| //+------------------------------------------------------------------+
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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 = 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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| 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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| //---
|
| 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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| 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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| if(!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)))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| Stop = true;
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| break;
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| }
|
| //---
|
| targets = matrix<float>::Zeros(PrecoderBars, 3);
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| for(int t = 0; t < PrecoderBars; t++)
|
| {
|
| target.Assign(Buffer[tr].States[i + 1 + t].state);
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| if(size > BarDescr)
|
| {
|
| 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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| Result.AssignArray(target);
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| if(!Autobot.backProp(Result, GetPointer(Ones), GetPointer(Gradient)))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| Stop = true;
|
| break;
|
| }
|
| }
|
| //---
|
| if(GetTickCount() - ticks > 500)
|
| {
|
| 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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| }
|
| }
|
| }
|
| Comment("");
|
| //---
|
| PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Autobot", Autobot.getRecentAverageError());
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| ExpertRemove();
|
| //---
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
