fixed Sigmoid function and modifications

include/NeuralNetwork/ActivationFunction/ActivationFunction.h

@@ -14,12 +14,21 @@ namespace ActivationFunction {
 
 			virtual ~ActivationFunction() {}
 
+			/**
+			 * @brief Returns derivation of output, it is slower than version with output as it needs to compute output
+			 * @param input is input of function
+			 */
+			inline float derivatedOutput(const float &input) {
+				return derivatedOutput(input,operator()(input));
+			};
+
 			/**
 			 * @brief Returns derivation of output
 			 * @param input is input of function
 			 * @param output is output of function
+			 * @see derivatedOutput
 			 */
-			virtual float derivatedOutput(const float &input,const float &output)=0;
+			virtual float derivatedOutput(const float &input, const float &output) =0;
 
 			/**
 			 * @brief Returns value of output

include/NeuralNetwork/ActivationFunction/Heaviside.h

@@ -8,6 +8,7 @@ namespace ActivationFunction {
 	class Heaviside: public ActivationFunction {
 		public:
 			Heaviside(const float &lambdaP=1.0): lambda(lambdaP) {}
+
 			inline virtual float derivatedOutput(const float &,const float &) override { return 1.0;  }
 			inline virtual float operator()(const float &x) override { return x>lambda ? 1.0f : 0.0f; };
 

include/NeuralNetwork/ActivationFunction/HyperbolicTangent.h

@@ -10,7 +10,9 @@ namespace ActivationFunction {
 	class HyperbolicTangent: public ActivationFunction {
 		public:
 			HyperbolicTangent(const float& lam=1):lambda(lam) {}
-			inline virtual float derivatedOutput(const float&,const float &output) override { return lambda*(1-output*output); }
+
+			inline virtual float derivatedOutput(const float &,const float &output) override { return lambda*(1-output*output); }
+
 			inline virtual float operator()(const float &x) override { return tanh(lambda*x); };
 			virtual ActivationFunction* clone() const override {
 				return new HyperbolicTangent(lambda);

include/NeuralNetwork/ActivationFunction/Linear.h

@@ -8,7 +8,9 @@ namespace ActivationFunction {
 	class Linear: public ActivationFunction {
 		public:
 			Linear(const float &lambdaP=1.0): lambda(lambdaP) {}
+
 			inline virtual float derivatedOutput(const float &,const float &) override { return lambda;  }
+
 			inline virtual float operator()(const float &x) override { return x*lambda; };
 
 			virtual ActivationFunction* clone() const override {

include/NeuralNetwork/ActivationFunction/Sigmoid.h

@@ -15,12 +15,15 @@ namespace ActivationFunction {
 	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 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);
 			}

include/NeuralNetwork/ActivationFunction/StreamingActivationFunction.h

@@ -13,7 +13,7 @@ namespace ActivationFunction {
 	*/
 	class StreamingActivationFunction : public ActivationFunction {
 		public:
-			virtual float derivatedOutput(const float &input,const float &output)=0;
+
 			virtual float operator()(const float &x)=0;
 
 			/**