How can i implement callback parameter in fit moder Autoencoder ?
There is not parameter.

from keras.callbacks.callbacks import EarlyStopping
cb_earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0,
mode='auto', baseline=None, restore_best_weights=False)
pyod_model.fit(scaler, callbacks=[cb_earlystop])

TypeError: fi

trex@beast:~/Development/siren/sentinl$ grep -rne "indices\"" docs/
docs/Watcher-Examples.md:227:          "indices": [
docs/Watcher-Examples.md:268:          "indices": [
docs/HOWTO-Alerts-in-Nagios-NRDP.md:157:        "indices": [

Datastream.io is not working because some of the library dependencies are not set to the correct version (tornado and elasticsearch in my case).
Here is the pip freeze (python3.5) of the fully working datastream.io:

`bokeh==1.3.0
dateparser==0.7.1
-e git+https://github.com/MentatInnovations/datastream.io@a243b89ec3c4e06473b5004c498c472ffd37ead2#egg=dsio
elasticsearch==5.5.3
Jinja2==2.10.1

In the following code

    @Override
    public DetectorDocument findByUuid(String uuid) {
        val queryBuilder = QueryBuilders.termQuery("uuid", uuid);
        val searchSourceBuilder = elasticsearchUtil.getSourceBuilder(queryBuilder).size(DEFAULT_ES_RESULTS_SIZE);
        val searchRequest = elasticsearchUtil.getSearchRequest(searchSourceBuilder, DETECTOR_INDEX, DETECTOR_DOC_TYPE)

The std_clip parameter makes the mean and covariance updates more stable. This can be important when the outliers arrive in batches instead of e.g. uniform. The effect is already visualized in https://github.com/SeldonIO/seldon-core/blob/master/components/outlier-detection/mahalanobis/doc.ipynb.

This is being worked on by @nyukyi . @emilystreetman, will you please facilitate the addition to help docs?
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Related to #572