Merge pull request #56 from cyber-physical-systems-group/reorganize

Reorganize

pydentification/data/__init__.py

@@ -1,11 +1,7 @@
-from .process import decay, unbatch
 from .sequences import generate_time_series_windows
 from .splits import compute_n_validation_samples, draw_validation_indices, time_series_train_test_split
 
 __all__ = [
-    # torch utils
-    "decay",
-    "unbatch",
     # windowing utils
     "generate_time_series_windows",
     # train-test-validation splitting utils

pydentification/models/README.md

@@ -7,4 +7,3 @@ stacking.
 This package contains following submodules:
 * `modules` - building blocks of the models, extending PyTorch modules with custom layers, activation functions, etc.
 * `networks` - models composed of building blocks, such as transformer etc.
-* `nonparametric` - non-parametric models and supporting utils

pydentification/models/modules/activations/__init__.py

@@ -1,9 +1,7 @@
-from .functional import bounded_linear_unit, universal_activation
-from .modules import BoundedLinearUnit, UniversalActivation
+from .functional import universal_activation
+from .modules import UniversalActivation
 
 __all__ = [
-    "bounded_linear_unit",
-    "BoundedLinearUnit",
     "universal_activation",
     "UniversalActivation",
 ]

pydentification/models/modules/activations/functional.py

@@ -2,15 +2,6 @@
 from torch import Tensor
 
 
-def bounded_linear_unit(inputs: Tensor, lower: float | Tensor, upper: float | Tensor, inplace: bool = False) -> Tensor:
-    """
-    Bounded linear activation function. It means that the output is linear in range [lower, upper] and clamped
-    outside of it to the values of the bounds. Bounds can be scalar of tensor of the same shape as inputs.
-    """
-    out = inputs if inplace else None
-    return torch.clamp(inputs, min=lower, max=upper, out=out)
-
-
 def universal_activation(inputs: Tensor, inplace: bool = False) -> Tensor:
     """
     Universal activation function, for which finite number of neurons can approximate any continuous function on

pydentification/models/modules/activations/modules.py

@@ -4,33 +4,6 @@
 from . import functional as func
 
 
-class BoundedLinearUnit(Module):
-    """
-    Bounded linear activation function. It means that the output is linear in range [-bounds, bounds] and clamped
-    outside of it to the values of the bounds. Bounds can be scalar of tensor of the same shape as inputs.
-    """
-
-    def __init__(
-        self,
-        lower: float | Tensor | None = None,
-        upper: float | Tensor | None = None,
-    ):
-        """
-        Bounds given in __init__ are static, applied irrespective of the input bounds
-        they can be scalar or tensor of the same shape as inputs
-        """
-        super(BoundedLinearUnit, self).__init__()
-
-        self.static_lower_bound = lower
-        self.static_upper_bound = upper
-
-    def forward(self, inputs: Tensor, bounds: float | Tensor | None = None) -> Tensor:
-        lower = bounds if self.static_lower_bound is None else self.static_lower_bound
-        upper = bounds if self.static_upper_bound is None else self.static_upper_bound
-
-        return func.bounded_linear_unit(inputs, lower=lower, upper=upper)
-
-
 class UniversalActivation(Module):
     """
     Universal activation function, for which finite number of neurons can approximate any continuous function on

pydentification/models/networks/transformer/feedforward.py

@@ -49,7 +49,7 @@ def forward(self, inputs: Tensor) -> Tensor:
 class CausalDelayLineFeedforward(nn.Module):
     """
     Linear transformation of the input signal using delay line with casual mask, allowing only connections from future
-    to past time steps, which means single row of weight matrix is multiplied with each time step of the input signal.
+    to past-time steps, which means single row of weight matrix is multiplied with each time step of the input signal.
 
     For MIMO systems, different weight matrix is applied for each dimension, where each of the matrices is stored in
     MaskedLinear module in torch.nn.ModuleList.

pyproject.toml

@@ -9,7 +9,7 @@ include = ["pydentification*"]
 
 [project]
 name = "pydentification"
-version = "0.4.1"
+version = "0.5.0"
 description = "Toolbox for dynamical system identification with neural networks"
 license = {text = "MIT"}
 readme = {file = "README.md", content-type = "text/markdown"}