public override void DeserializeAttributes(PersistenceReader ip)
{
m_Value = ip.GetString("v");
m_Format = Utility.Intern(ip.GetString("f"));
if (m_Format == null)
{
Utility.Intern(ref m_Value);
}
}
public void Deserialize(PersistenceReader ip)
{
DeserializeAttributes(ip);
if (ip.BeginChildren())
{
DeserializeChildren(ip);
ip.FinishChildren();
}
}
public override void DeserializeChildren(PersistenceReader ip)
{
while (ip.HasChild)
{
PersistableObject child = ip.GetChild();
if (child is ReportColumn)
{
m_Columns.Add((ReportColumn)child);
}
else if (child is ReportItem)
{
m_Items.Add((ReportItem)child);
}
}
}
public override void DeserializeAttributes(PersistenceReader ip)
{
m_Name = Utility.Intern(ip.GetString("n"));
m_Width = Utility.Intern(ip.GetString("w"));
}
public override void DeserializeAttributes(PersistenceReader ip)
{
m_Name = Utility.Intern(ip.GetString("n"));
m_Value = ip.GetInt32("v");
}
public override void DeserializeAttributes(PersistenceReader ip)
{
base.DeserializeAttributes(ip);
m_ShowPercents = ip.GetBoolean("p");
}
/// <summary>
/// Load an already trained model
/// </summary>
/// <param name="reader"></param>
protected override void LoadContent(PersistenceReader reader)
{
reader.OpenScope((PersistItemTag)Persistent.Parameters);
reader.GetValue(out C);
reader.GetValue(out cacheSize);
reader.GetValue(out maximumInput);
reader.GetValue(out lowerOrder);
reader.GetValue(out exponent);
reader.GetValue(out gamma);
reader.GetValue(out strKernelType);
reader.CloseScope();
reader.OpenScope((PersistItemTag)Persistent.Classifiers);
int length = 0;
reader.GetValue(out length);
classifiers = new Avanade.Datamining.SMO.SMO[length][];
for (int i = 0; i < classifiers.Length; i++)
{
classifiers[i] = new Avanade.Datamining.SMO.SMO[length];
for (int j = i + 1; j < classifiers.Length; j++)
{
classifiers[i][j] = new Avanade.Datamining.SMO.SMO();
reader.GetValue(out classifiers[i][j].b);
int instLength = 0;
reader.GetValue(out instLength);
Instances inst = new Instances(new Instance[instLength], this.getLabels(), 0);
for (int x = 0; x < instLength; x++)
{
int posLength = 0;
reader.GetValue(out posLength);
int[] positions = new int[posLength];
double[] values = new double[posLength];
for (int y = 0; y < posLength; y++)
{
reader.GetValue(out positions[y]);
reader.GetValue(out values[y]);
}
int label = 0;
reader.GetValue(out label);
Instance instance = new Instance(1, label, new double[] { });
instance.values = values;
instance.positions = positions;
inst.instances[x] = instance;
}
classifiers[i][j].kernel = getKernel(inst);
int supportvectorslength = 0;
reader.GetValue(out supportvectorslength);
LinkedList <int> supportVectors = new LinkedList <int>();
for (int x = 0; x < supportvectorslength; x++)
{
int s = 0;
reader.GetValue(out s);
supportVectors.AddLast(s);
}
classifiers[i][j].supportVectors = supportVectors;
int alphaLength = 0;
reader.GetValue(out alphaLength);
double[] alphas = new double[alphaLength];
for (int x = 0; x < alphas.Length; x++)
{
reader.GetValue(out alphas[x]);
}
classifiers[i][j].alpha = alphas;
int labelLength = 0;
reader.GetValue(out labelLength);
double[] labels = new double[labelLength];
for (int x = 0; x < labels.Length; x++)
{
reader.GetValue(out labels[x]);
}
classifiers[i][j].labels = labels;
}
}
reader.CloseScope();
}
public virtual void DeserializeChildren(PersistenceReader ip)
{
}
public virtual void DeserializeAttributes(PersistenceReader ip)
{
}
