public void BuildAttributesList()
{
Attributes = new List <DecisionClassAttribute>();
for (int i = 1; i < this.decisionClassVector.Length; i++)
{
string attributeName = this.decisionClassVector[i];
string decision = this.decisionsVector[i];
var attribute = Attributes
.Where(x => x.AttributeName == attributeName)
.FirstOrDefault();
if (attribute == null)
{
attribute = new DecisionClassAttribute(attributeName);
Attributes.Add(attribute);
}
if (decision == "No")
{
attribute.Negatives++;
}
else
{
attribute.Positives++;
}
}
}
private Leaf build(DecisionSystem ds, DecisionClassAttribute parentLink)
{
var leaf = new Leaf {
ParentLink = parentLink
};
// an end of recursion
// if entropy equals 0 decision is deterministic
if (parentLink != null && parentLink.Entropy == 0)
{
return(leaf);
}
// divide decision system into decision classes, assuming that
// first row contains labels,
// first column contains ordinal,
// last column contains decision
var records = ds.Records;
var decisionClasses = new List <DecisionClass>();
var decisionsVector = ds.
GetDecisionClassVector(ds.Records[0].Length - 1);
for (int i = 1; i < records[0].Length - 1; i++)
{
var decisionClassVector = ds.GetDecisionClassVector(i);
var dc = new DecisionClass(decisionClassVector,
decisionsVector);
decisionClasses.Add(dc);
}
decisionClasses.ForEach(x => x.BuildAttributesList());
// sort decision classes by their gain
decisionClasses.Sort();
// get class with the highest gain as a leaf
leaf.DecisionClass = decisionClasses.Last();
// we continue to build tree,
// searching for the next decision class for leaf
foreach (var attribute in leaf.DecisionClass.Attributes)
{
var subset = ds.GetSubset(leaf.DecisionClass.Name,
attribute.AttributeName);
leaf.Children.Add(build(subset, attribute));
}
return(leaf);
}
private Leaf build(DecisionSystem ds, DecisionClassAttribute parentLink)
{
var leaf = new Leaf { ParentLink = parentLink };
// an end of recursion
// if entropy equals 0 decision is deterministic
if (parentLink != null && parentLink.Entropy == 0)
return leaf;
// divide decision system into decision classes, assuming that
// first row contains labels,
// first column contains ordinal,
// last column contains decision
var records = ds.Records;
var decisionClasses = new List<DecisionClass>();
var decisionsVector = ds.
GetDecisionClassVector(ds.Records[0].Length - 1);
for (int i = 1; i < records[0].Length - 1; i++)
{
var decisionClassVector = ds.GetDecisionClassVector(i);
var dc = new DecisionClass(decisionClassVector,
decisionsVector);
decisionClasses.Add(dc);
}
decisionClasses.ForEach(x => x.BuildAttributesList());
// sort decision classes by their gain
decisionClasses.Sort();
// get class with the highest gain as a leaf
leaf.DecisionClass = decisionClasses.Last();
// we continue to build tree,
// searching for the next decision class for leaf
foreach (var attribute in leaf.DecisionClass.Attributes)
{
var subset = ds.GetSubset(leaf.DecisionClass.Name,
attribute.AttributeName);
leaf.Children.Add(build(subset, attribute));
}
return leaf;
}
