moved Linear sintrigification to .cpp file and fixed err in neuron weights

src/NeuralNetwork/BasisFunction/Linear.cpp

@@ -0,0 +1,69 @@
+#include <NeuralNetwork/BasisFunction/Linear.h>
+
+float NeuralNetwork::BasisFunction::Linear::operator()(const std::vector<float> &weights, const std::vector<float> &input) const {
+	assert(input.size()== weights.size());
+	std::size_t inputSize=input.size();
+
+#ifdef USE_AVX
+
+	std::size_t alignedPrev=inputSize-inputSize%8;
+
+	const float* weightsData=weights.data();
+	const float* inputData=input.data();
+
+	union {
+		__m256 avx;
+		float f[8];
+	} partialSolution;
+
+	partialSolution.avx=_mm256_setzero_ps();
+
+	for(size_t k=0;k<alignedPrev;k+=8) {
+		//TODO: asignement!! -- possible speedup
+		partialSolution.avx=_mm256_add_ps(partialSolution.avx,_mm256_mul_ps(_mm256_loadu_ps(weightsData+k),_mm256_loadu_ps(inputData+k)));
+	}
+
+	for(size_t k=alignedPrev;k<inputSize;k++) {
+		partialSolution.avx=_mm256_add_ps(partialSolution.avx,_mm256_mul_ps(_mm256_set_ps(weightsData[k],0,0,0,0,0,0,0),_mm256_set_ps(inputData[k],0,0,0,0,0,0,0)));
+	}
+
+	partialSolution.avx = _mm256_add_ps(partialSolution.avx,  _mm256_permute2f128_ps(partialSolution.avx , partialSolution.avx , 1));
+	partialSolution.avx = _mm256_hadd_ps(partialSolution.avx, partialSolution.avx);
+	partialSolution.avx = _mm256_hadd_ps(partialSolution.avx, partialSolution.avx);
+
+	return partialSolution.f[0];
+#elif USE_SSE
+
+	std::size_t alignedPrev=inputSize-inputSize%4;
+
+	const float* weightsData=weights.data();
+	const float* inputData=input.data();
+	vec4f partialSolution;
+	partialSolution.sse =_mm_setzero_ps();
+
+	//TODO prefetch ??
+	for(register size_t k=0;k<alignedPrev;k+=4) {
+	partialSolution.sse=_mm_add_ps(partialSolution.sse,_mm_mul_ps(_mm_load_ps(weightsData+k),_mm_load_ps(inputData+k)));
+	}
+
+	for(register size_t k=alignedPrev;k<inputSize;k++) {
+	partialSolution.sse=_mm_add_ps(partialSolution.sse,_mm_mul_ps(_mm_load_ss(weightsData+k),_mm_load_ss(inputData+k)));
+	}
+
+	#ifdef USE_SSE2 //pre-SSE3 solution
+		partialSolution.sse= _mm_add_ps(_mm_movehl_ps(partialSolution.sse, partialSolution.sse), partialSolution.sse);
+		partialSolution.sse=_mm_add_ss(partialSolution.sse, _mm_shuffle_ps(partialSolution.sse,partialSolution.sse, 1));
+	#else
+		partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
+		partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
+	#endif
+	return partialSolution.f[0];
+#else
+
+	register float tmp = 0;
+	for(size_t k=0;k<inputSize;k++) {
+		tmp+=input[k]*weights[k];
+	}
+	return tmp;
+#endif
+}

src/NeuralNetwork/FeedForward/Layer.cpp

@@ -7,4 +7,19 @@ void NeuralNetwork::FeedForward::Layer::solve(const std::vector<float> &input, s
 		output[neuron->id()]=neuron->operator()(input);
 	}
 
-}
+}
+
+void NeuralNetwork::FeedForward::Layer::stringify(std::ostream &out) const  {
+	out << "{"  << std::endl;
+	out << "\t \"class\": \"NeuralNetwork::FeedForward::Layer\"," << std::endl;
+	out << "\t \"neurons\": ["  << std::endl;
+	bool first=true;
+	for(auto &neuron: neurons) {
+		if(!first)
+			out << ", ";
+		out << neuron->stringify();
+		first=false;
+	}
+	out << "]";
+	out << "}";
+}