154 satır
12 KiB
MQL5
154 satır
12 KiB
MQL5
//+------------------------------------------------------------------+
|
|
//| Study.mq5 |
|
|
//| Copyright DNG® |
|
|
//| https://www.mql5.com/ru/users/dng |
|
|
//+------------------------------------------------------------------+
|
|
#property copyright "Copyright DNG®"
|
|
#property link "https://www.mql5.com/ru/users/dng"
|
|
#property version "1.00"
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#define Study
|
|
#include "Trajectory.mqh"
|
|
//+------------------------------------------------------------------+
|
|
//| Input parameters |
|
|
//+------------------------------------------------------------------+
|
|
input int Iterations = 1e7;
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
CNet Worker;
|
|
CNet Descrimitator;
|
|
//---
|
|
float dError;
|
|
datetime dtStudied;
|
|
//---
|
|
CBufferFloat *Data;
|
|
CBufferFloat *Result;
|
|
STrajectory Buffer[];
|
|
//+------------------------------------------------------------------+
|
|
//| Expert initialization function |
|
|
//+------------------------------------------------------------------+
|
|
int OnInit()
|
|
{
|
|
//--- load models
|
|
float temp;
|
|
if(!Worker.Load(FileName + "Work.nnw", temp, temp, temp, dtStudied, true) ||
|
|
!Descrimitator.Load(FileName + "Descr.nnw", temp, temp, temp, dtStudied, true))
|
|
{
|
|
CArrayObj *worker = new CArrayObj();
|
|
CArrayObj *descriminator = new CArrayObj();
|
|
if(!CreateWorkerDescriptions(worker, descriminator))
|
|
{
|
|
delete worker;
|
|
delete descriminator;
|
|
return INIT_FAILED;
|
|
}
|
|
if(!Worker.Create(worker) ||
|
|
!Descrimitator.Create(descriminator))
|
|
{
|
|
delete worker;
|
|
delete descriminator;
|
|
return INIT_FAILED;
|
|
}
|
|
delete worker;
|
|
delete descriminator;
|
|
//---
|
|
}
|
|
//---
|
|
Descrimitator.SetOpenCL(Worker.GetOpenCL());
|
|
//---
|
|
Worker.getResults(Data);
|
|
if(Data.Total() != NActions)
|
|
{
|
|
PrintFormat("The scope of the Worker does not match the actions count (%d <> %d)", NActions, Data.Total());
|
|
return INIT_FAILED;
|
|
}
|
|
//---
|
|
Descrimitator.GetLayerOutput(0, Data);
|
|
if(Data.Total() != NActions)
|
|
{
|
|
PrintFormat("Input size of Descriminator doesn't match Worker output (%d <> %d)", Data.Total(), NActions);
|
|
return INIT_FAILED;
|
|
}
|
|
Data.Clear();
|
|
Worker.TrainMode(true);
|
|
//---
|
|
if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
|
|
{
|
|
PrintFormat("Error of create study event: %d", GetLastError());
|
|
return INIT_FAILED;
|
|
}
|
|
//---
|
|
return(INIT_SUCCEEDED);
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Expert deinitialization function |
|
|
//+------------------------------------------------------------------+
|
|
void OnDeinit(const int reason)
|
|
{
|
|
//---
|
|
Worker.Save(FileName + "Work.nnw", 0, 0, 0, TimeCurrent(), true);
|
|
Descrimitator.Save(FileName + "Descr.nnw", 0, 0, 0, TimeCurrent(), true);
|
|
delete Data;
|
|
delete Result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| ChartEvent function |
|
|
//+------------------------------------------------------------------+
|
|
void OnChartEvent(const int id,
|
|
const long &lparam,
|
|
const double &dparam,
|
|
const string &sparam)
|
|
{
|
|
//---
|
|
if(id == 1001)
|
|
Train();
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Train function |
|
|
//+------------------------------------------------------------------+
|
|
void Train(void)
|
|
{
|
|
uint ticks = GetTickCount();
|
|
//---
|
|
bool StopFlag = false;
|
|
for(int iter = 0; (iter < Iterations && !IsStopped() && !StopFlag); iter ++)
|
|
{
|
|
Data.BufferInit(WorkerInput, 0);
|
|
int pos = int(MathRand() / 32767.0 * (WorkerInput - 1));
|
|
Data.Update(pos, 1.0f);
|
|
//--- Study
|
|
if(!Worker.feedForward(Data,1,false,(CBufferFloat*)NULL) ||
|
|
!Descrimitator.feedForward(GetPointer(Worker),-1,(CBufferFloat*)NULL))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
StopFlag = true;
|
|
break;
|
|
}
|
|
//vector<float> temp;
|
|
//Worker.getResults(temp);
|
|
//Descrimitator.getResults(Result);
|
|
if(!Descrimitator.backProp(Data,(CBufferFloat *)NULL, (CBufferFloat *)NULL) ||
|
|
!Worker.backPropGradient((CBufferFloat *)NULL, (CBufferFloat *)NULL))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
StopFlag = true;
|
|
break;
|
|
}
|
|
//---
|
|
if(GetTickCount() - ticks > 500)
|
|
{
|
|
string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Desciminator", iter * 100.0 / (double)(Iterations), Descrimitator.getRecentAverageError());
|
|
Comment(str);
|
|
ticks = GetTickCount();
|
|
}
|
|
}
|
|
Comment("");
|
|
//---
|
|
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Descriminator", Descrimitator.getRecentAverageError());
|
|
ExpertRemove();
|
|
//---
|
|
}
|
|
//+------------------------------------------------------------------+
|