34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
import pytest
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from pina import Trainer
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from pina.solver import PINN
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from pina.model import FeedForward
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from pina.loss import NeuralTangentKernelWeighting
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from pina.problem.zoo import Poisson2DSquareProblem
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# Initialize problem and model
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problem = Poisson2DSquareProblem()
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problem.discretise_domain(10)
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model = FeedForward(len(problem.input_variables), len(problem.output_variables))
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@pytest.mark.parametrize("alpha", [0.0, 0.5, 1.0])
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def test_constructor(alpha):
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NeuralTangentKernelWeighting(alpha=alpha)
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# Should fail if alpha is not >= 0
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with pytest.raises(ValueError):
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NeuralTangentKernelWeighting(alpha=-0.1)
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# Should fail if alpha is not <= 1
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with pytest.raises(ValueError):
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NeuralTangentKernelWeighting(alpha=1.1)
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@pytest.mark.parametrize("alpha", [0.0, 0.5, 1.0])
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def test_train_aggregation(alpha):
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weighting = NeuralTangentKernelWeighting(alpha=alpha)
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solver = PINN(problem=problem, model=model, weighting=weighting)
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trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
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trainer.train()
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