public Matrix Uniform(float a)
{
float a2 = 2 * a;
float an = -a;
ThreadSafeFastRandom.NextFloats(Data.AsSpan());
SIMD.Multiply(Data.AsSpan(), a2);
SIMD.Add(Data.AsSpan(), an);
return(this);
}
public Matrix Uniform(float a)
{
float a2 = 2 * a;
float an = -a;
Parallel.For(0, Rows, (i) =>
{
ThreadSafeFastRandom.NextFloats(Data[i]);
SIMD.Multiply(ref Data[i], a2);
SIMD.Add(ref Data[i], an);
});
return(this);
}
public void ResizeAndFillRows(int newRows, float a)
{
float a2 = 2 * a;
float an = -a;
Array.Resize(ref Data, newRows * Columns);
var toFill = Data.AsSpan().Slice(Rows * Columns);
ThreadSafeFastRandom.NextFloats(toFill);
SIMD.Multiply(toFill, a2);
SIMD.Add(toFill, an);
Rows = newRows;
}
public IList <DetectedLanguage> DetectAll(string text)
{
if (string.IsNullOrWhiteSpace(text))
{
return(Array.Empty <DetectedLanguage>());
}
var ngrams = ExtractNGrams(NormalizeText(text));
try
{
if (ngrams.Count == 0)
{
return(Array.Empty <DetectedLanguage>());
}
Span <double> languageProbabilities = Data.Languages.Count < 256 ? stackalloc double[Data.Languages.Count] : new double[Data.Languages.Count];
for (int t = 0; t < Trials; t++)
{
var probs = InitializeProbabilities();
double alpha = Alpha + ThreadSafeFastRandom.NextDouble() * AlphaWidth;
for (int i = 0; ; i++)
{
int r = ThreadSafeFastRandom.Next(ngrams.Count);
UpdateProbabilities(probs, ngrams[r], alpha);
if (i % 5 == 0 && (NormalizeProbabilities(probs) > ConvergenceThreshold || i >= MaxIterations))
{
break;
}
}
for (int j = 0; j < languageProbabilities.Length; j++)
{
languageProbabilities[j] += probs[j] / Trials;
}
}
return(SortProbabilities(languageProbabilities));
}
finally
{
_listPool.Return(ngrams);
}
}