basis functions

src/NeuralNetwork/BasisFunction/BasisFunction.h

@@ -0,0 +1,18 @@
+#ifndef _BASIS_FUN_H_
+#define  _BASIS_FUN_H_
+
+#include <math.h>
+
+namespace NeuralNetwork
+{
+namespace BasisFunction
+{
+	class BasisFunction
+	{
+		public:
+			virtual ~BasisFunction() {}
+			virtual float operator()(const size_t &inputSize, const float* weights, const float* input)=0;
+	};
+}
+}
+#endif

src/NeuralNetwork/BasisFunction/FeedForward.h

@@ -0,0 +1,63 @@
+#ifndef __BASIS_FEEDFORWARD_H_
+#define  __BASIS_FEEDFORWARD_H_
+
+#include "./StreamingBasisFunction.h"
+
+#include <mmintrin.h>
+#include <xmmintrin.h>
+#include <emmintrin.h>
+#include <xmmintrin.h>
+#include <pmmintrin.h>
+
+namespace NeuralNetwork
+{
+namespace BasisFunction
+{
+	class FeedForward: public StreamingBasisFunction
+	{
+		public:
+			FeedForward() {}
+
+			inline virtual __m128 operator()(const size_t& inputSize, const float* weights, const float* input, const size_t& alignedPrev)
+			{
+				__m128 partialSolution= _mm_setzero_ps();
+				__m128 w=_mm_setzero_ps();
+				__m128 sols;
+				for(register size_t k=alignedPrev;k<inputSize;k++)
+				{
+					w = _mm_load_ss(weights+k);
+					sols  = _mm_load_ss(input+k);
+					w=_mm_mul_ps(w,sols);
+					partialSolution=_mm_add_ps(partialSolution,w);
+				}
+				for(register size_t k=0;k<alignedPrev;k+=sizeof(float)) // TODO ??? sizeof(float)
+				{
+					w = _mm_load_ps(weights+k);
+					sols  = _mm_load_ps(input+k);
+					w=_mm_mul_ps(w,sols);
+					partialSolution=_mm_add_ps(partialSolution,w);
+				}
+#ifdef USE_SSE2 //pre-SSE3 solution
+				partialSolution= _mm_add_ps(_mm_movehl_ps(partialSolution, partialSolution), partialSolution);
+				partialSolution=_mm_add_ss(partialSolution, _mm_shuffle_ps(partialSolution,partialSolution, 1));
+#else
+				partialSolution = _mm_hadd_ps(partialSolution, partialSolution);
+				partialSolution = _mm_hadd_ps(partialSolution, partialSolution);
+#endif
+				return partialSolution;
+			}
+
+			inline virtual float operator()(const size_t &inputSize, const float* weights, const float* input)
+			{
+				register float tmp = 0;
+				for(register size_t k=0;k<inputSize;k++)
+				{
+					tmp+=input[k]*weights[k];
+				}
+				return tmp;
+			}
+				
+	};
+}
+}
+#endif

src/NeuralNetwork/BasisFunction/Radial.h

@@ -0,0 +1,17 @@
+#ifndef __BASIS_RADIAL_H_
+#define  __BASIS_RADIAL_H_
+
+#include "./BasisFunction.h"
+
+namespace NeuralNetwork
+{
+namespace BasisFunction
+{
+	class Radial: public BasisFunction
+	{
+		public:
+			Radial() {}
+	};
+}
+}
+#endif

src/NeuralNetwork/BasisFunction/StreamingBasisFunction.h

@@ -0,0 +1,22 @@
+#ifndef __STREAMINGBASIS_FUN_H_
+#define  __STREAMINGBASIS_FUN_H_
+
+#include <xmmintrin.h>
+
+#include "../../sse_mathfun.h"
+
+#include "./BasisFunction.h"
+
+namespace NeuralNetwork
+{
+namespace BasisFunction
+{
+	class StreamingBasisFunction : public BasisFunction
+	{
+		public:
+			virtual float operator()(const size_t &inputSize, const float* weights, const float* input) = 0;
+			virtual __m128 operator()(const size_t& inputSize, const float* weights, const float* input, const size_t& alignedPrev) =0;
+	};
+}
+}
+#endif