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2024-01-25 04:08:38 +03:30
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+///////////////////////////////////////////////////////
+//
+// SaherElm IT Center MQL5 NN Class Library
+// ----------------------------------------
+// Name: XNNClass
+// Description: provides all classes for implementing
+// Neural Network ...
+//
+//
+// Maintainer:
+// ------------
+// Hadi Khazaee Asl (hadi_khazaee_asl@yahoo.com)
+//
+//////////////////////////////////////////////////////
+//
+// Global Properties ...
+#property library
+#property copyright "Copyright 2023, SaherElm IT Center"
+#property link "https://www.saherelm.ir"
+#property version "1.00"
+#property strict
+
+//
+// START Global Definitions: Variables, Properties and etc ...
+//
+class XCNNBase
+{
+    //
+    // Public Definitions ...
+public:
+    //
+    // Constructor ...
+    void XCNNBase(
+        int nodes = 10, // Number of Input Nodes 
+        double starterWeight = 0.5, // start weight for each input 
+        double coEfficient = 0.1, // COEfficient Multiplier 
+        double upperRange = 1, // Upper Normal Range Value 
+        double lowerRange = -1, // Lower Normal Range Value 
+        double learningRates = 0.1 // Back Propagation Learning Rate
+    ) {
+        //
+        mNodes = nodes;
+        mUpperRange = upperRange;
+        mLowerRange = lowerRange;
+        mCoEfficient = coEfficient;
+        mLearningRates = learningRates;
+        mStarterWeight = starterWeight;
+
+        //
+        ReConstructWeights();
+    }
+
+    //
+    // Deconstructor ...
+    void ~XCNNBase() {}
+
+    //
+    // Protected Definitions ...
+    void NormalizeInputs(double &inputs[], double &result[])
+    {
+        //
+        ArrayFree(result);
+        ArrayResize(result, 1);
+
+        //
+        // Validate inputs ...
+        if (ArraySize(inputs) != mNodes)
+        {
+            return;
+        }
+
+        //
+        // Prepare Result ...
+        ArrayResize(result, mNodes);
+
+        //
+        // Calculating min and max range value ...
+        double minRangeValue = inputs[ArrayMinimum(inputs)];
+        double maxRangeValue = inputs[ArrayMaximum(inputs)];
+
+        //
+        // Loop through Inputs nd Normalize them ...
+        for (int i = 0; i < mNodes; i++)
+        {
+            //
+            double normalValue = (((inputs[i] - minRangeValue) * (mUpperRange - mLowerRange)) / (maxRangeValue - minRangeValue)) + mLowerRange;
+            result[i] = normalValue;
+        }
+    }
+
+    //
+    // Hyperbolic Tangent Activation Function ...
+    double TanHActivationFunction(double weightedInputs)
+    {
+        //
+        double result = (exp(weightedInputs) - exp(-weightedInputs)) / (exp(weightedInputs) + exp(-weightedInputs));
+        return result;
+    }
+
+    //
+    // Calculate Weighted Inputs ...
+    double CalculateWeightedInputs(
+        double &inputs[],
+        bool applyCOEfficient = true)
+    {
+        //
+        double result = 0;
+
+        //
+        // Validate Inputs ...
+        if (ArraySize(inputs) != mNodes)
+        {
+            //
+            result = -1;
+            return result;
+        }
+
+        //
+        for (int i = 0; i < mNodes; i++)
+        {
+            result += inputs[i] * mWeights[i];
+        }
+
+        //
+        // Multiply if required ...
+        if (applyCOEfficient)
+        {
+            result *= mCoEfficient;
+        }
+
+        //
+        return result;
+    }
+
+    //
+    // Calculate Hidden Layers ...
+    virtual double CalculateHiddenLayer(double &inputs[])
+    {
+        //
+        // this is Default Activation Function which used ...
+        // you can override this by writing your own ...
+        double totalWeightedInputs = CalculateWeightedInputs(inputs);
+        double result = TanHActivationFunction(totalWeightedInputs);
+
+        //
+        return result;
+    }
+
+    //
+    // Calculate Output Layers ...
+    double CalculateOutputLayer(double &inputs[])
+    {
+        //
+        double result = -1;
+
+        //
+        // Validate Inputs ...
+        if (ArraySize(inputs) != mNodes) {
+            return result;
+        }
+
+        //
+        double normalizedInputs[];
+        NormalizeInputs(inputs, normalizedInputs);
+        if (ArraySize(normalizedInputs) != mNodes) {
+            return result;
+        }
+
+        //
+        double hiddenLayerResult = CalculateHiddenLayer(normalizedInputs);
+
+        //
+        result = 1 * hiddenLayerResult;
+
+        //
+        return result;
+    }
+
+    //
+    // Basck Propaggation Learning ...
+    void BackPropagation(
+        double &inputs[],
+        double &outputs,
+        double targetOutput
+    ) {
+        //
+        // Validate Inputs ...
+        if (ArraySize(inputs) != mNodes) {
+            return;
+        }
+
+        //
+        double normalizedInputs[];
+        NormalizeInputs(inputs, normalizedInputs);
+        if (ArraySize(normalizedInputs) != mNodes) {
+            return;
+        }
+
+        //
+        double error = targetOutput - outputs;
+        double derivative = 1 - MathPow(outputs, 2);
+
+        //
+        for (int i = 0; i < mNodes; i++) {
+            //
+            double gradient = error * derivative * inputs[i];
+            mWeights[i] = mLearningRates * gradient;
+        }
+    }
+
+protected:
+    //
+    // Private Definitions ...
+private:
+    //
+    // Number of Input Nodes ...
+    int mNodes;
+
+    //
+    // Specify Upper range of Normal Values ...
+    double mUpperRange;
+
+    //
+    // Specify Lower range of Normal Values ...
+    double mLowerRange;
+
+    //
+    // Starter Weight for each input node ...
+    double mStarterWeight;
+
+    //
+    // double Input Node Weights ...
+    double mWeights[];
+
+    //
+    // CoEfficient is a multiplyer for weighted inputs ...
+    double mCoEfficient;
+
+    //
+    // Back Propagation Learning Rates ...
+    double mLearningRates;
+
+    //
+    // ReConstruct Weights ...
+    void ReConstructWeights() {
+        //
+        ArrayFree(mWeights);
+        ArrayResize(mWeights, mNodes);
+
+        //
+        for (int i = 0; i< mNodes; i++) {
+            mWeights[i] = mStarterWeight;
+        }
+    }
+}
+//
+// END Global Definitions: Variables, Properties and etc ...
+//