public void BuildTree(string fileName, string outputDirectory = "Output")
{
var data = LoadDataFromfCSV(fileName);
var decisionTreeName = Path.GetFileNameWithoutExtension(fileName);
var algorithm = new C45Algorithm();
var fixedData = TableFixedData.FromTableData(data);
if (!Directory.Exists(outputDirectory))
{
Directory.CreateDirectory(outputDirectory);
}
var decisionalTree = algorithm.BuildConditionalTree(fixedData, new TreeOptions());
var decisionTreeRenderer = new DecisionTreeRenderer();
var bitmap = decisionTreeRenderer.RenderTree(decisionalTree, new Size(100, 50));
var decisionTreeFileName = Path.Combine(outputDirectory, string.Format("{0}.jpg", decisionTreeName));
bitmap.Save(decisionTreeFileName, ImageFormat.Jpeg);
var img = new BitmapImage(new Uri(decisionTreeFileName, UriKind.RelativeOrAbsolute));
img.Freeze();
//img.EndInit();
//mainGrid.Children.Remove(tree);
tree.Source = img;
tree.UpdateLayout();
//tree = new Image {Source = img};
//mainGrid.Children.Add(tree);
//Grid.SetRow(tree,1);
//Grid.SetColumn(tree, 1);
txtMessage.Text = decisionalTree.ToPseudocode();
}
public static void TestC45()
{
var data = LoadDataFromfCSV("Data2.csv");
var algorithm = new C45Algorithm();
var fixedData = TableFixedData.FromTableData(data);
var decisionalTree = algorithm.BuildConditionalTree(fixedData, new TreeOptions());
var decisionTreeRenderer = new DecisionTreeRenderer();
var bitmap = decisionTreeRenderer.RenderTree(decisionalTree, new Size(100, 50));
bitmap.Save("test.jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
var pseudocode = decisionalTree.ToPseudocode();
using (var textString = new StreamReader(@"C:\Research\Kaggle\Expedia\trainData\train.csv"))
{
while (!textString.EndOfStream)
{
var line = textString.ReadLine();
}
}
}
private static void Main(string[] args)
{
ExpediaReader reader = new ExpediaReader();
var dateTime = DateTime.Parse("9/3/2015 5:09:54 PM");
TestC45();
TestSantander.TestData();
//Tools.Test();
var data = LoadDataFromfCSV("Data.csv");
// TestNaiveBayes();
var algorithm = new C45Algorithm();
var fixedData = TableFixedData.FromTableData(data);
// var val = algorithm.ComputeGain(data, "Attribute1");
double splitValue;
var delta = 0.0;
var watch1 = new Stopwatch();
var watch2 = new Stopwatch();
var decisionalTree = algorithm.BuildConditionalTree(fixedData, new TreeOptions());
var randomForestAlgorithm = new RandomForestAlgorithm(70, null);
var forest = randomForestAlgorithm.BuildForest(fixedData);
for (int index = 0; index < 50; index++)
{
//var className1 = ret1.GetClass(data.ToList()[8]);
var row = data.ToList()[index];
var estimatedClassName = decisionalTree.GetClass(row);
var result = decisionalTree.Compute(row);
var result2 = forest.Compute(row);
var classsForest = fixedData.ClassesValue[forest.GetClass(row)];
if (estimatedClassName != row.Class || classsForest!= row.Class)
{
// missed++;
}
}
for (int index = 0; index < 100; index++)
{
watch1.Start();
var ret = algorithm.BuildConditionalTree(data, new TreeOptions());
watch1.Stop();
watch2.Start();
var ret1 = algorithm.BuildConditionalTree(fixedData, new TreeOptions());
watch2.Stop();
//var className = ret.GetClass(data.ToList()[8]);
var className1 = ret1.GetClass(data.ToList()[8]);
}
delta = watch1.Elapsed.Subtract(watch2.Elapsed).TotalMilliseconds;
}
public static void TestC45()
{
var data = LoadDataFromfCSV("Data2.csv");
var algorithm = new C45Algorithm();
var fixedData = TableFixedData.FromTableData(data);
var decisionalTree = algorithm.BuildConditionalTree(fixedData, new TreeOptions());
var decisionTreeRenderer = new DecisionTreeRenderer();
var bitmap = decisionTreeRenderer.RenderTree(decisionalTree, new Size(100, 50));
bitmap.Save("test.jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
var pseudocode = decisionalTree.ToPseudocode();
using (var textString = new StreamReader(@"C:\Research\Kaggle\Expedia\trainData\train.csv"))
{
while (!textString.EndOfStream)
{
var line = textString.ReadLine();
}
}
}
public void BuildTree(string fileName, string outputDirectory = "Output")
{
var data = LoadDataFromfCSV(fileName);
var decisionTreeName = Path.GetFileNameWithoutExtension(fileName);
var algorithm = new C45Algorithm();
var fixedData = TableFixedData.FromTableData(data);
if (!Directory.Exists(outputDirectory))
{
Directory.CreateDirectory(outputDirectory);
}
var decisionalTree = algorithm.BuildConditionalTree(fixedData, new TreeOptions());
var decisionTreeRenderer = new DecisionTreeRenderer();
var bitmap = decisionTreeRenderer.RenderTree(decisionalTree, new Size(100, 50));
var decisionTreeFileName = Path.Combine(outputDirectory, string.Format("{0}.jpg", decisionTreeName));
bitmap.Save(decisionTreeFileName, ImageFormat.Jpeg);
var img = new BitmapImage(new Uri(decisionTreeFileName, UriKind.RelativeOrAbsolute));
img.Freeze();
//img.EndInit();
//mainGrid.Children.Remove(tree);
tree.Source = img;
tree.UpdateLayout();
//tree = new Image {Source = img};
//mainGrid.Children.Add(tree);
//Grid.SetRow(tree,1);
//Grid.SetColumn(tree, 1);
txtMessage.Text = decisionalTree.ToPseudocode();
}
private static void Main(string[] args)
{
ExpediaReader reader = new ExpediaReader();
var dateTime = DateTime.Parse("9/3/2015 5:09:54 PM");
TestC45();
TestSantander.TestData();
//Tools.Test();
var data = LoadDataFromfCSV("Data.csv");
// TestNaiveBayes();
var algorithm = new C45Algorithm();
var fixedData = TableFixedData.FromTableData(data);
// var val = algorithm.ComputeGain(data, "Attribute1");
double splitValue;
var delta = 0.0;
var watch1 = new Stopwatch();
var watch2 = new Stopwatch();
var decisionalTree = algorithm.BuildConditionalTree(fixedData, new TreeOptions());
var randomForestAlgorithm = new RandomForestAlgorithm(70, null);
var forest = randomForestAlgorithm.BuildForest(fixedData);
for (int index = 0; index < 50; index++)
{
//var className1 = ret1.GetClass(data.ToList()[8]);
var row = data.ToList()[index];
var estimatedClassName = decisionalTree.GetClass(row);
var result = decisionalTree.Compute(row);
var result2 = forest.Compute(row);
var classsForest = fixedData.ClassesValue[forest.GetClass(row)];
if (estimatedClassName != row.Class || classsForest != row.Class)
{
// missed++;
}
}
for (int index = 0; index < 100; index++)
{
watch1.Start();
var ret = algorithm.BuildConditionalTree(data, new TreeOptions());
watch1.Stop();
watch2.Start();
var ret1 = algorithm.BuildConditionalTree(fixedData, new TreeOptions());
watch2.Stop();
//var className = ret.GetClass(data.ToList()[8]);
var className1 = ret1.GetClass(data.ToList()[8]);
}
delta = watch1.Elapsed.Subtract(watch2.Elapsed).TotalMilliseconds;
}
