public float[] Transform(
IVectorPack <float> x,
IList <float> weights,
bool normalize,
float[] result,
CancellationToken cancellationToken)
{
if (result == null)
{
result = new float[this.K];
}
if (weights == null)
{
for (int i = 0, ii = x.Count, len = x.Length, off = 0; i < ii; i++, off += len)
{
int cluster = this.Assign(new DenseVectorProxyF(len, x.X, off));
result[cluster] += 1.0f;
}
}
else
{
if (weights.Count != x.Count)
{
throw new ArgumentException("The number of weights must match the number of input vectors.", nameof(weights));
}
for (int i = 0, ii = x.Count, len = x.Length, off = 0; i < ii; i++, off += len)
{
int cluster = this.Assign(new DenseVectorProxyF(len, x.X, off));
result[cluster] += weights[i];
}
}
if (normalize)
{
float sum = Vectors.Sum(result.Length, result, 0);
if (sum != 0.0f)
{
Mathematics.DivC(result.Length, sum, result, 0);
}
}
return(result);
}
private static float[] PrepareVector(KMeans kmeans, IVectorPack <float> x, CancellationToken cancellationToken)
{
return(kmeans.Transform(x, null, false, null, cancellationToken));
}
