public IEnumerable <SuspiciousNode> SelectSuspiciousAcceptedNodes(
IReadOnlyList <List <SuspiciousNode> > candidateNodesLists,
Classifier classifier)
{
for (int i = 0; i < candidateNodesLists.Count; i++)
{
var candidates = candidateNodesLists[i];
foreach (var candidate in candidates)
{
candidate.BitsCount =
LearningExperimentUtil.CountBits(candidate.Vector & _acceptingFeatureBitMask);
}
candidates.Sort((t1, t2) => t1.BitsCount.CompareTo(t2.BitsCount));
var count = DetermineCount(classifier, i);
foreach (var candidate in candidates)
{
if (candidate.Used)
{
continue;
}
yield return(candidate);
candidate.Used = true;
if (--count == 0)
{
break;
}
}
}
}
private IEnumerable <SuspiciousNode> SelectSuspiciousElementsWithMaskWithSmallGrowing(
IReadOnlyList <List <SuspiciousNode> > candidateNodesLists, BigInteger xor, BigInteger mask)
{
for (int i = 0; i < candidateNodesLists.Count; i++)
{
var vector = BigInteger.Zero;
var candidates = candidateNodesLists[i];
while (true)
{
var minDiffCount = int.MaxValue;
SuspiciousNode newNode = null;
foreach (var candidate in candidates)
{
var newVector = (vector | (candidate.Vector ^ xor)) & mask;
var diff = newVector ^ vector;
var diffCount = LearningExperimentUtil.CountBits(diff);
if (diffCount > 0 && minDiffCount > diffCount)
{
minDiffCount = diffCount;
vector = newVector;
newNode = candidate;
}
}
if (newNode != null)
{
yield return(newNode);
}
else
{
break;
}
}
}
}
private IEnumerable <SuspiciousNode> SelectSuspiciousElementsWithMaskForFastRejectionLearning(
List <List <SuspiciousNode> > candidateNodesLists, BigInteger xor, BigInteger mask,
Classifier classifier)
{
for (int i = 0; i < candidateNodesLists.Count; i++)
{
var vector = BigInteger.Zero;
var classifierUnit = classifier.Units[i];
var candidates = candidateNodesLists[i]
.OrderBy(
t => LearningExperimentUtil.CountBits(
classifierUnit.Accepting
& ((t.Vector & _rejectingFeatureBitMask) ^ _rejectingFeatureBitMask)))
.ToList();
foreach (var candidate in candidates)
{
var newVector = (vector | (candidate.Vector ^ xor)) & mask;
if (newVector == vector)
{
continue;
}
vector = newVector;
yield return(candidate);
}
}
}
public IEnumerable <SuspiciousNode> SelectNodesForFastRejectionLearning(
IReadOnlyList <List <SuspiciousNode> > candidateNodesLists,
Classifier classifier)
{
for (int i = 0; i < candidateNodesLists.Count; i++)
{
var candidates = candidateNodesLists[i];
var rejectingVector = classifier.Units[i].Rejecting;
foreach (var cnadidate in candidates)
{
cnadidate.BitsCount = LearningExperimentUtil.CountBits(
(cnadidate.Vector & _rejectingFeatureBitMask) | rejectingVector);
}
candidates.Sort((t1, t2) => t1.BitsCount.CompareTo(t2.BitsCount));
var count = DetermineCount(classifier, i);
for (int j = candidates.Count - 1; j >= 0; j--)
{
var candidate = candidates[j];
if (candidate.Used)
{
continue;
}
yield return(candidate);
candidate.Used = true;
if (--count == 0)
{
break;
}
}
}
}
public IEnumerable <SuspiciousNode> SelectNodesForSlowAcceptanceLearning(
IReadOnlyList <List <SuspiciousNode> > candidateNodesLists,
Classifier classifier)
{
for (int i = 0; i < candidateNodesLists.Count; i++)
{
var candidates = candidateNodesLists[i];
var acceptingVector = classifier.Units[i].Accepting;
foreach (var target in candidates)
{
target.BitsCount = LearningExperimentUtil.CountBits(target.Vector & acceptingVector);
}
candidates.Sort((t1, t2) => t1.BitsCount.CompareTo(t2.BitsCount));
var count = DetermineCount(classifier, i);
for (int j = candidates.Count - 1; j >= 0; j--)
{
var candidate = candidates[j];
if (candidate.Used)
{
continue;
}
yield return(candidate);
candidate.Used = true;
if (--count == 0)
{
break;
}
}
}
}
public IEnumerable <SuspiciousNode> SelectNodesForSlowRejectionLearningStrongly(
IReadOnlyList <List <SuspiciousNode> > candidateNodesLists,
Classifier classifier)
{
for (int i = 0; i < candidateNodesLists.Count; i++)
{
var candidates = candidateNodesLists[i];
var rejectingUnit = classifier.Units[i].Rejecting;
foreach (var target in candidates)
{
target.BitsCount = LearningExperimentUtil.CountBits(
(target.Vector & _rejectingFeatureBitMask) | rejectingUnit);
}
candidates.Sort((t1, t2) => t1.BitsCount.CompareTo(t2.BitsCount));
var vector = _rejectingFeatureBitMask;
var count = DetermineStrongCount(i, classifier);
foreach (var candidate in candidates)
{
if (!candidate.Used)
{
continue;
}
var newVector = vector & candidate.Vector;
if (newVector == vector)
{
continue;
}
vector = newVector;
yield return(candidate);
candidate.Used = true;
if (--count == 0)
{
break;
}
}
}
}
private SuspiciousNode SelectMostDifferentElement(
IEnumerable <BigInteger> existings, IEnumerable <SuspiciousNode> candidates,
BigInteger mask)
{
if (!existings.Any())
{
return(candidates.FirstOrDefault());
}
var maxDiff = 0;
SuspiciousNode ret = null;
foreach (var candidate in candidates)
{
var vector = candidate.Vector & mask;
var diff = existings.Min(f => LearningExperimentUtil.CountBits((f & mask) ^ vector));
if (maxDiff < diff)
{
maxDiff = diff;
ret = candidate;
}
}
return(ret);
}
public IEnumerable <SuspiciousNode> SelectNodesForFastAcceptanceLearningStrongly(
IReadOnlyList <List <SuspiciousNode> > candidateNodesLists,
Classifier classifier)
{
for (int i = 0; i < candidateNodesLists.Count; i++)
{
var candidates = candidateNodesLists[i];
var acceptingVector = classifier.Units[i].Accepting;
foreach (var target in candidates)
{
target.BitsCount = LearningExperimentUtil.CountBits(target.Vector & acceptingVector);
}
candidates.Sort((t1, t2) => t1.BitsCount.CompareTo(t2.BitsCount));
var vector = BigInteger.Zero;
var count = DetermineStrongCount(i, classifier);
foreach (var candidate in candidates)
{
if (candidate.Used)
{
continue;
}
var newVector = (vector | candidate.Vector) & _acceptingFeatureBitMask;
if (newVector == vector)
{
continue;
}
vector = newVector;
yield return(candidate);
candidate.Used = true;
if (--count == 0)
{
break;
}
}
}
}
public IEnumerable <int> CountRejectingFeatures()
{
return(Units.Select(c =>
LearningExperimentUtil.CountBits(c.Rejecting >> AcceptingFeatureCount)));
}
public IEnumerable <int> CountAcceptingFeatures()
{
return(Units.Select(c =>
LearningExperimentUtil.CountBits(c.Accepting & AcceptingFeatureBitMask)));
}