prepare workspace by eu5, add some scripts and node modules on Documents folder, extends compile script and also node module to automate usefull most commonly used compilation tasks and also create required npm scripts and vscode debug lunch tasks based on them ...
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///////////////////////////////////////////////////////
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//
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// SaherElm IT Center MQL5 NN Class Library
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// ----------------------------------------
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// Name: XNNClass
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// Description: provides all classes for implementing
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// Neural Network ...
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//
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//
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// Maintainer:
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// ------------
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// Hadi Khazaee Asl (hadi_khazaee_asl@yahoo.com)
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//
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//////////////////////////////////////////////////////
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//
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// Global Properties ...
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#property library
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#property copyright "Copyright 2023, SaherElm IT Center"
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#property link "https://www.saherelm.ir"
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#property version "1.00"
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#property strict
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//
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// START Global Definitions: Variables, Properties and etc ...
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//
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class XCNNBase
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{
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//
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// Public Definitions ...
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public:
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//
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// Constructor ...
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void XCNNBase(
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int nodes = 10, // Number of Input Nodes
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double starterWeight = 0.5, // start weight for each input
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double coEfficient = 0.1, // COEfficient Multiplier
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double upperRange = 1, // Upper Normal Range Value
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double lowerRange = -1, // Lower Normal Range Value
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double learningRates = 0.1 // Back Propagation Learning Rate
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) {
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//
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mNodes = nodes;
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mUpperRange = upperRange;
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mLowerRange = lowerRange;
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mCoEfficient = coEfficient;
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mLearningRates = learningRates;
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mStarterWeight = starterWeight;
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//
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ReConstructWeights();
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}
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//
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// Deconstructor ...
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void ~XCNNBase() {}
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//
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// Protected Definitions ...
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void NormalizeInputs(double &inputs[], double &result[])
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{
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//
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ArrayFree(result);
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ArrayResize(result, 1);
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//
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// Validate inputs ...
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if (ArraySize(inputs) != mNodes)
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{
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return;
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}
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//
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// Prepare Result ...
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ArrayResize(result, mNodes);
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//
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// Calculating min and max range value ...
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double minRangeValue = inputs[ArrayMinimum(inputs)];
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double maxRangeValue = inputs[ArrayMaximum(inputs)];
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//
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// Loop through Inputs nd Normalize them ...
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for (int i = 0; i < mNodes; i++)
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{
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//
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double normalValue = (((inputs[i] - minRangeValue) * (mUpperRange - mLowerRange)) / (maxRangeValue - minRangeValue)) + mLowerRange;
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result[i] = normalValue;
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}
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}
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//
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// Hyperbolic Tangent Activation Function ...
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double TanHActivationFunction(double weightedInputs)
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{
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//
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double result = (exp(weightedInputs) - exp(-weightedInputs)) / (exp(weightedInputs) + exp(-weightedInputs));
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return result;
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}
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//
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// Calculate Weighted Inputs ...
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double CalculateWeightedInputs(
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double &inputs[],
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bool applyCOEfficient = true)
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{
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//
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double result = 0;
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//
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// Validate Inputs ...
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if (ArraySize(inputs) != mNodes)
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{
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//
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result = -1;
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return result;
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}
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//
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for (int i = 0; i < mNodes; i++)
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{
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result += inputs[i] * mWeights[i];
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}
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//
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// Multiply if required ...
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if (applyCOEfficient)
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{
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result *= mCoEfficient;
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}
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//
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return result;
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}
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//
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// Calculate Hidden Layers ...
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virtual double CalculateHiddenLayer(double &inputs[])
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{
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//
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// this is Default Activation Function which used ...
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// you can override this by writing your own ...
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double totalWeightedInputs = CalculateWeightedInputs(inputs);
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double result = TanHActivationFunction(totalWeightedInputs);
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//
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return result;
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}
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//
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// Calculate Output Layers ...
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double CalculateOutputLayer(double &inputs[])
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{
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//
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double result = -1;
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//
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// Validate Inputs ...
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if (ArraySize(inputs) != mNodes) {
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return result;
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}
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//
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double normalizedInputs[];
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NormalizeInputs(inputs, normalizedInputs);
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if (ArraySize(normalizedInputs) != mNodes) {
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return result;
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}
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//
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double hiddenLayerResult = CalculateHiddenLayer(normalizedInputs);
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//
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result = 1 * hiddenLayerResult;
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//
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return result;
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}
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//
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// Basck Propaggation Learning ...
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void BackPropagation(
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double &inputs[],
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double &outputs,
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double targetOutput
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) {
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//
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// Validate Inputs ...
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if (ArraySize(inputs) != mNodes) {
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return;
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}
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//
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double normalizedInputs[];
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NormalizeInputs(inputs, normalizedInputs);
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if (ArraySize(normalizedInputs) != mNodes) {
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return;
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}
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//
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double error = targetOutput - outputs;
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double derivative = 1 - MathPow(outputs, 2);
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//
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for (int i = 0; i < mNodes; i++) {
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//
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double gradient = error * derivative * inputs[i];
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mWeights[i] = mLearningRates * gradient;
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}
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}
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protected:
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//
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// Private Definitions ...
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private:
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//
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// Number of Input Nodes ...
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int mNodes;
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//
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// Specify Upper range of Normal Values ...
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double mUpperRange;
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//
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// Specify Lower range of Normal Values ...
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double mLowerRange;
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//
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// Starter Weight for each input node ...
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double mStarterWeight;
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//
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// double Input Node Weights ...
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double mWeights[];
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//
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// CoEfficient is a multiplyer for weighted inputs ...
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double mCoEfficient;
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//
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// Back Propagation Learning Rates ...
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double mLearningRates;
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//
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// ReConstruct Weights ...
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void ReConstructWeights() {
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//
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ArrayFree(mWeights);
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ArrayResize(mWeights, mNodes);
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//
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for (int i = 0; i< mNodes; i++) {
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mWeights[i] = mStarterWeight;
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}
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}
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}
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//
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// END Global Definitions: Variables, Properties and etc ...
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//
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