17 lines
1.1 KiB
Plaintext
17 lines
1.1 KiB
Plaintext
The testing for the loglinear package uses functional invariants over randomly generated inputs, which in general yields
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much more durable software.
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This is aided by the JUnit port of Quickcheck. The general tactic for testing is to randomly generate inputs, then use
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the slow definitional approach on tractably small inputs and test that the output of our algorithms always matches
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exactly. The GitHub for the Quickcheck port is https://github.com/pholser/junit-quickcheck. The dependencies for that
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are in the lib/ folder in test1.
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Some of the general testing approaches are listed below:
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message passing -> tested against brute force factor multiplication and marginalization
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partition function -> tested against brute force multiplication and summation
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log likelihood gradient -> tested against definition of derivative
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optimization -> tested by making thousands of random perturbations around the function to check if any values are better
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concatVector -> tested against a non-sparse version
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table factor -> tested against functional invariants of results, and re-implementations using different algorithms
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