eric-moreno / in Goto Github PK

The following changes can work!
acc_vals_validation = acc_vals_validation[:(final_epoch + 1)]
loss_vals_training = loss_vals_training[:(final_epoch + 1)]
loss_vals_validation = loss_vals_validation[:(final_epoch + 1)]
loss_std_validation = loss_std_validation[:(final_epoch + 1)]
loss_std_training = loss_std_training[:(final_epoch + 1)]
I have checked this code .

Hi author,

For the evaluation part in the instruction in README.md, I cannot access the output.pkl file using command 'xrdcp root://eosuser.cern.ch//eos/user/w/woodson/IN/output.pkl .'
I have installed the Xrootd package， but the xrdcp command seems not to work for downloading, I wonder how can I get this .pkl file?

I am new to this tool and I would be grateful if you can give me any advice! Thanks!

ModuleNotFoundError: No module named 'type_func'
is there any package except install.sh?

Dear eric，
How to get training data?I want to find benchmark dataset to confirm the algorithm's effectiveness.
I'm not a physicist, but I'm focused on deep learning.Could you give me some advice?
Thanks！

Hi authors,

How do you reliably compute the Jensen-Shannon divergence between the continuous mass distributions passing/failing the threshold? As far as I understand, the KL Divergence and by extension the JS divergence b/w continuous distributions are intractable and hard to estimate reliably.

I looked through the code base but found it mildly confusing. From what I can gather, you discretize the distributions somehow and use an estimator for the entropy of the discretized distribution? But how do you calculate the cross entropy as well? A quick rundown would be very helpful, as this would be a very useful metric to quantify the extent of decorrelation to a given pivot variable.

Cheers,
Justin

I fit the easy random forest model，just like this
from sklearn.ensemble import RandomForestClassifier
RandomForestClassifier(n_estimators=10, random_state=2019)

TABLE IV. High-level features in yours paper

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 16 out of 16 | elapsed: 3.2min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 16 out of 16 | elapsed: 1.2s finished
Validation Accuracy: 0.996