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What are fractals?
Is this is one of the concepts that is brought over from Mathematics to programming to simplify or solve a particular set of problems?
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If you want to know about fractals in a general non-programming way, I would suggest looking at a general non-programming site. Wikipedia has a good article on them. If you want to know about programming fractals, I would suggest looking at this already asked question:
How to program a fractal
It even has a fractal tag.
A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole," a property called self-similarity. The term was coined by Benoît Mandelbrot in 1975 and was derived from the Latin fractus meaning "broken" or "fractured." A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion.
A fractal often has the following features:
It has a fine structure at arbitrarily small scales.
It is too irregular to be easily described in traditional Euclidean geometric language.
It is self-similar (at least approximately or stochastically).
It has a Hausdorff dimension which is greater than its topological dimension (although this requirement is not met by space-filling curves such as the Hilbert curve).
It has a simple and recursive definition.
http://en.wikipedia.org/wiki/Fractal
its a type of self-similar shape, often grounded in a repeated mathematical function (but not necessarily). It has nothing to do with programming technique, but the easiest way to view one is to write a program to draw it. (drawing a fractal with pen-and-paper is pretty time-consuming)
By 'self-similar' i mean, if you keep zooming in on different parts of the fractal, it doesn't get any "smoother" or more linear, as would happen with a non-fractal shape. It's degree of complexity is invariant of the zoom level.
the Wikipedia page is pretty useful
Look up Procedural Generation for one way of how fractals are used in programming. They are an excellent way of generating chaotic/seemingly complex data from a very simple source. The generated data often benefits from self-similarity and other bits of organzation that make the content make more sense to people.
Related
I wrote a basic O(n^2) algorithm for a nearest neighbor search. As usual Matlab 2013a's knnsearch(..) method works a lot faster.
Can someone tell me what kind of optimization they used in their implementation?
I am okay with reading any documentation or paper that you may point me to.
PS: I understand the documentation on the site mentions the paper on kd trees as a reference. But as far as I understand kd trees are the default option when column number is less than 10. Mine is 21. Correct me if I'm wrong about it.
The biggest optimization MathWorks have made in implementing nearest-neighbors search is that all the hard stuff is implemented in a MEX file, as compiled C, rather than MATLAB.
With an algorithm such as kNN that (in my limited understanding) is quite recursive and difficult to vectorize, that's likely to give such an improvement that the O() analysis will only be relevant at pretty high n.
In more detail, under the hood the knnsearch command uses createns to create a NeighborSearcher object. By default, when X has less than 10 columns, this will be a KDTreeSearcher object, and when X has more than 10 columns it will be an ExhaustiveSearcher object (both KDTreeSearcher and ExhaustiveSearcher are subclasses of NeighborSearcher).
All objects of class NeighbourSearcher have a method knnsearch (which you would rarely call directly, using instead the convenience command knnsearch rather than this method). The knnsearch method of KDTreeSearcher calls straight out to a MEX file for all the hard work. This lives in matlabroot\toolbox\stats\stats\#KDTreeSearcher\private\knnsearchmex.mexw64.
As far as I know, this MEX file performs pretty much the algorithm described in the paper by Friedman, Bentely, and Finkel referenced in the documentation page, with no structural changes. As the title of the paper suggests, this algorithm is O(log(n)) rather than O(n^2). Unfortunately, the contents of the MEX file are not available for inspection to confirm that.
The code builds a KD-tree space-partitioning structure to speed up nearest neighbor search, think of it like building indexes commonly used in RDBMS to speed up lookup operations.
In addition to nearest neighbor(s) searches, this structure also speeds up range-searches, which finds all points that are within a distance r from a query point.
As pointed by #SamRoberts, the core of the code is implemented in C/C++ as a MEX-function.
Note that knnsearch chooses to build a KD-tree only under certain conditions, and falls back to an exhaustive search otherwise (by naively searching all points for the nearest one).
Keep in mind that in cases of very high-dimensional data (and few instances), the algorithm degenerates and is no better than an exhaustive search. In general as you go with dimensions d>30, the cost of searching KD-trees will increase to searching almost all the points, and could even become worse than a brute force search due to the overhead involved in building the tree.
There are other variations to the algorithm that deals with high dimensions such as the ball trees which partitions the data in a series of nesting hyper-spheres (as opposed to partitioning the data along Cartesian axes like KD-trees). Unfortunately those are not implemented in the official Statistics toolbox. If you are interested, here is a paper which presents a survey of available kNN algorithms.
(The above is an illustration of searching a kd-tree partitioned 2d space, borrowed from the docs)
I need to program an algorithm to navigate a robot through a "maze" (a rectangular grid with a starting point, a goal, empty spaces and uncrossable spaces or "walls"). It can move in any cardinal direction (N, NW, W, SW, S, SE, E, NE) with constant cost per move.
The problem is that the robot doesn't "know" the layout of the map. It can only view it's 8 surrounding spaces and store them (it memorizes the surrounding tiles of every space it visits). The only other input is the cardinal direction in which the goal is on every move.
Is there any researched algorithm that I could implement to solve this problem? The typical ones like Dijkstra's or A* aren't trivialy adapted to the task, as I can't go back to revisit previous nodes in the graph without cost (retracing the steps of the robot to go to a better path would cost the moves again), and can't think of a way to make a reasonable heuristic for A*.
I probably could come up with something reasonable, but I just wanted to know if this was an already solved problem, and I need not reinvent the wheel :P
Thanks for any tips!
The problem isn't solved, but like with many planning problems, there is a large amount of research already available.
Most of the work in this area is based on the original work of R. E. Korf in the paper "Real-time heuristic search". That paper seems to be paywalled, but the preliminary results from the paper, along with a discussion of the Real-Time A* algorithm are still available.
The best recent publications on discrete planning with hidden state (path-finding with partial knowledge of the graph) are by Sven Koenig. This includes the significant work on the Learning Real-Time A* algorithm.
Koenig's work also includes some demonstrations of a range of algorithms on theoretical experiments that are far more challenging that anything that would be likely to occur in a simulation. See in particular "Easy and Hard Testbeds for Real-Time Search Algorithms" by Koenig and Simmons.
I find genetic algorithm simulations like this to be incredibly entrancing and I think it'd be fun to make my own. But the problem with most simulations like this is that they're usually just hill climbing to a predictable ideal result that could have been crafted with human guidance pretty easily. An interesting simulation would have countless different solutions that would be significantly different from each other and surprising to the human observing them.
So how would I go about trying to create something like that? Is it even reasonable to expect to achieve what I'm describing? Are there any "standard" simulations (in the sense that the game of life is sort of standardized) that I could draw inspiration from?
Depends on what you mean by interesting. That's a pretty subjective term. I once programmed a graph analyzer for fun. The program would first let you plot any f(x) of your choice and set the bounds. The second step was creating a tree holding the most common binary operators (+-*/) in a random generated function of x. The program would create a pool of such random functions, test how well they fit to the original curve in question, then crossbreed and mutate some of the functions in the pool.
The results were quite cool. A totally weird function would often be a pretty good approximation to the query function. Perhaps not the most useful program, but fun nonetheless.
Well, for starters that genetic algorithm is not doing hill-climbing, otherwise it would get stuck at the first local maxima/minima.
Also, how can you say it doesn't produce surprising results? Look at this vehicle here for example produced around generation 7 for one of the runs I tried. It's a very old model of a bicycle. How can you say that's not a surprising result when it took humans millennia to come up with the same model?
To get interesting emergent behavior (that is unpredictable yet useful) it is probably necessary to give the genetic algorithm an interesting task to learn and not just a simple optimisation problem.
For instance, the Car Builder that you referred to (although quite nice in itself) is just using a fixed road as the fitness function. This makes it easy for the genetic algorithm to find an optimal solution, however if the road would change slightly, that optimal solution may not work anymore because the fitness of a solution may have grown dependent on trivially small details in the landscape and not be robust to changes to it. In real, cars did not evolve on one fixed test road either but on many different roads and terrains. Using an ever changing road as the (dynamic) fitness function, generated by random factors but within certain realistic boundaries for slopes etc. would be a more realistic and useful fitness function.
I think EvoLisa is a GA that produces interesting results. In one sense, the output is predictable, as you are trying to match a known image. On the other hand, the details of the output are pretty cool.
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A professor asked me to help making a specification for a college project.
By the time the students should know the basics of programming.
The professor is a mathematician and has little experience in other programming languages, so it should really be in MATLAB.
I would like some projects ideas. The project should
last about 1 to 2 months
be done individually
have web interface would be great
doesn't necessary have to go deep in maths, but some would be great
use a database (or store data in files)
What kind of project would make the students excited?
If you have any other tips I'll appreciate.
UPDATE: The students are sophomores and have already studied vector calculus. This project is for an one year Discrete Mathematics course.
UPDATE 2: The topics covered in the course are
Formal Logic
Proofs, Recursion, and Analysis of Algorithms
Sets and Combinatorics
Relations, Functions, and Matrices
Graphs and Trees
Graph Algorithms
Boolean Algebra and Computer Logic
Modeling Arithmetic, Computation, and Languages
And it'll be based on this book Mathematical Structures for Computer Science: A Modern Approach to Discrete Mathematics by Judith L. Gersting
General Suggestions:
There are many teaching resources at The MathWorks that may give you some ideas for course projects. Some sample links:
The MATLAB Central blogs, specifically some posts by Loren that include using LEGO Mindstorms in teaching and a webinar about MATLAB for teaching (note: you will have to sign up to see the webinar)
The Curriculum Exchange: a repository of course materials
Teaching with MATLAB and Simulink: a number of other links you may find useful
Specific Suggestions:
One of my grad school projects in non-linear dynamics that I found interesting dealt with Lorenz oscillators. A Lorenz oscillator is a non-linear system of three variables that can exhibit chaotic behavior. Such a system would provide an opportunity to introduce the students to numerical computation (iterative methods for simulating systems of differential equations, stability and convergence, etc.).
The most interesting thing about this project was that we were using Lorenz oscillators to encode and decode signals. This "encrypted communication" aspect was really cool, and was based on the following journal article:
Kevin M. Cuomo and Alan V. Oppenheim,
Circuit Implementation of Synchronized Chaos with Applications
to Communications, Physical Review
Letters 71(1), 65-68 (1993)
The article addresses hardware implementations of a chaotic communication system, but the equivalent software implementation should be simple enough to derive (and much easier for the students to implement!).
Some other useful aspects of such a project:
The behavior of the system can be visualized in 2-D and 3-D plots, thus exposing the students to a number of graphing utilities in MATLAB (PLOT, PLOT3, COMET, COMET3, etc.).
Audio signals can be read from files, encrypted using the Lorenz equations, written out to a new file, and then decrypted once again. You could even have the students each encrypt a signal with their Lorenz oscillator code and give it to another student to decrypt. This would introduce them to various file operations (FREAD, FWRITE, SAVE, LOAD, etc.), and you could even introduce them to working with audio data file formats.
You can introduce the students to the use of the PUBLISH command in MATLAB, which allows you to format M-files and publish them to various output types (like HTML or Word documents). This will teach them techniques for making useful help documentation for their MATLAB code.
I have found that implementing and visualizing Dynamical systems is great
for giving an introduction to programming and to an interesting branch of
applied mathematics. Because one can see the 'life' in these systems,
our students really enjoy this practical module.
We usually start off by visualizing a 1D attractor, so that we can
overlay the evolution rule/rate of change with the current state of
the system. That way you can teach computational aspects (integrating the system) and
visualization, and the separation of both in implementation (on a simple level, refreshing
graphics at every n-th computation step, but in C++ leading to threads, unsure about MATLAB capabilities here).
Next we add noise, and then add a sigmoidal nonlinearity to the linear attractor. We combine this extension with an introduction to version control (we use a sandbox SVN repository for this): The
students first have to create branches, modify the evolution rule and then merge
it back into HEAD.
When going 2D you can simply start with a rotation and modify it to become a Hopf oscillator, and visualize either by morphing a grid over time or by going 3D when starting with a distinct point. You can also visualize the bifurcation diagram in 3D. So you again combine generic MATLAB skills like 3D plotting with the maths.
To link in other topics, browse around in wikipedia: you can bring in hunter/predator models, chaotic systems, physical systems, etc.etc.
We usually do not teach object-oriented-programming from within MATLAB, although it is possible and you can easily make up your own use cases in the dynamical systems setting.
When introducing inheritance, we will already have moved on to C++, and I'm again unaware of MATLAB's capabilities here.
Coming back to your five points:
Duration is easily adjusted, because the simple 1D attractor can be
done quickly and from then on, extensions are ample and modular.
We assign this as an individual task, but allow and encourage discussion among students.
About the web interface I'm at a loss: what exactly do you have in mind, why is it
important, what would it add to the assignment, how does it relate to learning MATLAB.
I would recommend dropping this.
Complexity: A simple attractor is easily understood, but the sky's the limit :)
Using a database really is a lot different from config files. As to the first, there
is a database toolbox for accessing databases from MATLAB. Few institutes have the license though, and apart from that: this IMHO does not belong into such a course. I suggest introducing to the concept of config files, e.g. for the location and strength of the attractor, and later for the system's respective properties.
All this said, I would at least also tell your professor (and your students!) that Python is rising up against MATLAB. We are in the progress of going Python with our tutorials, but I understand if someone wants to stick with what's familiar.
Also, we actually need the scientific content later on, so the usefulness for you will probably depend on which department your course will be related to.
A lot of things are possible.
The first example that comes in mind is to model a public transportation network (the network of your city, with underground, buses, tramways, ...). It is represented by a weighted directed graph (you can use sparse matrix to represent it, for example).
You may, for example, ask them to compute the shortest path from one station to another one (Moore-dijkistra algorithm, for example) and display it.
So, for the students, the several steps to do are:
choose an appropriate representation for the network (it could be some objects to represent the properties of the stations and the lines, and a sparse matrix for the network)
load all the data (you can provide them the data in an XML file)
be able to draw the network (since you will put the coordinates of the stations)
calculate the shortest path from one point to another and display it in a pretty way
create a fronted (with GUI)
Of course, this could be complicated by adding connection times (when you change from one line to another), asking for several options (shortest path with minimum connections, take in considerations the time you loose by waiting for a train/bus, ...)
The level of details will depend on the level of the students and the time they could spend on it (it could be very simple, or very realist)
You want to do a project with a web interface and a database, but not any serious math... and you're doing it in MATLAB? Do you understand that MATLAB is especially designed to be used for "deep math", and not for web interfaces or databases?
I think if this is an intro to a Discrete Mathematics course, you should probably do something involving Discrete Mathematics, and not waste the students' time as they learn a bunch of things in that language that they'll never actually use.
Why not do something involving audio? I did an undergraduate project in which we used MATLAB to automatically beat-match different tunes and DJ mix between them. The full program took all semester, but you could do a subset of it. wavread() and the like are built in and easy to use.
Or do some simple image processing like finding Waldo using cross-correlation.
Maybe do something involving cryptography, have them crack a simple encryption scheme and feel like hackers.
MATLAB started life as a MATrix LAB, so maybe concentrating on problems in linear algebra would be a natural fit.
Discrete math problems using matricies include:
Spanning trees and shortest paths
The marriage problem (bipartite graphs)
Matching algorithms
Maximal flow in a network
The transportation problem
See Gil Strang's "Intro to Applied Math" or Knuth's "Concrete Math" for ideas.
You might look here: http://www.mathworks.com/academia/student_center/tutorials/launchpad.html
on the MathWorks website. The interactive tutorial (second link) is quite popular.
--Loren
I always thought the one I was assigned in grad school was a good choice-a magnetic lens simulator. The math isn't completely overwhelming so you can focus more on learning the language, and it's a good intro to the graphical capabilities (e.g., animating the path of an off-axis electron going through the lens).
db I/O and fancy interfaces are out of place in a discrete math course.
my matlab labs were typically algorithm implementations, with charts as output, and simple file input.
how hard is the material? image processing is really easy in matlab, can you do some discrete 2D filtering? blurs and stuff. http://homepages.inf.ed.ac.uk/rbf/HIPR2/filtops.htm
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Would learning to program fractals help think clearly about certain set of programming problems?
Fractal programming would introduce you to recursion, iteration, graphics programming concepts, image processing, and user interface design. From a mathematics point of view, you would learn about geometry, complex numbers, Mobius transformations (Kleinian fractals), Affine transformation (IFS fractals), root-finding methods (Newton fractals).
And on top of all this, you get the reward of seeing your efforts result in strange and unusual images.
From what I remember you'll get a good handle on recursion if nothing else...maybe a little bitmap level programming as well...
If you are a beginner such activity will surely help you to improve your skills. Apart from that programming fractal visualizations depending on the fractal type and the goal you set may give you some specific skills or knowledge like:
working with graphics, image processing;
understanding recursion and recursive
structures;
optimization techniques;
low-level program optimizations;
understanding how computer operates
(e.g. why resolution would be
normally limited -> floating point
precision and error accumulation);
parallel programming;
some mathematical improvement and
extend your range of interest;
understanding various technologies
(e.g. you can code Mandelbrot
set in PixelBender which is
really fast since may be executed on
GPU);
understanding complex compression
algorithms (e.g. some kind of fractal
compression);
creativity (e.g. you invent your own
fractal set coloring algorithm);
much more else :)
It is indeed a versatile and interesting field, lots of things to explore and learn. I used to draw fractals a lot :)
Fractals got me thinking about complex numbers and branch-points. Whether that was a good thing is, I suppose, a matter of opinion. :-)
Any kind of programming experience is useful. So yes it is.
Especially for:
math problems
basic algorithms
and of course fractal programming
It'll maybe give you practice in implementing mathematical formulae.
Some fractals are good visual examples for explicit recursion; if you have a hard time with that concept, they might be good problems to work. You can start with "turtle graphics" style fractal paths like the Hilbert curve, or the classic "snowflake" fractal.
Many fractal-generation methods use heavy-duty number crunching (e.g., Mandelbrot and Julia sets). Number crunching is of course a field in itself, and tweaking your fractal generator to run as fast as possible can be a nice exercise in optimization.
I don't think programming fractals will teach you anything in particular. Depending on the fractal, I suppose it might teach you a bit about math or fractals in general.
However, I do think fractals are fun as an introduction to programming, and beginners/students are often fascinated by the result, be it more graphic fractals like mandelbrot or julia sets, or more easy to understand L-systems.
Of course, if you're new to programming, it'll also hopefully teach you a lot about programming in general. If nothing else, fractals are interesting to look at.
when I was an undergrad, we used fractal drawing to power our work in parellel processing. It gets fairly computationally intensive quickly, so having multiple CPUs available to do the work lets you see a visible increase in efficiency.
So, along with recursion, I'd say it helps with learning how to balance CPU load across parallel processors.
... or if the equipment isn't available, it probably teaches you Zen-like patience. :)
Great idea! I think coding up fractals makes a great "etude" (study) sized program. It has some nice features this way: generally you won't require much 3rd party code, they can be implemented in a reasonably short amount of time (and complexity) and you get something nice too look at in the end which also verifies your work.
Also there are loads of basic issues in both mathematics and the implementation of numerical algorithms that you will bump into if you do this.
From something as simple as a basic Mandelbrot set generator you can branch out into all sorts of issues as commenters have mentioned. Even sticking with just that, though, you can learn about optimization techniques (why is my generator so slow) and numerical issues (why can't I zoom past here), but also if you want to a bit of color theory (what's Lab* space anyway) and other bits and pieces.
Have fun!
Fractals is a very intellectually interesting topic and well even the simplest implementation will make you learn about complex number maths,graphics generation,scaling images, and general programming.