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License: Other
hCRF-light Library 3.0
License: Other
I followed the instructions in compile.sh
and got no error.
After make
and make distribute
, I tried to run the command
./distribute/bin/hcrf-light
and got the following error :
./distribute/bin/hcrf-light: error while loading shared libraries: liblbfgs-1.8.so: cannot open shared object file: No such file or directory
The library liblbfgs-1.8.so
indeed exists in ./lib/liblbfgs/lib/.libs/
. How can I solve this problem ?
I am using Debian 8.7.
Dear Yale,
Looks like this light version HCRF does not support window size for dependency range. Currently just consider consecutive nodes, right? Or is there a way to specify such window size in your current code? Thanks a lot.
When I run the file 'test_oc.m', I get the error "Undefined function or variable 'matHCRF'." How could I solve this problem?
Hi Song, I know it's been some time since this paper is published. I'm trying to run the code for OCCRF model for a few days but I couldn't figure out the correct configuration. FYI, I could run the hcrf
model in README.md and I hope to have some guidance on running the occrf
model.
Here's what I'm trying:
./hCRF-light/distribute/bin/hcrf-light -m occrf \
-Fd ./hCRF-light/data/toy/dataAnomalyTrain.csv -Fl ./hCRF-light/data/toy/labelsAnomalyTrain.csv \
-FD ./hCRF-light/data/toy/dataAnomalyTest.csv -FL ./hCRF-light/data/toy/labelsAnomalyTest.csv
And here's what I'm getting:
L-BFGS optimization terminated with status code = -1000
fx = 0.0151543
Reading testing set...
Starting testing ...
Press a key to continue...
Here's the cat stats.txt
. Looks like it's predicting all data points in test set to be normal (label 0).
I noticed that the status code is -1000, and I've checked the lbfgs library and this error code links to LBFGSERR_ROUNDING_ERROR. I'm not sure why this is happening, I'm guessing that the gradient became too small during optimization.
Then, I try to solve it by changing to use nrbm optimizer by adding -o nrbm
, and here's the cat stats.txt
result. It's predicting all rows in test set as anomalies:
(1) I'll be very pleased if you would guide me on the configuration of occrf
for the toy anomaly dataset / other dataset. ๐
(2) I also have another question in mind: Shouldn't occrf
be predicting just one instead of multiple labels per sequence? According to the publised paper, there is only 1 anomaly score per employee. But in this code, it's considered a continuousModel, and uses fl
& fL
(1 label / timestep) instead of fq
& fQ
(1 label / sequence)...
Thank you! ๐
When I ran this library with a dataset of 4200 files. Dimension of seqs data(93 columns and rows [not fixed]) It gave me accuracy to be somewhere around 0.0015 which is very low. I gave the split valid parameters to be same as split test. Can anyone explain the reason for this low accuracy?
Do I need to change seeds or any other parameter?
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