geforkt von dng/NN_in_Trading
//+------------------------------------------------------------------+
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//| QR-DQN.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 defQRDQN 0x7791 ///<Neuron Net \details Identified class #CQRDQN
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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class CQRDQN : protected CNet
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{
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private:
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uint iCountBackProp;
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protected:
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uint iNumbers;
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uint iActions;
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uint iUpdateTarget;
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matrix<float> mTaus;
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//---
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CNet cTargetNet;
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public:
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/** Constructor */
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CQRDQN(void);
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CQRDQN(CArrayObj *Description) { Create(Description, iActions); }
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bool Create(CArrayObj *Description, uint actions);
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/** Destructor */~CQRDQN(void);
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bool feedForward(CArrayFloat *inputVals, int window = 1, bool tem = 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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{ return CNet::feedForward(inputVals, window, tem); }
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bool backProp(CBufferFloat *targetVals, float discount, CArrayFloat *nextState=NULL, int window = 1, bool tem = true); ///< Back propagation method. @param[in] targetVals Target values
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void getResults(CBufferFloat *&resultVals); ///< Method to get results of feed forward process.@param[out] resultVals Array of result values
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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, datetime time, bool common = true)
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{ return CNet::Save(file_name, getRecentAverageError(), (float)iActions, 0, time, common); }
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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 Save(const int file_handle);
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virtual bool Load(string file_name, datetime &time, 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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virtual bool Load(const int file_handle);
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//---
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virtual int Type(void) const { return defQRDQN; }///< Identificator of class.@return Type of class
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virtual bool TrainMode(bool flag) { return CNet::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 void SetUpdateTarget(uint batch) { iUpdateTarget = batch; }
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virtual bool UpdateTarget(string file_name);
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//---
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virtual bool SetActions(uint actions);
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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CQRDQN::CQRDQN() : iNumbers(31),
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iActions(2),
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iUpdateTarget(1000)
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{
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mTaus = matrix<float>::Ones(1, iNumbers) / iNumbers;
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mTaus[0, 0] /= 2;
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mTaus = mTaus.CumSum(0);
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cTargetNet.Create(NULL);
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Create(NULL, iActions);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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CQRDQN::~CQRDQN()
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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 CQRDQN::Create(CArrayObj *Description, uint actions)
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{
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if(actions <= 0 || !CNet::Create(Description))
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return false;
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int last_layer = Description.Total() - 1;
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CLayer *layer = layers.At(last_layer);
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if(!layer)
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return false;
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CNeuronBaseOCL *neuron = layer.At(0);
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if(!neuron)
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return false;
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iActions = actions;
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iNumbers = neuron.Neurons() / actions;
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mTaus = matrix<float>::Ones(1, iNumbers) / iNumbers;
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mTaus[0, 0] /= 2;
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mTaus = mTaus.CumSum(0);
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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 CQRDQN::backProp(CBufferFloat *targetVals, float discount, CArrayFloat *nextState=NULL, int window = 1, bool tem = true)
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{
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//---
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if(!targetVals)
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return false;
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vectorf target;
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if(!targetVals.GetData(target) || target.Size() != iActions)
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return false;
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//---
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if(!!nextState)
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{
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if(!cTargetNet.feedForward(nextState, window, tem))
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return false;
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vectorf temp;
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cTargetNet.getResults(targetVals);
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if(!targetVals.GetData(temp))
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return false;
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matrixf q = matrixf::Zeros(1, temp.Size());
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if(!q.Row(temp, 0) || !q.Reshape(iActions, iNumbers))
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return false;
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temp = q.Mean(0);
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target = target + discount * temp.Max();
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}
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//---
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vectorf quantils;
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getResults(targetVals);
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if(!targetVals.GetData(quantils))
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return false;
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matrixf Q = matrixf::Zeros(1, quantils.Size());
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if(!Q.Row(quantils, 0) || !Q.Reshape(iActions, iNumbers))
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return false;
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//---
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for(uint a = 0; a < iActions; a++)
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{
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vectorf q = Q.Row(a);
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vectorf dp = q - target[a], dn = dp;
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if(!dp.Clip(0, FLT_MAX) || !dn.Clip(-FLT_MAX, 0))
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return false;
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dp = (mTaus.Row(0) - 1) * dp;
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dn = mTaus.Row(0) * dn * (-1);
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if(!Q.Row(dp + dn + q, a))
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return false;
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}
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if(!targetVals.AssignArray(Q))
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return false;
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//---
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if(iCountBackProp >= iUpdateTarget)
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{
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#ifdef FileName
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if(UpdateTarget(FileName + ".nnw"))
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#else
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if(UpdateTarget("QRDQN.upd"))
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#endif
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iCountBackProp = 0;
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}
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else
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iCountBackProp++;
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//---
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return CNet::backProp(targetVals);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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void CQRDQN::getResults(CBufferFloat *&resultVals)
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{
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CNet::getResults(resultVals);
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if(!resultVals)
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return;
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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;
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}
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matrixf q;
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if(!q.Init(1, temp.Size()) || !q.Row(temp, 0) || !q.Reshape(iActions, iNumbers))
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{
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delete resultVals;
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return;
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}
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//---
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if(!resultVals.AssignArray(q.Mean(1)))
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{
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delete resultVals;
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return;
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}
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//---
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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int CQRDQN::getAction(void)
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{
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CBufferFloat *temp;
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getResults(temp);
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if(!temp)
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return -1;
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//---
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return temp.Maximum(0, temp.Total());
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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int CQRDQN::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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matrixf q;
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if(!q.Init(1, temp.Size()) || !q.Row(temp, 0) || !q.Reshape(iActions, iNumbers))
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{
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delete resultVals;
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return -1;
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}
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//---
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if(!q.Mean(1).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)Math::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 CQRDQN::UpdateTarget(string file_name)
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{
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if(!Save(file_name, 0, false))
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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, false))
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return false;
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iCountBackProp = 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 CQRDQN::Save(const int file_handle)
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{
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if(!CNet::Save(file_handle))
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return false;
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if(FileWriteInteger(file_handle, (int)iUpdateTarget) < INT_VALUE)
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return false;
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if(FileWriteInteger(file_handle, (int)iActions) < INT_VALUE)
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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 CQRDQN::Load(const int file_handle)
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{
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if(!CNet::Load(file_handle))
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return false;
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if(FileIsEnding(file_handle))
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return true;
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iUpdateTarget = (uint)FileReadInteger(file_handle);
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if(!SetActions((uint)FileReadInteger(file_handle)))
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return false;
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if(!UpdateTarget("DQDQN.upd"))
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return false;
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iCountBackProp = 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 CQRDQN::SetActions(uint actions)
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{
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if(actions <= 0)
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return false;
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iActions = actions;
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CLayer *layer = layers.At(layers.Total() - 1);
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if(!layer)
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return false;
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CNeuronBaseOCL *neuron = layer.At(0);
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if(!neuron)
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return false;
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iNumbers = neuron.Neurons() / iActions;
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mTaus = matrix<float>::Ones(1, iNumbers) / iNumbers;
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mTaus[0, 0] /= 2;
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mTaus = mTaus.CumSum(1);
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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 CQRDQN::Load(string file_name, datetime &time, bool common = true)
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{
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float undefine, forecast;
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if(!CNet::Load(file_name, recentAverageError, undefine, forecast, time, common))
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return false;
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//---
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return (undefine>0 ? SetActions((uint)undefine) : true);
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}
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//+------------------------------------------------------------------+
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