public static double resultPrepareWithCrossFold(string classifierFileName, int baseClasses, string clasifier)
{
double performance = 0.0;
if (clasifier == "svm")
{
weka.classifiers.Classifier svm = new SMO();
performance = Weka.classifyCrossFold_Train_Test(classifierFileName, baseClasses, svm);
}
else if (clasifier == "nb")
{
weka.classifiers.Classifier nb = new NaiveBayes();
performance = Weka.classifyCrossFold_Train_Test(classifierFileName, baseClasses, nb);
}
else if (clasifier == "rf")
{
weka.classifiers.Classifier rf = new RandomForest();
performance = Weka.classifyCrossFold_Train_Test(classifierFileName, baseClasses, rf);
}
else if (clasifier == "j48")
{
weka.classifiers.Classifier j48 = new J48();
performance = Weka.classifyCrossFold_Train_Test(classifierFileName, baseClasses, j48);
}
return(performance);
}
protected void Button1_Click(object sender, EventArgs e)
{
try
{
SMO.publicationDatabase = TextBox5.Text;
SMO s = new SMO();
if (s.IsDBExsit())
{
Label1.ForeColor = Color.Green;
Label1.Text = "数据库已存在";
TextBox3.Text += "数据库已存在 \r\n";
}
else
{
Label1.ForeColor = Color.Brown;
Label1.Text = "数据库不存在";
TextBox3.Text += "数据库不存在 \r\n";
}
}
catch (Exception ex)
{
Label1.ForeColor = Color.Brown;
Label1.Text = "操作异常: " + ex.Message;
TextBox3.Text += "操作异常: " + ex.Message + " \r\n";
}
}
public static int work(int kassaCount, int clients, int servTime, int totalTime)
{
int timeUnit = 1000;
int currentTime = 0;
SMO smo = new SMO(kassaCount);
while (currentTime <= totalTime)
{
Console.WriteLine("тик " + currentTime / 1000);
if (currentTime > 0)
{
smo.clientComming(clients);
}
Console.WriteLine(smo.getSMO());
if (currentTime % servTime == 0 && currentTime != 0)
{
smo.doneSevice();
Console.WriteLine("Обслужено");
Console.WriteLine(smo.getSMO());
}
currentTime += timeUnit;
System.Threading.Thread.Sleep(timeUnit);
}
Console.WriteLine("end");
return(smo.doneClients);
}
public static void TrainSMO(Instances data)
{
//grab SMO, config
weka.classifiers.functions.SMO smo = new SMO();
smo.setOptions(weka.core.Utils.splitOptions(" -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
//train
smo.buildClassifier(data);
//test on self should get 100%
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
eval.evaluateModel(smo, data);
Training_Output.printWekaResults(eval.toSummaryString("\nResults\n======\n", false));
//save model serialize model
weka.core.SerializationHelper.write(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG.model", smo);
//load model deserialize model
smo = (weka.classifiers.functions.SMO)weka.core.SerializationHelper.read(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG.model");
//test loaded model
eval = new weka.classifiers.Evaluation(data);
eval.evaluateModel(smo, data);
Training_Output.printWekaResults(eval.toSummaryString("\nResults\n======\n", false));
}
public Boolean CreateSubscriptionRequest(SubscriptionInfo info)
{
SMO smo = new SMO();
return(smo.RegisterSubscriptionOnPublisher(info.subscriberName, info.subscriptionDbName));
return(true);
}
public smo2(int _k, int _q)
{
kanal_quality = _k;
query_quality = _q * _k;
for (int i = 0; i < kanal_quality; i++)
{
SMO.Add(new Chanel(_q));
}
}
public static void classifierOne(string classifierFileName, string predictionModel)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances wekaInsts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
wekaInsts.setClassIndex(wekaInsts.numAttributes() - 1);
Classifier cl = new SMO();
//Classifier cl = new NaiveBayes();
java.util.Random random = new java.util.Random(1);
Evaluation evaluation = new Evaluation(wekaInsts);
evaluation.crossValidateModel(cl, wekaInsts, 10, random);
foreach (object o in evaluation.getMetricsToDisplay().toArray())
{
}
int count = 0;
StringBuilder sb = new StringBuilder();
foreach (object o in evaluation.predictions().toArray())
{
NominalPrediction prediction = o as NominalPrediction;
if (prediction != null)
{
double[] distribution = prediction.distribution();
double predicted = prediction.predicted();
double actual = prediction.actual();
string revision = prediction.getRevision();
double weight = prediction.weight();
double margine = prediction.margin();
//bool equals = prediction.@equals();
string distributions = String.Empty;
for (int i = 0; i < distribution.Length; i++)
{
//System.Console.WriteLine(distribution[i]);
distributions += distribution[i];
}
var predictionLine = String.Format("{0} - {1} - {2} - {3} - {4} - {5}\n", actual, predicted, revision, weight, margine, distributions);
sb.Append(predictionLine);
//System.Console.WriteLine(predicted);
}
count++;
}
File_Helper.WriteToFile(sb, predictionModel + "NbCl.txt");
System.Console.WriteLine(count);
System.Console.ReadKey();
}
protected void Button3_Click(object sender, EventArgs e)
{
try
{
SMO.publicationDatabase = TextBox5.Text;
SMO s = new SMO();
s.DeleteDB();
Label1.ForeColor = Color.Green;
Label1.Text = "操作成功";
TextBox3.Text += "操作成功 \r\n";
}
catch (Exception ex)
{
Label1.ForeColor = Color.Brown;
TextBox3.Text += "操作异常: " + ex.Message + " \r\n";
}
}
protected void Button2_Click(object sender, EventArgs e)
{
try
{
SMO.publicationDatabase = TextBox5.Text;
SMO s = new SMO();
s.CreatDB(Server.MapPath("~/App_Data"));
Label1.ForeColor = Color.Green;
Label1.Text = "操作成功";
TextBox3.Text += "操作成功 \r\n";
}
catch (Exception ex)
{
Label1.ForeColor = Color.Brown;
Label1.Text = "操作异常: " + ex.Message;
TextBox3.Text += "操作异常: " + ex.Message + " \r\n";
}
}
public Boolean CreateSubscriptionRequest(SubscriptionInfo info)
{
SMO smo = new SMO();
return smo.RegisterSubscriptionOnPublisher(info.subscriberName,info.subscriptionDbName);
return true;
}
protected void Button13_Click(object sender, EventArgs e)
{
SMO smo = new SMO();
smo.RegisterSubscriptionOnPublisher(TextBox9.Text, TextBox10.Text);
}
protected void Button3_Click(object sender, EventArgs e)
{
try
{
SMO.publicationDatabase = TextBox5.Text;
SMO s = new SMO();
s.DeleteDB();
Label1.ForeColor = Color.Green;
Label1.Text = "操作成功";
TextBox3.Text += "操作成功 \r\n";
}
catch (Exception ex)
{
Label1.ForeColor = Color.Brown;
TextBox3.Text += "操作异常: " + ex.Message +" \r\n";
}
}
protected void Button2_Click(object sender, EventArgs e)
{
try
{
SMO.publicationDatabase = TextBox5.Text;
SMO s = new SMO();
s.CreatDB(Server.MapPath("~/App_Data"));
Label1.ForeColor = Color.Green;
Label1.Text = "操作成功";
TextBox3.Text += "操作成功 \r\n";
}
catch (Exception ex)
{
Label1.ForeColor = Color.Brown;
Label1.Text = "操作异常: " + ex.Message;
TextBox3.Text += "操作异常: " + ex.Message +" \r\n";
}
}
protected void Button1_Click(object sender, EventArgs e)
{
try
{
SMO.publicationDatabase = TextBox5.Text;
SMO s = new SMO();
if (s.IsDBExsit())
{
Label1.ForeColor = Color.Green;
Label1.Text = "数据库已存在";
TextBox3.Text += "数据库已存在 \r\n";
}
else
{
Label1.ForeColor = Color.Brown;
Label1.Text = "数据库不存在";
TextBox3.Text += "数据库不存在 \r\n";
}
}
catch (Exception ex)
{
Label1.ForeColor = Color.Brown;
Label1.Text = "操作异常: " + ex.Message;
TextBox3.Text += "操作异常: " + ex.Message + " \r\n";
}
}
protected void Button13_Click(object sender, EventArgs e)
{
SMO smo = new SMO();
smo.RegisterSubscriptionOnPublisher(TextBox9.Text,TextBox10.Text);
}
public void LearnModel()
{
Init();
foreach (Feature currFeature in DomPool.SelectorFeatures)
{
String featureString = currFeature.ToString();
HashSet <HtmlNode> resNodes = DomPool.RunXpathQuery(featureString);
foreach (HtmlNode nd in resNodes)
{
if (!allNodes.Contains(nd))
{
continue;
}
nodeFeatures[nd].Add(featureString);
}
}
FastVector fvWekaAttributes = GetDataSetAtts();
Instances trainingSet = new Instances("TS", fvWekaAttributes, 10);
trainingSet.setClassIndex(fvWekaAttributes.size() - 1);
foreach (HtmlNode currNode in allNodes)
{
Instance item = new SparseInstance(fvWekaAttributes.size());
for (int i = 0; i < fvWekaAttributes.size() - 1; i++)
{
weka.core.Attribute currFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(i);
if (nodeFeatures[currNode].Contains(currFeature.name()))
{
item.setValue(currFeature, 1);
}
else
{
item.setValue(currFeature, 0);
}
}
//set the class
weka.core.Attribute classFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(fvWekaAttributes.size() - 1);
item.setValue(classFeature, (DomPool.TargetNodes.Contains(currNode)?"yes":"no"));
item.setDataset(trainingSet);
if (DomPool.TargetNodes.Contains(currNode))
{
for (int t = 0; t < (DomPool.NonTargetNodes.Count() / DomPool.TargetNodes.Count()); t++)
{
trainingSet.add(new SparseInstance(item));
}
}
else
{
trainingSet.add(item);
}
}
//String[] options = new String[2];
//options = new string[] { "-C", "0.05" }; // unpruned tree
SMO cls = new SMO(); // new instance of tree
cls.setOptions(weka.core.Utils.splitOptions("-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
// cls.setOptions(options); // set the options
cls.buildClassifier(trainingSet); // build classifier
//save the resulting classifier
classifier = cls;
// Reader treeDot = new StringReader(tree.graph());
// TreeBuild treeBuild = new TreeBuild();
// Node treeRoot = treeBuild.create(treeDot);
FeaturesUsed = new HashSet <string>();
foreach (Feature f in DomPool.SelectorFeatures)
{
FeaturesUsed.Add(f.ToString());
}
}
