internal void MoveTo(ClusterList s, int toID)
{//移动元素操作
Cluster from = s.clusters[ClusterID];
Cluster to = s.clusters[toID];
//if it's the first element
if (Before == null)
{
from.First = After;
if (After != null)
{
After.Before = null;
}
}
else
{
Before.After = After;
if (After != null)
{
After.Before = Before;
}
}
from.Num--;
from.NumberLinks = from.NumberLinks - from.Num;
Before = null;
After = null;
//remove then insert
to.Add(this);
}
public ClusterList ConsolidateLCS(int minScoreAllowed, out int lcsBest)
{ // Consolidate best LCS by combining 1 cluster with another
int iBest = -1, jBest = -1;
lcsBest = 0;
for (int i = 0; i < clusters.Count; i++)
{
for (int j = i + 1; j < clusters.Count; j++)
{
int lcs = clusters[i].ScoreLCS(clusters[j]);
if (lcs >= lcsBest)
{
lcsBest = lcs;
iBest = i;
jBest = j;
}
}
}
// Return this list unchanged
if (lcsBest < minScoreAllowed)
{
return(this);
}
else
{ // Get best candidates
Cluster iCluster = clusters[iBest];
Cluster jCluster = clusters[jBest];
// Combine clusters
ClusterList newClusters = new ClusterList();
for (int i = 0; i < clusters.Count; i++)
{ // Create new cluster copying the old one
Cluster cluster = new Cluster();
clusters[i].CopyTo(cluster);
// If this is the i match
if (i == iBest)
{ // Combine j with cluster i
cluster.Combine(jCluster);
// Add combined cluster
newClusters.clusters.Add(cluster);
}
else if (i == jBest)
{ // Do nothing already combined in a previous step above
}
else
{ // Need to add the cluster to the list
newClusters.clusters.Add(cluster);
}
}
// Return new cluster list
return(newClusters);
}
}
public void AddClusterToHyperCluster(Cluster cluster)
{
ClusterList.Add(cluster);
AdaptiveIntersect.UpdateClusterIntersectionByLast(ClusterList, HyperClusterVector);
AdaptiveIntersect.UpdateClusterSummaryByLast(ClusterList, HyperClusterVectorSummary);
ValidHyperClusterItemList = false;
//TODO: rendutant analyse to remove it
//for (int i = 0; i < cluster.ClusterItemList.Count; i++)
//{
// HyperClusterItemList.Add(cluster.ClusterItemList[i]);
//}
}
private void initClustListData()
{
ListViewItem lvi;
//先添加0（好友设置）和10000（默认群设置）
string friendSwitch = SQLiteHandler.getClusterIsEnabled("0");
string defaultSwitch = SQLiteHandler.getClusterIsEnabled("10000");
lvi = new ListViewItem("0");
lvi.SubItems.Add("好友设置");
if (friendSwitch == "0")
{
lvi.SubItems.Add("关");
}
else
{
lvi.SubItems.Add("开");
}
lvi = listView_clusterList.Items.Add(lvi);
lvi = new ListViewItem("10000");
lvi.SubItems.Add("默认群设置");
if (defaultSwitch == "0")
{
lvi.SubItems.Add("关");
}
else
{
lvi.SubItems.Add("开");
}
lvi = listView_clusterList.Items.Add(lvi);
//获取群列表
ClusterList clusterlist = Plugin.Client.ClusterList;
foreach (KeyValuePair <uint, ClusterInfo> kv in clusterlist)
{
ClusterInfo clusterInfo = kv.Value;
string clusterId = kv.Key.ToString();
string clusterSwitch = SQLiteHandler.getClusterIsEnabled(clusterId);
lvi = new ListViewItem(clusterId);
lvi.SubItems.Add(clusterInfo.Name);
if (clusterSwitch == "1")
{
lvi.SubItems.Add("开");
}
else
{
lvi.SubItems.Add("关");
}
lvi = listView_clusterList.Items.Add(lvi);
}
}
public void SetInitialClusterCenters()
{
//for (int i = 0; i < NumberOfClusters; i++)
//{
// Random rnd = new Random();
// int num = rnd.Next(0, ElementList.Count - 1);
// ClusterList.Add(new Cluster(ElementList[num]), i+1);
//}
ClusterList.Add(new Cluster(ElementList[0], 1));
ClusterList.Add(new Cluster(ElementList[8], 2));
ClusterList.Add(new Cluster(ElementList[15], 3));
ClusterList.Add(new Cluster(ElementList[17], 4));
ClusterList.Add(new Cluster(ElementList[23], 5));
}
public ClusterGenerator(IGpsValueConverter converter, int NumZoomLevels)
{
this.cachedConvertedObjects = new Dictionary <Object, BasicGeoposition>();
this.keyToItems = new Dictionary <string, IList <Object> >();
this.zoomLevelToClusters = new List <ClusterList>();
this.validItemsToCluster = new ClusterList();
this.gpsConverter = converter;
this.numZoomLevels = NumZoomLevels;
// This is a 100 pixels for the minimum hitbox length
this.MinHitBoxSizeInMiles = GeospatialHelperStatic.ConvertPixelsToMiles(100, NumZoomLevels);
this.LeastClustersZoomLevel = 1;
this.MostClustersZoomLevel = 1;
}
private void Initialize()
{
IP = new byte[4];
ServerIp = new byte[4];
LastLoginIp = new byte[4];
IsLoggedIn = false;
LoginMode = QQStatus.ONLINE;
IsUdp = true;
ContactInfo = new ContactInfo();
IsShowFakeCam = false;
Friends = new FriendList(this);
QQList = new QQList();
ClusterList = new ClusterList();
this.QQKey = new QQKey(this);
}
public void AddItemToClusters(Object item)
{
if (!GeospatialHelperStatic.IsValidGPS(ConvertObjToGPS(item)))
{
return;
}
for (int i = 1; i <= this.numZoomLevels; i++)
{
// List of Clusters for this zoom level
ClusterList clusters = this.zoomLevelToClusters[i - 1];
bool addedToCluster = false;
BasicGeoposition itemCoord = ConvertObjToGPS(item);
for (int j = 0; j < clusters.Count; j++)
{
if (IsWithinBoundary(itemCoord, clusters[j].Location, i))
{
clusters[j].Count += 1;
clusters[j].Objects.Add(item);
string id = String.Format("ZL{0}_C{1}", i, j);
this.keyToItems[id].Add(item);
addedToCluster = true;
break;
}
}
if (addedToCluster == false)
{
string id = String.Format("ZL{0}_C{1}", i, clusters.Count);
List <Object> singleItem = new List <Object>()
{
item
};
clusters.Add(new Cluster()
{
ClusterId = id,
Objects = singleItem,
Count = 1,
Location = itemCoord
});
this.keyToItems.Add(id, singleItem);
}
this.zoomLevelToClusters[i - 1] = clusters;
}
}
public async Task <Response <ClusterList> > ListBySubscriptionAsync(string subscriptionId, CancellationToken cancellationToken = default)
{
Argument.AssertNotNullOrEmpty(subscriptionId, nameof(subscriptionId));
using var message = CreateListBySubscriptionRequest(subscriptionId);
await _pipeline.SendAsync(message, cancellationToken).ConfigureAwait(false);
switch (message.Response.Status)
{
case 200:
{
ClusterList value = default;
using var document = await JsonDocument.ParseAsync(message.Response.ContentStream, default, cancellationToken).ConfigureAwait(false);
value = ClusterList.DeserializeClusterList(document.RootElement);
return(Response.FromValue(value, message.Response));
}
public void Create()
{
ClusterList.Clear();
HyperClusterList.Clear();
ItemToClusterMap.Clear();
ClusterToHyperClusterMap.Clear();
for (int i = 0; i < FeatureItemList.Count; i++)
{
AssignCluster(FeatureItemList[i]);
}
//Get items assigned to hyperClusters
for (int i = 0; i < HyperClusterList.Count; i++)
{
HyperClusterList[i].GetHyperClusterItemList();
}
}
public void Create(ICollection <FeatureItem> itemCollection)
{
FeatureItemList.AddRange(itemCollection);
ClusterList.Clear();
HyperClusterList.Clear();
ItemToClusterMap.Clear();
ClusterToHyperClusterMap.Clear();
for (int i = 0; i < FeatureItemList.Count; i++)
{
FeatureItemList[i].Id = i;
AssignCluster(FeatureItemList[i]);
}
//Get items assigned to hyperClusters
for (int i = 0; i < HyperClusterList.Count; i++)
{
HyperClusterList[i].GetHyperClusterItemList();
}
}
public ClusterGenerator(IGpsValueConverter converter, int NumZoomLevels, double lengthValue, BoxLengthType hitBoxType)
{
this.cachedConvertedObjects = new Dictionary <Object, BasicGeoposition>();
this.keyToItems = new Dictionary <string, IList <Object> >();
this.zoomLevelToClusters = new List <ClusterList>();
this.validItemsToCluster = new ClusterList();
this.gpsConverter = converter;
this.numZoomLevels = NumZoomLevels;
if (hitBoxType == BoxLengthType.Distance)
{
this.MinHitBoxSizeInMiles = lengthValue;
}
else if (hitBoxType == BoxLengthType.Pixels)
{
this.MinHitBoxSizeInMiles = GeospatialHelperStatic.ConvertPixelsToMiles(lengthValue, NumZoomLevels);
}
this.LeastClustersZoomLevel = 1;
this.MostClustersZoomLevel = 1;
}
// Removes item from the clusters, also will need to delete clusters if they are empty
public void RemoveItemFromClusters(Object item)
{
if (!GeospatialHelperStatic.IsValidGPS(ConvertObjToGPS(item)))
{
return;
}
for (int i = 1; i <= this.numZoomLevels; i++)
{
// List of Clusters for this zoom level
ClusterList clusters = this.zoomLevelToClusters[i - 1];
for (int j = 0; j < clusters.Count; j++)
{
for (int k = 0; k < clusters[j].Objects.Count; k++)
{
if (item == clusters[j].Objects[k])
{
// Remove the entire cluster if there is only one element
if (clusters[j].Objects.Count == 1)
{
clusters.RemoveAt(j);
}
// Remove just that object from the list
else
{
clusters[j].Objects.RemoveAt(k);
}
// only one object removed per zoom level and since we can't do multi-loop breaks
// have this assignment to break out of the 2nd loops
j = clusters.Count;
break;
}
}
}
}
}
public bool RemoveClusterFromHyperCluster(Cluster cluster)
{
if (ClusterList.Remove(cluster) == true)
{
if (ClusterList.Count > 0)
{
AdaptiveIntersect.CalculateClusterIntersection(ClusterList, HyperClusterVector);
AdaptiveIntersect.CalculateClusterSummary(ClusterList, HyperClusterVectorSummary);
//TODO: redundant analyse to remove it
// nie ma senzu sa kazdym razem tworzyc listy, tylko wtedy gdy bedzie do niej potrzebny dostep
//HyperClusterItemList.Clear();
//for(int i=0; i< ClusterList.Count; i++)
//{
// for(int j = 0; j < ClusterList[i].ClusterItemList.Count; j++)
// {
// HyperClusterItemList.Add(ClusterList[i].ClusterItemList[j]);
// }
//}
}
ValidHyperClusterItemList = false;
}
return(ClusterList.Count > 0);
}
private void Initialize()
{
IP = new byte[4];
ServerIp = new byte[4];
LastLoginIp = new byte[4];
IsLoggedIn = false;
LoginMode = QQStatus.我在线上;
IsUdp = true;
ContactInfo = new ContactInfo();
IsShowFakeCam = false;
Friends = new FriendList(this);
QQList = new QQList();
ClusterList = new ClusterList();
this.QQKey = new QQKey(this);
}
private void bgw_DoWork(object sender, DoWorkEventArgs e)
{
bgw.ReportProgress(0, "Reading data file..."); //读取in文件
readDataFile();
bgw.ReportProgress(10, "Initializing ClusterList..."); //初始化聚类对象
clusterList = new ClusterList();
bgw.ReportProgress(15, "Loading data..."); //导入数据
clusterList.importData(dataFileParser.inputScore, dataFileParser.normlizedScore);
//record the start time 记录算法开始时间
startTime = DateTime.Now;
//构造初始解
bgw.ReportProgress(20, "Constructing initial solution...");
clusterList.doInitial(initMethod);
bgw.ReportProgress(25, "Initializing Tabu Search...");
clusterList.initTSPhase();
bgw.ReportProgress(30, "Executing Tabu Search...");
for (int i = 0; i < Globals.EleNum; i++)
{
Util.WriteLine("Element(" + i + "):" + dataFileParser.nameFormater.getName(i));
}
Util.WriteLine("********************************\n");
Util.WriteLine("Movelog.");
clusterList.execTSPhase(bgw, diverMethod);
endTime = DateTime.Now;
FileStream fileCluster = new FileStream(outputPath + "\\cluster_" + Globals.FileName + "_" + startTime.ToString("yyyyMMddHHmmss") + ".txt", FileMode.OpenOrCreate);
using (StreamWriter sw = new StreamWriter(fileCluster))
{
bgw.ReportProgress(85, "Writing Cluster Result..");
clusterList.exportResult(sw, dataFileParser.normlizedScore);
if (normMethod != Normalization.NONE)
{
sw.WriteLine("\n\r\n\r");
sw.WriteLine("\nClustering Result mapping to the original data:");
clusterList.exportResult(sw, dataFileParser.inputScore);
}
sw.WriteLine("\n\r\n\r");
//输出界面参数
sw.WriteLine("LBound = " + m_LB + "\tUbound = " + m_UB);
sw.WriteLine("A1 = " + Globals.A1 + "\tA2 = " + Globals.A2 + "\tA3 = 0");
sw.WriteLine("Time Cost = " + (endTime - startTime).ToString("g"));
}
FileStream fileElement = new FileStream(outputPath + "\\element_" + Globals.FileName + "_" + startTime.ToString("yyyyMMddHHmmss") + ".txt", FileMode.OpenOrCreate);
using (StreamWriter sw = new StreamWriter(fileElement))
{
bgw.ReportProgress(90, "Writing Elements Distribution Result..");
if (dataType != DataType.DISTANCE)
{
sw.WriteLine("CID\t EID\t EName\t \tX \tY \tZ");
for (int i = 1; i <= Globals.CluNum; i++)
{
for (int j = 0; j < Globals.EleNum; j++)
{
if (clusterList.elements[j].ClusterID == i)
{
MyPoint p = dataFileParser.points[j];
sw.WriteLine(i + "\t" + j + "\t" + dataFileParser.nameFormater.getName(j) + "\t" + p.X + "\t" + p.Y + "\t" + p.Z + '\n');
}
}
}
}
else//非点集的输出
{
sw.WriteLine("CID\t EID\t EName\t ");
for (int i = 1; i <= Globals.CluNum; i++)
{
for (int j = 0; j < Globals.EleNum; j++)
{
if (clusterList.elements[j].ClusterID == i)
{
sw.WriteLine(i + "\t" + j + "\t" + dataFileParser.nameFormater.getName(j) + '\n');
}
}
}
}
}
bgw.ReportProgress(100, "Done! Time Cost =" + (endTime - startTime).ToString("g"));
}
public void AssignCluster(FeatureItem item)
{
int iterationCounter = IterationLimit; //assign IterationLimit
bool isAssignementChanged = true;
double itemVectorMagnitude = CalculateVectorMagnitude(item.FeatureVector);
while (isAssignementChanged && iterationCounter > 0)
{
isAssignementChanged = false;
List <KeyValuePair <Cluster, double> > clusterToProximityList = new List <KeyValuePair <Cluster, double> >();
double proximityThreshold = itemVectorMagnitude / (bValue + rangeLimit * FeatureItemSize); // ||E_i||/(b+1)
//Calculate proximity values for item and clusters
for (int i = 0; i < ClusterList.Count; i++)
{
double clusterVectorMagnitude = CalculateVectorMagnitude(ClusterList[i].ClusterVector);
double proximity = CaulculateVectorIntersectionMagnitude(item.FeatureVector, ClusterList[i].ClusterVector) / (bValue + clusterVectorMagnitude); //prox = ||C_j and E_i ||/ (b + ||E_i||) > proxThres
if (proximity > proximityThreshold)
{
clusterToProximityList.Add(new KeyValuePair <Cluster, double>(ClusterList[i], proximity));
}
}
if (clusterToProximityList.Count > 0) //???? tutaj zobaczyc, czy nie trzeba sprawdzic dodania lub ominiecia dodania
{
clusterToProximityList.Sort((x, y) => - 1 * x.Value.CompareTo(y.Value)); //sorting in place in descending order
//search from the maximum proximity to smallest
for (int i = 0; i < clusterToProximityList.Count; i++)
{
Cluster newCluster = clusterToProximityList[i].Key;
double vigilance = CaulculateVectorIntersectionMagnitude(newCluster.ClusterVector, item.FeatureVector) / itemVectorMagnitude;
if (vigilance >= pValue) //passed all tests and has max proximity
{
if (ItemToClusterMap.ContainsKey(item.Id)) //find cluster with this item
{
Cluster previousCluster = ItemToClusterMap[item.Id];
if (ReferenceEquals(newCluster, previousCluster))
{
break; //if the best is the same, then it will break (not considered others)
}
if (previousCluster.RemoveItemFromCluster(item) == false) //the cluster is empty
{
ClusterList.Remove(previousCluster);
}
}
//Add item to the current cluster
newCluster.AddItemToCluster(item);
ItemToClusterMap[item.Id] = newCluster;
isAssignementChanged = true;
break;
}
}
}
if (ItemToClusterMap.ContainsKey(item.Id) == false)
{
Cluster newCluster = new Cluster(item);
ClusterList.Add(newCluster);
ItemToClusterMap.Add(item.Id, newCluster);
isAssignementChanged = true;
}
iterationCounter--;
}
AssignHyperCluster();
}
public WebModule() : base()
{
Get["/"] = parameters =>
{
RestoreSession();
ApplicationModel appModel = new ApplicationModel(Application.APPLICATION_NAME);
return(Render("index.pt", context: appModel,
view: new ApplicationView(appModel, CurrentSession)));
};
Get["/objs"] = parameters => // Это страница сайта с квартирами.
{
RestoreSession();
ObjectList objList = new ObjectList();
// Надо отлаживать в монодевелоп...
ObjectListView objView = new ObjectListView(objList);
return(Render("objlist.pt", context: objList, view: objView));
};
Get["/offers"] = parameters =>
{
RestoreSession();
OfferList model = new OfferList(null);
OfferListView view = new OfferListView(model);
return(Render("offerlist.pt", context: model, view: view));
};
Get["/offers/{clid}"] = parameters =>
{
int clid = int.Parse(parameters.clid);
RestoreSession();
OfferList model = new OfferList(clid: clid);
OfferListView view = new OfferListView(model);
return(Render("offerlist.pt", context: model, view: view));
};
Get["/offer/{GUID}"] = parameters => // Эта страница с индивидуальной квартирой
{
RestoreSession();
string GUID = parameters.GUID;
IOffer model = Application.Context.Offers.Where(x => x.GUID == GUID).FirstOrDefault();
// По идее в BrightStarDB есть у каждого объекта свой ID и наш
// GUID можно к нему привязать. FIXME: Привязать!
string msg = "Объект (Offer) не найден!: " + GUID;
if (model == null)
{
Console.WriteLine(msg);
// и я НЕ понял почему....
return("msg");
}
else
{
Console.WriteLine(model);
}
// Надо нудно искать ошибку в основном шаблоне....
// Завтра. Вырубает....
OfferView view = new OfferView(model);
return(Render("offer.pt", context: model, view: view));
};
Get["/agents"] = parameters =>
{
RestoreSession();
AgentList model = new AgentList();
AgentListView view = new AgentListView(model);
return(Render("agentlist.pt", context: model, view: view));
};
Get["/login"] = parameters => // Эта страница уже лет 20 не нужна.
{
RestoreSession();
LoginObject model = new LoginObject();
LoginView view = new LoginView(model, this.Request, CurrentSession);
// return View["login.pt", testModel]; // Оставим для истории.
// Это, к стати правильный вариант отрисовки по шаблону.
return(Render("login.pt", context: model, view: view));
};
// Принимаем данные пользователя из формы регистрации
Post["/login"] = parameters =>
{
RestoreSession();
LoginObject model = new LoginObject();
LoginView view = new LoginView(model, this.Request, CurrentSession);
Response response = null;
bool res = view.Process();
CurrentSession = view.Session; // Обновление сессии
if (res)
{
response = Response.AsRedirect("/");
}
else // Неуданая идентификация
{
response = Response.AsRedirect("/login");
}
// Перенаправить браузер на домашнюю страницу.
return(InSession(response));
};
Get["/logout"] = parameters => // Эта страница уже лет 20 не нужна.
{
RestoreSession();
LoginObject model = new LoginObject();
LoginView view = new LoginView(model, this.Request, CurrentSession);
// return View["login.pt", testModel]; // Оставим для истории.
// Это, к стати правильный вариант отрисовки по шаблону.
view.Logout();
CurrentSession = view.Session;
return(Render("login.pt", context: model, view: view));
};
Post["/clustering"] = parameters =>
{
RestoreSession();
Response response = null;
int num = 0;
try
{
num = int.Parse(this.Request.Form.max);
int clnum = 5;
FlatClusterAnalyzer a = FlatClusterAnalyzer.AnalyzeFlatWithCluster(num);
a.Store(clnum);
CurrentSession["message"] = info("Обработано для " + num + " квартир, " + clnum + " кластеров",
msg: "Успешный запуск");
CurrentSession["analysis_data"] = a;
}
catch (FormatException)
{
CurrentSession["message"] = error("Неправильное число квартир", msg: "Неуспешный запуск");
}
response = Response.AsRedirect("/analysis");
return(InSession(response));
};
Get["/analysis"] = parameters =>
{
RestoreSession();
ClusterList model = new ClusterList();
ClusterListView view = new ClusterListView(model);
return(Render("clusters.pt", context: model, view: view));
};
Post["/analysis"] = parameters =>
{
RestoreSession();
ClusterList model = new ClusterList();
ClusterListView view = new ClusterListView(model);
FlatClusterAnalyzer analyzer = null;
var form = this.Request.Form;
if (form.reconstruct != null)
{
try
{
analyzer = (FlatClusterAnalyzer)CurrentSession["analysis_data"];
int k = int.Parse(form.numclusters);
Console.WriteLine("---> K=" + k);
analyzer.Store(k);
CurrentSession["message"] = info("Произведена перестройка кластера", msg: "Удачное завершение операции");
}
catch
{
// В сессии нет данных по кластеру.
CurrentSession["message"] = error("Похоже кластер не рассчитан", msg: "Неудачная операция");
}
}
return(InSession(Response.AsRedirect("/analysis")));
};
}
// Perf? Do it by pictures first and then by zoom level?
// The assumption is that the d
public void GenerateClusteringData(IEnumerable items)
{
this.cachedConvertedObjects.Clear();
this.keyToItems.Clear();
this.zoomLevelToClusters.Clear();
this.LeastClustersZoomLevel = 1;
this.MostClustersZoomLevel = 1;
CreateValidItemsAsClusters(items);
// 1 is space view
// 20 is ground view
for (int i = 1; i <= this.numZoomLevels; i++)
{
// List of Clusters for this zoom level
ClusterList clusters = new ClusterList();
foreach (var item in items)
{
BasicGeoposition itemCoord = ConvertObjToGPS(item);
if (!GeospatialHelperStatic.IsValidGPS(itemCoord))
{
continue;
}
bool addedToCluster = false;
for (int j = 0; j < clusters.Count; j++)
{
if (IsWithinBoundary(itemCoord, clusters[j].Location, i))
{
clusters[j].Count += 1;
clusters[j].Objects.Add(item);
string id = String.Format("ZL{0}_C{1}", i, j);
this.keyToItems[id].Add(item);
addedToCluster = true;
break;
}
}
if (addedToCluster == false)
{
string id = String.Format("ZL{0}_C{1}", i, clusters.Count);
List <Object> singleItem = new List <Object>()
{
item
};
clusters.Add(new Cluster()
{
ClusterId = id,
Objects = singleItem,
Count = 1,
Location = itemCoord
});
List <Object> otherSingleItem = new List <Object>()
{
item
};
this.keyToItems.Add(id, otherSingleItem);
}
}
if (this.zoomLevelToClusters.Count > 1)
{
int minClusters = this.zoomLevelToClusters[this.LeastClustersZoomLevel - 1].Count;
int maxClusters = this.zoomLevelToClusters[this.MostClustersZoomLevel - 1].Count;
// eg: 1 1 1 3 5 5 7 7 7, this will do the 1s. See definition for what these two are
if (clusters.Count <= minClusters)
{
this.LeastClustersZoomLevel = i;
}
if (clusters.Count > maxClusters)
{
this.MostClustersZoomLevel = i;
}
}
this.zoomLevelToClusters.Add(clusters);
}
RecalculateCenters();
}
public static void Main(String[] args)
{
//String A = "ACBDEA";
//String B = "ABCDA";
//int[] Aint = { 12, 17, 15, 11, 6, 1, 2, 5, 5, 7, 11, 10, 12, 12, 15, 15, 17, -17, -18, 17, 17, 16, 13, 9, 6, 4, 4, 7, 9, 9, 9, 8, 14, 9, -1, 9, -16, 3, 3, 8, -11, -8, 9, -10, 9, -3, -9, -1, 3, -8, 1 };
//int[] Bint = { 11, 11, 10, 9, 7, 4, 4, 3, 3, 4, 4, 3, 4, 1, -3, -3, 1, 5, 10, 16, 17, -18, 14, 9, 0, -2, -3, -2, -1, -1, -1, 0, 0, 0, 1, 1, 1, 2, 3, 4, 6, 8, 10, 11, 10, 10, 10, 8, 10, 10, 8, 8, 9, 8, 6, 8, 9, -18, 14, -9 };
//int tempResult = findInt(Aint, Bint);
//UNNAMED_01_1851,0,-1,0,1,-1,0,0,2,4,7,9,9,10,16,
List <SortedSet <string> > resultSet = new List <SortedSet <string> >();
using (TextFieldParser parser = new TextFieldParser(@"C:\Users\User\Desktop\HurricaneProject\sequences.txt"))
{
parser.TextFieldType = Microsoft.VisualBasic.FileIO.FieldType.Delimited;
parser.SetDelimiters(",");
Dictionary <string, int[]> sequenceDict = new Dictionary <string, int[]>();
while (!parser.EndOfData)
{
//Process row
string[] fields = parser.ReadFields();
string name = fields[0];
int length = fields.Length - 1;
while (fields[length] == "")
{
length--;
}
int[] tempInt = new int[length];
for (int i = 1; i <= length; i++)
{
tempInt[i - 1] = Int32.Parse(fields[i]);
}
sequenceDict.Add(name, tempInt);
}
/* int[] Aint = sequenceDict["UNNAMED_05_1864"];
* int[] Bint = sequenceDict["HUMBERTO_09_2007"];
*
* string lcsMatch;
* int lcs = Hurricane.findInt(Aint, Bint, out lcsMatch);*/
StreamWriter logFile = new StreamWriter("C:\\Users\\User\\Desktop\\HurricaneProject\\ClusterLog.txt", false);
ClusterList fullSet = new ClusterList();
// Build set of clusters
for (int i = 0; i < sequenceDict.Keys.Count; i++)
{
//if (i > 200) break;
string nameA = sequenceDict.Keys.ElementAt(i);
int[] intA = sequenceDict[sequenceDict.Keys.ElementAt(i)];
Hurricane hur = new Hurricane();
hur.id = nameA;
hur.bearing = intA;
Cluster cluster = new Cluster();
cluster.id = i;
cluster.set.Add(hur);
fullSet.clusters.Add(cluster);
}
// Test print matrix
Console.WriteLine("Iter 0 (full matrix)");
logFile.WriteLine("Iter 0 (full matrix)");
fullSet.Print(logFile);
//fullSet.PrintMatrix(logFile);
// Reqiured minimum clustering score
int minScoreAllowed = 5;
// Begin clustering on full list
ClusterList curCluster = fullSet;
// Iterate
for (int i = 1; ; i++)
{
// Try to find two clusters to combine
int lcsScore;
ClusterList newCluster = curCluster.ConsolidateLCS(minScoreAllowed, out lcsScore);
// No more good clustering
if (newCluster == curCluster)
{ // No more clusters match to within minScoreAllowed
logFile.WriteLine("Clustering completed sorting by cluster size");
curCluster.SortByCount();
curCluster.Print(logFile, true);
// Write cluster groups
StreamWriter grpFile = new StreamWriter("C:\\Users\\User\\Desktop\\HurricaneProject\\ClusterGrps.txt", false);
curCluster.WriteIds(grpFile);
grpFile.Close();
break;
}
else
{ // Accept the new cluster as the current
curCluster = newCluster;
}
// Log progress
logFile.WriteLine("Iter " + i.ToString() + " clusters " + curCluster.ClusterCount().ToString() + " LCSscore=" + lcsScore.ToString());
Console.WriteLine("Iter " + i.ToString() + " clusters " + curCluster.ClusterCount().ToString() + " LCSscore=" + lcsScore.ToString());
curCluster.Print(logFile);
// Print if reasonable
// curCluster.PrintMatrix(logFile);
}
Console.WriteLine("Done");
logFile.Close();
}
}