initial cleaning

2014-12-10 19:57:54 +01:00
parent aab9a073e9
commit 5d0fa9301b
38 changed files with 906 additions and 583 deletions
--- a/tests/nn-rl-xor2.cpp
+++ b/tests/nn-rl-xor2.cpp
@@ -0,0 +1,99 @@
+#include "../src/NeuronNetwork/Learning/QLearning.h"
+
+#include <iostream>
+#include <vector>
+
+
+class X: public Shin::NeuronNetwork::Problem
+{
+	public:
+		X(const X& a) :Problem(a) {}
+		X(const std::vector<float> &a):Problem() {data=a;}
+};
+
+float atof(char *s)
+{
+    int f, m, sign, d=1;
+    f = m = 0;
+
+    sign = (s[0] == '-') ? -1 : 1;
+    if (s[0] == '-' || s[0] == '+') s++;
+
+    for (; *s != '.' && *s; s++) {
+            f = (*s-'0') + f*10;
+    }
+    if (*s == '.')
+            for (++s; *s; s++) {
+                    m = (*s-'0') + m*10;
+                    d *= 10;
+            }
+    return sign*(f + (float)m/d);
+}
+
+float AA=10;
+float getQuality(X& p, int action)
+{
+	if((p[0]==0&& p[1]==0) ||(p[0]==1&& p[1]==1)) //should be 0
+	{
+ 		return action==1?-AA:AA;
+	}else // should be 1
+	{
+		return action==0?-AA:AA;
+	}
+}
+
+int main(int argc, char **argv)
+{
+	srand(time(NULL));
+
+	Shin::NeuronNetwork::Learning::QLearning l(2,45,2);
+	if(argc==4 && argv[3][0]=='o')
+	{
+		std::cerr << "USING Optical Backpropagation\n";
+		l.opticalBackPropagation();
+	}
+	if(argc>=3)
+	{
+		std::cerr << "Setting learning coefficients to:" << atof(argv[1]) << "," << atof(argv[2]) << "\n";
+		l.setLearningCoeficient(atof(argv[1]),atof(argv[2]));
+	}
+	std::vector <std::pair<Shin::NeuronNetwork::Solution,Shin::NeuronNetwork::Problem>> p1x;
+
+	std::vector <X> states;
+	states.push_back(X(std::vector<float>({1,0})));
+	states.push_back(X(std::vector<float>({0,0})));
+	states.push_back(X(std::vector<float>({1,1})));
+	states.push_back(X(std::vector<float>({0,1})));
+
+	unsigned long step=0;
+	double quality=0;
+	while(step< 600000 && quality < (3.9*AA))
+	{
+		quality=0;
+		if(step%10000==0)
+			std::cerr << "STEP " << step << "\n";
+		for(unsigned i=0;i<states.size();i++)
+		{
+			int choice=l.getChoice(states[i]);
+			l.learn(states[i],choice,quality);
+		}
+		for(unsigned i=0;i<states.size();i++)
+		{
+			int choice=l.getChoice(states[i]);
+			quality+=getQuality(states[i],choice);
+			if(step%10000==0)
+			{
+				Shin::NeuronNetwork::Solution sol=l.getSolution(states[i]);
+				std::cerr << "\tState: [" << states[i][0] << "," << states[i][1] << "] Q-function:  [" << sol[0] << "," <<sol[1] << "] Action " << choice << "\n";
+			}
+		}
+		step++;
+	}
+	std::cerr << step << "\n";
+	for(unsigned i=0;i<states.size();i++)
+	{
+		Shin::NeuronNetwork::Solution sol=l.getSolution(states[i]);
+		int choice=l.getChoice(states[i]);
+		std::cerr << "State: [" << states[i][0] << "," << states[i][1] << "] Q-function:  [" << sol[0] << "," <<sol[1] << "] Action " << choice << "\n";
+	}
+}