66 lines
3.1 KiB
MQL5
66 lines
3.1 KiB
MQL5
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//+----------------------------------------------------------------------+
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//| BPNN_MQL.mqh |
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//| Copyright (c) 2009-2019, gpwr, Marketeer |
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//| https://www.mql5.com/en/users/marketeer |
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//| https://www.mql5.com/en/users/gpwr |
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//| Based on original idea and source codes of gpwr |
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//| rev.18.12.2019 |
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//+----------------------------------------------------------------------+
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#ifndef BPNN_LIBRARY_DESC
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#define BPNN_LIBRARY_DESC "\nBPNN MQL library is imported from BPNN_MQL.ex5"
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#endif
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#define NN_STATE_ABORTED -1
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#define NN_STATE_UNDEFINED 0
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#define NN_STATE_TRAINED_BY_VALIDATION 1
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#define NN_STATE_TRAINED_BY_ACCURACY 2
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#define NN_STATE_TRAINED_BY_EPOCH_LIMIT 3
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struct NNStatus
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{
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int code;
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string message;
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NNStatus(const int n, const string text): code(n), message(text) {}
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NNStatus(const NNStatus &other)
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{
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code = other.code;
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message = other.message;
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}
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};
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#ifndef BPNN_LIBRARY
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//======================================= Ex-DLL =========================================
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#import "BPNN_MQL.ex5"
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NNStatus Train(
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const double &inpTrain[], // Input training data (1D array carrying 2D data, old first)
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const double &outTarget[], // Output target data for training (2D data as 1D array, oldest 1st)
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double &outTrain[], // Output 1D array to hold net outputs from training
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const int ntr, // # of training sets
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const int UEW, // Use Ext. Weights for initialization (1=use extInitWt, 0=use rnd)
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const double &extInitWt[], // Input 1D array to hold 3D array of external initial weights
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double &trainedWt[], // Output 1D array to hold 3D array of trained weights
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const int numLayers, // # of layers including input, hidden and output
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const int &lSz[], // # of neurons in layers. lSz[0] is # of net inputs
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const int AFT, // Type of neuron activation function (0:sigm, 1:tanh, 2:x/(1+x))
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const int OAF, // 1 enables activation function for output layer; 0 disables
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const int nep, // Max # of training epochs
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const double maxMSE // Max MSE; training stops once maxMSE is reached
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);
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void Test(
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const double &inpTest[], // Input test data (2D data as 1D array, oldest first)
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double &outTest[], // Output 1D array to hold net outputs from training (oldest first)
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const int ntt, // # of test sets
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const double &extInitWt[], // Input 1D array to hold 3D array of external initial weights
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const int numLayers, // # of layers including input, hidden and output
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const int &lSz[], // # of neurons in layers. lSz[0] is # of net inputs
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const int AFT, // Type of neuron activation function (0:sigm, 1:tanh, 2:x/(1+x))
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const int OAF // 1 enables activation function for output layer; 0 disables
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);
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#import
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#endif
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