jansturm1

# *-----------------------------------------------------------------
# | PROGRAM NAME: ex VAR.py
# | DATE: 2/23/21 
# | CREATED BY: MATT BOGARD 
# | PROJECT FILE:            
# *----------------------------------------------------------------
# | PURPOSE: source: https://www.machinelearningplus.com/time-series/vector-autoregression-examples-python/
# *----------------------------------------------------------------

# see also my blog post: http://econometricsense.blogspot.com/2011/05/vector-autoregressions-and-bayesian.html

jansturm1

Either via 
$ docker run --dns 10.0.0.2 busybox nslookup google.com

or edit your /etc/docker/daemon.json to have something like:

{
    "dns": ["10.0.0.2", "8.8.8.8"]
}

then restart docker service  

jansturm1

A dendrogram is a common way to represent hierarchical data. For Python users, Scipy has a hierarchical clustering module that performs hierarchical clustering and outputs the results as dendrogram plots via matplotlib. When it's time to make a prettier, more customized, or web-version of the dendogram, however, it can be tricky to use Scipy's dendrogram to create a suitable visualization. My preferred method of visualizing data -- especially on the web -- is D3. This example includes a script to convert a Scipy dendrogram into JSON format used by D3's cluster method.

In the example, I cluster six genes by their expression values from two experiments. You can easily replace that data with your own, larger data set, to harness the power of both Scipy and D3 for analyzing hierarchical data. The D3 code I used to generate this example is straigh