public static void TestData()
{
var data = Program.LoadDataFromfCSV(@"C:\Work\NLP\train.csv", ",");
var fixedData = TableFixedData.FromTableData(data);
var dt = DateTime.Now;
var attributes =
fixedData.Attributes.Select((item, index) => index)
.Where(item => !fixedData.IsClassAttribute(item) && fixedData.Attributes[item].ToLower() != "id")
.ToArray();
RandomForestAlgorithm rfa = new RandomForestAlgorithm(70);
var forest = rfa.BuildForest(fixedData, new TreeOptions {
MaxTreeDepth = 10
}, attributes);
//var ret4 = forest.Compute(data[0]);
//var ret3 = forest.GetClass(data[0]);
var ts = DateTime.Now.Subtract(dt);
Console.WriteLine(ts.TotalMilliseconds);
GC.Collect();
var testData = Program.LoadDataFromfCSV(@"C:\Work\NLP\test.csv", ",");
var configuration = new CsvConfiguration();
configuration.Delimiter = ",";
configuration.HasExcelSeparator = false;
configuration.IgnoreQuotes = true;
configuration.HasHeaderRecord = true;
configuration.QuoteNoFields = true;
using (var writer = new StreamWriter("test.csv"))
{
using (var csvWriter = new CsvWriter(writer))
{
foreach (var testItem in testData)
{
var classId = forest.Compute(testItem);
csvWriter.WriteField(classId[0]);
csvWriter.NextRecord();
}
}
}
}
public static void TestData()
{
var data = Program.LoadDataFromfCSV(@"C:\Work\NLP\train.csv", ",");
var fixedData = TableFixedData.FromTableData(data);
var dt = DateTime.Now;
var attributes =
fixedData.Attributes.Select((item, index) => index)
.Where(item => !fixedData.IsClassAttribute(item) && fixedData.Attributes[item].ToLower() != "id")
.ToArray();
RandomForestAlgorithm rfa = new RandomForestAlgorithm(70);
var forest = rfa.BuildForest(fixedData, new TreeOptions {MaxTreeDepth = 10}, attributes);
//var ret4 = forest.Compute(data[0]);
//var ret3 = forest.GetClass(data[0]);
var ts = DateTime.Now.Subtract(dt);
Console.WriteLine(ts.TotalMilliseconds);
GC.Collect();
var testData = Program.LoadDataFromfCSV(@"C:\Work\NLP\test.csv", ",");
var configuration = new CsvConfiguration();
configuration.Delimiter = "," ;
configuration.HasExcelSeparator = false;
configuration.IgnoreQuotes = true;
configuration.HasHeaderRecord = true;
configuration.QuoteNoFields = true;
using (var writer = new StreamWriter("test.csv"))
{
using (var csvWriter = new CsvWriter(writer))
{
foreach (var testItem in testData)
{
var classId = forest.Compute(testItem);
csvWriter.WriteField(classId[0]);
csvWriter.NextRecord();
}
}
}
}
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;
}
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;
}
