95 lines
1.4 KiB
C++
95 lines
1.4 KiB
C++
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#include "../src/NeuralNetwork/FeedForward"
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#include <iostream>
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class X: public Shin::Problem
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{
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protected:
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std::vector<float> representation() const
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{
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return std::vector<float>({1,1});
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}
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};
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class X1: public Shin::Problem
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{
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protected:
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std::vector<float> representation() const
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{
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return std::vector<float>({1});
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}
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};
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int main()
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{
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NeuralNetwork::FeedForward n({2,4,2});
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NeuralNetwork::FeedForward nq({2,4,2});
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if(n[2].size() != 4)
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{
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std::cout << "1) Actual size:" << n[1].size();
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return 1;
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}
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if(nq[1].size() != 4)
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{
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std::cout << "QUICK Actual size:" << nq[1].size();
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return 1;
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}
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n[2][0].setPotential(25);
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nq[2][0].setPotential(25);
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std::cout << "Potential: " << n[2][0].getPotential() << "\n";
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std::cout << "Potential: " << nq[2][0].getPotential() << "\n";
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Shin::Solution s =n.solve(X());
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Shin::Solution sq =nq.solve(X());
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if(s.size()!=2)
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{
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std::cout << "1";
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return 1;
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}
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for(int i=0;i<2;i++)
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{
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if(s[i]!=sq[i])
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{
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std::cout << " 4 - " << i << " expected "<<s[i] << " was " <<sq[i];
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return 1;
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}
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}
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n[2][0].setWeight(0,26.0);
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nq[2][0].setWeight(0,26.0);
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s =n.solve(X());
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sq =n.solve(X());
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if(s.size()!=2)
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{
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std::cout << "a1";
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return 1;
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}
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if(s[0]!=1)
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{
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std::cout << "a2";
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return 1;
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}
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if(s[1]!=1)
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{
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std::cout << "a3";
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return 1;
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}
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for(int i=0;i<2;i++)
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{
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if(s[i]!=sq[i])
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{
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std::cout << " a4 - " << i << " expected "<<s[i] << " was " <<sq[i];
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return 1;
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}
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}
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return 0;
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} |