/// <summary>
/// Builds the tree by choosing the best dividing criteria for the current set
/// </summary>
public static DecisionNode BuildTree(Set rows, MeasuringMetric mode = MeasuringMetric.Entropy)
{
Fun scoreCalc = Entropy;
switch (mode)
{
case MeasuringMetric.Entropy:
scoreCalc = Entropy;
break;
case MeasuringMetric.GiniImpurity:
scoreCalc = GiniImpurity;
break;
case MeasuringMetric.Variance:
scoreCalc = Variance;
break;
}
if (rows.Count == 0)
{
return(new DecisionNode());
}
double currentScore = scoreCalc(rows);
// Set up some variables to track the best criteria
double bestGain = 0;
Pair bestCriteria = new Pair();
DividedSet bestSets = new DividedSet();
int columnCount = rows[0].Length - 1;
for (int col = 0; col < columnCount; ++col)
{
// Generate the list of different values in this column
List <string> columnValues = new List <string>();
foreach (var row in rows)
{
if (!columnValues.Contains(row[col]))
{
columnValues.Add(row[col]);
}
}
// Now try dividing the rows up for each value in this column
foreach (var value in columnValues)
{
DividedSet divSet = DivideSet(rows, col, value);
// Information gain
double p = (double)divSet.Key.Count / rows.Count;
double gain = currentScore - p * scoreCalc(divSet.Key) - (1 - p) * scoreCalc(divSet.Value);
if (gain > bestGain && divSet.Key.Count > 0 && divSet.Value.Count > 0)
{
bestGain = gain;
bestCriteria = new Pair(col, value);
bestSets = divSet;
}
}
}
// Create the subbranches
if (bestGain > 0)
{
DecisionNode trueBranch = BuildTree(bestSets.Key);
DecisionNode falseBranch = BuildTree(bestSets.Value);
return(new DecisionNode(column: bestCriteria.Key, value: bestCriteria.Value, nextTrueNode: trueBranch,
nextFalseNode: falseBranch));
}
return(new DecisionNode(results: UniqueCounts(rows)));
}
/// <summary>
/// Builds the tree by choosing the best dividing criteria for the current set
/// </summary>
public static DecisionNode BuildTree(Set rows, MeasuringMetric mode = MeasuringMetric.Entropy)
{
Fun scoreCalc = Entropy;
switch (mode)
{
case MeasuringMetric.Entropy:
scoreCalc = Entropy;
break;
case MeasuringMetric.GiniImpurity:
scoreCalc = GiniImpurity;
break;
case MeasuringMetric.Variance:
scoreCalc = Variance;
break;
}
if (rows.Count == 0)
{
return new DecisionNode();
}
double currentScore = scoreCalc(rows);
// Set up some variables to track the best criteria
double bestGain = 0;
Pair bestCriteria = new Pair();
DividedSet bestSets = new DividedSet();
int columnCount = rows[0].Length - 1;
for (int col = 0; col < columnCount; ++col)
{
// Generate the list of different values in this column
List<string> columnValues = new List<string>();
foreach (var row in rows)
{
if (!columnValues.Contains(row[col]))
{
columnValues.Add(row[col]);
}
}
// Now try dividing the rows up for each value in this column
foreach (var value in columnValues)
{
DividedSet divSet = DivideSet(rows, col, value);
// Information gain
double p = (double)divSet.Key.Count / rows.Count;
double gain = currentScore - p * scoreCalc(divSet.Key) - (1 - p) * scoreCalc(divSet.Value);
if (gain > bestGain && divSet.Key.Count > 0 && divSet.Value.Count > 0)
{
bestGain = gain;
bestCriteria = new Pair(col, value);
bestSets = divSet;
}
}
}
// Create the subbranches
if (bestGain > 0)
{
DecisionNode trueBranch = BuildTree(bestSets.Key);
DecisionNode falseBranch = BuildTree(bestSets.Value);
return new DecisionNode(column: bestCriteria.Key, value: bestCriteria.Value, nextTrueNode: trueBranch,
nextFalseNode: falseBranch);
}
return new DecisionNode(results: UniqueCounts(rows));
}
