2026-08-13 23:48:47 +03:00 | | | //+------------------------------------------------------------------+
|
| | | //| Study.mq5 |
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| | | //| Copyright DNG® |
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| | | //| https://www.mql5.com/ru/users/dng |
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2026-06-05 22:00:59 +03:00 | | | //+------------------------------------------------------------------+
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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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2026-08-13 23:48:47 +03:00 | | | //| |
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2026-06-05 22:00:59 +03:00 | | | //+------------------------------------------------------------------+
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| | | #define Study
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| | | #include "Trajectory.mqh"
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| | | //+------------------------------------------------------------------+
|
| | | //| Input parameters |
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| | | //+------------------------------------------------------------------+
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| | | input int Iterations = 10000;
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| | | input float Tau = 0.001f;
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| | | //+------------------------------------------------------------------+
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2026-08-13 23:48:47 +03:00 | | | //| |
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2026-06-05 22:00:59 +03:00 | | | //+------------------------------------------------------------------+
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| | | STrajectory Buffer[];
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| | | CNet Actor;
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| | | CNet Critic1;
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| | | CNet Critic2;
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| | | CNet TargetCritic1;
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| | | CNet TargetCritic2;
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| | | CNet Convolution;
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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 TargetState;
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| | | CBufferFloat Account;
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| | | CBufferFloat Actions;
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| | | CBufferFloat Gradient;
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| | | CBufferFloat *Result;
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| | | vector<float> check;
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| | | int StartTargetIter;
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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(!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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| | | !Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true) ||
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| | | !TargetCritic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
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| | | !TargetCritic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true))
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| | | {
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| | | Print("Init new models");
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| | | CArrayObj *actor = new CArrayObj();
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| | | CArrayObj *critic = new CArrayObj();
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| | | CArrayObj *convolution = new CArrayObj();
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| | | if(!CreateDescriptions(actor, critic, convolution))
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| | | {
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| | | delete actor;
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| | | delete critic;
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| | | delete convolution;
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| | | return INIT_FAILED;
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| | | }
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| | | if(!Actor.Create(actor) || !Critic1.Create(critic) || !Critic2.Create(critic) ||
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| | | !Convolution.Create(convolution))
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| | | {
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| | | delete actor;
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| | | delete critic;
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| | | delete convolution;
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| | | return INIT_FAILED;
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| | | }
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| | | if(!TargetCritic1.Create(critic) || !TargetCritic2.Create(critic))
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| | | {
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| | | delete actor;
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| | | delete critic;
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| | | delete convolution;
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| | | return INIT_FAILED;
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| | | }
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| | | delete actor;
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| | | delete critic;
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| | | delete convolution;
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| | | //---
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| | | TargetCritic1.WeightsUpdate(GetPointer(Critic1), 1.0f);
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| | | TargetCritic2.WeightsUpdate(GetPointer(Critic2), 1.0f);
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| | | StartTargetIter = StartTargetIteration;
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| | | }
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| | | else
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| | | StartTargetIter = 0;
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| | | //---
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| | | OpenCL = Actor.GetOpenCL();
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| | | Critic1.SetOpenCL(OpenCL);
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| | | Critic2.SetOpenCL(OpenCL);
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| | | TargetCritic1.SetOpenCL(OpenCL);
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| | | TargetCritic2.SetOpenCL(OpenCL);
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| | | Convolution.SetOpenCL(OpenCL);
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| | | //---
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| | | Actor.getResults(Result);
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| | | if(Result.Total() != 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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| | | Actor.GetLayerOutput(0, Result);
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| | | if(Result.Total() != (HistoryBars * BarDescr))
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| | | {
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| | | PrintFormat("Input size of Actor 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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| | | 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(AccountDescr, 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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| | | TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
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| | | TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
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| | | Actor.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
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| | | TargetCritic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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| | | TargetCritic2.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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| | | 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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| | | if(id == 1001)
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| | | Train();
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| | | }
|
| | | //+------------------------------------------------------------------+
|
| | | //| Train function |
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| | | //+------------------------------------------------------------------+
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| | | void Train(void)
|
| | | {
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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;
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| | | for(int i = 1; i < total_tr; i++)
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| | | total_states += Buffer[i].Total;
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| | | vector<float> temp, next;
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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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| | | matrix<float> rewards = matrix<float>::Zeros(total_states,NRewards);
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| | | int state = 0;
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| | | for(int tr = 0; tr < total_tr; tr++)
|
| | | {
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| | | for(int st = 0; st < Buffer[tr].Total; 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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| | | if(!Convolution.feedForward(GetPointer(State),1,false,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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| | | Convolution.getResults(temp);
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| | | state_embedding.Row(temp,state);
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| | | temp.Assign(Buffer[tr].States[st].rewards);
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| | | next.Assign(Buffer[tr].States[st + 1].rewards);
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| | | rewards.Row(temp - next * DiscFactor,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);
|
| | | ticks = GetTickCount();
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| | | }
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| | | }
|
| | | }
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| | | if(state != total_states)
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| | | {
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| | | rewards.Resize(state,NRewards);
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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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| | | //---
|
| | | vector<float> rewards1, rewards2;
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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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| | | vector<float> reward, target_reward = vector<float>::Zeros(NRewards);
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| | | reward.Assign(Buffer[tr].States[i].rewards);
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| | | //--- Target
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| | | TargetState.AssignArray(Buffer[tr].States[i + 1].state);
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| | | if(iter >= StartTargetIter)
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| | | {
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| | | float PrevBalance = Buffer[tr].States[i].account[0];
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| | | float PrevEquity = Buffer[tr].States[i].account[1];
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| | | Account.Clear();
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| | | Account.Add((Buffer[tr].States[i + 1].account[0] - PrevBalance) / PrevBalance);
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| | | Account.Add(Buffer[tr].States[i + 1].account[1] / PrevBalance);
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| | | Account.Add((Buffer[tr].States[i + 1].account[1] - PrevEquity) / PrevEquity);
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| | | Account.Add(Buffer[tr].States[i + 1].account[2]);
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| | | Account.Add(Buffer[tr].States[i + 1].account[3]);
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| | | Account.Add(Buffer[tr].States[i + 1].account[4] / PrevBalance);
|
| | | Account.Add(Buffer[tr].States[i + 1].account[5] / PrevBalance);
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| | | Account.Add(Buffer[tr].States[i + 1].account[6] / PrevBalance);
|
| | | double x = (double)Buffer[tr].States[i + 1].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
|
| | | Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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| | | x = (double)Buffer[tr].States[i + 1].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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| | | Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
|
| | | x = (double)Buffer[tr].States[i + 1].account[7] / (double)PeriodSeconds(PERIOD_W1);
|
| | | Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| | | x = (double)Buffer[tr].States[i + 1].account[7] / (double)PeriodSeconds(PERIOD_D1);
|
| | | Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| | | //---
|
| | | if(Account.GetIndex() >= 0)
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| | | Account.BufferWrite();
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| | | if(!Actor.feedForward(GetPointer(TargetState), 1, false, GetPointer(Account)))
|
| | | {
|
| | | PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| | | break;
|
| | | }
|
| | | //---
|
| | | if(!TargetCritic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)) ||
|
| | | !TargetCritic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)))
|
| | | {
|
| | | PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| | | break;
|
| | | }
|
| | | TargetCritic1.getResults(rewards1);
|
| | | TargetCritic2.getResults(rewards2);
|
| | | if(rewards1.Sum() <= rewards2.Sum())
|
| | | target_reward = rewards1;
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| | | else
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| | | target_reward = rewards2;
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| | | for(ulong r = 0; r < target_reward.Size(); r++)
|
| | | target_reward -= Buffer[tr].States[i + 1].rewards[r];
|
| | | target_reward *= DiscFactor;
|
| | | }
|
| | | //--- Q-function study
|
| | | State.AssignArray(Buffer[tr].States[i].state);
|
| | | float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
|
| | | float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
|
| | | Account.Clear();
|
| | | Account.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
|
| | | Account.Add(Buffer[tr].States[i].account[1] / PrevBalance);
|
| | | Account.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
|
| | | Account.Add(Buffer[tr].States[i].account[2]);
|
| | | Account.Add(Buffer[tr].States[i].account[3]);
|
| | | Account.Add(Buffer[tr].States[i].account[4] / PrevBalance);
|
| | | Account.Add(Buffer[tr].States[i].account[5] / PrevBalance);
|
| | | Account.Add(Buffer[tr].States[i].account[6] / PrevBalance);
|
| | | double x = (double)Buffer[tr].States[i].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
|
| | | Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| | | x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_MN1);
|
| | | Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
|
| | | x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_W1);
|
| | | Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| | | x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_D1);
|
| | | Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| | | if(Account.GetIndex() >= 0)
|
| | | Account.BufferWrite();
|
| | | //---
|
| | | if(!Actor.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
|
| | | {
|
| | | PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| | | break;
|
| | | }
|
| | | //---
|
| | | Actions.AssignArray(Buffer[tr].States[i].action);
|
| | | if(Actions.GetIndex() >= 0)
|
| | | Actions.BufferWrite();
|
| | | //---
|
| | | if(!Critic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actions)) ||
|
| | | !Critic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actions)))
|
| | | {
|
| | | PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| | | break;
|
| | | }
|
| | | //---
|
| | | Critic1.getResults(rewards1);
|
| | | Result.AssignArray(CAGrad(reward + target_reward - rewards1) + rewards1);
|
| | | if(!Critic1.backProp(Result, GetPointer(Actions), GetPointer(Gradient)) ||
|
| | | !Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient), LatentLayer))
|
| | | {
|
| | | PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| | | break;
|
| | | }
|
| | | Critic2.getResults(rewards2);
|
| | | Result.AssignArray(CAGrad(reward + target_reward - rewards2) + rewards2);
|
| | | if(!Critic2.backProp(Result, GetPointer(Actions), GetPointer(Gradient)) ||
|
| | | !Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient), LatentLayer))
|
| | | {
|
| | | PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| | | break;
|
| | | }
|
| | | //--- Policy study
|
| | | CNet *critic = NULL;
|
| | | if(Critic1.getRecentAverageError() <= Critic2.getRecentAverageError())
|
| | | critic = GetPointer(Critic1);
|
| | | else
|
| | | critic = GetPointer(Critic2);
|
| | | //---
|
| | | Actor.getResults(rewards1);
|
| | | double cl_op = Buffer[tr].States[i + 1].state[bar];
|
| | | double prof_1l = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_PROFIT) * cl_op /
|
| | | SymbolInfoDouble(_Symbol, SYMBOL_POINT);
|
| | | vector<float> forecast = ForecastAccount(Buffer[tr].States[i].account,rewards1,prof_1l,Buffer[tr].States[i + 1].account[7]);
|
| | | TargetState.AddArray(forecast);
|
| | | if(!critic.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)) ||
|
| | | !Convolution.feedForward(GetPointer(TargetState)))
|
| | | {
|
| | | PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| | | break;
|
| | | }
|
| | | next.Assign(Buffer[tr].States[i + 1].rewards);
|
| | | target_reward+=next;
|
| | | Convolution.getResults(rewards1);
|
| | | target_reward=KNNReward(7,rewards1,state_embedding,rewards) + next * DiscFactor;
|
| | | if(forecast[3] == 0.0f && forecast[4] == 0.0f)
|
| | | target_reward[2] -= (Buffer[tr].States[i + 1].state[bar + 6] / PrevBalance) / DiscFactor;
|
| | | critic.getResults(reward);
|
| | | reward += CAGrad(target_reward - reward);
|
| | | //---
|
| | | Result.AssignArray(reward);
|
| | | critic.TrainMode(false);
|
| | | if(!critic.backProp(Result, GetPointer(Actor)) ||
|
| | | !Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient)))
|
| | | {
|
| | | PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| | | critic.TrainMode(true);
|
| | | break;
|
| | | }
|
| | | critic.TrainMode(true);
|
| | | //--- Update Target Nets
|
| | | TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
|
| | | TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
|
| | | //---
|
| | | if(GetTickCount() - ticks > 500)
|
| | | {
|
| | | string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Critic1", iter * 100.0 / (double)(Iterations), Critic1.getRecentAverageError());
|
| | | str += StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Critic2", iter * 100.0 / (double)(Iterations), Critic2.getRecentAverageError());
|
| | | Comment(str);
|
| | | ticks = GetTickCount();
|
| | | }
|
| | | }
|
| | | Comment("");
|
| | | //---
|
| | | PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Critic1", Critic1.getRecentAverageError());
|
| | | PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Critic2", Critic2.getRecentAverageError());
|
| | | ExpertRemove();
|
| | | //---
|
| | | }
|
| | | //+------------------------------------------------------------------+
|
2026-08-13 23:48:47 +03:00 | | | //| |
|
| | | //+------------------------------------------------------------------+
|
| | | //| | Implements KNNReward. |
|
2026-06-05 22:00:59 +03:00 | | | //+------------------------------------------------------------------+
|
| | | vector<float> KNNReward(ulong k, vector<float> &embedding, matrix<float> &state_embedding, matrix<float> &rewards)
|
| | | {
|
| | | if(embedding.Size() != state_embedding.Cols())
|
| | | {
|
| | | PrintFormat("%s -> %d Inconsistent embedding size", __FUNCTION__, __LINE__);
|
| | | return vector<float>::Zeros(0);
|
| | | }
|
| | | //---
|
| | | ulong size = embedding.Size();
|
| | | ulong states = state_embedding.Rows();
|
| | | k = MathMin(k,states);
|
| | | ulong rew_size = rewards.Cols();
|
| | | 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> distance = MathSqrt(temp.Sum(1));
|
| | | matrix<float> k_rewards = matrix<float>::Zeros(k,rew_size);
|
| | | matrix<float> k_embeding = matrix<float>::Zeros(k + 1,size);
|
| | | for(ulong i = 0; i < k; i++)
|
| | | {
|
| | | ulong pos = distance.ArgMin();
|
| | | k_rewards.Row(rewards.Row(pos) * (1 - MathLog(distance[pos] + 1)),i);
|
| | | k_embeding.Row(state_embedding.Row(pos),i);
|
| | | distance[pos] = FLT_MAX;
|
| | | }
|
| | | k_embeding.Row(embedding,k);
|
| | | //---
|
| | | matrix<float> U,V;
|
| | | vector<float> S;
|
| | | k_embeding.SVD(U,V,S);
|
| | | //---
|
| | | vector<float> result = k_rewards.Mean(0);
|
| | | result[rew_size - 1] = S.Sum() / (MathSqrt(MathPow(k_embeding,2.0f).Sum() * MathMax(k + 1,size)));
|
| | | //---
|
| | | return (result);
|
| | | }
|
| | | //+------------------------------------------------------------------+
|
2026-08-13 23:48:47 +03:00 | | | //| |
|
| | | //+------------------------------------------------------------------+
|
| | | //| | Creates and manages object lifecycle for CAGrad. |
|
2026-06-05 22:00:59 +03:00 | | | //+------------------------------------------------------------------+
|
| | | vector<float> CAGrad(vector<float> &grad)
|
| | | {
|
| | | matrix<float> GG = grad.Outer(grad);
|
| | | GG.ReplaceNan(0);
|
| | | if(MathAbs(GG).Sum() == 0)
|
| | | return grad;
|
| | | float scale = MathSqrt(GG.Diag() + 1.0e-4f).Mean();
|
| | | GG = GG / MathPow(scale,2);
|
| | | vector<float> Gg = GG.Mean(1);
|
| | | float gg = Gg.Mean();
|
| | | vector<float> w = vector<float>::Zeros(grad.Size());
|
| | | float c = MathSqrt(gg + 1.0e-4f) * fCAGrad_C;
|
| | | vector<float> w_best = w;
|
| | | float obj_best = FLT_MAX;
|
| | | vector<float> moment = vector<float>::Zeros(w.Size());
|
| | | for(int i = 0; i < iCAGrad_Iters; i++)
|
| | | {
|
| | | vector<float> ww;
|
| | | w.Activation(ww,AF_SOFTMAX);
|
| | | float obj = ww.Dot(Gg) + c * MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f);
|
| | | if(MathAbs(obj) < obj_best)
|
| | | {
|
| | | obj_best = MathAbs(obj);
|
| | | w_best = w;
|
| | | }
|
| | | if(i < (iCAGrad_Iters - 1))
|
| | | {
|
| | | float loss = -obj;
|
| | | vector<float> derev = Gg + GG.MatMul(ww) * c / (MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f) * 2) + ww.MatMul(GG) * c / (MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f) * 2);
|
| | | vector<float> delta = derev * loss;
|
| | | ulong size = delta.Size();
|
| | | matrix<float> ident = matrix<float>::Identity(size, size);
|
| | | vector<float> ones = vector<float>::Ones(size);
|
| | | matrix<float> sm_der = ones.Outer(ww);
|
| | | sm_der = sm_der.Transpose() * (ident - sm_der);
|
| | | delta = sm_der.MatMul(delta);
|
| | | if(delta.Ptp() != 0)
|
| | | delta = delta / delta.Ptp();
|
| | | moment = delta * 0.8f + moment * 0.5f;
|
| | | w += moment;
|
| | | if(w.Ptp() != 0)
|
| | | w = w / w.Ptp();
|
| | | }
|
| | | }
|
| | | w_best.Activation(w,AF_SOFTMAX);
|
| | | float gw_norm = MathSqrt(w.MatMul(GG).Dot(w) + 1.0e-4f);
|
| | | float lmbda = c / (gw_norm + 1.0e-4f);
|
| | | vector<float> result = ((w * lmbda + 1.0f / (float)grad.Size()) * grad) / (1 + MathPow(fCAGrad_C,2));
|
| | | //---
|
| | | return result;
|
| | | }
|
| | | //+------------------------------------------------------------------+
|
2026-08-13 23:48:47 +03:00 | | |
|
