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Maybe. My primary problem is figuring out how to sausage grind the math into code. There may be some library that can help me, but I probably wouldn't know if I saw it.
Unfortunately, my Youtube level education on LSH is not measuring up well to the math in this dissertation I am reading.
So far I have googled a lot of LSH tutorials and had min-hashing and Jaccard distance between text documents explained to me a half dozen times.
@julianpistorius, so I added stanford-corenlp-3.9.2-models.jar to the lib directory, and now I get a different error: Execution failed for task ':run'.
Process 'command '/Library/Java/JavaVirtualMachines/jdk1.8.0_181.jdk/Contents/Home/bin/java'' finished with non-zero exit value 1
What went wrong:
Execution failed for task ':run'.
Process 'command '/Library/Java/JavaVirtualMachines/jdk1.8.0_181.jdk/Contents/Home/bin/java'' finished with non-zero exit value 1
Try:
Run with --stacktrace option to get the stack trace. Run with --info or --debug option to get more log output.
Hi all, hoping someone more numerically savvy can help me understand this unexpected output from an eigenvector solver. For a real, symmetric matrix, the syevr
(eigenvectors + values) and gesdd
(SVD) functions should produce the same results. I'm finding the eigenvalues/singular values agree to numerical precision, but the vectors do not. The vectors in both outputs behave as expected (A v = lambda v
and |v| = 1
), and I don't have any repeated eigenvalues in the output so I don't suspect degenerate eigenvalues.
Any ideas?