public ServiceNN(int learningIterations)
{
const int receptors = 3;
const int numberOfOutputClasses = 3;
int[] neuronByLayer = { 10, numberOfOutputClasses };
_fileManager = new FileManager();
_nnTeacher = new NetworkTeacher(neuronByLayer, receptors, 1, _fileManager)
{
Iteration = learningIterations,
TestVectors = _fileManager.ReadVectors("inputDataTest.txt")
};
}
public void SinWaveTrainingTest()
{
NodeFuncGenerator funcGenerator = new NodeFuncGenerator();
Dictionary <NodeType, double> nodeProbabilityMap = new Dictionary <NodeType, double>()
{
{ NodeType.add, 1.0d },
{ NodeType.compare, 1.0d },
{ NodeType.copy, 0.0d },
{ NodeType.inverse, 1.0d },
{ NodeType.log, 1.0d },
{ NodeType.multiply, 1.0d },
{ NodeType.reverse, 1.0d }
};
NodeFactory nodeFactory = new NodeFactory(funcGenerator, nodeProbabilityMap);
List <DataPoint> dataPoints = GenerateSinWaveDataPoints(100, 0.35d);
Network network = new Network(nodeFactory, dataPoints, 1000);
double addRemoveChance = 0.5;
PatchFactory patchFactory = new PatchFactory(network, nodeFactory, addRemoveChance);
NetworkTeacher networkTeacher = new NetworkTeacher(network, patchFactory);
networkTeacher.Teach(TimeSpan.FromSeconds(0.1d), 2000, 3, 30);
}
public void SetUp()
{
NodeFuncGenerator nodeFuncGenerator = new NodeFuncGenerator();
NodeFactory nodeFactory = new NodeFactory(
nodeFuncGenerator,
new Dictionary <NodeType, double>()
{
{ NodeType.add, 1.0d }
});
network = new Network(
nodeFactory: nodeFactory,
trainingData: new List <DataPoint>()
{
new DataPoint(new List <double>()
{
2.0d, 2.0d, 2.0d
}, 5.0d) //IMPORTANT: TODO: was modified from "new DataPoint(new List<double>(){2.0d, 2.0d}, 5.0d)" - now test can work incorrectly
},
maxHierarchy: 1000
);
patchFactory = new PatchFactory(network, nodeFactory, 1.0d);
networkTeacher = new NetworkTeacher(network, patchFactory);
}
