542 lines
45 KiB
MQL5
542 lines
45 KiB
MQL5
//+------------------------------------------------------------------+
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//| ICM.mqh |
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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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//| Includes |
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//+------------------------------------------------------------------+
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#include "..\NeuroNet_DNG\NeuroNet.mqh"
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//---
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#define defICM 0x7793 ///<Neuron Net \details Identified class #CICM
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#define TargetNetFile "ICM.upd"
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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class CReplayState : public CObject
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{
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protected:
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CBufferFloat cState;
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int iAction;
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double dReaward;
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public:
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CReplayState(CBufferFloat *state, int action, double reward);
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~CReplayState(void) {};
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bool GetCurrent(CBufferFloat *&state, int &action);
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bool GetNext(CBufferFloat *&state, double &reward);
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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CReplayState::CReplayState(CBufferFloat *state, int action, double reward)
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{
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cState.AssignArray(state);
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iAction = action;
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dReaward = reward;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CReplayState::GetCurrent(CBufferFloat *&state, int &action)
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{
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action = iAction;
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double reward;
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return GetNext(state, reward);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CReplayState::GetNext(CBufferFloat *&state, double &reward)
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{
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reward = dReaward;
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if(!state)
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{
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state = new CBufferFloat();
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if(!state)
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return false;
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}
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return state.AssignArray(GetPointer(cState));
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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class CReplayBuffer : protected CArrayObj
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{
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protected:
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uint iMaxSize;
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public:
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CReplayBuffer(void) : iMaxSize(500) {};
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~CReplayBuffer(void) {};
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//---
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void SetMaxSize(uint size) { iMaxSize = size; }
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bool AddState(CBufferFloat *state, int action, double reward);
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bool GetRendomState(CBufferFloat *&state1, int &action, double &reward, CBufferFloat*& state2);
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bool GetState(int position, CBufferFloat *&state1, int &action, double &reward, CBufferFloat*& state2);
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int Total(void) { return CArrayObj::Total(); }
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CReplayBuffer::AddState(CBufferFloat *state, int action, double reward)
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{
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if(!state)
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return false;
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//---
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if(!Add(new CReplayState(state, action, reward)))
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return false;
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while(Total() > (int)iMaxSize)
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Delete(0);
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CReplayBuffer::GetRendomState(CBufferFloat *&state1, int &action, double &reward, CBufferFloat *&state2)
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{
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int position = (int)(MathRand() * MathRand() / pow(32767.0, 2.0) * (Total() - 1));
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return GetState(position, state1, action, reward, state2);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CReplayBuffer::GetState(int position, CBufferFloat *&state1, int &action, double &reward, CBufferFloat *&state2)
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{
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if(position < 0 || position >= (Total() - 1))
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return false;
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CReplayState* element = m_data[position];
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if(!element || !element.GetCurrent(state1, action))
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return false;
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element = m_data[position + 1];
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if(!element.GetNext(state2, reward))
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return false;
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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class CICM : protected CNet
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{
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protected:
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uint iMinBufferSize;
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uint iStateEmbedingLayer;
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double dPrevBalance;
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//---
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CNet cTargetNet;
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CReplayBuffer cReplay;
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CNet cInverseNet;
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CNet cForwardNet;
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virtual bool AddInputData(CArrayFloat *inputVals);
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public:
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/** Constructor */
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CICM(void);
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CICM(CArrayObj *Description, CArrayObj *Forward, CArrayObj *Inverse);
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bool Create(CArrayObj *Description, CArrayObj *Forward, CArrayObj *Inverse);
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/** Destructor */~CICM(void);
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int feedForward(CArrayFloat *inputVals, int window = 1, bool tem = true, bool sample = true); ///< Feed Forward method.@param[in] prevLayer Pointer to previos layer. @param[in] window Window of input data. @param[in] tem Use Time Embedding.
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bool backProp(int batch, float discount = 0.999f);
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int getAction(void); ///< Method to get results of feed forward process.@param[out] resultVals Array of result values
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int getSample(void);
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float getRecentAverageError() { return recentAverageError; } ///< Method to check quality of study. @return Average error
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bool Save(string file_name, bool common = true);
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bool Save(string dqn, string forward, string invers, bool common = true);
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///< Save method. @param[in] file_name File name to save @param[in] error Average error @param[in] undefine Undefined percent @param[in] Foecast percent @param[in] time Last study time @param[in] common Common flag
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virtual bool Load(string file_name, bool common = true);
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bool Load(string dqn, string forward, string invers, uint state_layer, bool common = true);
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///< Load method. @param[in] file_name File name to save @param[out] error Average error @param[out] undefine Undefined percent @param[out] Foecast percent @param[out] time Last study time @param[in] common Common flag
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//---
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virtual int Type(void) const { return defICM; }///< Identificator of class.@return Type of class
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virtual bool TrainMode(bool flag) { return (CNet::TrainMode(flag) && cForwardNet.TrainMode(flag) && cInverseNet.TrainMode(flag)); } ///< Set Training Mode Flag
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virtual bool GetLayerOutput(uint layer, CBufferFloat *&result) ///< Retutn Output data of layer. @param[in] layer Number of layer @param[out] return Buffer with data
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{ return CNet::GetLayerOutput(layer, result); }
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//---
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virtual bool UpdateTarget(string file_name);
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virtual void SetStateEmbedingLayer(uint layer) { iStateEmbedingLayer = layer; }
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virtual void SetBufferSize(uint min, uint max);
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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CICM::CICM(void) : iMinBufferSize(100)
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{
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cTargetNet.Create(NULL);
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Create(NULL, NULL, NULL);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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CICM::~CICM()
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{
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CICM::Create(CArrayObj *Description, CArrayObj *Forward, CArrayObj *Inverse)
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{
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if(!CNet::Create(Description))
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return false;
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if(!cForwardNet.Create(Forward))
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return false;
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if(!cInverseNet.Create(Inverse))
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return false;
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cTargetNet.Create(NULL);
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CICM::backProp(int batch, float discount = 0.999000f)
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{
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//---
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if(cReplay.Total() < (int)iMinBufferSize)
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return true;
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//if(!UpdateTarget(TargetNetFile))
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// return false;
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//---
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CLayer *currentLayer, *nextLayer, *prevLayer;
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CNeuronBaseOCL *neuron;
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CBufferFloat *state1, *state2, *targetVals = new CBufferFloat();
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vector<float> target, actions, st1, st2, result;
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double reward;
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int action;
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//--- цикл обучения в размере batch
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for(int i = 0; i < batch; i++)
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{
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//--- получаем случайное состояние и реплай буфера
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if(!cReplay.GetRendomState(state1, action, reward, state2))
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return false;
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//--- прямой проход обучаемой моделм ("текущее" состояие)
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if(!CNet::feedForward(state1, 1, false, (CBufferFloat*)NULL))
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return false;
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//--- выгружаем эмбединг состояния
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if(!GetLayerOutput(iStateEmbedingLayer, state1))
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return false;
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//--- подготавливаем one-hote вектор действия и конкатенируем с вектором текущего состояния
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getResults(target);
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actions = vector<float>::Zeros(target.Size());
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actions[action] = 1;
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if(!targetVals.AssignArray(actions) || !targetVals.AddArray(state1))
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return false;
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//--- прямой проход forward net - прогноз следующего состояния
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if(!cForwardNet.feedForward(targetVals, 1, false, (CBufferFloat*)NULL))
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return false;
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//--- прямой проход
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if(!cTargetNet.feedForward(state2, 1, false, (CBufferFloat*)NULL))
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return false;
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//--- выгружаем эмбединг состояния и соединяем с эмбедингом "текущего" состояния
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if(!cTargetNet.GetLayerOutput(iStateEmbedingLayer, state2))
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return false;
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//--- прямой проход inverse net - определение совершенного действия.
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if(!state1.AddArray(state2) || !cInverseNet.feedForward(state1, 1, false, (CBufferFloat*)NULL))
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return false;
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//--- обратный проход inverse net
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if(!targetVals.AssignArray(actions) || !cInverseNet.backProp(targetVals, (CBufferFloat*)NULL))
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return false;
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//--- обратный проход forward net
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if(!cForwardNet.backProp(state2, (CBufferFloat*)NULL))
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return false;
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//--- корректировка вознаграждения
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cForwardNet.getResults(st1);
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state2.GetData(st2);
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reward += (MathPow(st2 - st1, 2)).Sum();
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cTargetNet.getResults(targetVals);
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target[action] = (float)(reward + discount * targetVals.Maximum());
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if(!targetVals.AssignArray(target))
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return false;
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//--- обратный проход обучаемой модели
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{
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getResults(result);
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float error = result.Loss(target, LOSS_MSE);
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//---
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currentLayer = layers.At(layers.Total() - 1);
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if(CheckPointer(currentLayer) == POINTER_INVALID)
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return false;
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neuron = currentLayer.At(0);
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if(!neuron.calcOutputGradients(targetVals, error))
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return false;
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//---
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backPropCount++;
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recentAverageError += (error - recentAverageError) / fmin(recentAverageSmoothingFactor, (float)backPropCount);
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//--- Calc Hidden Gradients
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int total = layers.Total();
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for(int layerNum = total - 2; layerNum > 0; layerNum--)
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{
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nextLayer = currentLayer;
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currentLayer = layers.At(layerNum);
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neuron = currentLayer.At(0);
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if(!neuron.CalcHiddenGradients(nextLayer.At(0)))
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return false;
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if(layerNum == iStateEmbedingLayer)
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{
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CLayer* temp = cInverseNet.layers.At(0);
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CNeuronBaseOCL* inv = temp.At(0);
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uint global_work_offset[1] = {0};
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uint global_work_size[1];
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global_work_size[0] = neuron.Neurons();
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opencl.SetArgumentBuffer(def_k_MatrixSum, def_k_sum_matrix1, neuron.getGradientIndex());
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opencl.SetArgumentBuffer(def_k_MatrixSum, def_k_sum_matrix2, inv.getGradientIndex());
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opencl.SetArgumentBuffer(def_k_MatrixSum, def_k_sum_matrix_out, neuron.getGradientIndex());
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opencl.SetArgument(def_k_MatrixSum, def_k_sum_dimension, 1);
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opencl.SetArgument(def_k_MatrixSum, def_k_sum_multiplyer, 1.0f);
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if(!opencl.Execute(def_k_MatrixSum, 1, global_work_offset, global_work_size))
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{
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printf("Error of execution kernel MatrixSum: %d", GetLastError());
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return false;
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}
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}
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}
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//---
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prevLayer = layers.At(total - 1);
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for(int layerNum = total - 1; layerNum > 0; layerNum--)
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{
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currentLayer = prevLayer;
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prevLayer = layers.At(layerNum - 1);
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neuron = currentLayer.At(0);
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if(!neuron.UpdateInputWeights(prevLayer.At(0)))
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return false;
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}
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//---
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for(int layerNum = 0; layerNum < total; layerNum++)
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{
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currentLayer = layers.At(layerNum);
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CNeuronBaseOCL *temp = currentLayer.At(0);
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if(!temp.TrainMode())
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continue;
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if((layerNum + 1) == total && !temp.getGradient().BufferRead())
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return false;
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break;
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}
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}
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}
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//---
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delete state1;
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delete state2;
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delete targetVals;
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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int CICM::getAction(void)
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{
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CBufferFloat *temp;
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CNet::getResults(temp);
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if(!temp)
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return -1;
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//---
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return temp.Argmax();
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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int CICM::getSample(void)
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{
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CBufferFloat* resultVals;
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CNet::getResults(resultVals);
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if(!resultVals)
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return -1;
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vectorf temp;
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if(!resultVals.GetData(temp))
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{
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delete resultVals;
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return -1;
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}
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delete resultVals;
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//---
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if(!temp.Activation(temp, AF_SOFTMAX))
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return -1;
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temp = temp.CumSum();
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int err_code;
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float random = (float)MathRandomNormal(0.5, 0.5, err_code);
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if(random >= 1)
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return (int)temp.Size() - 1;
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for(int i = 0; i < (int)temp.Size(); i++)
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if(random <= temp[i] && temp[i] > 0)
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return i;
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//---
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return -1;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CICM::UpdateTarget(string file_name)
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{
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if(!CNet::Save(file_name, 0, 0, 0, true))
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return false;
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float error, undefine, forecast;
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datetime time;
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if(!cTargetNet.Load(file_name, error, undefine, forecast, time, true))
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return false;
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CICM::AddInputData(CArrayFloat *inputVals)
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{
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if(!inputVals)
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return false;
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if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_BALANCE)))
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return false;
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if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_EQUITY)))
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return false;
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if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_MARGIN_FREE)))
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return false;
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if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_MARGIN_LEVEL)))
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return false;
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if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_PROFIT)))
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return false;
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//---
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double buy_value = 0, sell_value = 0, buy_profit = 0, sell_profit = 0;
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int total = PositionsTotal();
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for(int i = 0; i < total; i++)
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{
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if(PositionGetSymbol(i) != _Symbol)
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continue;
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switch((int)PositionGetInteger(POSITION_TYPE))
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{
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case POSITION_TYPE_BUY:
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buy_value += PositionGetDouble(POSITION_VOLUME);
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buy_profit += PositionGetDouble(POSITION_PROFIT);
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break;
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case POSITION_TYPE_SELL:
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sell_value += PositionGetDouble(POSITION_VOLUME);
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sell_profit += PositionGetDouble(POSITION_PROFIT);
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break;
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}
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}
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if(!inputVals.Add((float)buy_value))
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return false;
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if(!inputVals.Add((float)sell_value))
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return false;
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if(!inputVals.Add((float)buy_profit))
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return false;
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if(!inputVals.Add((float)sell_profit))
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return false;
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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int CICM::feedForward(CArrayFloat *inputVals, int window = 1, bool tem = true, bool sample = true)
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{
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if(!AddInputData(inputVals))
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return -1;
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//---
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if(!CNet::feedForward(inputVals, window, tem, (CBufferFloat*)NULL))
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return -1;
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double balance = AccountInfoDouble(ACCOUNT_BALANCE);
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double reward = (dPrevBalance == 0 ? 0 : balance - dPrevBalance);
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dPrevBalance = balance;
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int action = (sample ? getSample() : getAction());
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if(!cReplay.AddState(inputVals, action, reward))
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return -1;
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//---
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return action;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CICM::Save(string file_name, bool common = true)
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{
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if(file_name == NULL)
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return false;
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//---
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int handle = FileOpen(file_name, (common ? FILE_COMMON : 0) | FILE_BIN | FILE_WRITE);
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if(handle == INVALID_HANDLE)
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return false;
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//---
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if(FileWriteInteger(handle, iMinBufferSize) <= 0 || FileWriteInteger(handle, iStateEmbedingLayer) <= 0)
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{
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FileClose(handle);
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return false;
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}
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bool result = true;
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if(!CNet::Save(handle) || !cForwardNet.Save(handle) || !cInverseNet.Save(handle))
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result = false;
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FileFlush(handle);
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FileClose(handle);
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//---
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return result;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CICM::Load(string file_name, bool common = true)
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{
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if(file_name == NULL)
|
|
return false;
|
|
//---
|
|
int handle = FileOpen(file_name, (common ? FILE_COMMON : 0) | FILE_BIN | FILE_READ);
|
|
if(handle == INVALID_HANDLE)
|
|
return false;
|
|
//---
|
|
iMinBufferSize = (uint)FileReadInteger(handle);
|
|
iStateEmbedingLayer = (uint)FileReadInteger(handle);
|
|
bool result = true;
|
|
if(!CNet::Load(handle))
|
|
result = false;
|
|
if(!cForwardNet.Load(handle))
|
|
result = false;
|
|
if(!cInverseNet.Load(handle))
|
|
result = false;
|
|
FileFlush(handle);
|
|
FileClose(handle);
|
|
UpdateTarget(TargetNetFile);
|
|
//---
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CICM::Save(string dqn, string forward, string invers, bool common = true)
|
|
{
|
|
if(dqn == NULL || forward == NULL || invers == NULL)
|
|
return false;
|
|
bool result = true;
|
|
if(!CNet::Save(dqn, getRecentAverageError(), 0, 0, 0, common) ||
|
|
!cForwardNet.Save(forward, cForwardNet.getRecentAverageError(), 0, 0, 0, common) ||
|
|
!cInverseNet.Save(invers, cInverseNet.getRecentAverageError(), 0, 0, 0, common))
|
|
result = false;
|
|
//---
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CICM::Load(string dqn, string forward, string invers, uint state_layer, bool common = true)
|
|
{
|
|
if(dqn == NULL || forward == NULL || invers == NULL)
|
|
return false;
|
|
bool result = true;
|
|
float err, undef, forecast;
|
|
datetime date;
|
|
if(!CNet::Load(dqn, err, undef, forecast, date, common) ||
|
|
!cForwardNet.Load(forward, err, undef, forecast, date, common) ||
|
|
!cInverseNet.Load(invers, err, undef, forecast, date, common))
|
|
result = false;
|
|
iStateEmbedingLayer = state_layer;
|
|
cTargetNet.Load(dqn, err, undef, forecast, date, common);
|
|
//---
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
void CICM::SetBufferSize(uint min, uint max)
|
|
{
|
|
iMinBufferSize = MathMin(min, max);
|
|
cReplay.SetMaxSize(max);
|
|
}
|
|
//+------------------------------------------------------------------+
|