Comments (3)
Hi,
You could use the NFA to evaluate the "normalcy level" of each track in the test set.
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Good advice at making sense of the output, thanks for that.
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Hi again -
Any thoughts on the following? Inside the contrario_detection( ) function in contrario_utils.py, what if there is a loop added above the NFA function with additional (more than the default 0.0091) epsilon values and multiple sensitivities approaching 1e-10 to100? That way the totals can be tallied and sorted? Do you think this make sense to you as well?
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Related Issues (20)
- VRNN Model HOT 1
- contrario detection Issues HOT 3
- Ship type HOT 5
- Using my own dataset. HOT 15
- Dataset Details HOT 3
- Logits Vs Probs. Are they being mixed up? HOT 2
- Using new dataset HOT 9
- 4 hot vector with dataset HOT 1
- An error when run "save_logprob"
- Tensorflow 2.x version HOT 1
- GPU Power required to run GeoTrackNet HOT 2
- Suitable Cuda and Graphic card used with environment?
- Question on results HOT 2
- Validation data explanation / strategy? HOT 3
- ValueError while training the embedding layer HOT 7
- ValueError: ./chkpt/elbo-ct_2017010203_10_20_train.pkl-data_dim-702-latent_size-100-batch_size-1 doesnt exist.
- Inputs for csv2pkl.py HOT 2
- LAT_MIN, LAT_MAX, LON_MIN, LON_MAX Values HOT 1
- 请问这个csv文件在哪里
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