public DiscernibilityMatrix(DecisionSystem ds,IEnumerable<int> reduct, double ruleValue)
{
var sa = new StandardApproximationSpace(ds, reduct);
RuleValue = ruleValue;
var columns = sa.LowerApproximation(RuleValue);
//so sánh với các phần tử không thuộc lower approximation
var rows =
ds.Universe.Where(p => columns.All(p2 => p2[0] != p[0])).Where(p => p[ds.DecisionAttributes[0]] != RuleValue);
Matrix = new List<List<List<pairID>>>();
foreach (var column in columns)
{
var dataRow = new List<List<pairID>>();
foreach (var row in rows)
{
var dataInstances = new List<pairID>();
foreach (var header in reduct)
{
if (row[header] != column[header])
{
dataInstances.Add(new pairID(header, int.Parse(column[header].ToString())));
}
}
if(dataInstances.Count!=0)
dataRow.Add(dataInstances);
}
if(dataRow.Count!=0)
Matrix.Add(dataRow);
}
}
// Current only support only one denpendent attribute,
// which is decision attr in DecisionSystem object
private float CalcDependencyDegree(IEnumerable<int> targetAttrs)
{
// cardinal number of object univers
StandardApproximationSpace apprSpace = new StandardApproximationSpace(this.DS, targetAttrs);
int posCardNum = apprSpace.PositiveRegion(this.DS.DecisionAttribute).Count();
float dependDegree = (float)posCardNum / this.DS.ObjectCount;
return dependDegree;
}
