code (5)


Random Web Radio Station

To pull a random radio station from my stream list, using vlc, jshon and curl on the terminal, enter the following:

cvlc $(curl -s http://johnvidler.co.uk/radio/data/streams.js | jshon -e $(shuf -i 0-`curl -s http://johnvidler.co.uk/radio/data/streams.js | tr -cd ‘{‘ | wc -c` -n 1) -e stream -u)

It’s not terribly efficient, but what the heck, it’s only ran once per radio station 🙂




uLaunch

Having had my feet wet a little with Android development, I decided to have a go at a bigger project – hence; uLaunch.

uLaunch is a very basic home screen replacement, with the intent to design for usability rather than fancy graphics (Oh, and I suck as an artist!).

Anyway – progress on Rev.0 continues thus:




Android ‘R’ Class

Just putting this up here in case anyone else falls into the trap I just did.

If you’re writing an Android application, and add a new Activity, you might want to refer to a generated class (from XML) using the ‘R’ class.

In Eclipse, it automagicly imports “android.R” as soon as you use it to attempt to access R.layout for a setContentView( R.layout.something ) call – this then breaks the class!

This is because the class is included as part of your project, but can be accessed via static calls from the Android API, causing a fun name conflict.

Simple workaround – remove the import line! Then your local copy will be used over the static methods of the class described in the API, fixing the issue.




GeneTest

Written for part of my university coursework, so vaguely well structured for once!
It can be downloaded here: GA.tar.gz
What follows is taken directly from the included readme.txt.

This program attempts to solve the Travelling Salesman problem using
a Genetic Algorithm approach.

The basic sequence of events is as follows (omitting special cases and
display code of any kind);

generation = new RandomGeneration( size );
   
    iteration = 0;
    while( iteration < maxGenerations ){
   
        //Breed
        Perform ERO on quads from top half of ‘generation’.
       
        //Mutate
        Randomly select ‘n’ children{
            For each child{
                Randomly swap city positions in its genome ‘X’ times.
            }
        }
       
        //Sort the generation by path length (shortest first)
        generation.sort();
       
        if( generation.best not changed in X generations )
            exit(done)
   
        iteration++;
    }

If the ‘–attempt_autocorrection’ (or -ac) flag is set upon starting the
program, the following sequence is executed immediately following the
mutate stage;

//Child Mutate
    Randomly select ‘n’ children{
        For each child{
            Randomly swap city positions in its genome ‘X’ times.
        }
    }

In this implementation, genes are stored as array offsets, allowing very
fast manipulation of data without extensive memory access.
Additionally, this allows modifications to the agents to be made without
details on the search space, allowing very generic representations of
agents.

Edge Recombination is used to breed new agent sequences from a
pair of parents. This has the advantage of only creating offspring
which are very similar to their parents, keeping ‘good’ sequences in the
gene pool and allowing them to spread to other agents genome in the next
breeding cycle.

The Edge Recombination Operator (ERO) is defined thus;

For each parent{
        Generate adjacency matrix for their genome.
    }
    Combine the matrices to one, removing any duplicated entries.
   
    Start from a parent‘s first point.
    Remove all instances of the current point from the matrix
   
    While( Matrix not empty ){
        if( Points exist on this row )
            Select next point from matrix.
        else
            Choose a random point on the matrix.
       
        Remove all instances of the current point from the matrix
    }
   
    Return new child( from points );

Selection is simple, the entire generation is ordered by how close to
zero they are, and the bottom half are removed. The top half then breed
with their ‘rightmost’ neighbours, stepping twice for each breed
( 1&2&3&4, 3&4&5&6, 4&5&6&7, etc. ) and populate the generation with
pairs of children.

Once they have bred, the breeders then move into the next generation
themselves, such that they can persist unless better candidates are
found.

The algorithm will exit if the best solution has not changed in a set
number of generations, as determined by the –stagnate (or -st)
parameter.

While this is not a brilliant metric for completion, it works most of
the time as the GA tends to plateu after a while as the probability of
it finding a better solution reduces the closer it gets to the perfect
route.

BUILDING:

With javac and javadoc on your PATH, simply calling;

make

which will generate both the binaries and the java documentation in
./bin and ./docs respectively.

To perform a clean build;

make clean && make

Logs of example runs can be found in ./logs, and are stored such
that the short parameter names follow their values; ie.

150 agents, 25 cities = 150a25c.txt
300 agents, 100 cities = 300a25c.txt

Logs are in CSV with the column order of:

Generation
Best Length
Worst Length
Average Length
SteveScore Best
SteveScore Worst
SteveScore Average

USAGE: java GeneTest
            [–width=X | -w=X]
            [–height=X | -h=X]
            [–agents=X | -a=X]
            [–cities=X | -c=X]
            [–mutate=X | -m=X]
            [–mutate_factor=X | -f=X]
            [–simple | -s]
            [–stagnate=X | -st=X]
            [–noreturn | -nr]
            [–stevescore | -ss]
            [–show_worst | -b]
            [–seed=X | -se=X]
            [–manual | -e]
            [–log=FILE | -l=FILE]
            [–attempt_autocorrection | -ac]
            [–help]
           
    –width=X | -w=X
        Sets the width of the search space to X.

    –height=X | -h=X
        Sets the height of the search space to X.

    –agents=X | -a=X
        Sets the number of agents in each generation to X.

    –cities=X | -c=X
        Sets the number of cities to pathfind through.

    –mutate=X | -m=X
        Sets the number of agents to run mutations on.

    –mutate_factor=X | -f=X
        Sets the number of mutations to occur in a mutated agent.

    –stagnate=X | -st=X
        Will halt the GA after no better route is found after X generations.

    –noreturn | -nr
        Performs calculations without a return path – gives open-ended routes.

    –stevescore | -ss
        Displays the scores in ‘steve’ format, for benchmarking purposes.

    –simple | -s
        Uses simple random mutation rather than edge recombination breeding.

    –show_worst | -b
        Displays the worst path in red on the search space window.

    –seed | -se=X
        Sets the random seed for city position generation.

    –manual | -e
        Allows you to tweak the best route produced for each generation.
        ‘swap [a] [b]’ swaps two points in the tour array, and
        ‘skip [generations]’ skips edits until ‘n’ generations have passed.

    –attempt_autocorrection | -ac
        Attempts to reduce the number of small, crossed over routes by
        an additional form of mutation. This takes the form of selectively swapping
        the points in edges with very short lengths.
        This uses the mutate factor to determine where to start reassembling
        edges in the generation (distance from best).

    –log | -l
        Sets the log file and starts logging to that file

    –help
        Prints this help.




OS Development

I’m working on a hobby OS to keep my C skills sharp, I’ll document stuff about it here when I remember

Progress

So far, I have a bootable kernel, with ISRs, IRQs, working, no memory manager yet, and no multitasking!

Grub on my bootable ISO

Grub on my bootable ISO