public void TestLearning()
{
NetworkStartInfo startInfo = new NetworkStartInfo(
name: "myTestTwo",
layers: new[] { 3, 25, 25, 1 },
learningRate: 0.03,
location: @"C:\Users\Roopal\Documents\Aashish\Shellshock",
activationMethod: 0);
NeuralNetwork _aiMain = new NeuralNetwork(startInfo);
for (int i = 0; i < 5000; i++)
{
_aiMain.FindOutput(new double[] { 0, 0, 0 });
_aiMain.Learn(new double[] { 0.91 });
_aiMain.FindOutput(new double[] { 0, 0, 1 });
_aiMain.Learn(new double[] { 1 });
_aiMain.FindOutput(new double[] { 0, 1, 0 });
_aiMain.Learn(new double[] { 0.14 });
_aiMain.FindOutput(new double[] { 0, 1, 1 });
_aiMain.Learn(new double[] { 0 });
_aiMain.FindOutput(new double[] { 1, 0, 0 });
_aiMain.Learn(new double[] { -0.21 });
_aiMain.FindOutput(new double[] { 1, 0, 1 });
_aiMain.Learn(new double[] { 0 });
_aiMain.FindOutput(new double[] { 1, 1, 0 });
_aiMain.Learn(new double[] { 0.63 });
_aiMain.FindOutput(new double[] { 1, 1, 1 });
_aiMain.Learn(new double[] { -0.8 });
}
var actualOutputs = _aiMain.FindOutput(new double[] { 1, 1, 1 });
Assert.AreEqual(-0.8, actualOutputs[0], 0.05);
actualOutputs = _aiMain.FindOutput(new double[] { 1, 1, 0 });
Assert.AreEqual(0.63, actualOutputs[0], 0.05);
actualOutputs = _aiMain.FindOutput(new double[] { 1, 0, 0 });
Assert.AreEqual(-0.21, actualOutputs[0], 0.05);
actualOutputs = _aiMain.FindOutput(new double[] { 0, 0, 0 });
Assert.AreEqual(0.91, actualOutputs[0], 0.05);
actualOutputs = _aiMain.FindOutput(new double[] { 0, 1, 0 });
Assert.AreEqual(0.14, actualOutputs[0], 0.05);
_aiMain.SaveWeightsAndBiases();
}
public void TestExistingTrainingData()
{
List <InputsAndOutputs> listOfIO = new List <InputsAndOutputs>();
InputsAndOutputs inputsAndOutputs1 = CsvHandler.GetInputsAndOutputs(19, 2, testTrainingData1);
InputsAndOutputs inputsAndOutputs2 = CsvHandler.GetInputsAndOutputs(19, 2, testTrainingData2);
InputsAndOutputs inputsAndOutputs3 = CsvHandler.GetInputsAndOutputs(19, 2, testTrainingData3);
InputsAndOutputs inputsAndOutputs4 = CsvHandler.GetInputsAndOutputs(19, 2, testTrainingData4);
listOfIO.Add(inputsAndOutputs1);
listOfIO.Add(inputsAndOutputs2);
listOfIO.Add(inputsAndOutputs3);
listOfIO.Add(inputsAndOutputs4);
//InputsAndOutputs io = CsvHandler.GetInputsAndOutputs(19, 2, testTrainingData1);
NetworkStartInfo startInfo = new NetworkStartInfo(
name: "ArtificialShellshock_Final",
layers: new[] { 19, 25, 25, 2 },
learningRate: 0.03,
location: @"C:\Users\Roopal\Documents\Aashish\Shellshock",
activationMethod: 0);
NeuralNetwork aiMain = new NeuralNetwork(startInfo);
List <double> averageCost = new List <double>();
foreach (var io in listOfIO)
{
for (int i = 0; i < 5001; i++)
{
double[] costs = new double[io.InputsList.Count];
for (int j = 0; j < io.InputsList.Count; j++)
{
aiMain.FindOutput(io.InputsList[j]);
costs[j] = (Math.Abs(aiMain.MyLayers[3].Values[0] - io.OutputsList[j][0]) +
Math.Abs(aiMain.MyLayers[3].Values[1] - io.OutputsList[j][1])) / 2;
aiMain.Learn(io.OutputsList[j]);
}
if (i % 1000 == 0)
{
aiMain.SaveWeightsAndBiases();
}
if (i % 100 == 0)
{
averageCost.Add(costs.Average());
}
}
}
double[] avgCost = new double[averageCost.Count];
averageCost.CopyTo(avgCost, 0);
CsvHandler.SaveVector(avgCost,
@"C:\Users\Roopal\Documents\Aashish\Shellshock\ArtificialShellshock_Final" + @"\AverageCost.csv");
var actualOutput = aiMain.FindOutput(listOfIO[3].InputsList[0]);
Assert.AreEqual(actualOutput[0], listOfIO[0].OutputsList[0][0], 0.1);
}
public StartPowerAngleFinder()
{
NetworkStartInfo startInfo = new NetworkStartInfo(Name, Layers, LearningRate, Location, 0);
neuralNetwork = new NeuralNetwork(startInfo);
}
