kaira.models.binary.soft_bit_thresholding.FixedThresholder

Inheritance diagram for FixedThresholder

class kaira.models.binary.soft_bit_thresholding.FixedThresholder(threshold: float = 0.5, input_type: InputType = InputType.PROBABILITY, *args: Any, **kwargs: Any)

Bases: SoftBitThresholder

Simple fixed threshold for soft bit values.

Applies a fixed threshold to convert soft bit values to hard decisions. For probability inputs (in range [0,1]), the default threshold is 0.5. For LLR inputs, the default threshold is 0.0.

Example

With threshold=0.5 and input [0.2, 0.7, 0.4, 0.9]: Output will be [0.0, 1.0, 0.0, 1.0]

Methods

__init__	Initialize the fixed thresholder.
forward	Apply fixed thresholding to convert soft bit values to hard decisions.
to	Move the model to the specified device.

__init__(threshold: float = 0.5, input_type: InputType = InputType.PROBABILITY, *args: Any, **kwargs: Any)

Initialize the fixed thresholder.

Parameters:

• threshold – The threshold value to use. Default is 0.5 for probabilities.

• input_type – Type of soft input, can be ‘prob’ (probabilities between 0 and 1) or ‘llr’ (log-likelihood ratios). Affects the default threshold if not specified.

• *args – Variable positional arguments passed to the base class.

• **kwargs – Variable keyword arguments passed to the base class.

forward(x: Tensor, *args: Any, **kwargs: Any) → Tensor

Apply fixed thresholding to convert soft bit values to hard decisions.

Parameters:

• x – Input tensor of soft bit values.

• *args – Additional positional arguments (unused).

• **kwargs – Additional keyword arguments (unused).

Returns:

Tensor of hard bit decisions (0.0 or 1.0).

to(device: str | device, *args, **kwargs) → SoftBitThresholder

Move the model to the specified device.

Parameters:

• device – The device to move the model to.

• *args – Additional positional arguments for nn.Module.to().

• **kwargs – Additional keyword arguments for nn.Module.to().

Returns:

Self for method chaining.