//
//You can use the following additional attributes as you write your tests:
//
//Use ClassInitialize to run code before running the first test in the class
//[ClassInitialize()]
//public static void MyClassInitialize(TestContext testContext)
//{
//}
//
//Use ClassCleanup to run code after all tests in a class have run
//[ClassCleanup()]
//public static void MyClassCleanup()
//{
//}
//
//Use TestInitialize to run code before running each test
//[TestInitialize()]
//public void MyTestInitialize()
//{
//}
//
//Use TestCleanup to run code after each test has run
//[TestCleanup()]
//public void MyTestCleanup()
//{
//}
//
#endregion
internal virtual IBackPropagation CreateIBackPropagation()
{
// TODO: Instantiate an appropriate concrete class.
IBackPropagation target = null;
return(target);
}
public void BackPropagateTest()
{
IBackPropagation target = CreateIBackPropagation(); // TODO: Initialize to an appropriate value
target.BackPropagate();
Assert.Inconclusive("A method that does not return a value cannot be verified.");
}
public void LoadNetwork(string path)
{
FileStream FS = new FileStream(path, FileMode.Open);
BinaryFormatter BF = new BinaryFormatter();
NeuralNet = (IBackPropagation <T>)BF.Deserialize(FS);
FS.Close();
}
public void InitializeNetworkTest()
{
IBackPropagation target = CreateIBackPropagation(); // TODO: Initialize to an appropriate value
List <FaceImage> trainingSet = null; // TODO: Initialize to an appropriate value
target.InitializeNetwork(trainingSet);
Assert.Inconclusive("A method that does not return a value cannot be verified.");
}
public NeuralNetwork(IBackPropagation IBackPro, GlobalSettingsModel settingsModel)
{
_neuralNet = IBackPro;
_trainingSet = settingsModel.TrainingSet;
_distractionTrainingSet = settingsModel.DistractionTrainingSet;
_neuralNet.InitializeNetwork(_trainingSet);
_settingsModel = settingsModel;
}
public void LoadNetwork(string path)
{
FileStream fileStream = new FileStream(path, FileMode.Open);
BinaryFormatter binaryFormatter = new BinaryFormatter();
_neuralNet = (IBackPropagation)binaryFormatter.Deserialize(fileStream);
fileStream.Close();
}
/// <summary>
///A test for NeuralNetwork`1 Constructor
///</summary>
public void NeuralNetworkConstructorTest1Helper <T>()
where T : IComparable <T>
{
IBackPropagation IBackPro = null; // TODO: Initialize to an appropriate value
List <FaceImage> trainingSet = null; // TODO: Initialize to an appropriate value
NeuralNetwork <T> target = new NeuralNetwork <T>(IBackPro, trainingSet);
Assert.Inconclusive("TODO: Implement code to verify target");
}
public void GetErrorTest()
{
IBackPropagation target = CreateIBackPropagation(); // TODO: Initialize to an appropriate value
double expected = 0F; // TODO: Initialize to an appropriate value
double actual;
actual = target.GetError();
Assert.AreEqual(expected, actual);
Assert.Inconclusive("Verify the correctness of this test method.");
}
public void ForwardPropagateTest()
{
IBackPropagation target = CreateIBackPropagation(); // TODO: Initialize to an appropriate value
double[] pattern = null; // TODO: Initialize to an appropriate value
string output = string.Empty; // TODO: Initialize to an appropriate value
target.ForwardPropagate(pattern, output);
Assert.Inconclusive("A method that does not return a value cannot be verified.");
}
public NeuralNetwork(IBackPropagation IBackPro, List <FaceImage> trainingSet)
{
if (trainingSet == null)
{
throw new ArgumentNullException("trainingSet");
}
_maxError = Constants.MAX_ERROR;
_maxIter = Constants.MAX_ITERATION;
_neuralNet = IBackPro;
_trainingSet = trainingSet;
_neuralNet.InitializeNetwork(_trainingSet);
}
/* constructors. */
public RandomSimulation(IMCTSPlayer thisPlayer, IMCTSPlayer opponent, Board b, string expansion, string backPropagation)
{
board = new Board(b);
this.thisPlayer = thisPlayer;
this.opponent = opponent;
wins = visits = 0;
exp = ExpansionFactory.Create(expansion);
bp = BackPropagationFactory.Create(backPropagation);
rand = new Random();
workers = new List <Thread>(threadNo);
mutex = new Mutex();
isSimulating = false;
root = null;
treeRoot = null;
}
void InitializeBackPropagation()
{
switch (CostFunction)
{
case CostFuntionKind.Quadratic:
backPropagation = new BackPropagationQuadratic(neuralNetAccessor);
break;
case CostFuntionKind.CrossEntropy:
backPropagation = new BackPropagationCrossEntropy(neuralNetAccessor);
break;
default:
throw new System.NotImplementedException();
}
}
public void RecognizeTest()
{
IBackPropagation target = CreateIBackPropagation(); // TODO: Initialize to an appropriate value
double[] Input = null; // TODO: Initialize to an appropriate value
string MatchedHigh = string.Empty; // TODO: Initialize to an appropriate value
string MatchedHighExpected = string.Empty; // TODO: Initialize to an appropriate value
double OutputValueHight = 0F; // TODO: Initialize to an appropriate value
double OutputValueHightExpected = 0F; // TODO: Initialize to an appropriate value
string MatchedLow = string.Empty; // TODO: Initialize to an appropriate value
string MatchedLowExpected = string.Empty; // TODO: Initialize to an appropriate value
double OutputValueLow = 0F; // TODO: Initialize to an appropriate value
double OutputValueLowExpected = 0F; // TODO: Initialize to an appropriate value
target.Recognize(Input, ref MatchedHigh, ref OutputValueHight, ref MatchedLow, ref OutputValueLow);
Assert.AreEqual(MatchedHighExpected, MatchedHigh);
Assert.AreEqual(OutputValueHightExpected, OutputValueHight);
Assert.AreEqual(MatchedLowExpected, MatchedLow);
Assert.AreEqual(OutputValueLowExpected, OutputValueLow);
Assert.Inconclusive("A method that does not return a value cannot be verified.");
}
public void LoadNetwork(string path)
{
try
{
using (var fs = new FileStream(path, FileMode.Open))
{
var bf = new BinaryFormatter();
_neuralNet = (IBackPropagation)bf.Deserialize(fs);
fs.Close();
}
}
catch (Exception ex)
{
bool rethrow = ExceptionPolicy.HandleException(ex, "IO Pocily");
if (rethrow)
{
throw;
}
MessageBox.Show("Failed load network");
}
}
public NeuralNetwork(IBackPropagation <T> IBackPro, Dictionary <T, double[]> trainingSet)
{
NeuralNet = IBackPro;
TrainingSet = trainingSet;
NeuralNet.InitializeNetwork(TrainingSet);
}
