Sign

class deeptrack.elementwise.Sign(feature: Feature | None = None, **kwargs: Any)

Bases: ElementwiseFeature

Apply the sign function elementwise.

This class uses a backend-aware dispatch to handle NumPy and PyTorch tensors safely. It returns: - -1 for negative values, - 0 for zero, - +1 for positive values.

For complex numbers, it returns x / abs(x) when x != 0.

Parameters

feature: Feature | None, optional

The input feature to which the sign function will be applied. If None, the function is applied directly to the input array or tensor.

Examples

>>> import deeptrack as dt
>>> from deeptrack.elementwise import Sign

Use with NumPy directly:

>>> import numpy as np
>>> result = Sign()(np.array([-5.0, 0.0, 2.0]))
>>> result
array([-1.,  0.,  1.])

Use with PyTorch directly:

>>> import torch
>>> result = Sign()(torch.tensor([-5.0, 0.0, 2.0]))
>>> result
tensor([-1.,  0.,  1.])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([-5.0, 0.0, 2.0]))
>>> pipeline = value >> Sign()
>>> result = pipeline()
>>> result
array([-1.,  0.,  1.])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor([-5.0, 0.0, 2.0]))
>>> pipeline = value >> Sign()
>>> result = pipeline()
>>> result
tensor([-1.,  0.,  1.])

These are equivalent to:

>>> pipeline = Sign(value)