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leeoniya / uPlot

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Tests

leeoniya
leeoniya commented Dec 14, 2019

it's becoming more time-consuming and error-prone to manually re-test all the demos following internal refactorings and API adjustments.

now that the API is fleshed out a bit, it's possible to test a large amount of code (non-granularly) without having to simulate all interactions via Puppeteer or similar.

a lot of code can already be regression-tested by simply running all the demos and val

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good first issue

tslearn-team / tslearn

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KMeans question

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ssimontacchi
ssimontacchi commented Jun 20, 2020

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually

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bug good first issue

shubhomoydas / ad_examples

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A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack
  • Updated Sep 25, 2020
  • Python

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