# Rewrite of the original file in DeepXDE: https://github.com/lululxvi/deepxde
# ==============================================================================
from typing import Sequence
import brainstate
from pinnx.utils.sampler import BatchSampler
from .base import Problem
__all__ = [
'QuadrupleDataset',
'QuadrupleCartesianProd',
]
[docs]
class QuadrupleDataset(Problem):
"""
Dataset with each data point as a quadruple.
The couple of the first three elements are the input, and the fourth element is the
output. This dataset can be used with the network ``MIONet`` for operator
learning.
Args:
X_train: A tuple of three NumPy arrays.
y_train: A NumPy array.
"""
def __init__(
self,
X_train,
y_train,
X_test,
y_test,
approximator: brainstate.nn.Module = None,
loss_fn: str = 'MSE',
loss_weights: Sequence[float] = None,
):
super().__init__(
approximator=approximator,
loss_fn=loss_fn,
loss_weights=loss_weights
)
self.train_x = X_train
self.train_y = y_train
self.test_x = X_test
self.test_y = y_test
self.train_sampler = BatchSampler(len(self.train_y), shuffle=True)
[docs]
def losses(self, inputs, outputs, targets, **kwargs):
return self.loss_fn(targets, outputs)
[docs]
def train_next_batch(self, batch_size=None):
if batch_size is None:
return self.train_x, self.train_y
indices = self.train_sampler.get_next(batch_size)
return (
(self.train_x[0][indices],
self.train_x[1][indices]),
self.train_x[2][indices],
self.train_y[indices],
)
[docs]
def test(self):
return self.test_x, self.test_y
[docs]
class QuadrupleCartesianProd(Problem):
"""Cartesian Product input data format for MIONet architecture.
This dataset can be used with the network ``MIONetCartesianProd`` for operator
learning.
Args:
X_train: A tuple of three NumPy arrays. The first element has the shape (`N1`,
`dim1`), the second element has the shape (`N1`, `dim2`), and the third
element has the shape (`N2`, `dim3`).
y_train: A NumPy array of shape (`N1`, `N2`).
"""
def __init__(self, X_train, y_train, X_test, y_test):
if (
len(X_train[0]) * len(X_train[2]) != y_train.size
or len(X_train[1]) * len(X_train[2]) != y_train.size
or len(X_train[0]) != len(X_train[1])
):
raise ValueError(
"The training dataset does not have the format of Cartesian product."
)
if (
len(X_test[0]) * len(X_test[2]) != y_test.size
or len(X_test[1]) * len(X_test[2]) != y_test.size
or len(X_test[0]) != len(X_test[1])
):
raise ValueError(
"The testing dataset does not have the format of Cartesian product."
)
self.train_x, self.train_y = X_train, y_train
self.test_x, self.test_y = X_test, y_test
self.branch_sampler = BatchSampler(len(X_train[0]), shuffle=True)
self.trunk_sampler = BatchSampler(len(X_train[2]), shuffle=True)
[docs]
def losses(self, inputs, outputs, targets, **kwargs):
return loss_fn(targets, outputs)
[docs]
def train_next_batch(self, batch_size=None):
if batch_size is None:
return self.train_x, self.train_y
if not isinstance(batch_size, (tuple, list)):
indices = self.branch_sampler.get_next(batch_size)
return (
self.train_x[0][indices],
self.train_x[1][indices],
self.train_x[2],
), self.train_y[indices]
indices_branch = self.branch_sampler.get_next(batch_size[0])
indices_trunk = self.trunk_sampler.get_next(batch_size[1])
return (
self.train_x[0][indices_branch],
self.train_x[1][indices_branch],
self.train_x[2][indices_trunk],
), self.train_y[indices_branch][:, indices_trunk]
[docs]
def test(self):
return self.test_x, self.test_y
