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Dataprocessing.aspx.cs
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Dataprocessing.aspx.cs
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using System;
using System.Collections.Generic;
using System.Linq;
using System.Web;
using System.Web.UI;
using System.Web.UI.WebControls;
using System.Data;
using weka;
using weka.classifiers.evaluation;
using System.IO;
public partial class Dataprocessing : System.Web.UI.Page
{
Connection db = new Connection();
string sq = "";
DataSet ds = new DataSet();
protected void Page_Load(object sender, EventArgs e)
{
}
protected void Button1_Click(object sender, EventArgs e)
{
try
{
string path = Server.MapPath(".") + "\\datasets\\" + dataset.Text + ".csv";
string fname = path;
File.Delete("d:\\train.arff");
weka.core.converters.CSVLoader loader = new weka.core.converters.CSVLoader();
loader.setSource(new java.io.File(fname));
weka.core.Instances inst2 = loader.getDataSet();
weka.core.converters.ArffSaver saver = new weka.core.converters.ArffSaver();
saver.setInstances(inst2);
saver.setFile(new java.io.File("d:\\train.arff"));
saver.writeBatch();
// Response.Write("<html><script>alert('File Converted..');</script></html>");
}
catch (Exception ex)
{
/// Response.Write("<html><script>alert('" + ex.Message.ToString() + "');</script></html>");
}
// weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("d:\\train.arff"));
// data.setClassIndex(data.numAttributes() - 1);
// weka.classifiers.Classifier cls = new weka.classifiers.bayes.NaiveBayes();
//// weka.classifiers.functions.supportVector.SMOset();
// int runs = 1;
// int folds = 10;
// //string sq = "delete from nbresults";
// //dbc.execfn(sq);
// // perform cross-validation
// for (int i = 0; i < runs; i++)
// {
// // randomize data
// int seed = i + 1;
// java.util.Random rand = new java.util.Random(seed);
// weka.core.Instances randData = new weka.core.Instances(data);
// randData.randomize(rand);
// if (randData.classAttribute().isNominal())
// randData.stratify(folds);
// weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
// for (int n = 0; n < folds; n++)
// {
// weka.core.Instances train = randData.trainCV(folds, n);
// weka.core.Instances test = randData.testCV(folds, n);
// // build and evaluate classifier
// weka.classifiers.Classifier clsCopy = weka.classifiers.Classifier.makeCopy(cls);
// clsCopy.buildClassifier(train);
// eval.evaluateModel(clsCopy, test);
// }
// preci_value.Text = eval.precision(0).ToString();
// recall_value.Text = eval.recall(0).ToString();
// acc_value.Text = eval.fMeasure(0).ToString();
// string s = "NB";
// string str = "insert into evaluation values('" + instid.Text + "','" + courid.Text.ToString() + "','" + preci_value.Text.ToString() + "','" + recall_value.Text.ToString() + "','" + acc_value.Text.ToString() + "','"+ s+ "' )";
// dbc.execfn(str);
// MessageBox.Show("saved");
// }
// }
}
protected void DropDownList1_SelectedIndexChanged(object sender, EventArgs e)
{
sq = "select * from " + dataset.Text + "";
db.dbSelect(sq);
ds = db.fillfn(sq);
GridView1.DataSource = ds.Tables["t1"];
GridView1.DataBind();
}
protected void Button2_Click(object sender, EventArgs e)
{
weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("d:\\train.arff"));
data.setClassIndex(data.numAttributes() - 1);
weka.classifiers.Classifier cls = new weka.classifiers.bayes.NaiveBayes();
// weka.classifiers.functions.supportVector.SMOset();
int runs = 1;
int folds = 10;
//string sq = "delete from nbresults";
//dbc.execfn(sq);
// perform cross-validation
for (int i = 0; i < runs; i++)
{
// randomize data
int seed = i + 1;
java.util.Random rand = new java.util.Random(seed);
weka.core.Instances randData = new weka.core.Instances(data);
randData.randomize(rand);
if (randData.classAttribute().isNominal())
randData.stratify(folds);
// weka.classifiers.trees.j48 jj;
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
for (int n = 0; n < folds; n++)
{
weka.core.Instances train = randData.trainCV(folds, n);
weka.core.Instances test = randData.testCV(folds, n);
// build and evaluate classifier
weka.classifiers.Classifier clsCopy = weka.classifiers.Classifier.makeCopy(cls);
clsCopy.buildClassifier(train);
eval.evaluateModel(clsCopy, test);
}
preci_value.Text = eval.precision(0).ToString();
recall_value.Text = eval.recall(0).ToString();
acc_value.Text = eval.fMeasure(0).ToString();
string s = "NB";
// string str = "insert into evaluation values('" + instid.Text + "','" + courid.Text.ToString() + "','" + preci_value.Text.ToString() + "','" + recall_value.Text.ToString() + "','" + acc_value.Text.ToString() + "','" + s + "' )";
// db.execfn(str);
// MessageBox.Show("saved");
}
}
}