static void Main(string[] args)
{
Attribute ceu = new Attribute("ceu", new string[] { "sol", "nublado", "chuva" });
Attribute temperatura = new Attribute("temperatura", new string[] { "alta", "baixa", "suave" });
Attribute humidade = new Attribute("humidade", new string[] { "alta", "normal" });
Attribute vento = new Attribute("vento", new string[] { "sim", "nao" });
Attribute[] attributes = new Attribute[] { ceu, temperatura, humidade, vento };
DataTable samples = getDataTable();
DecisionTreeID3 id3 = new DecisionTreeID3();
TreeNode root = id3.mountTree(samples, "result", attributes);
printNode(root, "");
}
static void Main(string[] args)
{
Attribute ceu = new Attribute("ceu", new string[] {"sol", "nublado", "chuva"});
Attribute temperatura = new Attribute("temperatura", new string[] {"alta", "baixa", "suave"});
Attribute humidade = new Attribute("humidade", new string[] {"alta", "normal"});
Attribute vento = new Attribute("vento", new string[] {"sim", "nao"});
Attribute[] attributes = new Attribute[] {ceu, temperatura, humidade, vento};
DataTable samples = getDataTable();
DecisionTreeID3 id3 = new DecisionTreeID3();
TreeNode root = id3.mountTree(samples, "result", attributes);
printNode(root, "");
}
private TreeNode internalMountTree(DataTable samples, string targetAttribute, Attribute[] attributes)
{
if (allSamplesPositives(samples, targetAttribute) == true)
return new TreeNode(new Attribute(true));
if (allSamplesNegatives(samples, targetAttribute) == true)
return new TreeNode(new Attribute(false));
if (attributes.Length == 0)
return new TreeNode(new Attribute(getMostCommonValue(samples, targetAttribute)));
mTotal = samples.Rows.Count;
mTargetAttribute = targetAttribute;
mTotalPositives = countTotalPositives(samples);
mEntropySet = calcEntropy(mTotalPositives, mTotal - mTotalPositives);
Attribute bestAttribute = getBestAttribute(samples, attributes);
TreeNode root = new TreeNode(bestAttribute);
DataTable aSample = samples.Clone();
foreach(string value in bestAttribute.values)
{
aSample.Rows.Clear();
DataRow[] rows = samples.Select(bestAttribute.AttributeName + " = " + "'" + value + "'");
foreach(DataRow row in rows)
{
aSample.Rows.Add(row.ItemArray);
}
ArrayList aAttributes = new ArrayList(attributes.Length - 1);
for(int i = 0; i < attributes.Length; i++)
{
if (attributes[i].AttributeName != bestAttribute.AttributeName)
aAttributes.Add(attributes[i]);
}
if (aSample.Rows.Count == 0)
{
return new TreeNode(new Attribute(getMostCommonValue(aSample, targetAttribute)));
}
else
{
DecisionTreeID3 dc3 = new DecisionTreeID3();
TreeNode ChildNode = dc3.mountTree(aSample, targetAttribute, (Attribute[])aAttributes.ToArray(typeof(Attribute)));
root.AddTreeNode(ChildNode, value);
}
}
return root;
}
private TreeNode internalMountTree(DataTable samples, string targetAttribute, Attribute[] attributes)
{
if (allSamplesPositives(samples, targetAttribute) == true)
{
return(new TreeNode(new Attribute(true)));
}
if (allSamplesNegatives(samples, targetAttribute) == true)
{
return(new TreeNode(new Attribute(false)));
}
if (attributes.Length == 0)
{
return(new TreeNode(new Attribute(getMostCommonValue(samples, targetAttribute))));
}
mTotal = samples.Rows.Count;
mTargetAttribute = targetAttribute;
mTotalPositives = countTotalPositives(samples);
mEntropySet = calcEntropy(mTotalPositives, mTotal - mTotalPositives);
Attribute bestAttribute = getBestAttribute(samples, attributes);
TreeNode root = new TreeNode(bestAttribute);
DataTable aSample = samples.Clone();
foreach (string value in bestAttribute.values)
{
aSample.Rows.Clear();
DataRow[] rows = samples.Select(bestAttribute.AttributeName + " = " + "'" + value + "'");
foreach (DataRow row in rows)
{
aSample.Rows.Add(row.ItemArray);
}
ArrayList aAttributes = new ArrayList(attributes.Length - 1);
for (int i = 0; i < attributes.Length; i++)
{
if (attributes[i].AttributeName != bestAttribute.AttributeName)
{
aAttributes.Add(attributes[i]);
}
}
if (aSample.Rows.Count == 0)
{
return(new TreeNode(new Attribute(getMostCommonValue(aSample, targetAttribute))));
}
else
{
DecisionTreeID3 dc3 = new DecisionTreeID3();
TreeNode ChildNode = dc3.mountTree(aSample, targetAttribute, (Attribute[])aAttributes.ToArray(typeof(Attribute)));
root.AddTreeNode(ChildNode, value);
}
}
return(root);
}