protected override int Test(FeatureVector vector, out double[] details)
{
var leaf = Root.FindLeaf(vector);
details = leaf.GetDistributionByClass();
return(StatisticsHelper.ArgMax(details));
}
protected override int Test(FeatureVector vector, out double[] details)
{
// classify(v) = argmax(P(c)P(v|c)) = argmax_c(prob_c * prob_v_c)
double[] v_logProb_c = new double[NoOfClasses];
for (int c_i = 0; c_i < NoOfClasses; c_i++)
{
v_logProb_c[c_i] = CalculateLogProb_v_c(vector, c_i);
}
// Convert back from log to decimal format.
NormalizationHelper.NormalizeLogs(v_logProb_c, 10);
details = v_logProb_c;
return(StatisticsHelper.ArgMax(details));
}
protected override int Test(FeatureVector vector, out double[] details)
{
double[] votes_c = new double[NoOfClasses];
var nearestNeighbors = new List <IdValuePair <double> >();
for (int v_i = 0; v_i < TrainingVectors.Count; v_i++)
{
double distance = distanceFunc(TrainingVectors[v_i], vector);
// If this neighbor is closer than our furthest neighbor, ...
// If the list of nearest neighbors is empty OR this is the closest distance we'Ve seen,
// then add neighbor as the closest neighbor (i.e. insert at position 0).
if (nearestNeighbors.Count == 0 || distance < nearestNeighbors[0].Value)
{
nearestNeighbors.Insert(0, new IdValuePair <double>(v_i, distance));
votes_c[TrainingVectors[v_i].Headers[Gold_i]]++;
// If we have too many neighbors, then remove the furthest one.
if (nearestNeighbors.Count > K)
{
votes_c[TrainingVectors[nearestNeighbors[nearestNeighbors.Count - 1].Id].Headers[Gold_i]]--;
nearestNeighbors.RemoveAt(nearestNeighbors.Count - 1);
}
}
else if (nearestNeighbors.Count < K || distance < nearestNeighbors[nearestNeighbors.Count - 1].Value)
{
var newNeighbor = new IdValuePair <double>(v_i, distance);
int insert_b = SearchHelper.FindInsertIndex(nearestNeighbors, newNeighbor);
if (insert_b <= K)
{
nearestNeighbors.Insert(insert_b, newNeighbor);
votes_c[TrainingVectors[v_i].Headers[Gold_i]]++;
}
// If we have too many neighbors, then remove the furthest one.
if (nearestNeighbors.Count > K)
{
votes_c[TrainingVectors[nearestNeighbors[nearestNeighbors.Count - 1].Id].Headers[Gold_i]]--;
nearestNeighbors.RemoveAt(nearestNeighbors.Count - 1);
}
}
Debug.Assert(nearestNeighbors.Count <= K);
}
if (nearestNeighbors.Count < K)
{
Console.Error.WriteLine("Warning: K nearest neighbors could not be found.");
}
details = NormalizationHelper.CreateNormalizedDistribution(votes_c);
return(StatisticsHelper.ArgMax(votes_c));
}
protected override int Test(FeatureVector vector, out double[] details)
{
double[] distribution = new double[NoOfClasses];
// Do two things:
// 1) Calculate the probability of a document belonging to class c_i, given document <c>vector</c>, and
// 2) Calculate Z, which will be used to normalize the distribution shortly.
for (int c_i = 0; c_i < NoOfClasses; c_i++)
{
double logProb = _lambda_c[c_i];
for (int u_i = 0; u_i < vector.UsedFeatures.Length; u_i++)
{
int f_i = vector.UsedFeatures[u_i];
logProb += CalculateLogProb_c_f(c_i, f_i);
}
distribution[c_i] = logProb;
}
NormalizationHelper.NormalizeLogs(distribution, Math.E);
details = distribution;
return(StatisticsHelper.ArgMax(details));
}