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All items from February 2018
25 Feb 2018 : Being successful as a thief #
Ars has a great video interviewing Paul Neurath about the troubled development of Thief. I loved sneaking around in the Thief games, from the original right through Deadly Shadows and up to the latest remake. But apart from a wonderful excuse to replay the games in my head, the real message of the video is about the challenges and time pressures of development, something I'm acutely aware of right now with Pico.
"You have to make mistakes. You try things, you go down a lot of dead ends. In this case a lot of those dead ends didn't pan out. But we were learning... That was the key thing. We finally had the mental model after doggedly pursuing this for a year. Now we know what we need to do to get this done and we figured it out and got it done."
When I was young game developers were my heroes. It's good to know that such an inspirational series of games suffered failures and challenges, but still came out as the amazing games they were. We're all working towards the moments they experienced, when "it worked and it felt great."
Comment
"You have to make mistakes. You try things, you go down a lot of dead ends. In this case a lot of those dead ends didn't pan out. But we were learning... That was the key thing. We finally had the mental model after doggedly pursuing this for a year. Now we know what we need to do to get this done and we figured it out and got it done."
When I was young game developers were my heroes. It's good to know that such an inspirational series of games suffered failures and challenges, but still came out as the amazing games they were. We're all working towards the moments they experienced, when "it worked and it felt great."
12 Feb 2018 : Countdown #
I'm not convinced it was good use of my time, but I spent the weekend writing some code to solve the Countdown numbers game. In case you're not familiar with Countdown, here's a clip.
There are lots of ways to do this, but my solution hinges on being able to enumerate all binary trees with a given number of nodes. Doing this efficiently (both in terms of time and memory) turned out to be tricky, and there's a hinge for this too, based on how the trees are represented. The key is to note that each layer can't have more than n nodes, where n is the number of nodes the tree can have overall.
Each tree is stored as a list, with each item in the list representing the nodes at a given depth in the tree (a layer). Each item is a bit sequence representing which nodes in the layer have children.
These bit sequences would get long quickly if they represented every possible node in a layer (since there are 2, 4, 8, 16, 32, ... possible nodes at each layer). Instead, the index of the bit represents the index into the actual nodes in the layer, rather than the possible nodes. This greatly limits the length of the bit sequence, because there can no more than n actual nodes in each layer, and there can be no more than n layers in total. The memory requirement is therefore n2.
Here's an example:
It's really easy to cycle through all of these, because you can just enumerate each layer individually, which involves cyclying through all sequences of binary strings.
It's not a new problem, but it was a fun exercise to figure out.
The code is up on GitHub in Python if you want to play around with it yourself.
Comment
There are lots of ways to do this, but my solution hinges on being able to enumerate all binary trees with a given number of nodes. Doing this efficiently (both in terms of time and memory) turned out to be tricky, and there's a hinge for this too, based on how the trees are represented. The key is to note that each layer can't have more than n nodes, where n is the number of nodes the tree can have overall.
Each tree is stored as a list, with each item in the list representing the nodes at a given depth in the tree (a layer). Each item is a bit sequence representing which nodes in the layer have children.
These bit sequences would get long quickly if they represented every possible node in a layer (since there are 2, 4, 8, 16, 32, ... possible nodes at each layer). Instead, the index of the bit represents the index into the actual nodes in the layer, rather than the possible nodes. This greatly limits the length of the bit sequence, because there can no more than n actual nodes in each layer, and there can be no more than n layers in total. The memory requirement is therefore n2.
Here's an example:
T = [[1], [1, 1], [1, 1, 0, 0], [0 ,1, 0, 0]]which represents a tree like this:
It's really easy to cycle through all of these, because you can just enumerate each layer individually, which involves cyclying through all sequences of binary strings.
It's not a new problem, but it was a fun exercise to figure out.
The code is up on GitHub in Python if you want to play around with it yourself.