Fuzzify module

class nnodely.layers.fuzzify.Fuzzify(output_dimension: int | None = None, range: list | None = None, *, centers: list | None = None, functions: str | list | Callable = 'Triangular')[source]

Represents a Fuzzify relation in the neural network model.

Parameters:

  • output_dimension (int, optional) – The output dimension of the Fuzzify relation. If provided, range must also be provided and centers must be None.

  • range (list, optional) – A list containing the start and end values for the range. Required if output_dimension is provided.

  • centers (list, optional) – A list of center values for the fuzzy functions. Required if output_dimension is None. The output_dimension will be inferred from the number of centers provided.

  • functions (str, list, or Callable, optional) – The fuzzy functions to use. Can be a string specifying a predefined function type, a custom function, or a list of callable functions. Default is ‘Triangular’.

Notes

Note

The predefined function types are ‘Triangular’ and ‘Rectangular’. It is also possible to pass a list of custom functions. In this case, each center will be associated with the respective function in the list.

relation_name

The name of the relation.

Type:

str

output_dimension

The output dimension of the Fuzzify relation.

Type:

dict

json

A dictionary containing the configuration of the Fuzzify relation.

Type:

dict

Examples

Basic usage:

x = Input('x')
fuz = Fuzzify(output_dimension=5, range=[1,5])
out = Output('out', fuz(x.last()))

Passing the centers:

fuz = Fuzzify(centers=[-1,0,3,5], functions='Rectangular')
out = Output('out', fuz(x.last()))

Using a custom function:

def fun(x):
    import torch
    return torch.tanh(x)

fuz = Fuzzify(output_dimension=11, range=[-5,5], functions=fun)
out = Output('out', fuz(x.last()))

For more examples of how to use the fuzzify module, please refer to the Fuzzify tutorial.