/// <summary>
/// Computes the complementary error function of input.
/// </summary>
/// <param name="input">The input tensor</param>
/// <returns></returns>
public static TorchTensor erfc(TorchTensor input)
{
var res = THSSpecial_erfc(input.Handle);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new TorchTensor(res));
}
/// <summary>
/// Computes input * log1p(other). Similar to SciPy’s scipy.special.xlog1py.
/// </summary>
/// <param name="input">The input tensor</param>
/// <param name="other"></param>
/// <returns></returns>
public static TorchTensor xlog1py(TorchTensor input, TorchTensor other)
{
var res = THSSpecial_xlog1py(input.Handle, other.Handle);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new TorchTensor(res));
}
public static TorchTensor matrix_rank(TorchTensor input, double?tol = null, bool hermitian = false)
{
unsafe {
var res = THSLinalg_matrix_rank(input.Handle, tol ?? double.NegativeInfinity, tol.HasValue, hermitian);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new TorchTensor(res));
}
}
public static TorchTensor tensorsolve(TorchTensor input, TorchTensor other, long[] dims)
{
unsafe
{
fixed(long *pdims = dims)
{
var res = THSLinalg_tensorsolve(input.Handle, other.Handle, (IntPtr)pdims, dims.Length);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new TorchTensor(res));
}
}
}
public static TorchTensor norm(TorchTensor input, long[]?dims = null, bool keepdim = false)
{
unsafe
{
fixed(long *pdims = dims)
{
var res = THSLinalg_norm_opt(input.Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new TorchTensor(res));
}
}
}