private void runTreeInThreads(DecisionTree tree, DataSet data)
{
int tuplesInThread = data.DataValues.Count / MAX_THREADS;
int from = 0;
List <int> froms = new List <int>();
froms.Add(0);
for (int i = 1; i < MAX_THREADS; i++)
{
froms.Add(froms[i - 1] + tuplesInThread);
}
foreach (var v in froms)
{
Console.WriteLine(v);
}
List <System.Threading.Thread> tasks = new List <System.Threading.Thread>();
for (int i = 0; i < MAX_THREADS - 1; i++)
{
System.Threading.Monitor.Enter(semaphore);
Object fr = new object();
fr = from;
System.Threading.Thread thread = new System.Threading.Thread(delegate() {
runTree(data, tree, (int)fr, (int)fr + tuplesInThread);
});
tasks.Add(thread);
thread.Start();
from += tuplesInThread;
System.Threading.Monitor.Exit(semaphore);
}
System.Threading.Thread lastThread = new System.Threading.Thread(delegate() {
runTree(data, tree, from, data.DataValues.Count);
});
tasks.Add(lastThread);
lastThread.Start();
foreach (var temp in tasks)
{
temp.Join();
}
}
public void SprintClassification(string treePath, string treeName, string dataPath, string dataName, char separator = ';')
{
DecisionTree tree = new DecisionTree();
Globals.stopWatch.Start();
tree.loadFromTxt(treePath + "\\" + treeName + ".txt");
Globals.stopWatch.Stop();
Globals.printElapsedTimeResetWatch(Globals.stopWatch.Elapsed, "Loading tree");
DataSet dataSet = new DataSet(dataPath + "\\" + dataName + ".txt", separator, true);
Globals.stopWatch.Start();
runTreeInThreads(tree, dataSet);
//foreach (var v in dataSet.DataValues) tree.classifyTuple(v);
Globals.stopWatch.Stop();
Globals.printElapsedTimeResetWatch(Globals.stopWatch.Elapsed, "Classifying");
Globals.stopWatch.Start();
dataSet.saveToFile(dataPath, dataName, separator);
Globals.stopWatch.Stop();
Globals.printElapsedTimeResetWatch(Globals.stopWatch.Elapsed, "Saving result to file");
}
public void loadFromTxt(string path)
{
try
{
using (var sr = new StreamReader(path))
{
string newLine = sr.ReadLine();
var attributes = newLine.Split(';');
nodeAttribute = Int32.Parse(attributes[0]);
nodeOperator = attributes[1];
nodeValue = attributes[2];
while (true)
{
newLine = sr.ReadLine();
if (newLine == null)
{
break;
}
attributes = newLine.Split(';');
DecisionTree toAdd;
if (attributes.Count() == 1)
{
toAdd = new DecisionTree(0, "-1", "-1", attributes[0]);
}
else
{
toAdd = new DecisionTree(Int32.Parse(attributes[0]), attributes[1], attributes[2]);
}
addChild(toAdd);
}
}
}
catch (IOException e)
{
Console.WriteLine("The file could not be read:");
Console.WriteLine(e.Message);
}
}
public void createTree(string trainingPath, string trainingName, char separator)
{
Globals.stopWatch.Start();
DataSet trainingSet = new DataSet(trainingPath + "\\" + trainingName + ".txt", separator, true);
Globals.stopWatch.Stop();
Globals.printElapsedTimeResetWatch(Globals.stopWatch.Elapsed, "Training data set");
Globals.stopWatch.Start();
Sprint sprint = new Sprint(trainingSet);
Globals.stopWatch.Stop();
Globals.printElapsedTimeResetWatch(Globals.stopWatch.Elapsed, "Sprint created");
Globals.stopWatch.Start();
DecisionTree tree = sprint.createTree();
Globals.stopWatch.Stop();
Globals.printElapsedTimeResetWatch(Globals.stopWatch.Elapsed, "Tree created");
Globals.stopWatch.Start();
tree.saveToTxt(trainingPath, "Decision_Tree_" + trainingName);
Globals.stopWatch.Stop();
Globals.printElapsedTimeResetWatch(Globals.stopWatch.Elapsed, "Saving tree to file");
}
private bool addChild(DecisionTree toAdd)
{
if (leftChild == null)
{
leftChild = toAdd;
return(true);
}
else if (leftChild.Leaf == null)
{
bool b = leftChild.addChild(toAdd);
if (b)
{
return(true);
}
if (rightChild == null)
{
rightChild = toAdd;
return(true);
}
if (rightChild.Leaf != null)
{
return(false);
}
return(rightChild.addChild(toAdd));
}
else if (rightChild == null)
{
rightChild = toAdd;
return(true);
}
else if (rightChild.Leaf == null)
{
return(rightChild.addChild(toAdd));
}
return(false);
}
private void runTree(DataSet data, DecisionTree tree, int from, int to)
{
data.clasifyTreeThread(tree, from, to);
}