Model-level composition

Model-level composition deals with independently defined sub-models and their interactions. Sub-models are logical groups of output streams that can be added, removed, connected, or frozen independently. Use the Composer API to manage these operations and to prepare the whole definition for neuralization.

Key Composer operators:

  • addModel / removeModel : register and unregister named sub-models. See addModel() and removeModel().

  • addConnect / addClosedLoop / removeConnection : connect an output stream of one model to an input of another (or the same) model; create closed-loop feedback between streams and inputs. See addConnect(), addClosedLoop(), and removeConnection().

  • neuralizeModel : finalize the model definition and translate it into a trainable neural representation (builds time windows, slices, and internal tensors required for training/inference). See neuralizeModel().

nnodely.operators.composer.Composer.addModel(self, name: str, stream_list: list | Output) → None

Adds a new model with the given name along with a list of Outputs.

Parameters:

  • name (str) – The name of the model.

  • stream_list (list of Stream) – The list of Outputs stream in the model.

Example

model = Modely()
x = Input('x')
out = Output('out', Fir(x.last()))
model.addModel('example_model', [out])
nnodely.operators.composer.Composer.removeModel(self, name_list: list | str) → None

Removes models with the given list of names.

Parameters:

name_list (list of str) – The list of model names to remove.

Example

model.removeModel(['sub_model1', 'sub_model2'])
nnodely.operators.composer.Composer.addConnect(self, stream_out: str | Output | Stream, input_in: str | Input, *, local: bool = False) → None

Adds a connection from a relation stream to an input.

Parameters:

  • stream_out (Stream) – The relation stream to connect from.

  • input_in (Input or list of inputs) – The input or list of input to connect to.

Examples

model = Modely()
x = Input('x')
y = Input('y')
relation = Fir(x.last())
model.addConnect(relation, y)
nnodely.operators.composer.Composer.addClosedLoop(self, stream_out: str | Output | Stream, input_in: str | Input, *, local: bool = False) → None

Adds a closed loop connection from a relation stream to an input.

Parameters:

  • stream_out (Stream) – The relation stream to connect from.

  • input_in (Input or list of inputs) – The Input or the list of inputs to connect to.

Examples

model = Modely()
x = Input('x')
y = Input('y')
relation = Fir(x.last())
model.addClosedLoop(relation, y)
nnodely.operators.composer.Composer.removeConnection(self, input_in: str | Input) → None

Remove a closed loop or connect connection from an input.

Parameters:

input_in (Input or name of the input of inputs) – The Input to disconnect.

Examples

model = Modely()
x = Input('x')
y = Input('y')
relation = Fir(x.last())
model.addConnect(relation, y)
model.removeConnection(y)
nnodely.operators.composer.Composer.neuralizeModel(self, sample_time: float | int | None = None, *, clear_model: bool = False, model_def: dict | None = None) → None

Neuralizes the model, preparing it for inference and training. This method creates a neural network model starting from the model definition. It will also create all the time windows and correct slicing for all the inputs defined.

Parameters:

  • sample_time (float or None, optional) – The sample time for the model. Default is 1.0

  • clear_model (bool, optional) – Whether to clear the existing model definition. Default is False.

  • model_def (dict or None, optional) – A dictionary defining the model. If provided, it overrides the existing model definition. Default is None.

Raises:

ValueError – If sample_time is not None and model_def is provided. If clear_model is True and model_def is provided.

Example

model = Modely(name='example_model')
model.neuralizeModel(sample_time=0.1, clear_model=True)

For further examples please refer to the relative tutorial.