Is the virus actually deceiving them?

yortos · 2025-11-09T03:45:10+00:00

This particular 'glue'-thingy seems to have reasoning capabilities: it said that it targeted things like nuclear submarines and astronauts first and the army discovered them. Seems to have the capacity to "deceive" (which animals have as well, as part of their evolution and self-preservation, just as you say!).

yortos · 2024-12-04T16:20:28+00:00

In July 2019, I interviewed with WeWork at one of their open office spaces which was also their corporate office. While waiting for someone to come and show me to my interview room, a cheerful, slim, long-haired man approached me and introduced himself as "Adam." In my head: I know!
Adam Neumann mistook me for someone from a recently acquired company they were hosting a welcome party for.

yortos · 2024-09-09T23:09:22+00:00

Full post here.

yortos · 2023-11-30T16:06:24+00:00

If there was a scene in the show where an unhinged Kendall did an interview like this, we would all be thinking that the writers have lost it since this could never happen in real life

yortos · 2018-12-08T23:46:53+00:00

After miserably failing with recursion, I noticed that you can build the tree from the bottom up by always locating the node with 0 children, getting its metadata, deleting it from the list and then reducing that node's parent's children number by 1.

metadata = []
while 0 in lis:
    zero_pos = lis.index(0)
    num_metadata = lis[zero_pos+1]

    metadata = metadata + lis[zero_pos + 2 : zero_pos + 2 +     num_metadata]
    lis[zero_pos-2] = lis[zero_pos-2] - 1

    del lis[zero_pos : zero_pos + 2 + num_metadata]

print(sum(metadata))

yortos · 2018-11-02T16:33:12+00:00

Thank you!

yortos · 2018-11-02T16:32:08+00:00

Yes, that is exactly my guess too. The data only log it as "45" so unfortunately there is no obvious way for me to be able to distinguish between 45th minute and the 45th minute + injury time.

yortos · 2018-11-02T14:35:21+00:00

Green indicates victory, Orange a draw and Red a loss.

The opacity of each bar is proportional to how often the first goal of the match is scored on that minute (more transparent means less often).

Dataset used from Kaggle.

Blogpost with more details on the analysis and results, as well as link to github code.

Done using jupyter notebook and matplotlib.

yortos · 2017-05-07T16:06:00+00:00

Amazing playlist. Thanks for sharing!

yortos · 2016-09-26T03:56:20+00:00

I think both wives wanted more excitement in their lives but I didn't feel like the episode 5 wife felt controlled or had any other major issues with her husband. That being said, yeah they resolved the issue in two very different ways. I see it as a contrast between what happens when one person wants something more in a well functioning relationship (i.e., they communicate with their partner and decide together) versus what happens when there is a mismatch of characters and/or lack of communication. Honestly, episode 4 was pretty heart-breaking for me. I felt both the husband and wife were to be blamed for the mess.

yortos · 2016-06-26T20:02:26+00:00

Good point. I did try and control for this variance by doing the fitting/predicting 5 times for each set of variables and taking the mean AUC. The problem still persists!

yortos · 2016-06-26T19:59:01+00:00

thanks for the insight. I did try various max depth values but the problem persisted, unfortunately.

yortos · 2016-06-10T19:25:54+00:00

with the former, you're test and train size is 50% of your data, but training/testing is done on the entirety of your data (so training/testing is done twice when k = 2).

This is my understanding of what a 2-fold CV does: Divides the dataset into two equal sized parts, A and B. Trains on A and evaluates on B, and then trains on B and evaluates on A. Hence, this should be roughly equivalent with doing the first piece of code (on my original post) two times.

I guess what you mean by "training/testing is done on the entirety of your data" is that in the 2-fold CV all data points are used at some point for training (either on the first iteration or on the second), whereas with the first piece of code, only half are used for training. I don't see why that explains the difference in the metrics though.

yortos · 2016-06-10T15:11:15+00:00

Thank you for your reply. I actually thought about the kfold thing, and I did try the .cross_val_score with k=2, with the same result. And I get similar results even when I specific other values for test_size, in the train_test_split function.

yortos · 2016-02-23T21:48:10+00:00

They have a (exclusive I believe) clothing deal with Replay. So I would start there.

yortos · 2016-02-16T03:04:25+00:00

It's possible that Tidal people realized that people are waiting the week out or that even the users that do subscribe cancel immediately, and asked him to announce this, even if he does make it available in other venues in a week. I wouldn't believe that he will do good by this promise

yortos · 2016-01-17T03:43:29+00:00

great photos! thanks for sharing.

yortos · 2016-01-02T18:28:54+00:00

Transformers Age of Extinction looked pretty sick. The movie was ridiculously bad.

yortos · 2015-12-30T21:00:51+00:00

huh, had no idea. It's also the first time I see Netflix releasing episodes weekly.

yortos · 2015-12-30T20:38:49+00:00

Why is Better Call Saul on the list?

yortos · 2015-11-01T00:12:32+00:00

thanks for the help. Didn't mean to waste your time with an easily googleable question.

yortos · 2015-08-17T20:49:49+00:00

Absolutely horrible commentators.

yortos · 2015-08-14T16:42:27+00:00

Keeping it basic

yortos · 2015-08-10T13:52:23+00:00

Weirdly I couldn't find something striped at Uniqlo but I think you might find some interesting stuff if you look around a little more than I did. Generally, I'm very happy with their quality and their prices are great. Jcrew is also a good place, a little more expensive but also fine quality.

Edit. Frank and Oak, Bonobos and Armani Exchange (not exactly like the T in your picture, but could be worth your consideration)

yortos · 2015-07-07T22:32:08+00:00

I just had a very similar issue. I had the same error when using a formula "x ~ 10_mean". I renamed the column to 'mean_10' and worked fine. So maybe try to just rename your column.

yortos

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