modified SSE code building
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@@ -8,13 +8,11 @@
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int main() {
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{
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NeuralNetwork::BasisFunction::Linear l;
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assert(39.0==l.compute({1,2,3,5},{1,2,3,5}));
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assert(39.0==l.computeStreaming({1,2,3,5},{1,2,3,5}));
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assert(39.0==l({1,2,3,5},{1,2,3,5}));
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}
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{
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NeuralNetwork::BasisFunction::Linear l;
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assert(88.0==l.computeStreaming({1,2,3,5,7},{1,2,3,5,7}));
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assert(88.0==l.compute({1,2,3,5,7},{1,2,3,5,7}));
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assert(88.0==l({1,2,3,5,7},{1,2,3,5,7}));
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}
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{
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NeuralNetwork::BasisFunction::Linear l;
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@@ -22,8 +20,7 @@ int main() {
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for(int in=0;in<100;in++) {
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w.push_back(2);
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}
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assert(400.0==l.computeStreaming(w,w));
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assert(400.0==l.compute(w,w));
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assert(400.0==l(w,w));
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}
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{
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NeuralNetwork::BasisFunction::Linear l;
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@@ -31,8 +28,7 @@ int main() {
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for(int in=0;in<55;in++) {
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w.push_back(2);
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}
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assert(220.0==l.computeStreaming(w,w));
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assert(220.0==l.compute(w,w));
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assert(220.0==l(w,w));
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}
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{
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NeuralNetwork::BasisFunction::Product l;
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@@ -3,13 +3,6 @@
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#include <cassert>
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#include <iostream>
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void printVec(const std::vector<float> &v) {
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for(int i=0;i<v.size();i++) {
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std::cout << v[i] << ", ";
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}
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std::cout<< "\n";
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}
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int main() {
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{ // XOR problem
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NeuralNetwork::FeedForward::Network n(2);
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