public DecisionSystem GetSubset(string decisionClassName,
string attributeName)
{
int classNumber;
for (classNumber = 1;
classNumber < Records[0].Length - 1;
classNumber++)
{
if (Records[0][classNumber] == decisionClassName)
{
break;
}
}
var recordsList = new List <string[]>();
recordsList.Add(Records[0]);
for (int i = 1; i < Records.Length; i++)
{
if (Records[i][classNumber] == attributeName)
{
recordsList.Add(Records[i]);
}
}
var subset = new DecisionSystem();
subset.Records = recordsList.ToArray();
return(subset);
}
public DecisionSystem GetSubset(string decisionClassName,
string attributeName)
{
int classNumber;
for (classNumber = 1;
classNumber < Records[0].Length - 1;
classNumber++)
if (Records[0][classNumber] == decisionClassName)
break;
var recordsList = new List<string[]>();
recordsList.Add(Records[0]);
for (int i = 1; i < Records.Length; i++)
if (Records[i][classNumber] == attributeName)
recordsList.Add(Records[i]);
var subset = new DecisionSystem();
subset.Records = recordsList.ToArray();
return subset;
}
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;
}
public static void Main(string[] args)
{
string outputFileName = "graph.dot";
if (args.Length == 0)
{
printHelp();
return;
}
for (int i = 1; i < args.Length; i++)
{
switch (args[i])
{
case "--output-file-name":
i++;
outputFileName = args[i];
break;
default:
break;
}
}
string path = args[0];
var ds = new DecisionSystem();
ds.Load(path);
var tree = new DecisionTree();
tree.Build(ds);
var graph = new DecisionTreeGraph(tree);
graph.BuildGraph();
graph.DrawGraph(outputFileName);
}
public static void Main(string[] args)
{
string outputFileName = "graph.dot";
if (args.Length == 0)
{
printHelp();
return;
}
for (int i = 1; i < args.Length; i++)
{
switch (args[i])
{
case "--output-file-name":
i++;
outputFileName = args[i];
break;
default:
break;
}
}
string path = args[0];
var ds = new DecisionSystem();
ds.Load(path);
var tree = new DecisionTree();
tree.Build(ds);
var graph = new DecisionTreeGraph(tree);
graph.BuildGraph();
graph.DrawGraph(outputFileName);
}
public void Build(DecisionSystem ds)
{
Root = build(ds, null);
}
public void Build(DecisionSystem ds)
{
Root = build(ds, null);
}
