------------------------------------------------+
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#property copyright "Copyright DNG®"
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#property link "https://www.mql5.com/en/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 = 1e4;
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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STrajectory Buffer[];
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CNet Autobot;
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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 *Result;
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CBufferFloat Ones;
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CBufferFloat Gradient;
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vector<float> STE;
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vector<float> STE_Noise;
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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(!Autobot.Load(FileName + "Traj.nnw", temp, temp, temp, dtStudied, true))
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{
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Print("Init new models");
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CArrayObj *autobot = new CArrayObj();
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if(!CreateTrajNetDescriptions(autobot))
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{
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delete autobot;
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return INIT_FAILED;
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}
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if(!Autobot.Create(autobot))
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{
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delete autobot;
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return INIT_FAILED;
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}
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delete autobot;
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//---
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}
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//---
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Autobot.getResults(Result);
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if(Result.Total() != PrecoderBars * 3)
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{
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PrintFormat("The scope of the Autobot does not match the precoder bars (%d <> %d)", PrecoderBars * 3, Result.Total());
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return INIT_FAILED;
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}
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//---
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Autobot.GetLayerOutput(0, Result);
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if(Result.Total() != (HistoryBars * BarDescr))
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{
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PrintFormat("Input size of Autobot 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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OpenCL = Autobot.GetOpenCL();
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if(!Ones.BufferInit(EmbeddingSize, 1) ||
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!Gradient.BufferInit(EmbeddingSize, 0) ||
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!Ones.BufferCreate(OpenCL) ||
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!Gradient.BufferCreate(OpenCL))
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{
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PrintFormat("Error of create buffers: %d", GetLastError());
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return INIT_FAILED;
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}
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State.BufferInit(HistoryBars * BarDescr, 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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if(!(reason == REASON_INITFAILED || reason == REASON_RECOMPILE))
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Autobot.Save(FileName + "Traj.nnw", 0, 0, 0, TimeCurrent(), true);
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delete Result;
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delete OpenCL;
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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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//---
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vector<float> probability = GetProbTrajectories(Buffer, 0.9);
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//---
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vector<float> result, target, inp;
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matrix<float> targets;
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matrix<float> delta;
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STE = vector<float>::Zeros(PrecoderBars * 3);
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int std_count = 0;
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int batch = GPTBars + 50;
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bool Stop = false;
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uint ticks = GetTickCount();
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ulong size = HistoryBars * BarDescr;
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//---
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for(int iter = 0; (iter < Iterations && !IsStopped() && !Stop); iter ++)
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{
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int tr = SampleTrajectory(probability);
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int state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 3 - PrecoderBars - batch));
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if(state < 0)
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{
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iter--;
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continue;
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}
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int end = MathMin(state + batch, Buffer[tr].Total - PrecoderBars);
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Autobot.Clear();
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delta = matrix<float>::Zeros(end - state - 1, Buffer[tr].States[state].state.Size());
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for(int i = state; i < end; i++)
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{
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inp.Assign(Buffer[tr].States[i].state);
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State.AssignArray(inp);
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int row = i - state;
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if(i < (end - 1))
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delta.Row(inp, row);
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if(row > 0)
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delta.Row(delta.Row(row - 1) - inp, row - 1);
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//---
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if(!Autobot.feedForward((CBufferFloat*)GetPointer(State), 1, false, (CBufferFloat*)GetPointer(Ones)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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//---
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targets = matrix<float>::Zeros(PrecoderBars, 3);
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for(int t = 0; t < PrecoderBars; t++)
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{
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target.Assign(Buffer[tr].States[i + 1 + t].state);
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if(size > BarDescr)
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{
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matrix<float> temp(1, size);
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temp.Row(target, 0);
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temp.Reshape(size / BarDescr, BarDescr);
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temp.Resize(size / BarDescr, 3);
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target = temp.Row(temp.Rows() - 1);
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}
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targets.Row(target, t);
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}
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targets.Reshape(1, targets.Rows()*targets.Cols());
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target = targets.Row(0);
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Autobot.getResults(result);
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vector<float> error = target - result;
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std_count = MathMin(std_count, 999);
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STE = MathSqrt((MathPow(STE, 2) * std_count + MathPow(error, 2)) / (std_count + 1));
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std_count++;
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vector<float> check = MathAbs(error) - STE * STE_Multiplier;
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if(check.Max() > 0)
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{
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//---
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Result.AssignArray(target);
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if(!Autobot.backProp(Result, GetPointer(Ones), GetPointer(Gradient)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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}
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//---
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if(GetTickCount() - ticks > 500)
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{
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double percent = (double(i - state) / (2 * (end - state)) + iter) * 100.0 / (Iterations);
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string str = StringFormat("%-20s %6.2f%% -> Error %15.8f\n", "Autobot", percent, Autobot.getRecentAverageError());
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Comment(str);
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ticks = GetTickCount();
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}
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}
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//--- With noise
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vector<float> std_delta = delta.Std(0) * STD_Delta_Multiplier;
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vector<float> mean_delta = delta.Mean(0);
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ulong inp_total = std_delta.Size();
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vector<float> noise = vector<float>::Zeros(inp_total);
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double ar_noise[];
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//---
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tr = SampleTrajectory(probability);
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state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 3 - PrecoderBars - batch));
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if(state < 0)
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{
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iter--;
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continue;
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}
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end = MathMin(state + batch, Buffer[tr].Total - PrecoderBars);
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Autobot.Clear();
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for(int i = state; i < end; i++)
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{
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if(!Math::MathRandomNormal(0, 1, (int)inp_total, ar_noise))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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noise.Assign(ar_noise);
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noise = mean_delta + std_delta * noise;
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inp.Assign(Buffer[tr].States[i].state);
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inp = inp + noise;
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State.AssignArray(inp);
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//---
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if(!Autobot.feedForward((CBufferFloat*)GetPointer(State), 1, false, (CBufferFloat*)GetPointer(Ones)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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//---
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targets = matrix<float>::Zeros(PrecoderBars, 3);
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for(int t = 0; t < PrecoderBars; t++)
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{
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target.Assign(Buffer[tr].States[i + 1 + t].state);
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if(size > BarDescr)
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{
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matrix<float> temp(1, size);
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temp.Row(target, 0);
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temp.Reshape(size / BarDescr, BarDescr);
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temp.Resize(size / BarDescr, 3);
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target = temp.Row(temp.Rows() - 1);
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}
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targets.Row(target, t);
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}
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targets.Reshape(1, targets.Rows()*targets.Cols());
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target = targets.Row(0);
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Autobot.getResults(result);
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vector<float> error = target - result;
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std_count = MathMin(std_count, 999);
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STE = MathSqrt((MathPow(STE, 2) * std_count + MathPow(error, 2)) / (std_count + 1));
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std_count++;
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vector<float> check = MathAbs(error) - STE * STE_Multiplier;
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if(check.Max() > 0)
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{
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//---
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Result.AssignArray(target);
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if(!Autobot.backProp(Result, GetPointer(Ones), GetPointer(Gradient)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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}
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//---
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if(GetTickCount() - ticks > 500)
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{
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double percent = (double(i - state) / (2 * (end - state)) + iter + 0.5) * 100.0 / (Iterations);
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string str = StringFormat("%-20s %6.2f%% -> Error %15.8f\n", "Autobot", percent, Autobot.getRecentAverageError());
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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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Comment("");
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//---
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PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Autobot", Autobot.getRecentAverageError());
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ExpertRemove();
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//---
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}
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//+------------------------------------------------------------------+
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