public static double Predict(Model model, Node[] vx)
{
if (model.NumberOfClasses > 2)
{
int labelIndex = 0;
int count = 0;
for (int i = 0; i < model.NumberOfClasses; i++)
{
if ( !double.IsNaN(model.BinaryClassifiers[i].Classify(vx)) )
{
labelIndex = i;
count++;
}
}
if (count == 1)
{
return model.BinaryClassifiers[labelIndex].PosLabel;
}
else
return double.NaN;
}
else if (model.NumberOfClasses == 2)
{
return model.BinaryClassifiers[0].Classify(vx);
}
else
return double.NaN;;
}
public static Model Train(Problem prob, TrainingArg arg)
{
List<double> labels = GetLabels(prob);
if (labels.Count > 2)
{
List<BinaryClassifier> classifiers = new List<BinaryClassifier>(labels.Count);
foreach (double label in labels)
{
Console.WriteLine("{0} vs all:",label);
BinaryClassifier classifier = BinaryClassifier.Train(prob, arg, label);
classifiers.Add(classifier);
Console.WriteLine("finish");
}
Model model = new Model();
model.NumberOfClasses = classifiers.Count;
model.BinaryClassifiers = classifiers.ToArray();
return model;
}
else if (labels.Count == 2)
{
BinaryClassifier[] classifiers = new BinaryClassifier[1];
classifiers[0] = BinaryClassifier.Train(prob, arg, labels[0], labels[1]);
Model model = new Model();
model.NumberOfClasses = 2;
model.BinaryClassifiers = classifiers;
return model;
}
else
throw new Exception(Messege.CouldNotClassify);
}
/// <summary>
/// Reads a Model from the provided stream.
/// </summary>
/// <param name="stream">The stream from which to read the Model.</param>
/// <returns>the Model</returns>
public static Model Read(Stream stream)
{
XmlDocument xmldoc = new XmlDocument();
xmldoc.Load(stream);
Model model = new Model();
XmlNode modelNode = xmldoc.FirstChild;
XmlNodeList classifyNodes = modelNode.ChildNodes;
model.NumberOfClasses = int.Parse(modelNode.Attributes["NumberOfClasses"].Value);
if ( (model.NumberOfClasses == 2 && classifyNodes.Count != 1) &&
(classifyNodes.Count != model.NumberOfClasses) )
throw new Exception(Messege.ReadModelFail);
model.BinaryClassifiers = new BinaryClassifier[classifyNodes.Count];
for (int i = 0; i < classifyNodes.Count; i++ )
{
XmlElement binaryClassifierNode = (XmlElement)classifyNodes.Item(i);
BinaryClassifier binaryClassifer = BinaryClassifier.DeserializeFromXML(binaryClassifierNode);
model.BinaryClassifiers[i] = binaryClassifer;
}
return model;
}
/// <summary>
/// Reads a Model from the provided file.
/// </summary>
/// <param name="filename">The name of the file containing the Model</param>
/// <returns>the Model</returns>
public static void Write(string filename, Model model)
{
FileStream stream = File.Open(filename, FileMode.Create);
try
{
Write(stream, model);
}
finally
{
stream.Close();
}
}
/// <summary>
/// Number of support vectors per class.
/// </summary>
public static void Write(Stream stream, Model model)
{
XmlDocument xmlDoc = new XmlDocument();
//XmlNode declaration = xmlDoc.CreateXmlDeclaration("1.0", "utf-8", null);
//xmlDoc.AppendChild(declaration);
XmlElement modelRoot = xmlDoc.CreateElement("model");
modelRoot.SetAttribute("NumberOfClasses", model.NumberOfClasses.ToString());
xmlDoc.AppendChild(modelRoot);
for(int i = 0; i<model.BinaryClassifiers.Length; i++)
{
XmlElement binaryClassifierNode = xmlDoc.CreateElement("BinaryClassifier");
model.BinaryClassifiers[i].SerializeToXml(ref binaryClassifierNode);
modelRoot.AppendChild(binaryClassifierNode);
}
xmlDoc.Save(stream);
}
