public static TorchTensor Sparse(TorchTensor indices, TorchTensor values, long[] size, string device = "cpu", bool requiresGrad = false)
{
TorchTensor.CheckForCUDA(device);
unsafe
{
fixed(long *psizes = size)
{
return(new TorchTensor(THSTensor_sparse(indices.Handle, values.Handle, (IntPtr)psizes, size.Length, (sbyte)ATenScalarMapping.Float, device, requiresGrad)));
}
}
}
/// <summary>
/// Create a new tensor filled with random values taken from a normal distribution with mean 0 and variance 1.
/// </summary>
static public TorchTensor RandomN(long[] size, string device = "cpu", bool requiresGrad = false)
{
TorchTensor.CheckForCUDA(device);
unsafe
{
fixed(long *psizes = size)
{
return(new TorchTensor(THSTensor_randn((IntPtr)psizes, size.Length, (sbyte)ATenScalarMapping.Byte, device, requiresGrad)));
}
}
}
/// <summary>
/// Create a new tensor filled with random values taken from a uniform distribution in [0, 1).
/// </summary>
static public TorchTensor Random(long[] size, string device = "cpu", bool requiresGrad = false)
{
TorchTensor.CheckForCUDA(device);
unsafe
{
fixed(long *psizes = size)
{
var tptr = THSTensor_rand((IntPtr)psizes, size.Length, (sbyte)ATenScalarMapping.Float, device, requiresGrad);
Torch.AssertNoErrors();
return(new TorchTensor(tptr));
}
}
}
/// <summary>
/// Creates 1-D tensor of size [(end - start) / step] with values from interval [start, end) and
/// common difference step, starting from start
/// </summary>
static public TorchTensor Arange(float start, float stop, float step, string device = "cpu", bool requiresGrad = false)
{
TorchTensor.CheckForCUDA(device);
return(new TorchTensor(THSTensor_arange(start.ToScalar().Handle, stop.ToScalar().Handle, step.ToScalar().Handle, (sbyte)ATenScalarMapping.Float, device, requiresGrad)));
}
