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fix doc model part 2

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This commit is contained in:
giovanni
2025-03-14 16:07:08 +01:00
committed by Nicola Demo
parent 001d1fc9cf
commit f9881a79b5
18 changed files with 887 additions and 851 deletions
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63
pina/model/block/pod_block.py
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@@ -5,23 +5,26 @@ import torch
class PODBlock(torch.nn.Module):
"""
POD layer: it projects the input field on the proper orthogonal
decomposition basis. It needs to be fitted to the data before being used
with the method :meth:`fit`, which invokes the singular value decomposition.
The layer is not trainable.
Proper Orthogonal Decomposition block.
This block projects the input field on the proper orthogonal decomposition
basis. Before being used, it must be fitted to the data with the ``fit``
method, which invokes the singular value decomposition. This block is not
trainable.
.. note::
All the POD modes are stored in memory, avoiding to recompute them when
the rank changes but increasing the memory usage.
the rank changes, leading to increased memory usage.
"""
def __init__(self, rank, scale_coefficients=True):
"""
Build the POD layer with the given rank.
Initialization of the :class:`PODBlock` class.
:param int rank: The rank of the POD layer.
:param bool scale_coefficients: If True, the coefficients are scaled
:param bool scale_coefficients: If ``True``, the coefficients are scaled
after the projection to have zero mean and unit variance.
Default is ``True``.
"""
super().__init__()
self.__scale_coefficients = scale_coefficients
@@ -34,12 +37,19 @@ class PODBlock(torch.nn.Module):
"""
The rank of the POD layer.
:return: The rank of the POD layer.
:rtype: int
"""
return self._rank
@rank.setter
def rank(self, value):
"""
Set the rank of the POD layer.
:param int value: The new rank of the POD layer.
:raises ValueError: If the rank is not a positive integer.
"""
if value < 1 or not isinstance(value, int):
raise ValueError("The rank must be positive integer")
@@ -48,9 +58,10 @@ class PODBlock(torch.nn.Module):
@property
def basis(self):
"""
The POD basis. It is a matrix whose columns are the first `self.rank`
POD modes.
The POD basis. It is a matrix whose columns are the first ``rank`` POD
modes.
:return: The POD basis.
:rtype: torch.Tensor
"""
if self._basis is None:
@@ -61,9 +72,11 @@ class PODBlock(torch.nn.Module):
@property
def scaler(self):
"""
The scaler. It is a dictionary with the keys `'mean'` and `'std'` that
store the mean and the standard deviation of the coefficients.
Return the scaler dictionary, having keys ``mean`` and ``std``
corresponding to the mean and the standard deviation of the
coefficients, respectively.
:return: The scaler dictionary.
:rtype: dict
"""
if self._scaler is None:
@@ -77,9 +90,9 @@ class PODBlock(torch.nn.Module):
@property
def scale_coefficients(self):
"""
If True, the coefficients are scaled after the projection to have zero
mean and unit variance.
The flag indicating if the coefficients are scaled after the projection.
:return: The flag indicating if the coefficients are scaled.
:rtype: bool
"""
return self.__scale_coefficients
@@ -87,10 +100,10 @@ class PODBlock(torch.nn.Module):
def fit(self, X, randomized=True):
"""
Set the POD basis by performing the singular value decomposition of the
given tensor. If `self.scale_coefficients` is True, the coefficients
given tensor. If ``self.scale_coefficients`` is True, the coefficients
are scaled after the projection to have zero mean and unit variance.
:param torch.Tensor X: The tensor to be reduced.
:param torch.Tensor X: The input tensor to be reduced.
"""
self._fit_pod(X, randomized)
@@ -99,10 +112,8 @@ class PODBlock(torch.nn.Module):
def _fit_scaler(self, coeffs):
"""
Private merhod that computes the mean and the standard deviation of the
given coefficients, allowing to scale them to have zero mean and unit
variance. Mean and standard deviation are stored in the private member
`_scaler`.
Compute the mean and the standard deviation of the given coefficients,
which are then stored in ``self._scaler``.
:param torch.Tensor coeffs: The coefficients to be scaled.
"""
@@ -113,8 +124,8 @@ class PODBlock(torch.nn.Module):
def _fit_pod(self, X, randomized):
"""
Private method that computes the POD basis of the given tensor and
stores it in the private member `_basis`.
Compute the POD basis of the given tensor, which is then stored in
``self._basis``.
:param torch.Tensor X: The tensor to be reduced.
"""
@@ -136,9 +147,7 @@ class PODBlock(torch.nn.Module):
def forward(self, X):
"""
The forward pass of the POD layer. By default it executes the
:meth:`reduce` method, reducing the input tensor to its POD
representation. The POD layer needs to be fitted before being used.
The forward pass of the POD layer.
:param torch.Tensor X: The input tensor to be reduced.
:return: The reduced tensor.
@@ -148,10 +157,11 @@ class PODBlock(torch.nn.Module):
def reduce(self, X):
"""
Reduce the input tensor to its POD representation. The POD layer needs
to be fitted before being used.
Reduce the input tensor to its POD representation. The POD layer must
be fitted before being used.
:param torch.Tensor X: The input tensor to be reduced.
:raises RuntimeError: If the POD layer is not fitted.
:return: The reduced tensor.
:rtype: torch.Tensor
"""
@@ -176,6 +186,7 @@ class PODBlock(torch.nn.Module):
to be fitted before being used.
:param torch.Tensor coeff: The coefficients to be expanded.
:raises RuntimeError: If the POD layer is not fitted.
:return: The expanded tensor.
:rtype: torch.Tensor
"""