private void btnAprioriNew_Click(object sender, EventArgs e)
{
richAssociationAprioriNew.Text = string.Empty;
richLargeAprioriNew.Text = string.Empty;
//do apriori
double supportThreshold = double.Parse(txtSupportThreshold.Text);
System.Diagnostics.Stopwatch calcTime = System.Diagnostics.Stopwatch.StartNew();
ItemsetCollection L = AprioriMining.DoAprioriNew(db, supportThreshold);
richLargeAprioriNew.Text = (L.Count + " Large Itemsets (by Apriori)") + "\r\n";
txtTimesAprioriNew.Text = ((calcTime.ElapsedMilliseconds)) + " Milliseconds";
foreach (Itemset itemset in L)
{
richLargeAprioriNew.Text += itemset.ToString() + "\r\n";
}
//do mining
double confidenceThreshold = double.Parse(txtConfidenceThreshold.Text);
List <AssociationRule> allRules = AprioriMining.Mine(db, L, confidenceThreshold);
richAssociationAprioriNew.Text = (allRules.Count + " Association Rules \n");
foreach (AssociationRule rule in allRules)
{
richAssociationAprioriNew.Text += rule.ToString() + "\r\n";
}
}
//[Test]
public void TestMethod1()
{
ItemsetCollection collection = new ItemsetCollection {
list1, list2, list3, list4
};
ItemsetCollection L = AprioriMining.Apriori(collection, 50);
ItemsetCollection Check = GetItems();
Assert.AreEqual(Check, L);
}
public static void Main(string[] args)
{
//string Items = String.Empty;
ItemsetCollection db = new ItemsetCollection();
Itemset Items = new Itemset();
HashSet <string> ItemsSet = new HashSet <string>();
Console.WriteLine("Введите кол-во транзакций:");
int amount = int.Parse(Console.ReadLine());
for (int i = 0; i < amount; i++)
{
Console.WriteLine("Введите транзакцию номер {0}, по одному товару в ряд. Пустая строка для конца ввода", i);
Itemset transac = new Itemset();
for (;;)
{
string item = Console.ReadLine().ToLower();
if (item == "" || item == String.Empty)
{
break;
}
transac.Add(item);
}
db.Add(transac);
}
foreach (Itemset transac in db)
{
Console.WriteLine(transac);
}
foreach (Itemset transac in db)
{
foreach (string item in transac)
{
if (!IsHashSetContainsThisString(ItemsSet, item))
{
ItemsSet.Add(item);
Items.Add(item);
}
}
}
Console.Write("Все уникальные товары:{");
foreach (var item in Items)
{
Console.Write(item + '\t');
}
Console.WriteLine("}");
Console.WriteLine("Введите поддержку:");
double support = double.Parse(Console.ReadLine(), System.Globalization.CultureInfo.InvariantCulture);
Console.WriteLine("Введите уверенность:");
double confidence = double.Parse(Console.ReadLine(), System.Globalization.CultureInfo.InvariantCulture);
DoApriori(support, confidence, Items, db);
}
private ItemsetCollection PerformCollection(UserRecientBlock item)
{
var trainingDB = new ItemsetCollection();
foreach (var visitedUser in item.VisitedUsers)
{
var itemSet = new Itemset();
itemSet.AddRange(visitedUser.VisitedConcertsIds);
trainingDB.Add(itemSet);
}
return(trainingDB);
}
private void btnGenerate_Click(object sender, EventArgs e)
{
//get items specified by user
Itemset items = new Itemset();
//items.AddRange(txtInputItem.Text.Split(','));
foreach (string it in txtInputItem.Text.Split(','))
{
items.Add(int.Parse(it));
}
//items.Remove("");
int transactionCount = 5;
int.TryParse(txtTransaction.Text, out transactionCount);
Random rnd = new Random();
//create random transactions
db = new ItemsetCollection();
for (int transactionIndex = 0; transactionIndex < transactionCount;)
{
int itemCount = rnd.Next(2, items.Count);
Itemset transaction = new Itemset();
//for (int itemIndex = 0; itemIndex < itemCount; itemIndex++)
//{
// int randomItemIndex = rnd.Next(items.Count);
// if (!transaction.Contains(items[randomItemIndex]))
// {
// transaction.Add(items[randomItemIndex]);
// }
//}
while (itemCount > 0)
{
int randomItemIndex = rnd.Next(items.Count);
if (!transaction.Contains(items[randomItemIndex]))
{
transaction.Add(items[randomItemIndex]);
itemCount--;
}
}
if (transaction.Count > 0)
{
db.Add(transaction);
transactionIndex += 1;
}
}
richItems.Text = db.ToString();
}
private void btnAprioriNewSS_Click(object sender, EventArgs e)
{
richAssociationAprioriNewSS.Text = string.Empty;
richLargeAprioriNewSS.Text = string.Empty;
// do FP-Growth
double supportThreshold = double.Parse(txtSupportThreshold.Text);
System.Diagnostics.Stopwatch calcTime = System.Diagnostics.Stopwatch.StartNew();
ReturnLApriori();//FPGrowthMining.DoFPGrowthParallel(db, supportThreshold);
txtTimesAprioriNewSS.Text = ((calcTime.ElapsedMilliseconds)) + " Milliseconds";
//
string[] database = null;
try { database = System.IO.File.ReadAllLines("OutputAprioriNew.txt"); }
catch { }
ItemsetCollection L = new ItemsetCollection();
Itemset items;
foreach (string item in database)
{
items = new Itemset();
string[] itemsupport = item.Split(':');
//items.AddRange(itemsupport[0].Split(','));
foreach (string it in itemsupport[0].Split(','))
{
items.Add(int.Parse(it));
}
//items.Remove("");
items.Support = double.Parse(itemsupport[1]);
L.Add(items);
}
//
richLargeAprioriNewSS.Text = (L.Count + " Large Itemsets (by Apriori cải tiến song song)") + "\r\n";
foreach (Itemset itemset in L)
{
richLargeAprioriNewSS.Text += itemset.ToString() + "\r\n";
}
//do mining
double confidenceThreshold = double.Parse(txtConfidenceThreshold.Text);
List <AssociationRule> allRules = AprioriMining.Mine(db, L, confidenceThreshold);
richAssociationAprioriNewSS.Text = (allRules.Count + " Association Rules \n");
foreach (AssociationRule rule in allRules)
{
richAssociationAprioriNewSS.Text += rule.ToString() + "\r\n";
}
}
public void AprioriTid() //Test of AprioriTid for support = 50%
{
ItemsetCollection collection = new ItemsetCollection {
list1, list2, list3, list4
};
ItemsetCollection L = AprioriMining.AprioriTid(collection, 50);
ItemsetCollection Check = GetItems();
Assert.AreEqual(Check.Count, L.Count); //Checking items count in itemsetcollection
for (int k = 0; k < Check.Count; k++) //Loop for all itmesets
{
Assert.AreEqual(Check[k].Support, L[k].Support); //Checking support of itemset
Assert.AreEqual(Check[k].Count, L[k].Count); // Checking number count of itmes in itemset
Assert.AreEqual(Check[k].Sum(), L[k].Sum()); //Checking summary of all numbers in list
}
}
private void ResetData()
{
//sample items
Itemset items = new Itemset();
items.Add(0);
items.Add(1);
items.Add(2);
items.Add(3);
items.Add(4);
items.Add(5);
items.Add(6);
items.Add(7);
items.Add(8);
txtInputItem.Text = string.Join(",", items.ToArray());
//sample database
db = new ItemsetCollection();
db.Add(new Itemset()
{
items[0], items[1], items[2], items[3], items[4]
});
db.Add(new Itemset()
{
items[1], items[2]
});
db.Add(new Itemset()
{
items[0], items[1], items[5]
});
db.Add(new Itemset()
{
items[1], items[0], items[6]
});
db.Add(new Itemset()
{
items[0], items[5], items[7]
});
richItems.Text = db.ToString();
txtTransaction.Text = db.Count.ToString();
txtSupportThreshold.Text = "40";
txtConfidenceThreshold.Text = "70";
}
public static void DoApriori(double support, double confidence, Itemset _items, ItemsetCollection _db)
{
Itemset uniqueItems = _db.GetUniqueItems();
ItemsetCollection L = AprioriMining.DoApriori(_db, support);
Console.WriteLine((L.Count + " Large Itemsets (by Apriori)\n"));
foreach (Itemset itemset in L)
{
Console.WriteLine(itemset.ToString() + " Длина " + itemset.Count);
}
Console.WriteLine("\n");
int maxLSize = L[0].Count;
Console.WriteLine("maxLSize=" + maxLSize);
foreach (Itemset itemset in L)
{
if (maxLSize < itemset.Count)
{
maxLSize = itemset.Count;
}
}
Console.WriteLine("maxLSize=" + maxLSize);
foreach (Itemset itemset in L)
{
if (itemset.Count < maxLSize)
{
itemset.Clear();
}
}
foreach (Itemset itemset in L)
{
Console.WriteLine(itemset.ToString() + " Длина " + itemset.Count);
}
List <AssociationRule> allRules = AprioriMining.Mine(_db, L, confidence);
Console.WriteLine(allRules.Count + " Association Rules\n");
foreach (AssociationRule rule in allRules)
{
Console.WriteLine(rule.ToString() + "Длина=" + rule.getTotalLength() + "\n");
}
}
public void Mine() //Test of minig asscociationrules for conffidence = 80
{
ItemsetCollection collection = new ItemsetCollection {
list1, list2, list3, list4
};
ItemsetCollection L = AprioriMining.Apriori(collection, 50);
List <AssociationRule> AllRules = AprioriMining.Mine(collection, L, 80);
List <AssociationRule> Check = GetRules();
Assert.AreEqual(Check.Count, AllRules.Count); //Checking rules count in rules collection
for (int k = 0; k < Check.Count; k++) //Loop for all rules
{
Assert.AreEqual(Check[k].Confidence, AllRules[k].Confidence); //Checking confidence of rule
Assert.AreEqual(Check[k].X.Count, AllRules[k].X.Count); // Checking numbers count of itmes in X
Assert.AreEqual(Check[k].X.Sum(), AllRules[k].X.Sum()); //Checking summary of all numbers in X
Assert.AreEqual(Check[k].Y.Count, AllRules[k].Y.Count); // Checking numbers count of itmes in Y
Assert.AreEqual(Check[k].Y.Sum(), AllRules[k].Y.Sum()); //Checking summary of all numbers in Y
}
}
private void btnOpen_Click(object sender, EventArgs e)
{
OpenFileDialog openFileDialog = new OpenFileDialog();
openFileDialog.InitialDirectory = "E:\\";
openFileDialog.Filter = "txt files (*.txt)|*.txt|All files (*.*)|*.*";
openFileDialog.FilterIndex = 2;
openFileDialog.RestoreDirectory = true;
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
int i = 0;
try
{
if ((openFileDialog.OpenFile()) != null)
{
file = openFileDialog.FileName;
string[] database = System.IO.File.ReadAllLines(file);
db = new ItemsetCollection();
Itemset items;
foreach (string item in database)
{
items = new Itemset();
//items.AddRange(item.Split(','));
//items.Remove("");
foreach (string it in item.Split(','))
{
items.Add(int.Parse(it));
}
db.Add(items);
i++;
}
richItems.Text = db.ToString();
}
}
catch (Exception ex)
{
MessageBox.Show("Error: Could not read file from disk. Original error: " + ex.Message + " " + i);
}
}
}
private void TestApriori()
{
ItemsetCollection collection = new ItemsetCollection {
list1, list2, list3, list4
};
ItemsetCollection L = AprioriMining.Apriori(collection, 50);
ItemsetCollection Check = GetItems();
Itemset i = new Itemset();
i.Add(1);
i.Support = 10;
i.Tid = 0;
Itemset j = new Itemset();
j.Add(1);
j.Support = 10;
j.Tid = 0;
Assert.AreEqual(Check[0].Sum(), j.ToList());
}
//support = 50%
private ItemsetCollection GetItems()
{
ItemsetCollection allItems = new ItemsetCollection();
Itemset item1 = new Itemset();
item1.Add(1);
item1.Support = 1 / 2;
Itemset item2 = new Itemset();
item2.Add(2);
item2.Support = 3 / 4;
Itemset item3 = new Itemset();
item3.Add(3);
item3.Support = 3 / 4;
Itemset item4 = new Itemset();
item4.Add(5);
item4.Support = 3 / 4;
Itemset item5 = new Itemset {
1, 3
};
item5.Support = 1 / 2;
Itemset item6 = new Itemset {
2, 3
};
item6.Support = 1 / 2;
Itemset item7 = new Itemset {
2, 5
};
item7.Support = 3 / 4;
Itemset item8 = new Itemset {
2, 3, 5
};
item8.Support = 1 / 2;
return(allItems);
}
public static ItemsetCollection FindAllSubsets(Itemset itemset)
{
ItemsetCollection allSubsets = new ItemsetCollection();
int subsetCount = (int)Math.Pow(2, itemset.Count);
for (int i = 0; i < subsetCount; i++)
{
Itemset subset = new Itemset();
for (int bitIndex = 0; bitIndex < itemset.Count; bitIndex++)
{
if (GetBit(i, bitIndex) == 1)
{
subset.Add(itemset[bitIndex]);
}
}
allSubsets.Add(subset);
}
return(allSubsets);
}
/// <summary>
/// Connects to MySQL in order to run AprioriMiner
/// </summary>
private static void DataFromMysql()
{
double[] minsupminconf = PromptForMinsupAndConf();
var tablechosen = PromptForDatasetSize();
var tableToMine = string.Empty;
switch (tablechosen)
{
case 1:
tableToMine = "itemsets";
break;
case 5:
tableToMine = "itemsets5k";
break;
case 20:
tableToMine = "itemsets20k";
break;
case 75:
tableToMine = "itemsets75k";
break;
default:
Console.WriteLine("Whooops something very bad has happened... Try again.");
return;
}
Console.WriteLine("Connecting to mysql server.... Trying to find table with {0}K transactions", tablechosen);
var database = new ItemsetCollection();
var timeStartQuery = DateTime.Now;
Console.Write("Started querying DB at " + timeStartQuery.ToLocalTime());
using (var conn = new MySqlConnection("server=localhost;user=dmuser;database=DataMiningDb;port=3306;password=data;"))
{
try
{
conn.Open();
var rows = 0;
rows = CountTransactions(conn, tableToMine);
var cmd = conn.CreateCommand();
for (var i = 1; i <= rows; i++)
{
cmd.CommandText = i == 1
? (@"set profiling=1; SELECT itemID FROM dataminingdb." + tableToMine + " WHERE transId="
+ i)
: @"SELECT itemID FROM dataminingdb." + tableToMine + " WHERE transId=" + i;
var reader = cmd.ExecuteReader();
var listofItemsInRow = new Itemset();
while (reader.Read())
{
listofItemsInRow.Add(reader.GetInt32(0));
}
database.Add(listofItemsInRow);
reader.Close();
// ============== LOG PROFILER DATA
cmd.CommandText = "SHOW PROFILES";
var pfdata = cmd.ExecuteReader();
var rs = 0;
var list = new List <object[]>();
while (pfdata.Read())
{
var rowArray = new object[pfdata.FieldCount];
rs = pfdata.GetValues(rowArray);
list.Add(rowArray);
}
using (var swrtr = new StreamWriter(@"..\..\..\MysqlProfilerLog.csv", true))
{
// swrtr.WriteLine(DateTime.Now + " = LOG Success = {0}--------------------\n", rs);
// Query Number,Time,SQLStatement
foreach (var stringse in list)
{
if (stringse[2].Equals("SHOW WARNINGS"))
{
continue;
}
foreach (var s in stringse)
{
swrtr.Write(s + ",");
if (stringse.ElementAt(2) == s)
{
swrtr.Write("\n");
}
}
// swrtr.WriteLine(Environment.NewLine);
}
}
// ==============
pfdata.Close();
// Writing profiler info to log file
// RecordLog(conn);
} // End of For Loop
}
catch (Exception e)
{
Console.WriteLine("There was an error: {0}\n\n Consult the help menu.", e.Message);
}
}
// database (of ItemsetCollection should now be populated and ready for mining...
var endQueryT = DateTime.Now;
Console.WriteLine("\aEnded queries at: {0}\nTime elapsed: {1}\n\nNow running Apriori on fetched data...", endQueryT.ToLocalTime(), endQueryT.Subtract(timeStartQuery));
var large = AprioriMining.DoApriori(database, CreateSetOfUniqueItems(), minsupminconf[0]);
var results = "Results: \n\n " + large.Count + " supported Itemsets obtained by Apriori\n\n";
foreach (var itemset in large)
{
results += itemset.ToString() + "\n";
}
Console.WriteLine("DONE! Now mining association rules...");
var allRules = AprioriMining.Mine(database, large, minsupminconf[1]);
results += "\nAssociation Rules Found: \n";
if (allRules.Count == 0)
{
results += "No rules were found over minconf of " + minsupminconf[1] + "%";
}
else
{
foreach (var associationRule in allRules)
{
results += associationRule.ToString() + "\n";
}
}
Console.WriteLine(results);
var finaltimeEnd = DateTime.Now;
Console.Write("Total Apriori time was: {0}", endQueryT.Subtract(finaltimeEnd));
}
private static void DataFromMongo()
{
double[] minsupminconf = PromptForMinsupAndConf();
var collneeded = PromptForDatasetSize();
var collectionNeeded = string.Empty;
switch (collneeded)
{
case 1:
collectionNeeded = "receipts1k";
break;
case 5:
collectionNeeded = "receipts5k";
break;
case 20:
collectionNeeded = "receipts20k";
break;
case 75:
collectionNeeded = "receipts75k";
break;
case 0:
collectionNeeded = "receipts";
break;
default:
Console.WriteLine("Whooops something very bad has happened... Try again.");
return;
}
Console.WriteLine("\nConnecting to mongoDB...");
var database = new ItemsetCollection();
var start = DateTime.Now;
try
{
const string ConnStr = "mongodb://localhost:27017";
var clt = new MongoClient(ConnStr);
var svr = clt.GetServer();
var dblearn = svr.GetDatabase("learn");
// var tlist = dblearn.GetCollection("receipts");
var list = dblearn.GetCollection <Transaction>(collectionNeeded);
Console.WriteLine("Found Data!\nCollection Name: {0}\nSize: {1}", collectionNeeded, list.Count());
var all = list.FindAll();
foreach (var transaction in all)
{
var itemset = new Itemset();
foreach (var item in transaction.Items)
{
itemset.Add(item);
}
database.Add(itemset);
}
}
catch (Exception e)
{
Console.WriteLine("There was an error, here are the details: \n=====================Error Stuff Here=====================\n" + e.Message + "\n==========================================================");
Console.WriteLine("Is the mongodb server running?? See the Help/Info section for help...");
return;
}
var endqueryT = DateTime.Now;
var totalQueryT = start.Subtract(endqueryT);
var itemsunique = CreateSetOfUniqueItems();
Console.WriteLine("Disconnected from MongoDB server at {0}.\n Total query time was: {1}", endqueryT.ToLocalTime(), totalQueryT);
Console.WriteLine("Now running Apriori on fetched data...");
var large = AprioriMining.DoApriori(database, itemsunique, minsupminconf[0]);
var results = "Results: \n\n " + large.Count + " supported Itemsets obtained by Apriori\n\n";
foreach (var itemset in large)
{
results += itemset.ToString() + "\n";
}
Console.WriteLine("DONE! Now mining association rules...");
var allRules = AprioriMining.Mine(database, large, minsupminconf[1]);
results += "\nAssociation Rules Found: \n";
if (allRules.Count == 0)
{
results += "No rules were found over minconf of " + minsupminconf[1] + "%";
}
else
{
foreach (var associationRule in allRules)
{
results += associationRule.ToString() + "\n";
}
}
Console.WriteLine(results);
var finaltimeEnd = DateTime.Now;
Console.Write("It's now: {0}.\n\nTotal Apriori time was: {1}", finaltimeEnd.ToLocalTime(), endqueryT.Subtract(finaltimeEnd));
}
//support = 50%
private ItemsetCollection GetItems()
{
ItemsetCollection allItems = new ItemsetCollection();
Itemset item1 = new Itemset {
1
};
item1.Support = 50.0;
item1.Tid = 0;
allItems.Add(item1);
Itemset item2 = new Itemset {
3
};
item2.Support = 75.0;
item2.Tid = 0;
allItems.Add(item2);
Itemset item3 = new Itemset {
2
};
item3.Support = 75.0;
item3.Tid = 0;
allItems.Add(item3);
Itemset item4 = new Itemset {
5
};
item4.Support = 75.0;
item4.Tid = 0;
allItems.Add(item4);
Itemset item5 = new Itemset {
2, 5
};
item5.Support = 75.0;
item5.Tid = 0;
allItems.Add(item5);
Itemset item6 = new Itemset {
3, 5
};
item6.Support = 50.0;
item6.Tid = 0;
allItems.Add(item6);
Itemset item7 = new Itemset {
3, 2
};
item7.Support = 50.0;
item7.Tid = 0;
allItems.Add(item7);
Itemset item8 = new Itemset {
1, 3
};
item8.Support = 50.0;
item8.Tid = 0;
allItems.Add(item8);
Itemset item9 = new Itemset {
2, 3, 5
};
item9.Support = 50.0;
item9.Tid = 0;
allItems.Add(item9);
return(allItems);
}
private void PopulateChart()
{
try
{
var filmList = db.Movies.ToList();
Itemset myItemset = new Itemset();
foreach (var i in filmList)
{
myItemset.Add(i.MyMovieID);
}
ItemsetCollection myItemsetCollection = new ItemsetCollection();
var MovieList = db.UserMovieLists.Select(u => u);
foreach (var u in MovieList)
{
var query = from a in db.Ratings
join b in db.UserMovieLists
on a.UserMovieLIstID equals b.UserMovieListID
into temp
from b in temp.DefaultIfEmpty()
where a.UserMovieLIstID == u.UserMovieListID
where a.UserRatingID >= 5
select new
{
a.MyMovieID,
};
Itemset tempItemset = new Itemset();
;
if (myItemset != null)
{
if (query.Count() > 1)
{
foreach (var r in query.Take(25))
{
tempItemset.Add(r.MyMovieID);
}
myItemsetCollection.Add(tempItemset);
}
}
}
List <double> supportList = new List <double>();
double support1 = 30;
double support2 = 27.5;
double support3 = 25;
double support4 = 22.5;
double support5 = 20;
supportList.Add(support5);
supportList.Add(support4);
supportList.Add(support3);
supportList.Add(support2);
supportList.Add(support1);
foreach (var ele in supportList)
{
int timeStart, timeStop, timeElapsed;
timeStart = Environment.TickCount;
ItemsetCollection L = AprioriMining.Apriori(myItemsetCollection, ele);
timeStop = Environment.TickCount;
timeElapsed = (timeStop - timeStart) / 1000;
this.chart1.Series["Apriori"].Points.AddXY(ele, timeElapsed);
}
foreach (var ele in supportList)
{
int timeStart, timeStop, timeElapsed;
timeStart = Environment.TickCount;
ItemsetCollection L = AprioriMining.AprioriTid(myItemsetCollection, ele);
timeStop = Environment.TickCount;
timeElapsed = (timeStop - timeStart) / 1000;
this.chart1.Series["AprioriTid"].Points.AddXY(ele, timeElapsed);
}
}
catch (Exception exp)
{
MessageBox.Show(exp.ToString());
}
}
private void AddRules2()
{
try
{
var filmList = db.Movies.ToList();
Itemset myItemset = new Itemset();
foreach (var i in filmList)
{
myItemset.Add(i.MyMovieID);
}
ItemsetCollection myItemsetCollection = new ItemsetCollection();
var MovieList = db.UserMovieLists.Select(u => u);
foreach (var u in MovieList)
{
var query = from a in db.Ratings
join b in db.UserMovieLists
on a.UserMovieLIstID equals b.UserMovieListID
into temp
from b in temp.DefaultIfEmpty()
where a.UserMovieLIstID == u.UserMovieListID
where a.UserRatingID >= 5
select new
{
a.MyMovieID,
};
Itemset tempItemset = new Itemset();
;
if (myItemset != null)
{
if (query.Count() > 1)
{
foreach (var r in query.Take(25))
{
tempItemset.Add(r.MyMovieID);
}
myItemsetCollection.Add(tempItemset);
}
}
}
ItemsetCollection L = AprioriMining.AprioriTid(myItemsetCollection, 20);
List <AssociationRule> allRules = AprioriMining.Mine(myItemsetCollection, L, 20);
var ruleX = db.RulesX.Select(x => x);
db.RulesX.RemoveRange(ruleX);
db.SaveChanges();
var ruleY = db.RulesY.Select(x => x);
db.RulesY.RemoveRange(ruleY);
db.SaveChanges();
var myRule = db.Rules.Select(x => x);
db.Rules.RemoveRange(myRule);
db.SaveChanges();
var ruleXmovie = db.RuleXMovie.Select(x => x);
db.RuleXMovie.RemoveRange(ruleXmovie);
db.SaveChanges();
var ruleYMovie = db.RuleYMovie.Select(x => x);
db.RuleYMovie.RemoveRange(ruleYMovie);
foreach (var r in allRules)
{
var rule = new MyRule();
var X = new RuleX();
var Y = new RuleY();
db.RulesX.Add(X);
db.SaveChanges();
db.RulesY.Add(Y);
db.SaveChanges();
rule.Support = r.Support;
rule.Confidene = r.Confidence;
rule.RuleXID = X.RuleXID;
rule.RuleYID = Y.RuleYID;
db.Rules.Add(rule);
db.SaveChanges();
foreach (var x in r.X)
{
var rm = new RuleXMyMovie();
rm.RuleX_RuleXID = X.RuleXID;
rm.MyMovie_MyMovieID = x;
db.RuleXMovie.Add(rm);
db.SaveChanges();
}
foreach (var y in r.Y)
{
var rm = new RuleYMyMovie();
rm.RuleY_RuleYID = Y.RuleYID;
rm.MyMovie_MyMovieID = y;
db.RuleYMovie.Add(rm);
db.SaveChanges();
}
}
}
catch (Exception exp)
{
MessageBox.Show(exp.ToString());
}
}
