public void TrainTest1()
{
const int LearnCount = 100;
const int TestCount = 1000;
const int Length = 300;
const double PositiveRatio = 0.1;
// create samples
List <(float[] x, bool y, float weight)> samples = SupportVectorMachineTest.GenerateSamples(LearnCount + TestCount, Length, PositiveRatio)
.Select(x => (x.x.Select(w => (float)w).ToArray(), x.y, (float)x.weight))
.ToList();
// learn
SequentualMinimalOptimization smo = new SequentualMinimalOptimization(new ChiSquare())
{
Algorithm = SMOAlgorithm.LibSVM,
Tolerance = 0.01f,
};
SupportVectorMachine machine = SupportVectorMachine.Learn(
smo,
samples.Take(LearnCount).Select(x => x.x).ToList(),
samples.Take(LearnCount).Select(x => x.y).ToList(),
samples.Take(LearnCount).Select(x => x.weight).ToList(),
CancellationToken.None);
// test
List <ClassificationResult <bool?> > results = samples
.Skip(LearnCount)
.Select(x => new ClassificationResult <bool?>(null, machine.Classify(x.x) > 0.5f, x.y, 1.0f, true))
.ToList();
ClassificationReport <bool?> report = new ClassificationReport <bool?>(results);
}
public (List <string> classes, KMeans kmeans, OneVsAllSupportVectorMachine svm) FinishLearning(
int vectorLength,
CancellationToken cancellationToken)
{
// count classes
List <string> classes = new List <string>(this.features.Select(x => x.truth).ToLookup(x => x).Select(x => x.Key));
if (classes.Count < 2)
{
throw new ArgumentException();
}
classes.Sort();
// count vectors
int numberOfVectors = this.features.Sum(x => x.features.Count);
// copy vectors
Dictionary <IVector <float>, float> vectors = new Dictionary <IVector <float>, float>(numberOfVectors);
for (int i = 0, ii = this.features.Count; i < ii; i++)
{
FeatureDetectors.Features f = this.features[i].features;
for (int j = 0, jj = f.Count, len = f.Length, off = 0; j < jj; j++, off += len)
{
////DenseVectorF vector = new DenseVectorF(len, f.X, off);
SparseVectorF vector = SparseVectorF.FromDense(len, f.X, off);
vectors[vector] = vectors.TryGetValue(vector, out float weight) ? weight + 1.0f : 1.0f;
}
}
cancellationToken.ThrowIfCancellationRequested();
// learn k-means
KMeans kmeans = KMeans.Learn(
vectorLength,
KMeansSeeding.Random,
2,
default(EuclideanDistance),
vectors.Keys.ToList(),
vectors.Values.ToList(),
cancellationToken);
cancellationToken.ThrowIfCancellationRequested();
// learn svm
Dictionary <string, int> classesLookup = classes.ToDictionary((x, i) => x, (x, i) => i);
SequentualMinimalOptimization smo = new SequentualMinimalOptimization(new ChiSquare())
{
Algorithm = SMOAlgorithm.LibSVM,
Tolerance = 0.01f,
};
List <float[]> svmx = new List <float[]>(this.features.Count);
List <int> svmy = new List <int>(this.features.Count);
for (int i = 0, ii = this.features.Count; i < ii; i++)
{
(FeatureDetectors.Features features, string truth) = this.features[i];
svmx.Add(PointsOfInterestClassifier.PrepareVector(kmeans, features, cancellationToken));
svmy.Add(classesLookup[truth]);
}
cancellationToken.ThrowIfCancellationRequested();
OneVsAllSupportVectorMachine svm = OneVsAllSupportVectorMachine.Learn(
smo,
classes.Count,
svmx,
svmy,
null,
cancellationToken);
cancellationToken.ThrowIfCancellationRequested();
return(classes, kmeans, svm);
}
