public RuleSet(Transform spawnPoint, Course course)
{
boids = new List <GameObject>();
alignment = new RuleParameters();
separation = new RuleParameters {
considerOtherGroups = true
};
cohesion = new RuleParameters();
collisionAvoidance = new RuleParameters();
targetChasing = new RuleParameters();
this.spawnPoint = spawnPoint;
this.course = course;
}
public IEnumerable <Rule> FindNegativeRuleSecondApproach(RuleParameters initialParameters)
{
double minSupport = initialParameters.MinSupport;
double minConfidence = initialParameters.MinConfidence;
List <Rule> rulesResult = new List <Rule>();
List <Item> initialFrequentItemset = new List <Item>();
var transactionsSet = _minerHelper.GetTransactionSet(FilteredDataSet.Records, FilteredDataSet.Headers);
var headersList = FilteredDataSet.Headers;
foreach (var header in headersList)
{
if (_minerHelper.CalculateSupport(transactionsSet, new List <string> {
header
}, FilteredDataSet.Records.Count) >= minSupport)
{
initialFrequentItemset.Add(
new Item {
Name = header
}
);
}
}
int rulesCounter = 0;
for (int k = initialParameters.MinLength; k < initialParameters.MaxLength; k++)
{
// we are getting the candidates from 1-frequent itemset
// against the traditional convention - that set is always generated
// from 1-frequent itemset
//List<List<Item>> candidates = frequentItemsets.ElementAt(0);
// next we need to join them with theirselves to create
// possible combinations
var kFrequentCandidates = _minerHelper.GetCombinations(initialFrequentItemset, k).ToList();
var kFrequentItemset = new List <List <Item> >();
foreach (var subSet in kFrequentCandidates)
{
if (_minerHelper.CalculateSupport(transactionsSet, subSet.Select(s => s.Name).ToList(), FilteredDataSet.Records.Count) >= minSupport)
{
for (int i = 1; i < subSet.Count(); i++)
{
// we get all possible combinations
var lhs = _minerHelper.GetCombinations(subSet, i);
foreach (var lhsEl in lhs)
{
var rhs = subSet.Where(s => !lhsEl.Contains(s));
var candidateRule = new Rule
{
LeftItemSet = lhsEl,
RightItemSet = rhs
};
// for every candidate rule, we are suppose to check some
// conditions such as : correlation coeff; support and confidence
// base on the results, we can easily classify single rule as a positive either negative
// We also use a 'conviction' parameter
double unionSupport = _minerHelper.CalculateSupport(transactionsSet, lhsEl
.Union(rhs)
.Select(l => l.Name)
.ToList(), FilteredDataSet.Records.Count);
double lhsSupport = _minerHelper.CalculateSupport(transactionsSet, lhsEl
.Select(l => l.Name)
.ToList(), FilteredDataSet.Records.Count);
double rhsSupport = _minerHelper.CalculateSupport(transactionsSet, rhs
.Select(l => l.Name)
.ToList(), FilteredDataSet.Records.Count);
double supportANotB = lhsSupport - unionSupport;
double supportNotAB = rhsSupport - unionSupport;
double supportNotANotB = 1 - lhsSupport - rhsSupport + unionSupport;
double aNotBConfidence = supportANotB / lhsSupport;
double notABConfidence = supportNotAB / (1 - lhsSupport);
double notANotBConfidence = supportNotANotB / (1 - lhsSupport);
double ABConfidence = unionSupport / lhsSupport;
if (notANotBConfidence >= minConfidence && supportNotANotB >= minSupport)
{
candidateRule.Support = supportNotANotB;
candidateRule.Confidence = notANotBConfidence;
candidateRule.Type = RuleType.BothNegative;
//Logger.Log(string.Format("¬ {0} => ¬ {1} [{2}]",
// string.Join(" ,", lhsEl.Select(r => r.Name)),
// string.Join(" ,", rhs.Select(r => r.Name)),
// Math.Round(notANotBConfidence, 2)
// ));
_setOfFoundRules.Add(candidateRule);
rulesCounter++;
rulesResult.Add(candidateRule);
}
else if (supportANotB >= minSupport && aNotBConfidence >= minConfidence)
{
candidateRule.Support = supportANotB;
candidateRule.Confidence = aNotBConfidence;
candidateRule.Type = RuleType.RightNegative;
//Logger.Log(string.Format("{0} => ¬ {1} [{2}]",
// string.Join(" ,", lhsEl.Select(r => r.Name)),
// string.Join(" ,", rhs.Select(r => r.Name)),
// Math.Round(aNotBConfidence, 2)));
_setOfFoundRules.Add(candidateRule);
rulesCounter++;
rulesResult.Add(candidateRule);
}
else if (supportNotAB >= minSupport && notABConfidence >= minConfidence)
{
candidateRule.Support = supportNotAB;
candidateRule.Confidence = aNotBConfidence;
candidateRule.Type = RuleType.LeftNegative;
//Logger.Log(string.Format("¬ {0} => {1} [{2}]",
// string.Join(" ,", lhsEl.Select(r => r.Name)),
// string.Join(" ,", rhs.Select(r => r.Name)),
// Math.Round(notABConfidence, 2)
// ));
_setOfFoundRules.Add(candidateRule);
rulesCounter++;
rulesResult.Add(candidateRule);
}
}
}
}
}
}
return(rulesResult);
}
private void MineRules()
{
if (!sourceSelected)
{
Console.WriteLine("THe data source was not chosen.");
Start();
return;
}
try
{
Console.WriteLine("Please enter a minimal support (for example:0,4).");
var supp = GetNumber(Console.ReadLine());
if (supp < 0)
{
throw new Exception("Wpisano nie poprawną wartość.");
}
Console.WriteLine("Please enter a minimal confidence (for example:0,2).");
var conf = GetNumber(Console.ReadLine());
if (conf < 0)
{
throw new Exception("Wpisano nie poprawną wartość.");
}
Console.WriteLine("Please enter a minimal rule length (for example:2).");
var minlen = GetOption(Console.ReadLine());
if (minlen < 0)
{
throw new Exception("Wpisano nie poprawną wartość.");
}
Console.WriteLine("Please enter a maximal rule length (for example:4).");
var maxlen = GetOption(Console.ReadLine());
if (maxlen < 0)
{
throw new Exception("Wpisano nie poprawną wartość.");
}
RuleParameters param = new RuleParameters
{
MinSupport = supp,
MinConfidence = conf,
MinLength = minlen,
MaxLength = maxlen
};
Console.WriteLine("Which approach of mining do you want to use?(1 or 2)");
var opt = GetOption(Console.ReadLine());
if (opt < 1 || opt > 2)
{
throw new Exception("Wpisano nie poprawną wartość.");
}
manager.GetObservableRulesCollection().CollectionChanged += MinerConsole_CollectionChanged;
Console.WriteLine("Mining is being processed...");
if (opt == 1)
{
manager.FindRule(param);
}
else if (opt == 2)
{
manager.FindRuleSecondApproach(param);
}
if (manager.GetObservableRulesCollection().Count == 0)
{
Console.WriteLine("No results found");
}
else
{
Console.WriteLine("");
Console.WriteLine("Mining has finished!");
Console.WriteLine("");
}
Console.WriteLine("");
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
finally
{
Start();
}
}
public IEnumerable <Rule> FindRuleSecondApproach(RuleParameters parameters)
{
return(_miner.FindNegativeRuleSecondApproach(parameters));
}
