public void DataPoint_CanSerailize()
{
DataPoint point1 = new DataPoint(new[] { 1.0, 1.1 }, new[] { 2.0, 2.2 });
DataPoint point2 = new DataPoint(new[] { 2.0, 2.1 }, new[] { 4.0, 2.2 });
DataPoint point3 = new DataPoint(new[] { 3.0, 3.1 }, new[] { 6.0, 2.2 });
DataPoint point4 = new DataPoint(new[] { 4.0, 4.1 }, new[] { 8.0, 2.2 });
DataPoint point5 = new DataPoint(new[] { 5.0, 5.1 }, new[] { 10.0, 2.2 });
DataPointCollection dataPointCollection = new DataPointCollection();
dataPointCollection.Add(point1);
dataPointCollection.Add(point2);
dataPointCollection.Add(point3);
dataPointCollection.Add(point4);
dataPointCollection.Add(point5);
XmlSerializer serializer = new XmlSerializer(typeof(DataPointCollection));
serializer.Serialize(Console.Out, dataPointCollection);
}
public void CanTrainNetwork()
{
//XOR data
DataPoint _dp1 = new DataPoint(new[] { 1.0, 1.0 }, new[] { 0.0 });
DataPoint _dp2 = new DataPoint(new[] { 1.0, 0.0 }, new[] { 1.0 });
DataPoint _dp3 = new DataPoint(new[] { 0.0, 1.0 }, new[] { 1.0 });
DataPoint _dp4 = new DataPoint(new[] { 0.0, 0.0 }, new[] { 0.0 });
DataPointCollection _dataPointCollection = new DataPointCollection();
_dataPointCollection.Add(_dp1);
_dataPointCollection.Add(_dp2);
_dataPointCollection.Add(_dp3);
_dataPointCollection.Add(_dp4);
int[] _layerSizes = new int[3] { 2, 2, 1 };
TransferFunction[] _transferFunctions = new TransferFunction[3]
{
TransferFunction.None,
TransferFunction.Sigmoid,
TransferFunction.Linear
};
BackPropagationNetwork _bpn = new BackPropagationNetwork(_layerSizes, _transferFunctions);
SimpleNetworkTrainer _networkTrainer = new SimpleNetworkTrainer(_bpn, _dataPointCollection);
_networkTrainer.TargetError = 0.0001;
_networkTrainer.MaxIterations = 1000000;
_networkTrainer.NudgeScale = 0.8;
_networkTrainer.NudgeWindow = 100;
_networkTrainer.TrainNetwork();
Assert.IsTrue(true, "Never Reached Minimum Error");
for (int i = _networkTrainer.ErrorHistory.Count - 100; i < _networkTrainer.ErrorHistory.Count; i++)
{
Console.WriteLine("{0}: {1:0.00000000}", i, _networkTrainer.ErrorHistory[i]);
}
}
public void ConvolutionNetwork()
{
int[] layerSizes = new int[5]
{
841,
1014,
1250,
100,
10
};
TransferFunction[] transferFunctions = new TransferFunction[5]
{
TransferFunction.None,
TransferFunction.Convolution,
TransferFunction.Convolution,
TransferFunction.Linear,
TransferFunction.Linear
};
BackPropagationNetwork backPropagationNetwork = new BackPropagationNetwork(layerSizes, transferFunctions);
double[] input1 = new double[841];
for (int i = 0; i < input1.Length; i++)
{
if(i % 2 == 0)
input1[i] = 1;
}
double[] input2 = new double[841];
for (int i = 0; i < input2.Length; i++)
{
input2[i] = 1;
}
DataPoint _dp1 = new DataPoint(input1, new[] { 1.0 });
DataPoint _dp2 = new DataPoint(input2, new[] { 0.0 });
DataPointCollection _dataPointCollection = new DataPointCollection();
_dataPointCollection.Add(_dp1);
_dataPointCollection.Add(_dp2);
SimpleNetworkTrainer _networkTrainer = new SimpleNetworkTrainer(backPropagationNetwork, _dataPointCollection);
_networkTrainer.TargetError = 0.0001;
_networkTrainer.MaxIterations = 1000000;
_networkTrainer.NudgeScale = 0.8;
_networkTrainer.NudgeWindow = 100;
_networkTrainer.TrainNetwork();
Assert.IsTrue(true, "Never Reached Minimum Error");
for (int i = _networkTrainer.ErrorHistory.Count - 100; i < _networkTrainer.ErrorHistory.Count; i++)
{
Console.WriteLine("{0}: {1:0.00000000}", i, _networkTrainer.ErrorHistory[i]);
}
}
