Cosh

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

Bases: ElementwiseFeature

Apply the hyperbolic cosine function elementwise.

This feature applies xp.cosh to each element in a NumPy array or a PyTorch tensor. It supports both direct input and pipeline composition.

Parameters

feature: Feature | None, optional

The input feature to which the hyperbolic cosine 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 Cosh

Use with NumPy directly:

>>> import numpy as np
>>>
>>> result = Cosh()(np.array([-1.0, 0.0, 1.0]))
>>> result
array([1.54308063, 1.        , 1.54308063])

Use with PyTorch directly:

>>> import torch
>>>
>>> result = Cosh()(torch.tensor([-1.0, 0.0, 1.0]))
>>> result
tensor([1.5431, 1.0000, 1.5431])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([-1.0, 0.0, 1.0]))
>>> pipeline = value >> Cosh()
>>> result = pipeline()
>>> result
array([1.54308063, 1.        , 1.54308063])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor([-1.0, 0.0, 1.0]))
>>> pipeline = value >> Cosh()
>>> result = pipeline()
>>> result
tensor([1.5431, 1.0000, 1.5431])

These are equivalent to:

>>> pipeline = Cosh(value)