public ActionResult TestAlgorithm(TestAlgorithmModel model)
{
var list = _taiLieuVanBanService.GetDocuments().Take(model.Amount).ToList();
list.Add(model.Name);
var docCollection = new DocumentCollection()
{
DocumentList = list
};
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
List <Centroid> resultSet = DocumnetClustering.DocumentCluster(model.Cluster, vSpace, model.Name);
string docNear = DocumnetClustering.FindClosestDocument();
var mode = new TestAlgorithmModel
{
Name = model.Name,
Amount = model.Amount,
Cluster = model.Cluster,
Centroids = resultSet,
DocumentNear = docNear
};
return(View(mode));
}
public ActionResult StorageSuggestion(string document, string type)
{
string local = "Không tìm thấy tài liêu/văn bản có cùng nội dung! Tạo hồ sơ mới.";
var hosos = AutoCompleteTextHoSos(GetHoSos());
var list = _taiLieuVanBanService.GetDocuments();
list.Add(document);
var docCollection = new DocumentCollection()
{
DocumentList = list
};
var cluster = _taiLieuVanBanService.CountDocumentType(type);
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
List <Centroid> resultSet = DocumnetClustering.DocumentCluster(cluster, vSpace, document);
string documentNeedSearch = DocumnetClustering.FindClosestDocument();
if (!string.IsNullOrEmpty(documentNeedSearch))
{
var taiLieuVanBan = _taiLieuVanBanService.Get(p => p.NoiDung == documentNeedSearch);
local = hosos.FirstOrDefault(p => p.Id == taiLieuVanBan.HoSoId).Text;
}
return(Json(new { da = local }, JsonRequestBehavior.AllowGet));
}
private void btnStartClustering_Click(object sender, EventArgs e)
{
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
int totalIteration = 0;
List <Centroid> resultSet = DocumnetClustering.PrepareDocumentCluster(int.Parse(txtClusterNo.Text), vSpace, ref totalIteration);
string msg = string.Empty;
int count = 1;
foreach (Centroid c in resultSet)
{
msg += String.Format("------------------------------[ CLUSTER {0} ]-----------------------------{1}", count, System.Environment.NewLine);
foreach (DocumentVector document in c.GroupedDocument)
{
msg += document.Content + System.Environment.NewLine;
if (c.GroupedDocument.Count > 1)
{
msg += String.Format("{0}-------------------------------------------------------------------------------{0}", System.Environment.NewLine);
}
}
msg += "-------------------------------------------------------------------------------" + System.Environment.NewLine;
count++;
}
richTextBox1.Text = msg;
lblTotalIteration.Text = totalIteration.ToString();
}
private void btnStartClustering_Click(object sender, EventArgs e)
{
int totalIteration = 0;
DocumnetClustering.mainCentroids = DocumnetClustering.PrepareDocumentCluster(
int.Parse(txtClusterNo.Text), vSpace, ref totalIteration, ddl_sim.Text, mainCLusterNodeList, cboxDataSet.Text, maxNoDoc);
printAlll();
lblTotalIteration.Text = totalIteration.ToString();
//MessageBox.Show("Done");
}
/// <summary>
/// 開始訓練
/// </summary>
public void train()
{
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
totalIteration = 0;
resultSet = DocumnetClustering.PrepareDocumentCluster(txtClusterNum, vSpace, ref totalIteration);
Console.WriteLine("totalIteration: " + totalIteration.ToString());
}
public ActionResult Test()
{
var list = _taiLieuVanBanService.GetDocuments();
var docCollection = new DocumentCollection()
{
DocumentList = list
};
var cluster = _taiLieuVanBanService.CountDocumentType("Thông Báo");
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
List <Centroid> resultSet = DocumnetClustering.DocumentCluster(cluster, vSpace, "thông báo chính phủ mới");
return(View(resultSet));
}
public string GetFromCurrency(string priceToConvert)
{
var from_currency = "";
Preprocess(priceToConvert);
int totalIteration = 0;
int final_index = -1;
int collectionNumber = docCollection.DocumentList.Count - 1;
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
List <Centroid> resultSet = DocumnetClustering.PrepareDocumentCluster(collectionNumber, vSpace, ref totalIteration, ref final_index, currency);
from_currency = resultSet[final_index].GroupedDocument[0].Content.Split(',')[0];
return(from_currency);
}
private void btnStartClustering_Click(object sender, EventArgs e)
{
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
int totalIteration = 0;
List <Centroid> resultSet = DocumnetClustering.PrepareDocumentCluster(int.Parse(txtClusterNo.Text), vSpace, ref totalIteration);
string msg = string.Empty;
int count = 1;
string k = string.Empty;
string max = string.Empty;
List <string> topic = new List <string>();
foreach (Centroid c in resultSet)
{
msg += String.Format("------------------------------[ CLUSTER {0} ]-----------------------------{1}", count, System.Environment.NewLine);
k += String.Format("[ CLUSTER {0} ]", count, System.Environment.NewLine);
max = string.Empty;
foreach (DocumentVector document in c.GroupedDocument)
{
for (int i = 0; i < document.keys.Length; i++)
{
float m = document.VectorSpace[0];
if (document.VectorSpace[i] > 0.005 && document.keys[i] != ".")
{
k += document.keys[i] + ",";
}
msg += document.Content + System.Environment.NewLine;
if (c.GroupedDocument.Count > 1)
{
msg += String.Format("{0}-------------------------------------------------------------------------------{0}", System.Environment.NewLine);
}
}
msg += "-------------------------------------------------------------------------------" + System.Environment.NewLine;
k += System.Environment.NewLine;
topic.Add(max);
count++;
}
}
richTextBox2.Text = k;
richTextBox1.Text = msg;
label10.Text = totalIteration.ToString();
}
public decimal GetExchangedValue(string priceToConvert, string to_currency)
{
var from_currency = "";
decimal exchangedValue;
Preprocess(priceToConvert);
int totalIteration = 0;
int final_index = -1;
int collectionNumber = docCollection.DocumentList.Count - 1;
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
List <Centroid> resultSet = DocumnetClustering.PrepareDocumentCluster(collectionNumber, vSpace, ref totalIteration, ref final_index, currency);
from_currency = resultSet[final_index].GroupedDocument[0].Content.Split(',')[0];
WriteInCurrencyDocument(from_currency, currency);
decimal rate = GetRate(from_currency, to_currency);
exchangedValue = value * rate;
return(exchangedValue);
}
private void btnAdd_Click(object sender, EventArgs e)
{
int newDoc = 0;
if (!string.IsNullOrEmpty(txtDoc1.Text))
{
docCollection.DocumentList.Add(txtDoc1.Text);
newDoc++;
}
if (!string.IsNullOrEmpty(txtDoc2.Text))
{
newDoc++;
docCollection.DocumentList.Add(txtDoc2.Text);
}
if (!string.IsNullOrEmpty(txtDoc3.Text))
{
docCollection.DocumentList.Add(txtDoc3.Text);
newDoc++;
}
if (!string.IsNullOrEmpty(txtDoc4.Text))
{
newDoc++;
docCollection.DocumentList.Add(txtDoc4.Text);
}
int totalDoc = 0;
if (int.TryParse(docCollection.DocumentList.Count.ToString(), out totalDoc))
{
lblTotalDoc.Text = totalDoc.ToString();
}
txtDoc1.Clear();
txtDoc2.Clear();
txtDoc3.Clear();
txtDoc4.Clear();
if (ddlType.Text == "Incremental" && DocumnetClustering.mainCentroids.Count > 0)
{
switch (ddlIncAlg.Text)
{
case "KMeans":
List <DocumentVector> vSpace = VectorSpaceModel.ProcessDocumentCollection(docCollection);
for (int i = 1; i <= newDoc; i++)
{
DocumentVector obj = vSpace[vSpace.Count - i];
int index = DocumnetClustering.FindClosestClusterCenter(DocumnetClustering.mainCentroids, obj, ddl_sim.Text);
DocumnetClustering.mainCentroids[index].GroupedDocument.Add(obj);
}
break;
case "CMeans":
List <DocumentVector> vSpace2 = VectorSpaceModel.ProcessDocumentCollection(docCollection);
string outFilepath = @"E:\Dropbox\Masters\myMSc\PracticalPart\Sematic_K-MEANSClustering\FCM\HM_data_Out_centers.dat";
var reader = new StreamReader(File.OpenRead(outFilepath));
List <float[]> values = new List <float[]>();
int t = 0;
while (!reader.EndOfStream)
{
var line = reader.ReadLine();
values.Add(Array.ConvertAll(line.Split(','), float.Parse));
t++;
}
for (int i = 0; i < newDoc; i++)
{
int closeCenter = 0;
float min = 1000;
int counter = 1;
DocumentVector obj2 = vSpace2[vSpace2.Count - newDoc + i];
for (int l = 0; l < t; l++)
{
// float s = SimilarityMatrics.FindCosineSimilarity(values[l], obj2.VectorSpace);
float s = ArrayDistanceFunction(values[l], obj2.VectorSpace);
if (s < min)
{
min = s;
closeCenter = counter;
}
counter++;
}
MessageBox.Show("Doc:" + (i + 1) + " Close is:" + closeCenter);
DocumnetClustering.mainCentroids[closeCenter - 1].GroupedDocument.Add(obj2);
}
break;
}
printAlll();
}
}