public void CanReadTabSeparatedList()
{
var reader = new TabSeparatedListReader();
List <KeyValuePair <double, double[]> > set = reader.Read(@"..\..\trainingsets\dataset2\biased.txt");
Assert.AreEqual(208, set.Count);
}
public void FTClassifiesBiasedData()
{
// data has 2 double inputs and an expected binary out
var network = new PerceptronFactory().BuildFTPerceptron(2);
var reader = new TabSeparatedListReader();
List <KeyValuePair <double, double[]> > trainingSet = reader.Read(@"..\..\trainingsets\dataset2\biased.txt");
network.Train(trainingSet);
trainingSet.ForEach(kvp => Assert.AreEqual(kvp.Key, network.Classify(kvp.Value)));
}
public void CanReadTabSeparatedList()
{
var reader = new TabSeparatedListReader();
List<KeyValuePair<double, double[]>> set = reader.Read(@"..\..\trainingsets\dataset2\biased.txt");
Assert.AreEqual(208, set.Count);
}
public void FTClassifiesBiasedData()
{
// data has 2 double inputs and an expected binary out
var network = new PerceptronFactory().BuildFTPerceptron(2);
var reader = new TabSeparatedListReader();
List<KeyValuePair<double, double[]>> trainingSet = reader.Read(@"..\..\trainingsets\dataset2\biased.txt");
network.Train(trainingSet);
trainingSet.ForEach(kvp => Assert.AreEqual(kvp.Key, network.Classify(kvp.Value)));
}
