public void Normal(NDArray result, int?seed, float mean, float stdv)
{
using (var cpuCopy = new NDArray(cpuAllocator, result.ElementType, result.Shape))
{
cpuRandom.Normal(cpuCopy, seed, mean, stdv);
TOps.Copy(result, cpuCopy);
}
}
public void Exponential(NDArray result, int?seed, float lambda)
{
using (var cpuCopy = new NDArray(cpuAllocator, result.ElementType, result.Shape))
{
cpuRandom.Exponential(cpuCopy, seed, lambda);
TOps.Copy(result, cpuCopy);
}
}
public void Uniform(NDArray result, int?seed, float min, float max)
{
using (var cpuCopy = new NDArray(cpuAllocator, result.ElementType, result.Shape))
{
cpuRandom.Uniform(cpuCopy, seed, min, max);
TOps.Copy(result, cpuCopy);
}
}
public void Bernoulli(NDArray result, int?seed, float p)
{
using (var cpuCopy = new NDArray(cpuAllocator, result.ElementType, result.Shape))
{
cpuRandom.Bernoulli(cpuCopy, seed, p);
TOps.Copy(result, cpuCopy);
}
}
public void Cauchy(NDArray result, int?seed, float median, float sigma)
{
using (var cpuCopy = new NDArray(cpuAllocator, result.ElementType, result.Shape))
{
cpuRandom.Cauchy(cpuCopy, seed, median, sigma);
TOps.Copy(result, cpuCopy);
}
}
public void Geometric(Tensor result, int?seed, float p)
{
using (var cpuCopy = new Tensor(cpuAllocator, result.ElementType, result.Shape))
{
cpuRandom.Geometric(cpuCopy, seed, p);
TOps.Copy(result, cpuCopy);
}
}
public void LogNormal(Tensor result, int?seed, float mean, float stdv)
{
using (var cpuCopy = new Tensor(cpuAllocator, result.ElementType, result.Shape))
{
cpuRandom.LogNormal(cpuCopy, seed, mean, stdv);
TOps.Copy(result, cpuCopy);
}
}