public void LinkedHashSet_Generic_CopyTo_NegativeCount_ThrowsArgumentOutOfRangeException(int count)
{
LinkedHashSet <T> set = (LinkedHashSet <T>)GenericISetFactory(count);
T[] arr = new T[count];
Assert.Throws <ArgumentOutOfRangeException>(() => set.CopyTo(arr, 0, -1));
Assert.Throws <ArgumentOutOfRangeException>(() => set.CopyTo(arr, 0, int.MinValue));
}
public void LinkedHashSet_Generic_CopyTo_NoIndexDefaultsToZero(int count)
{
LinkedHashSet <T> set = (LinkedHashSet <T>)GenericISetFactory(count);
T[] arr1 = new T[count];
T[] arr2 = new T[count];
set.CopyTo(arr1);
set.CopyTo(arr2, 0);
Assert.True(arr1.SequenceEqual(arr2));
}
public void LinkedHashSet_Generic_RemoveWhere_NewObject() // Regression Dev10_624201
{
object[] array = new object[2];
object obj = new object();
LinkedHashSet <object> set = new LinkedHashSet <object>();
set.Add(obj);
set.Remove(obj);
foreach (object o in set)
{
}
set.CopyTo(array, 0, 2);
set.RemoveWhere((element) => { return(false); });
}
public CategoricalDistribution jointDistribution(
params IProposition[] propositions)
{
ProbabilityTable d = null;
IProposition conjProp = ProbUtil
.constructConjunction(propositions);
LinkedHashSet <RandomVariable> vars = new LinkedHashSet <RandomVariable>(
conjProp.getUnboundScope());
if (vars.Count > 0)
{
RandomVariable[] distVars = new RandomVariable[vars.Count];
vars.CopyTo(distVars);
ProbabilityTable ud = new ProbabilityTable(distVars);
Object[] values = new Object[vars.Count];
//ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// if (conjProp.holds(possibleWorld)) {
// int i = 0;
// for (RandomVariable rv : vars) {
// values[i] = possibleWorld.get(rv);
// i++;
// }
// int dIdx = ud.getIndex(values);
// ud.setValue(dIdx, ud.getValues()[dIdx] + probability);
// }
// }
//};
//distribution.iterateOverTable(di);
// TODO:
d = ud;
}
else
{
// No Unbound Variables, therefore just return
// the singular probability related to the proposition.
d = new ProbabilityTable();
d.setValue(0, prior(propositions));
}
return(d);
}
public CategoricalDistribution jointDistribution(
params IProposition[] propositions)
{
ProbabilityTable d = null;
IProposition conjProp = ProbUtil
.constructConjunction(propositions);
LinkedHashSet<RandomVariable> vars = new LinkedHashSet<RandomVariable>(
conjProp.getUnboundScope());
if (vars.Count > 0)
{
RandomVariable[] distVars = new RandomVariable[vars.Count];
vars.CopyTo(distVars);
ProbabilityTable ud = new ProbabilityTable(distVars);
Object[] values = new Object[vars.Count];
//ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// if (conjProp.holds(possibleWorld)) {
// int i = 0;
// for (RandomVariable rv : vars) {
// values[i] = possibleWorld.get(rv);
// i++;
// }
// int dIdx = ud.getIndex(values);
// ud.setValue(dIdx, ud.getValues()[dIdx] + probability);
// }
// }
//};
//distribution.iterateOverTable(di);
// TODO:
d = ud;
}
else
{
// No Unbound Variables, therefore just return
// the singular probability related to the proposition.
d = new ProbabilityTable();
d.setValue(0, prior(propositions));
}
return d;
}