Localmodel module

class nnodely.layers.localmodel.LocalModel(input_function: Callable | None = None, output_function: Callable | None = None, *, pass_indexes: bool = False)[source]

Represents a Local Model relation in the neural network model.

Parameters:

input_function (Callable, optional) – A callable function to process the inputs.
output_function (Callable, optional) – A callable function to process the outputs.
pass_indexes (bool, optional) – A boolean indicating whether to pass indexes to the functions. Default is False.

relation_name

The name of the relation.

Type:: str

pass_indexes

A boolean indicating whether to pass indexes to the functions.

Type:: bool

input_function

The function to process the inputs.

Type:: Callable

output_function

The function to process the outputs.

Type:: Callable

Examples

Basic usage:

x = Input('x')
activation = Fuzzify(2, [0, 1], functions='Triangular')(x.last())
loc = LocalModel(input_function=Fir())
out = Output('out', loc(x.tw(1), activation))

Passing a custom function:

def myFun(in1, p1, p2):
    return p1 * in1 + p2

x = Input('x')
activation = Fuzzify(2, [0, 1], functions='Triangular')(x.last())
loc = LocalModel(
    input_function=lambda: ParamFun(myFun),
    output_function=lambda: Fir
)(x.last(), activation)
out = Output('out', loc)

Custom function with multiple activations:

def myFun(in1, p1, p2):
    return p1 * in1 + p2

x = Input('x')
F = Input('F')
activationA = Fuzzify(2, [0, 1], functions='Triangular')(x.tw(1))
activationB = Fuzzify(2, [0, 1], functions='Triangular')(F.tw(1))

loc = LocalModel(
    input_function=lambda: ParamFun(myFun),
    output_function=Fir(3)
)(x.tw(1), (activationA, activationB))
out = Output('out', loc)

For more examples of how to use the local model module, please refer to the LocalModel tutorial.