surreal | Reviving the original Unreal Tournament on modern systems | Game Engine library

I'm currently trying to solve a little problem.

I have the following code. I try to filter and re-render the fetched movielist based on the chosen genre.

So far i am able to cast the selected option to an object in my js-script but i don't know where to go from here. The genre values in my observable array is an array of its own since one movie can have multiple genres.

Here's my script so far:

I have a text file consist of words, emotion-category, and corresponded score to each emotion.

How can I set an image to ImageView?

The idea of my app is to show the different type of RecyclerView's view holder according to the data class in a RecyclerView. Data classes look like this:

I'm trying to implement Conway's surreal numbers in Scala. A surreal number is defined recursively – as a pair of sets of surreal numbers, called left and right, such that no element in the right set is less than or equal to any element in the left set. Here the relation "less than or equal to" between surreal numbers is also defined recursively: we say that x ≤ y if

• there is no element a in the left set of x such that y ≤ a, and
• there is no element b in the right set of y such that b ≤ x.

We start with defining zero as a pair of empty sets, then use zero to define 1 and -1, and so on.

I cannot figure out how to enforce the definition of a surreal number at compile time. This is what I have now:

I would like to compare two list of different size using JaroWinkler similarity. List_1 has 5 elements and it comes from a column dataframe, e.g.

In this problem I want to map a function over multiple data inputs to create output showing frequencies of item responses.

First I create two data sets using the psych package:

Have been out of Swift coding for almost a year, I'm back.

Download Xcode 10 beta. Have I lost my mind? I've tried every trick I know, none of the UI elements show up in storyboard. Zero. The sidebar is empty.

Xcode 10:

Xcode 9, no problem there they are, all my UI buttons, etc.

thanks, this is kind of surreal. :-)

I've created a DMR Topic model (via Java API) which calculates the topic distribution based on the publication-year of the documents.

The resulting distribution is a bit confusing, because there are a lot of negative propabilities. Sometimes all propabilities for a whole topic are negative values. See:

Q1: Why are there negative values? The lowest possible possibility for a topic distribution for a given feature should be at least 0,0 ... I guess?

Additional I build a LDA model where the ModelLogLikelihood seems to be surreal. I trained the model with nearly 4 million documents and 20 topics. Alpha =1.0 ; Beta = 0.01 ; # iterations 1000;

Results in Model-Log likelihood: -8.895651309362761E8

Q2: Can this value be correct? Or am I doing something wrong?