I had a lot of fun testing and watching as the game progressed and, even if I didn’t have an automatic bias, I honesty have to say that the game came out amazing. It is getting rave reviews everywhere, with the only consistent complaint being about the controls. But it’s a dual-stick shooter on a touchscreen, so the controls are destined to be at least a little annoying. The bros did push out a final patch on Android (and that will eventually make its way through Apple’s iTunes guardians) that fixes the control issues as much as possible.
Go download it on Google Play, Amazon, or iTunes and start shooting animals out of your face! It does cost a little cash ($3) but is definitely worth skipping a latte for!
The weekend of my last post (over a month ago…) my brothers and I did indeed complete a Game Jam with a crazy game called “I know CPR!”. It was a little inspired by QWOP (though it is not even almost as difficult) and otherwise followed the theme for the weekend: the sound of a beating heart. The executables (PC/Mac) are free to download, so go try it out (and see the video below)!
Hm. So it appears that, two years ago, I wrote a post on calculating the average gene length in prokaryotes. I found a half-draft of the second part and decided to finish it off.
In part 1 we defined mGenes (“maybe-genes”) as the pieces you get after breaking the genome at each stop codon, and predicted that the probability of finding an mGene of length L is given by the following equation:
By plotting this function it is clear that the probability of a set of codons being an mGene plummets quickly, so that there is nearly a 0 probility of finding an mGene of 100 codons (300 bases) in a random sequence (black line in Fig. 1). I confirmed this with a 1,000,000 base synthetic genome (all code is at the end of this entry), resulting in the red circles in Fig. 1 that perfectly overlap with the prediction line.
So now we know what to expect from a completely random genome: Nearly all mGenes will be less than 100 codons (300 bases) in length. This is much shorter than your typical gene, and so I would expect there to be a large number of mGenes larger than this size in a real genome. So let’s check it out, using a fully sequenced prokaryotic genome!
I’ve been perusing a pretty good book by Michael McRoberts called “Beginning Arduino”, and after putting together one of the first projects I decided to have fun with it and write some more interesting code than the one provided. The original scheme gave a series of three LEDs that would turn on as if they were a stop light, and then allow someone to press a button to get the light to change to red so that another LED, representing the pedestrian walk sign, could turn on. This wasn’t very interesting to me, so I made it into a reaction game instead otherwise using the same circuit. See the video immediately below, and the circuit diagram (made using Fritzing) and code below the fold.
A few days ago I found myself having a vague recollection of an interesting statistics problem. All I could remember was that it had to do with having a room full of people and the probability that any two people in that room would have the same birthday. I remembered the point, which was that it is much more likely than you might think, but I was fuzzy on the details.
After trying to define the problem and find an answer mathematically, I remembered that I suck at statistical reasoning about as much as the average person. So I decided to model the problem with a short Python script and find the answer that way.
Sure, I could’ve looked it up, but where’s the fun in that?
The problem: There are n people (say, at a party) drawn randomly from a population in which the chances of having a birthday on any day is equal to having a birthday on any other (which is not true of real populations (probably)). What is the probability of there being at least two people with the same birthday in the sample?
To put this thing together, I figure we need three things:
The ability to generate random numbers (provided by Python’s random module);
An object representing each person;
A party object full of those people.
Then we can add things like the ability to choose how many people we want at the party and how many parties to have, as well as some output for making plots!
First, the Person object. All each person needs is a birthday:
def __init__( self ):
self.birthday = random.randint( 1, 365 )
I previously showed you (without a screencast) how to make NP++ default in XP. Of course, people have successfully done this for W7/Vista as well, but the various tutorials I saw were all a huge pain in the ass. Except for one: a comment on a more-complicated method. I made the following short screencast to demonstrate that method. This one has the advantages of being easy to reverse, easy to do, and not in any way intimidating!
A contestant is shown 3 doors. Only one of those three doors hides something of value to the contestant (perhaps a new car), while the other two contain nothing. The contestant chooses one door, but that door remains closed. The host then opens up a 2nd door, and this door is always a losing door. At this point, the contestant may choose to now open the originally-chosen door, or switch to and open the last remaining door.
So why is this interesting? It turns out that the way to maximize your chances of winning is to always switch, and this maximized chance is 67%. It also turns out that this is totally non-intuitive, and that most people think that, if the contestant always switches, the chances of winning are at best 50%. If you haven’t heard the solution to this problem before, you should think through it and see what you expect the chances of winning are under the two conditions: After the contestant chooses a door, and is subsequently shown that one of the other two is a losing door,  the contestant always switches to the remaining door, or  the contestant never switches. After the jump, I’ll explain this intuitively and then show a Python script to simulate this problem.