/// <summary>
/// Given a set of labeled RVFDatums, treats each as an instance vector of that
/// label and adds it to the examples used for classification.
/// </summary>
/// <remarks>
/// Given a set of labeled RVFDatums, treats each as an instance vector of that
/// label and adds it to the examples used for classification.
/// NOTE: l2NormalizeVectors is NOT applied here.
/// </remarks>
public virtual KNNClassifier <K, V> Train(ICollection <RVFDatum <K, V> > instances)
{
KNNClassifier <K, V> classifier = new KNNClassifier <K, V>(k, weightedVotes, l2NormalizeVectors);
classifier.AddInstances(instances);
return(classifier);
}
/// <summary>
/// Given a set of vectors, and a mapping from each vector to its class label,
/// generates the sets of instances used to perform classifications and returns
/// the corresponding K-NN classifier.
/// </summary>
/// <remarks>
/// Given a set of vectors, and a mapping from each vector to its class label,
/// generates the sets of instances used to perform classifications and returns
/// the corresponding K-NN classifier.
/// NOTE: if l2NormalizeVectors is T, creates a copy and applies L2Normalize to it.
/// </remarks>
public virtual KNNClassifier <K, V> Train(ICollection <ICounter <V> > vectors, IDictionary <V, K> labelMap)
{
KNNClassifier <K, V> classifier = new KNNClassifier <K, V>(k, weightedVotes, l2NormalizeVectors);
ICollection <RVFDatum <K, V> > instances = new List <RVFDatum <K, V> >();
foreach (ICounter <V> vector in vectors)
{
K label = labelMap[vector];
RVFDatum <K, V> datum;
if (l2NormalizeVectors)
{
datum = new RVFDatum <K, V>(Counters.L2Normalize(new ClassicCounter <V>(vector)), label);
}
else
{
datum = new RVFDatum <K, V>(vector, label);
}
instances.Add(datum);
}
classifier.AddInstances(instances);
return(classifier);
}
/// <summary>
/// Given a CollectionValued Map of vectors, treats outer key as label for each
/// set of inner vectors.
/// </summary>
/// <remarks>
/// Given a CollectionValued Map of vectors, treats outer key as label for each
/// set of inner vectors.
/// NOTE: if l2NormalizeVectors is T, creates a copy of each vector and applies
/// l2Normalize to it.
/// </remarks>
public virtual KNNClassifier <K, V> Train(CollectionValuedMap <K, ICounter <V> > vecBag)
{
KNNClassifier <K, V> classifier = new KNNClassifier <K, V>(k, weightedVotes, l2NormalizeVectors);
ICollection <RVFDatum <K, V> > instances = new List <RVFDatum <K, V> >();
foreach (K label in vecBag.Keys)
{
RVFDatum <K, V> datum;
foreach (ICounter <V> vector in vecBag[label])
{
if (l2NormalizeVectors)
{
datum = new RVFDatum <K, V>(Counters.L2Normalize(new ClassicCounter <V>(vector)), label);
}
else
{
datum = new RVFDatum <K, V>(vector, label);
}
instances.Add(datum);
}
}
classifier.AddInstances(instances);
return(classifier);
}
