static void LevelI()
{
// Load training samples
var signal = DataSet.ReadDataSet(path + "signal.dat");
var background = DataSet.ReadDataSet(path + "background.dat");
// Load data sample
var data = DataSet.ReadDataSet(path + "decisionTreeData.dat");
int bestVariableIndex = -1;
double bestSplitValue = 0;
// TODO: Insert code here that calculates the proper values of bestVariableIndex and bestSplitValue
using var file = File.CreateText(path + "decisionTreeResultsLevelI.txt");
file.WriteLine("Event\tPurity");
for (int i = 0; i < data.Points.Count; ++i)
{
// Note that you may have to change the order of the 1 and 0 here, depending on which one matches signal.
// 1 means signal and 0 means background
double output = data.Points[i].Variables[bestVariableIndex] > bestSplitValue ? 1 : 0;
file.WriteLine(i + "\t" + output);
}
}
static void LevelIIAndBeyond()
{
// Load training samples
var signal = DataSet.ReadDataSet(path + "signal.dat");
var background = DataSet.ReadDataSet(path + "background.dat");
// Load data sample
var data = DataSet.ReadDataSet(path + "decisionTreeData.dat");
var tree = new Tree();
// Train the tree
tree.Train(signal, background);
// Calculate output value for each event and write to file
tree.MakeTextFile(path + "decisionTreeResults.txt", data);
}
static void Main()
{
// Load training samples
var background = DataSet.ReadDataSet(path + "backgroundOverallTrainingSample.dat");
var signal = DataSet.ReadDataSet(path + "signalOverallTrainingSample.dat");
// Load data sample
//var data = DataSet.ReadDataSet(path + "project3Data.dat");
var data = DataSet.ReadDataSet(path + "project3Data.dat");
int numTrees = 30;
Forest forest = new Forest(numTrees);
forest.Train(signal, background);
List <double> outputs = new List <double>();
for (int i = 0; i < data.Points.Count; i++)
{
double output = forest.RunDataPoint(data.Points[i]);
outputs.Add(output);
//Need to normalize datapoint on new scale
}
//double normalizationFactor = outputs.Max();
double normalizationFactor = 1;
//It seems like numAbove is background and numBelow is signal
int numAbove = 0;
int numBelow = 0;
using var file = File.CreateText(path + "JayAyanSignals.txt");
int count = 0;
foreach (double d in outputs)
{
Console.WriteLine(d / normalizationFactor);
if (d / normalizationFactor >= numTrees)
{
numAbove += 1;
file.WriteLine(count);
}
else
{
numBelow += 1;
}
count += 1;
}
Console.WriteLine("num above: " + numAbove);
Console.WriteLine("num below: " + numBelow);
Console.WriteLine(count);
//Level1(signal, background, data);
//var tree = new Tree();
//// Train the tree
//tree.Train(signal, background);
//// Calculate output value for each event and write to file
//using var file = File.CreateText(path + "Level 2 Results.txt");
//file.WriteLine("Event\tPurity");
//for (int i = 0; i < data.Points.Count; ++i)
//{
// double output = tree.RunDataPoint(data.Points[i]);
// file.WriteLine(i + "\t" + output);
//}
}