python-intro | Python exercises led by hints in comments - suitable

Starting in SciPy version 0.16.0, linregress returns a namedtuple, which allows you to access elements of the tuple using attribute names. If you are using a version SciPy older than 0.16, the return value is a plain tuple, so you will have to get the slope using regular indexing, e.g.

I added {% if request.path == '/{{sub_cat.sub_cat_parent.cat_slug}}/{{sub_cat.sub_cat_slug}}/' %} active {% endif %} to check if the current URL matches with url of the current clicked link and if it matches add css active class. However, this had no effect. It didn't throw any error and didn't work either.

Of course not, cf my answer to your (almost duplicate) other question.

edit: I tried this, it didn't work either

{% url '{{request.path}}' category='python' sub_cat='python-introduction' as target %}

{% if target %}

Of course not. First because the first argument to {% url %} is the url's name, not request.path - which you should know since it's already in your original snippet:

f-strings were introduced in Python 3.6. In an earlier Python, this line is a syntax error:

The python input() function will always wait for response from the keyboard before returning. Take a look at this answer for a technique to accomplish what you want.

Note that you cannot directly apply k-means clustering to a network, as there does not necessarily exist a metric to measure distances between nodes and centroids. But...

.. provided you assume:

• The path length of the weighted shortest-path is a distance measure between a pair of nodes.
• centroids are nodes. Note: In traditional k-means clustering centroids are not necessarily data points themselves.

Under these assumptions the sum of distances to the centroids is minimal if you associate to each node the centroid with the shortest weighted shortest-path.

So the procedure could be:

• Associate each node to a centroid such that the sum of the distances from each node to its centroid is minimal (i.e. the withing cluster sum of distances)
• Update the centroids
• Repeat previous two steps until the centroids are stable.

This procedure corresponds loosely to the procedure of k-mean clustering, that is to minimize the within-cluster sum of squares (WCSS).

Although this procedure is similar to k-means clustering in data points in a metric-space, I would not call it k-means clustering. Especially because the position of the centroids is restricted to nodes in the network.

Here is how you could approach this with python:

1. Define the initial centroids: