public void AprioriExampleTest1()
{
// Example from https://en.wikipedia.org/wiki/Apriori_algorithm
SortedSet<int>[] dataset =
{
new SortedSet<int> { 1, 2, 3, 4 },
new SortedSet<int> { 1, 2, 4 },
new SortedSet<int> { 1, 2 },
new SortedSet<int> { 2, 3, 4 },
new SortedSet<int> { 2, 3 },
new SortedSet<int> { 3, 4 },
new SortedSet<int> { 2, 4 },
};
var apriori = new Apriori(threshold: 3, confidence: 0);
var classifier = apriori.Learn(dataset);
var expected = new Tuple<int[], int>[]
{
Tuple.Create(new[] {1}, 3),
Tuple.Create(new[] {2}, 6),
Tuple.Create(new[] {3}, 4),
Tuple.Create(new[] {4}, 5),
Tuple.Create(new[] {1,2}, 3),
//Tuple.Create(new[] {1,3}, 1),
//Tuple.Create(new[] {1,4}, 2),
Tuple.Create(new[] {2,3}, 3),
Tuple.Create(new[] {2,4}, 4),
Tuple.Create(new[] {3,4}, 3),
//Tuple.Create(new[] {2,3,4}, 2),
};
var frequent = apriori.Frequent;
foreach (var tuple in expected)
{
var a = frequent.Where(x => x.Key.SetEquals(tuple.Item1)).First();
Assert.AreEqual(tuple.Item2, a.Value);
}
}
public void ProcessTransactionTest()
{
//Arrange
IApriori target = new Apriori();
//Act
Output actual = target.ProcessTransaction(_minSupport, _minConfidence, _items, _transactions);
//Assert
Assert.AreEqual(9, actual.FrequentItems.Count);
Assert.AreEqual(2, actual.FrequentItems["a"].Support);
Assert.AreEqual(3, actual.FrequentItems["b"].Support);
Assert.AreEqual(3, actual.FrequentItems["c"].Support);
Assert.AreEqual(3, actual.FrequentItems["e"].Support);
Assert.AreEqual(2, actual.FrequentItems["ac"].Support);
Assert.AreEqual(2, actual.FrequentItems["bc"].Support);
Assert.AreEqual(3, actual.FrequentItems["be"].Support);
Assert.AreEqual(2, actual.FrequentItems["ce"].Support);
Assert.AreEqual(2, actual.FrequentItems["bce"].Support);
Assert.AreEqual(2, actual.MaximalItemSets.Count);
Assert.AreEqual("ac", actual.MaximalItemSets[0]);
Assert.AreEqual("bce", actual.MaximalItemSets[1]);
Assert.AreEqual(5, actual.StrongRules.Count);
}
private void Statistic()
{
var apriori = new Apriori(Tree);
string path = Path.Combine(DirectoryPath, "statistic.txt");
File.WriteAllText(path, $"Liczba transakcji: {Tree.NumberTransactions} Liczba towarów: {apriori.NumberOfUniqProducts()}");
}
public static void Main()
{
BL.DataHandle db = new BL.DataHandle();
Item[][] a = db.GetOrders().Select(order => order.Items.ToArray()).ToArray();
Apriori <Item> ab = new Apriori <Item>(15, 0);
//var clsfr = ab.Learn(a);
//var itemsDistinctByBarcode = db.GetAllItems().GroupBy(i => i.BarcodeNumber).Select(grp => grp.FirstOrDefault());
//AssociationRuleMatcher<Item> cl = new AssociationRuleMatcher<Item>(itemsDistinctByBarcode.Count(),
// clsfr.Rules.Where(r => r.Confidence > 0.6).ToArray());
//int abcde;
//System.Collections.Generic.Dictionary<Item, int> counter = new System.Collections.Generic.Dictionary<Item, int>();
//foreach (Order o in db.GetOrders()){
// foreach (Item item in o.Items)
// {
// if (counter.TryGetValue(item,out abcde))
// {
// }
// }
//}
//var abcd = cl.Decide(itemsDistinctByBarcode.ToList().GetRange(0,10).ToArray());
AssociationRule <Item>[] rules = ab.Learn(a).Rules;
foreach (var rule in rules)
{
Console.WriteLine(rule);
}
Console.ReadLine();
//BL.DataHandle.GenerateQRcodes();
//FlushData.FlushAll();
//Console.WriteLine("press key to exit");
//Console.ReadLine();
}
public void TestMethodAlgorithms()
{
List <double> supports = new List <double>(new double[] { 0.001, 0.002, 0.003, 0.004, 0.005, 0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.2 });
string stringResult = "";
foreach (double support in supports)
{
var start = DateTime.Now;
var transactions = new TransactionDatabase(@"\\small.txt");
var apriori = new Apriori(transactions, 0.2);
apriori.Run();
var end = DateTime.Now;
var result = end - start;
stringResult += $"APRIORI Support:{support} Total Time (ms): {result.TotalMilliseconds}" + Environment.NewLine;
}
foreach (double support in supports)
{
var start = DateTime.Now;
var transactions = new TransactionDatabase(@"\\small.txt");
var eclat = new Eclat(transactions, 0.2);
eclat.Run();
var end = DateTime.Now;
var result = end - start;
stringResult += $"ECLAT Support:{support} Total Time (ms): {result.TotalMilliseconds}" + Environment.NewLine;
System.IO.File.WriteAllText(@"PerformanceResultsAuto.csv", stringResult);
}
}
private void performanceTestToolStripMenuItem_Click(object sender, EventArgs e)
{
List <double> supports = new List <double>(new double[] { 0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5 });
string stringResult = "";
string readfile = File.ReadAllText(comboBoxTestDataSet.Text);
foreach (double support in supports)
{
var start = DateTime.Now;
var transactions = new TransactionDatabase(comboBoxTestDataSet.Text);
var apriori = new Apriori(transactions, support);
apriori.Run();
var end = DateTime.Now;
var result = end - start;
var resultMem = GC.GetTotalMemory(true);
stringResult += $"APRIORI Support:{support} Total Time (ms): {result.TotalMilliseconds} Memory Usage (bytes): {resultMem}" + Environment.NewLine;
}
foreach (double support in supports)
{
var start = DateTime.Now;
var transactions = new TransactionDatabase(comboBoxTestDataSet.Text);
var eclat = new Eclat(transactions, support);
eclat.Run();
var end = DateTime.Now;
var result = end - start;
var resultMem = GC.GetTotalMemory(true);
stringResult += $"ECLAT Support:{support} Total Time (ms): {result.TotalMilliseconds} Memory Usage (bytes): {resultMem}" + Environment.NewLine;
System.IO.File.WriteAllText(@"PerformanceResults.txt", stringResult);
}
}
public MainViewModel()
{
var tr = new TextReader();
Apriori apriori = new Apriori(tr.GetItems(), 0.2, 0.2);
Console.WriteLine();
}
static void Main(string[] args)
{
var events = File.ReadAllLines(@"C:\Users\leosm\Documents\Projects\TCC\DataSetByCNPJ\cartelFull.csv");
var list = events.Select(x =>
{
var split = x.Split(';');
return(new Participante
{
CodItemCompra = split[0],
CnpjParticipante = split[1]
});
});
var groups = list.GroupBy(x => x.CodItemCompra).ToDictionary(x => x.Key, x => x.Select(e => e.CnpjParticipante).ToArray()).ToArray();
var dataset = groups.Select(x => x.Value.ToArray()).ToArray();
// Create a new A-priori learning algorithm with the requirements
var apriori = new Apriori <string>(threshold: 3, confidence: 0.7);
// Use apriori to generate a n-itemset generation frequent pattern
AssociationRuleMatcher <string> classifier = apriori.Learn(dataset);
// Generate association rules from the itemsets:
AssociationRule <string>[] rules = classifier.Rules;
}
static void Main(string[] args)
{
var apriori = new Apriori();
var support = 2;
var confidence = 0.8;
var transactions = new[] { "milk~egg~flower", "honey~egg~siroup", "milk~honey~egg~siroup", "honey~siroup", "milk~egg~siroup" };
var items = new List <string> {
"milk", "honey", "egg", "flower", "siroup"
};
//var transactions = new[] { "1~3~4", "2~3~5", "1~2~3~5", "2~5", "1~3~5" };
//var items = new List<string> { "1", "2", "3", "4", "5" };
var transactionsTest = ReadTransactionsFile();
var itemsTest = ReadItemsFile();
var result = apriori.ProcessTransaction(support, confidence, itemsTest, transactionsTest);
WriteRules(result.StrongRules);
WriteFrequent(result.FrequentItems);
WriteClosest(result.ClosedItemSets);
WriteMaximal(result.MaximalItemSets);
Console.WriteLine("Done...");
Console.Read();
}
public string getRecommendedBook(string CustomerId)
{
if (CustomerId == null)
{
return("error - must have CustomerId");
}
var allLoans = db.Loans.Include(l => l.Book).Include(l => l.Customer).ToList();
var customers = db.Customers.ToList();
List <int[]> tempDataset = new List <int[]>();
foreach (var c in customers)
{
var booksPerC = allLoans.Where(x => x.CustomerId == c.Id).Select(b => b.BookId).ToList();
tempDataset.Add(booksPerC.ToArray());
}
int[][] dataset = tempDataset.ToArray();
Apriori apriori = new Apriori(threshold: 1, confidence: 0);
AssociationRuleMatcher <int> classifier = apriori.Learn(dataset);
var booksPerSpecC = allLoans.Where(x => x.Customer.PersonalID == (string)CustomerId).Select(b => b.BookId).ToArray();
int[][] matches = classifier.Decide(booksPerSpecC);
if (matches.Length > 0)
{
int BestBookID = matches[0][0];
string BestBookName = db.Books.Single(x => x.Id == BestBookID).Name;
return(BestBookName);
}
return("There is no recommended book for this customer.");
}
public List <int[]> FrequentItemsets_Apriori(double threshold)
{
List <int[]> itemsets = context.Items.Select(s => new int[] { s.Value.Code }).ToList();
List <List <int> > transactions = context.Transactions.Select(t => t.Value.Items).ToList();
return(Apriori.GenerateAllFrecuentItemsets(itemsets, transactions, threshold).ToList());
}
static void Main(string[] args)
{
Console.WriteLine("Hi, Thank you for choosing AgentNet. \n\n\nGetting latest transaction data from DB...");
InititalizeData();
Console.WriteLine("Following items found :");
Console.WriteLine(string.Join(",", ItemSets));
Console.WriteLine("Number of transactions found : " + TransactionSets.Count);
//Console.WriteLine(string.Join(",", TransactionSets));
Console.Write("Please enter a minimum support (%) :");
string minSupportStr = Console.ReadLine();//"50";//Console.ReadLine();
Console.Write("Please enter a minimum confidence (%) :");
string minConfidenceStr = Console.ReadLine(); //"80";//Console.ReadLine();
double minSupport = double.Parse(minSupportStr) / 100;
double minConfidence = double.Parse(minConfidenceStr) / 100;
string[] transactionArray = TransactionSets.ToArray();
IApriori _apriori = new Apriori();
Output output = _apriori.ProcessTransaction(minSupport, minConfidence, ItemSets, transactionArray);
Console.WriteLine(string.Join("", output.StrongRules.Select(i => i.X + "-->" + i.Y + "\t" + i.Confidence * 100 + "%\n").ToList()));
Console.ReadKey();
}
public void Predict(string SaleID, string CustomerID)
{
Dictionary <string, int> mapActors = new Dictionary <string, int>();
SortedSet <int>[] dataset = SalesTransactionConverter(mapActors);
Apriori apriori = new Apriori(threshold: 3, confidence: 0);
AssociationRuleMatcher <int> classifier = apriori.Learn(dataset);
var sale = _context.Sale.Include(s => s.Movies).Include("Movies.Movie").FirstOrDefault(s => s.SaleID == SaleID);
HashSet <string> actorsSet = new HashSet <string>();
List <int> sample = new List <int>();
foreach (var movie in sale.Movies)
{
string[] actors = movie.Movie.Actors.Split(",");
foreach (var actor in actors)
{
if (!actorsSet.Contains(actor))
{
actorsSet.Add(actor);
sample.Add(mapActors.GetValueOrDefault(actor));
}
}
}
int[][] matches = classifier.Decide(sample.ToArray());
}
public void GenerateFrequentItemSets()
{
var sut = new Apriori(null);
var test = new string[] { "banana", "apple", "potato", "grape" };
var result = sut.GenerateFrequentItemSets(test, 1);
}
public void UpdateRecommendedProducts()
{
var orders1 = _context.OrderClient.Include(order => order.ProductOrders).ThenInclude(po => po.Product);
var orders = _context.ApplicationUser.Include(user => user.Orders).ThenInclude(order => order.ProductOrders)
.ThenInclude(po => po.Product).Select(delegate(ApplicationUser user)
{
List <int> orderList = new List <int>();
foreach (ClientOrder co in user.Orders)
{
foreach (ProductOrder po in co.ProductOrders)
{
orderList.Add(po.Product.ProductID);
}
}
return(orderList);
}).ToArray();
SortedSet <int>[] dataset = ToSortedSet(orders);
// We will use Apriori to determine the frequent item sets of this database.
// To do this, we will say that an item set is frequent if it appears in at
// least 20% transactions of the database: the value _minSupport * dataset.Length is the support threshold.
// Create a new a-priori learning algorithm with support 3
Apriori <int> apriori =
new Apriori <int>(Convert.ToInt32(_minSupport * dataset.Length), _minConfidence);
// Use the algorithm to learn a set matcher
classifier = apriori.Learn(dataset);
}
public void Example()
{
List <Transaction <char> > database = new List <Transaction <char> >();
database.Add(new Transaction <char>('a', 'c', 'd', 'e')
{
ID = 10
});
database.Add(new Transaction <char>('a', 'b', 'e')
{
ID = 20
});
database.Add(new Transaction <char>('b', 'c', 'e')
{
ID = 30
});
database.Add(new Transaction <char>('b', 'c', 'e')
{
ID = 40
});
Apriori <char> method = new Apriori <char>();
ItemSets <char> itemsets = method.MinePatterns(database, 2, new List <char>()
{
'a', 'b', 'c', 'd', 'e'
});
for (int i = 0; i < itemsets.Count; ++i)
{
ItemSet <char> itemset = itemsets[i];
Console.WriteLine(itemset);
}
}
private void FrequentSets(double minSup, int maxSize)
{
var apriori = new Apriori(Tree);
Leaf[] leafs = apriori.GetFrequentSets(minSup, maxSize);
FrequentSets(leafs, $"FrequentSets {minSup} {maxSize} Find elements: {leafs.Length}", $"{TreeName}-frequentsets-{DoubleToString(minSup)}-{maxSize}.txt");
}
static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
KetNoi();
Program.apriori = new Apriori();
Application.Run(new FormMain());
}
public PlaylistController(ApplicationDbContext context, UserManager <ApplicationUser> userManager)
{
_context = context;
_userManager = userManager;
//Initializing aprior alg.
AprioriAlg = new Apriori(threshold: 1, confidence: 0);
}
public List <String> FrequentItemSetsByClient(String clientCode, double Support)
{
List <int[]> itemsets = context.Clients[clientCode].Transactions.SelectMany(t => t.Items).Distinct().Select(s => new int[] { s }).ToList();
List <List <int> > transactions = context.Clients[clientCode].Transactions.Select(t => t.Items).ToList();
var frequent = Apriori.GenerateAllFrecuentItemsets(itemsets, transactions, Support).ToList();
return(formatItemSets(frequent));
}
public List <String> FrequentItemSetsByDepartment(String department, double support)
{
List <int[]> itemsets = TransactionsByDepartment(department).SelectMany(t => t.Items).Distinct().Select(s => new int[] { s }).ToList();
List <List <int> > transactions = TransactionsByDepartment(department).Select(t => t.Items).ToList();
var frequent = Apriori.GenerateAllFrecuentItemsets(itemsets, transactions, support).ToList();
return(formatItemSets(frequent));
}
static void Main(string[] args)
{
/*
This sample demonstrate how-to use the Apriori algorithm.
As a test data I use a data from an imagine book store:
1) We have a list of authors, which the store sells.
2) We have an information about shopping cart of some users.
As a result we get the following information:
1. Order by popularity of the authors/set of authors.
2. What we can advise the user, basing on his shopping cart. It is ordered by probability (frequent).
*/
TypeRegister.Register<BookAuthor>((a, b) => a.Name.CompareTo(b.Name));
var itemsets = new List<Itemset<BookAuthor>>();
var transactions = new SampleHelper().Transactions.ToList();
var apriori = new Apriori<BookAuthor>
{
MinSupport = (double) 1/9,
SaveItemset = itemset => itemsets.Add(itemset),
GetTransactions = ()=> transactions
};
apriori.ProcessTransactions();
var rules = new List<Rule<BookAuthor>>();
var agrawal = new AgrawalFaster<BookAuthor>
{
MinLift = 0.01,
MinConfidence = 0.01,
TransactionsCount = transactions.Count(),
GetItemsets = ()=>itemsets,
SaveRule = rule=>rules.Add(rule)
};
agrawal.Run();
foreach (var item in itemsets.OrderByDescending(v => v.Support))
{
Console.WriteLine(string.Join("; ", item.Value.OrderBy(i => i.Name)) + " #SUP: " + item.Support);
}
Console.WriteLine("====");
foreach (var item in rules.OrderByDescending(r => r.Confidence))
{
Console.WriteLine(string.Join("; ", item.Combination) + " => " + string.Join("; ", item.Remaining) + " ===> Confidence: " + item.Confidence + " Lift: " + item.Lift);
}
Console.ReadLine();
}
private void aprioriAlgorithmToolStripMenuItem_Click(object sender, EventArgs e)
{
var transactions = new TransactionDatabase(comboBoxTestDataSet.Text);
var apriori = new Apriori(transactions, double.Parse(textBoxMinimumSupport.Text));
apriori.Run();
textBoxOutput.AppendText(apriori.ToString());
csvString = apriori.ToCSV();
}
private void button2_Click(object sender, EventArgs e)
{
Stream myStream = null;
OpenFileDialog openFileDialog1 = new OpenFileDialog();
openFileDialog1.Filter = "txt files (*.txt)|*.txt|All files (*.*)|*.*";
openFileDialog1.FilterIndex = 1;
openFileDialog1.RestoreDirectory = true;
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
try
{
if ((myStream = openFileDialog1.OpenFile()) != null)
{
using (myStream)
{
StreamReader sr = new StreamReader(myStream);
LoadTransactions lt = new LoadTransactions(sr);
List <List <string> > transactions;
transactions = lt.Load();
apriori = new Apriori(transactions);
}
}
}
catch (Exception ex)
{
MessageBox.Show("Error: Could not read file from disk. Original error: " + ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
}
listBox1.Items.Clear();
listBox2.Items.Clear();
listBox3.Items.Clear();
listBox4.Items.Clear();
foreach (var transacion in apriori.transactions)
{
StringBuilder tempString = new StringBuilder();
foreach (var item in transacion)
{
tempString.Append(item + ", ");
}
listBox4.Items.Add(tempString.ToString());
}
trackBar1.Maximum = apriori.FirstFrequent.Values.ToList().Max();
labelSupportMax.Text = trackBar1.Maximum.ToString();
textBoxSupportValue.Text = trackBar1.Value.ToString();
button1.Enabled = true;
trackBar1.Enabled = true;
trackBar2.Enabled = true;
button3.Enabled = false;
}
public void AprioriExampleTest1()
{
// Example from https://en.wikipedia.org/wiki/Apriori_algorithm
SortedSet <int>[] dataset =
{
new SortedSet <int> {
1, 2, 3, 4
},
new SortedSet <int> {
1, 2, 4
},
new SortedSet <int> {
1, 2
},
new SortedSet <int> {
2, 3, 4
},
new SortedSet <int> {
2, 3
},
new SortedSet <int> {
3, 4
},
new SortedSet <int> {
2, 4
},
};
var apriori = new Apriori(threshold: 3, confidence: 0);
var classifier = apriori.Learn(dataset);
var expected = new Tuple <int[], int>[]
{
Tuple.Create(new[] { 1 }, 3),
Tuple.Create(new[] { 2 }, 6),
Tuple.Create(new[] { 3 }, 4),
Tuple.Create(new[] { 4 }, 5),
Tuple.Create(new[] { 1, 2 }, 3),
//Tuple.Create(new[] {1,3}, 1),
//Tuple.Create(new[] {1,4}, 2),
Tuple.Create(new[] { 2, 3 }, 3),
Tuple.Create(new[] { 2, 4 }, 4),
Tuple.Create(new[] { 3, 4 }, 3),
//Tuple.Create(new[] {2,3,4}, 2),
};
var frequent = apriori.Frequent;
foreach (var tuple in expected)
{
var a = frequent.Where(x => x.Key.SetEquals(tuple.Item1)).First();
Assert.AreEqual(tuple.Item2, a.Value);
}
}
private void CallApriori(double support)
{
int result = 0;
Apriori algorithm = new Apriori(support);
algorithm.MileStoneEvent += algorithm_MileStoneEvent;
algorithm.InputDataFile = textDataFile.Text;
algorithm.Process();
}
static void Main(string[] args)
{
ProductRepository productRepository = new ProductRepository();
PurchaseRepository purchaseRepository = new PurchaseRepository();
AssociationRuleRepository associationRuleRepository = new AssociationRuleRepository();
List <Product> products = productRepository.GetAllProducts();
List <Purchase> purchases = purchaseRepository.GetAllPurchases();
Apriori apriori = new Apriori();
List <AssociationRule> associatiationRules = apriori.ProccessApriori(0.14, 0.1, products, purchases);
}
public void TestCandidateNull()
{
SetUp1();
candidates = Apriori.GenerateCandidate(input.ElementAt(0), input.ElementAt(1));
Assert.IsTrue(candidates == null);
candidates = Apriori.GenerateCandidate(input.ElementAt(0), input.ElementAt(2));
Assert.IsTrue(candidates == null);
candidates = Apriori.GenerateCandidate(input.ElementAt(0), input.ElementAt(3));
Assert.IsTrue(candidates == null);
}
public void UnionSetsTest_3Items()
{
var set = new List <string> {
"banana", "apple", "potato", "grape"
};
var sut = new Apriori(null);
var results = sut.UnionSets(set, 3);
Assert.AreEqual(results.Count, 4);
}
public async Task <string> getRecommendedMovie(string customerId)
{
if (customerId == null)
{
throw new ArgumentException("customer id is required!");
}
var loans = await _context.Loan
.Include(l => l.Movie)
.Include(l => l.Customer)
.ToListAsync();
var customers = await _context.Customer.ToListAsync();
List <int[]> tempDatasset = new List <int[]>();
// Creating a unsymmetric matrix that each row contains the movies that a certain
// customer has lent
foreach (var customer in customers)
{
var moviesIdsPerCustomer = loans.Where(l => l.CustomerId == customer.CustomerId)
.Select(m => m.MovieId).ToList();
tempDatasset.Add(moviesIdsPerCustomer.ToArray());
}
int[][] dataset = tempDatasset.ToArray();
// threshold represents that only 1 movie that the specified customer has lent
// needs to be found in the other loans of other customers in order to recommend it
// for him
Apriori apriori = new Apriori(threshold: 1, confidence: 0);
// machine learning section
AssociationRuleMatcher <int> classifier = apriori.Learn(dataset);
// movies ids of the specified customer according to his loans
var moviesPerSpecifiedCustomer = loans
.Where(l => l.Customer.PersonalId == (string)customerId)
.Select(b => b.MovieId).ToArray();
// make an intersection between his movies and try to find from the dataset
// all rows that contains atleast 1 of his movies that he had lent
int[][] matches = classifier.Decide(moviesPerSpecifiedCustomer);
if (matches.Length > 0)
{
int bestMovieId = matches[0][0];
string bestMovieName = _context.Movie.Single(m => m.MovieId == bestMovieId).Name;
return(bestMovieName);
}
return("There is no recommended movie for this customer.");
}
public void TestNextCandidates_Size3()
{
SetUp5();
var a = Apriori.GenerateNextCandidates(input);
int aux = 0;
foreach (String[] n in a)
{
n.SequenceEqual(solution.ToList().ElementAt(aux));
aux++;
}
}
public AssociationRuleMatcher <int> CreateAprioriClassifier()
{
// Create a new a-priori learning algorithm with support 3
Apriori apriori = new Apriori(threshold: 2, confidence: 0);
int[][] histories = GetAllViewingHistories();
// Use the algorithm to learn a set matcher
AssociationRuleMatcher <int> classifier = apriori.Learn(histories);
return(classifier);
}
public void ClassifierTest1()
{
// example from http://www3.cs.stonybrook.edu/~cse634/lecture_notes/07apriori.pdf
string[][] dataset =
{
new string[] { "1", "2", "5" },
new string[] { "2", "4" },
new string[] { "2", "3" },
new string[] { "1", "2", "4" },
new string[] { "1", "3" },
new string[] { "2", "3" },
new string[] { "1", "3" },
new string[] { "1", "2", "3", "5" },
new string[] { "1", "2", "3" },
};
var apriori = new Apriori<string>(threshold: 2, confidence: 0.7);
var classifier = apriori.Learn(dataset);
var rules = classifier.Rules;
Assert.AreEqual(6, rules.Length);
Assert.AreEqual(rules[0].ToString(), "[5] -> [1]; support: 2, confidence: 1");
Assert.AreEqual(rules[1].ToString(), "[5] -> [2]; support: 2, confidence: 1");
Assert.AreEqual(rules[2].ToString(), "[4] -> [2]; support: 2, confidence: 1");
Assert.AreEqual(rules[3].ToString(), "[5] -> [1 2]; support: 2, confidence: 1");
Assert.AreEqual(rules[4].ToString(), "[1 5] -> [2]; support: 2, confidence: 1");
Assert.AreEqual(rules[5].ToString(), "[2 5] -> [1]; support: 2, confidence: 1");
string[][] actual;
actual = classifier.Decide(new string[] { "1", "5" });
Assert.AreEqual("1", actual[0][0]);
Assert.AreEqual("2", actual[1][0]);
Assert.AreEqual("1", actual[2][0]);
Assert.AreEqual("2", actual[2][1]);
Assert.AreEqual("2", actual[3][0]);
actual = classifier.Decide(new string[] { "2", "5" });
Assert.AreEqual("1", actual[0][0]);
Assert.AreEqual("2", actual[1][0]);
Assert.AreEqual("1", actual[2][0]);
Assert.AreEqual("2", actual[2][1]);
Assert.AreEqual("1", actual[3][0]);
actual = classifier.Decide(new string[] { "0", "5" });
Assert.AreEqual("1", actual[0][0]);
Assert.AreEqual("2", actual[1][0]);
Assert.AreEqual("1", actual[2][0]);
Assert.AreEqual("2", actual[2][1]);
}
