I record a bunch of my activities with Strava. And there are novel routes that I try out and only have done once. The other part are routes that I do more than once. The thing that I am missing on Strava is a comparison of similar routes. It has segments, but I would have to make my whole commute one segment in order to see how I fare on it.

So what I would like to try here is to use a clustering algorithm to automatically identify clusters of similar rides. And also I would like find rides that have the same start and end point, but different routes in between. In my machine learning book I read that there are clustering algorithms, so this is the project that I would like to apply them to.

Incidentally Strava features a lot of apps, so I had a look but could not find what I was looking for. Instead I want to program this myself in Python. One can export the data from Strava and obtains a ZIP file with all the GPX files corresponding to my activities.

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I've been using clothespins all along. I know other people who do as well, and some who never use them. While discussing this over dinner, it seems there are two stances that people take:

1. Pins are not worth using at all. The clothing dries as fast as it does without them, perhaps insignificantly slower. The time needed to work with the pins does not make up for the benefit of having the laundry done faster.

2. Pins clearly must do a difference as the clothing is just in two and not four layers.

Well, I am clearly in the second team. But this is a hypotheses that one can test and negate. So apply the scientific method! As a setup I took four pieces of underwear and two t-shirts. Then I put half of them on the dryer with pins, the other just folded in half. Every now and then I measured their weight with a kitchen scale.

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Bei ALDI gibt es wegen der Mehrwertsteuersenkung aktuell 3 % auf alles. Mediamarkt hatte manchmal auch so Aktionen, bei denen es 19 % Rabatt unter dem Motto »Mediamarkt schenkt die Mehrwertsteuer« gibt. Interessant ist ja eigentlich, dass bei den Rabatten die Preise sogar noch weiter gesenkt werden als nötig.

Sei der Nettopreis $N$, dann ist der Bruttopreis $B$ bei einer Mehrwertsteuer $m$ gegeben durch $B = N \cdot (1 + m)$. Im Normalfall ist $m = 0.19$ und daher haben wir $B = 1.19 \cdot N$. Möchte man die Mehrwertsteuer erlassen, so muss man den einen Rabatt geben, der $1/1.19 \approx 0.8403361$ entspricht. Das ist aber ein Rabatt von $1 - 0.8403361 \approx 0.1596639$, also knapp unter 16 %. Würde Mediamarkt den Kunden aber nur 16 % Rabatt geben, wären wahrscheinlich viele empört. Also gibt es noch weitere 3 % Rabatt für alle, die in Prozentrechnung nicht so fit sind.

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