public static int Main(string[] args)
{
args = new string[]{"todos2"};
Stream s = File.Open(args[0],FileMode.Open,FileAccess.Read);
List<string> total = new List<string>(readLines(s));
s.Close();
string[] tags = new string[]{"shop","sport","travel","home","reading","work","mlcourse","family","appointment","chore","urgent","school","finance","leisure","friends"};
metrics.Add(new TruePositivesMetric());
metrics.Add(new FalsePositivesMetric());
metrics.Add(new TrueNegativesMetric(tags.Length));
metrics.Add(new FalseNegativesMetric());
metrics.Add(new PrecisionMetric());
metrics.Add(new RecallMetric());
metrics.Add(new AccuracyMetric());
metrics.Add(new FMeasureMetric());
metrics.Add(new HammingLossMetric(tags.Length));
FileStream fsin = File.Open("innerstats.dat",FileMode.Create,FileAccess.Write);
FileStream fsou = File.Open("outerstats.dat",FileMode.Create,FileAccess.Write);
TextWriter twin = new StreamWriter(fsin);
TextWriter twou = new StreamWriter(fsou);
twin.Write("#k");
twou.Write("#k");
foreach(EvaluationMetric m in metrics) {
twin.Write("\t{0}",m.Name);
twou.Write("\t{0}",m.Name);
}
twou.WriteLine();
twin.WriteLine();
for(int pct = 2; pct < 100; pct++) {
TRAIN_PERCENTAGE = pct;
for(int sam = 0; sam < 10; sam++) {
vs = new VotingSystem (tags);
//vs.AddRecommender(new NeuralNetworkRecommender());
//vs.AddRecommender (new CustomRecommender (tags.Length));
//vs.AddRecommender (new ID3Recommender(0.7d));
//vs.AddRecommender (new NearestNeighbourRecommender());
//vs.AddRecommender(new C45Recommender(tags));
//vs.AddRecommender(new MLkNNRecommender(tags.Count()));
vs.AddRecommender(new VectorClassif(tags.Count()));
//vs.AddRecommender(new C45Recommender());
Console.WriteLine("train");
List<string> test = new List<string>(total);
List<string> train = new List<string>();
int tp = TRAIN_PERCENTAGE*test.Count/100;
Random rand = new Random();
for(int i = 0; i < tp; i++) {
int k = rand.Next(test.Count);
train.Add(test[k]);
test.RemoveAt(k);
}
#region Training
foreach(string l in train) {
Query(l);
}
vs.EndTrainingSession();
#endregion
#region TestInner
foreach(string l in train) {
Test(l);
}
twin.Write(pct);
//Console.WriteLine("Inner Results:");
foreach(EvaluationMetric m in metrics) {
//Console.WriteLine("\t{0} = {1}",m.Name,m.Result);
twin.Write("\t{0}",m.Result.ToString(nfi));
m.Reset();
}
twin.WriteLine();
#endregion
#region TestOuter
foreach(string l in test) {
Test(l);
}
twou.Write(pct);
//Console.WriteLine("Outer Results:");
foreach(EvaluationMetric m in metrics) {
//Console.WriteLine("\t{0} = {1}",m.Name,m.Result);
twou.Write("\t{0}",m.Result.ToString(nfi));
m.Reset();
}
twou.WriteLine();
#endregion
}
twin.Flush();
twou.Flush();
}
twin.Close();
twou.Close();
fsin.Close();
fsou.Close();
#region testFree
string line = Console.ReadLine();
while(line != "exit") {
foreach(Tuple<string,double> kvp in vs.TagFiltered(line)) {
Console.WriteLine("{0} / {1}",kvp.Item1,kvp.Item2);
}
Console.WriteLine("-----------------");
line = Console.ReadLine();
}
#endregion
return 0;
}
public static int Main(string[] args)
{
//run met "mono MLTag.exe trainfile+testfile ?logfile"
args = new string[] { "S:\\thesis.txt" };
metrics.Add(new TruePositivesMetric());
metrics.Add(new FalsePositivesMetric());
metrics.Add(new TrueNegativesMetric(tags.Length));
metrics.Add(new FalseNegativesMetric());
metrics.Add(new PrecisionMetric());
metrics.Add(new RecallMetric());
metrics.Add(new AccuracyMetric());
metrics.Add(new FMeasureMetric());
metrics.Add(new HammingLossMetric(tags.Length));
vs = new VotingSystem(tags);
//vs.AddRecommender(new NeuralNetworkRecommender());
//vs.AddRecommender (new CustomRecommender (tags.Length));
//vs.AddRecommender (new ID3Recommender(0.7d));
//vs.AddRecommender (new NearestNeighbourRecommender());
//vs.AddRecommender(new C45Recommender(tags));
//vs.AddRecommender(new MLkNNRecommender(tags.Count()));
//vs.AddRecommender(new VectorClassif(tags.Count()));
vs.AddRecommender(new ConcreteCustomVectorRecommender(tags));
Console.WriteLine("train");
Stream s = File.Open(args[0], FileMode.Open, FileAccess.Read);
List<string> test = new List<string>(readLines(s));
List<string> train = new List<string>();
s.Close();
int tp = TRAIN_PERCENTAGE * test.Count / 100;
Random rand = new Random();
for (int i = 0; i < tp; i++) {
int k = rand.Next(test.Count);
train.Add(test[k]);
test.RemoveAt(k);
}
#region Training
foreach (string l in train) {
Query(l);
}
vs.EndTrainingSession();
#endregion
#region TestInner
foreach (string l in train) {
Test(l);
}
Console.WriteLine("Inner Results:");
foreach (EvaluationMetric m in metrics) {
Console.WriteLine("\t{0} = {1}", m.Name, m.Result);
m.Reset();
}
#endregion
#region TestOuter
foreach (string l in test) {
Test(l);
}
Console.WriteLine("Outer Results:");
foreach (EvaluationMetric m in metrics) {
Console.WriteLine("\t{0} = {1}", m.Name, m.Result);
}
#endregion
#region testFree
string line = Console.ReadLine();
while (line != "exit") {
foreach (Tuple<string, double> kvp in vs.TagFiltered(line)) {
Console.WriteLine("{0} / {1}", kvp.Item1, kvp.Item2);
}
Console.WriteLine("-----------------");
line = Console.ReadLine();
}
#endregion
return 0;
}
