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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

7
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 Jul 2, 2021
  • Python

timeseriesAI / tsai

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Memory Error with Very Large Dataset

3
HariWu1995
HariWu1995 commented Jun 3, 2021

Dear team,

I am in stuck when convert very large numpy array to your TSDatasets.
These are what I have tried to fix my issue:

  • when building time-series, I used tensorflow.keras.preprocessing.timeseries_dataset_from_array. After this step, the memory is still fine
  • I concatenate all batch data into numpy array, this step produces problem so I use numpy memmap to avoi
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good first issue

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