reffactored and recurrent implementation

include/NeuralNetwork/ActivationFunction/Sigmoid.h

@@ -0,0 +1,35 @@
+#pragma once
+
+#include <cmath>
+
+#include "./StreamingActivationFunction.h"
+#include "../../sse_mathfun.h"
+
+namespace NeuralNetwork {
+namespace ActivationFunction {
+
+	/**
+	* @author Tomas Cernik (Tom.Cernik@gmail.com)
+	* @brief Class for computing sigmoid
+	*/
+	class Sigmoid: public StreamingActivationFunction {
+		public:
+			Sigmoid(const float lambdaP = -0.5): lambda(lambdaP) {}
+			inline virtual float derivatedOutput(const float&,const float &output) override { return lambda*output*(1.0f-output); }
+			inline virtual float operator()(const float &x) override { return 1.0f / (1.0f +exp(lambda*x) ); };
+			inline virtual __m128 operator()(const __m128 &x) override {
+				// exp_ps is extremly slow!
+				return _mm_div_ps(_mm_set1_ps(1.0),_mm_add_ps(exp_ps(_mm_mul_ps(_mm_set1_ps(lambda),x)),_mm_set1_ps(1.0)));
+			}
+			virtual ActivationFunction* clone() const override {
+				return new Sigmoid(lambda);
+			}
+
+			virtual std::string stringify() const override {
+				return "{ \"class\": \"NeuralNetwork::ActivationFunction::Sigmoid\", \"lamba\" : "+std::to_string(lambda)+"}";
+			}
+		protected:
+			float lambda;
+	};
+}
+}