Floor#

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

Bases: ElementwiseFeature

Apply the floor function elementwise.

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

The floor function returns the greatest integer less than or equal to each element of the input.

Parameters#

feature: Feature | None, optional: The input feature to which the floor 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 Floor

Use with NumPy directly:

>>> import numpy as np
>>>
>>> result = Floor()(np.array([-1.7, -0.5, 0.0, 0.5, 1.7]))
>>> result
array([-2., -1.,  0.,  0.,  1.])

Use with PyTorch directly:

>>> import torch
>>>
>>> result = Floor()(torch.tensor([-1.7, -0.5, 0.0, 0.5, 1.7]))
>>> result
tensor([-2., -1.,  0.,  0.,  1.])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([-1.7, -0.5, 0.0, 0.5, 1.7]))
>>> pipeline = value >> Floor()
>>> result = pipeline()
>>> result
array([-2., -1.,  0.,  0.,  1.])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor([-1.7, -0.5, 0.0, 0.5, 1.7]))
>>> pipeline = value >> Floor()
>>> result = pipeline()
>>> result
tensor([-2., -1.,  0.,  0.,  1.])

These are equivalent to:

>>> pipeline = Floor(value)