public void EncodingAndNormalizationTest()
{
string[] rawData = new[]
{
"Sex Age Locale Income Politics",
"==============================================",
"Male 25 Rural 63,000.00 Conservative",
"Female 36 Suburban 55,000.00 Liberal",
"Male 40 Urban 74,000.00 Moderate",
"Female 23 Rural 28,000.00 Liberal"
};
char separator = ' ';
string[] encodedData = rawData.Skip(2).ToArray();
encodedData = ParsingUtilities.EncodeColumnInLine(encodedData, 4, separator, Encode.DependentVariable);
encodedData = ParsingUtilities.EncodeColumnInLine(encodedData, 2, separator, Encode.Predictor);
encodedData = ParsingUtilities.EncodeColumnInLine(encodedData, 0, separator, Encode.Predictor);
var numericalData = ParsingUtilities.ConvertToNumeric(encodedData, separator);
Normalize.Multiple(numericalData, Normalize.Gauss, 1);
Normalize.Multiple(numericalData, Normalize.MinMax, 4);
Assert.AreEqual("Moderate", ParsingUtilities.Decode(rawData.Skip(2).ToArray(), 4, ' ', new double[] { 0, 0, 1 }));
}
public void AutomaticallyEncodedNeuralNetworkDataTest()
{
string[] rawData = new[]
{
"Sex Age Locale Income Politics",
"==============================================",
"Male 25 Rural 63,000.00 Conservative",
"Female 36 Suburban 55,000.00 Liberal",
"Male 40 Urban 74,000.00 Moderate",
"Female 23 Rural 28,000.00 Liberal"
};
char separator = ' ';
var networkData = new NeuralNetworkData(rawData.Skip(2).ToArray(), separator);
var convertedData = networkData.ConvertedData;
Assert.AreEqual("Moderate", networkData.DecodeOutput(0, 0, 1));
Assert.AreEqual("Liberal", networkData.DecodeOutput(0, 1, 0));
Assert.AreEqual("Conservative", networkData.DecodeOutput(1, 0, 0));
Assert.AreEqual("Moderate", ParsingUtilities.Decode(rawData.Skip(2).ToArray(), 4, separator, new double[] { 0, 0, 1 }));
}
