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I assume you mean you get that error when you run exactly the code in the example? If so could you share scikit-learn version as that's the only place I can think it comes from.
If it's from adding sparsity the Positive Definite problem can happen and you can try changing the random seed a bit.
If you aren't bothered about having a random covariance structure then you can pass
LinearSimulatedData(structure='identity')
which I think works more generally but can make signal recovery more difficult.
Hi James, I tried to simulate data using this example, but I get:
LinAlgError: Matrix is not positive definite
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