I just wanted to know if any one had any pointers for a library or libraries that support Markov modelling and graphical graph representation, as for a project i must simulate a transport model and be able to develop an interface for it too. I am relatively new to c++.
Have a look at Boost Graph as it will simplify all your graph work. I am unsure if there is a Markov Chain algiorithm (I am looking for one too) but it should be easy to write once you have the graph -- a concurrent monte carlo approach maybe?
Numerical Recipes has many algorithm and code in both C and C++.
The graphviz tools are all you need to draw graphs.
Related
We're working on a machine learning project in which we'd like to see the influence of certain online sample embedding methods on SVMs.
In the process we've tried interfacing with Pegasos and dlib as well as designing (and attempting to write) our own SVM implementation.
dlib seems promising as it allows interfacing with user written kernels.
Yet kernels don't give us the desired "online" behavior (unless that assumption is wrong).
Therefor, if you know about an SVM library which supports online embedding and custom written embedders, it would be of great help.
Just to be clear about "online".
It is crucial that the embedding process will happen online in order to avoid heavy memory usage.
We basically want to do the following within Stochastic subGradient Decent(in very general pseudo code):
w = 0 vector
for t=1:T
i = random integer from [1,n]
embed(sample_xi)
// sample_xi is sent to sub gradient loss i as a parameter
w = w - (alpha/t)*(sub_gradient(loss_i))
end
I think in your case you might want to consider the Budgeted Stochastic Gradient Descent for Large-Scale SVM Training (BSGD) [1] by Wang, Crammer, Vucetic
This is because, as specified in the paper about the "Curse of Kernelization" you might want to explore this option instead what you have indicated in the pseudocode in your question.
The Shark Machine Learning Library implements BSGD. Check a quick tutorial here
Maybe you want to use something like dlib's empirical kernel map. You can read it's documentation and particularly the example program for the gory details of what it does, but basically it lets you project a sample into the span of some basis set in a kernel feature space. There are even algorithms in dlib that iteratively build the basis set, which is maybe what you are asking about.
I am in an Intro to data structures class and we are currently going over sorting algorithms. Is there any way in C++ to use a graph to trace the exchanging and sorting of sorting algorithms? There is an image illustrating shell sort if you follow this link and scroll down a bit. I also linked it down below. (Essentially what I would like to achieve). Addison Wesley, Algorithms 4th edition: http://algs4.cs.princeton.edu/21elementary/.
They spoke about using built in functions in Java to create the graph and spoke about it as if it is rather trivial. I'm assuming if its basic in Java its basic in C++.
Question being, how would I implement a function that would correspond numerically to how big the bars are and output the graph pre-sort, mid-sort and post-sort?
Note: I have intermediate level C++ knowledge and no experience with graphics.
Consider using a graphing package such as gnuplot. You would have to use it to plot, say as bar graphs, the array values your program is trying to sort.
Markov Random Fields are a really popular way to look at an image, but I can't find a direct reference to them being implemented in OpenCV. Perhaps they are named differently, or are built from some indirect method.
As the title states, are MRFs implemented in OpenCV? And if not, what is the popular way to represent them?
OpenCV deals mostly with statistical machine learning rather than things that go under the name Bayesian Networks, Markov Random Fields, or graphical models.
I am working on a research project that deals with social networks. I have done most of the backbone of the program in C++ and am now wanting to implement a way to create the graph of nodes and the connections as well as a way to visualize the connections between people. I have looked a little into Lemon and the Boost graph library, but was wondering which one would be easier to learn and implement or if I should just code my own.
If you use the BGL then you should also be able to make use of the Graph Toolkit for Algorithms and Drawings (GTAD). The GTAD is meant to be compatible with BGL and adds a number of graph algorithms not in BGL as well as algorithms for layouts.
For visualization the BGL allows you to read and write some common graph file types such as GraphML and Dot for use with GraphViz.
Lemon looks to be a well featured library with a good array of algorithms. You can also use gLemon to view Lemon graphs. This visualizer looks quite basic though and was last updated in 2008, unlike Lemon which is still under development.
I would suggest you first work out what you want to do with any graphs you create, ie what algorithms you require (shortest-path etc) and compare the two libraries from that respect.
Also take a look at the tutorials for both. They have very good documentation and should help you decide which you'll find easier to implement.
Unless you really want to get into the details of how certain graph structures and algorithms are implemented I would use a library.
i am trying to implement discrete curve evolution algorithm in c++ do any one help me with psudo code or c code or
some simple steps of your understanding
Discrete Curve Evolution is an algorithm to compute an everywhere convex curve from one that is concave. It moves concave sections of the curve outward along their normal in discrete steps until all concavities are eliminated. It is not a genetic algorithm, the term evolution refers to 'evolving' the position of the curve over time.
Having searched on this for quite some time the best source on the internet is here:
https://cis.temple.edu/~latecki/Software/Evo.zip
This is matlab code so it's not quite what you are looking for but you have three good options:
Port it to C++ (usually not to hard with matlab as long as it doesn't use matrix prims.)
Wrap the matlab code so you can call it from C (matlab provides libraries to do this)
Compile it to an executable and call that from C (matlab also allows this)
Option 2 would require anyone that want's to run it to have a copy of the matlab dynamic library on their computer which may be undesirable. I'm guessing option 3 would require this too, but I only have experience with options 1 and 2. Porting matlab to c++ is usually not that bad; it depends on how much the code utilizes matrix primitives and matrix operations which are easy to use in matlab and hard to use in C++ (because they aren't built-in). Still, I'd recommend giving it the old college try!
If you're just looking for DCE, check out the file evolution.m. That's the function that implements DCE. The full skeleton pruning algorithm this comes from can only be described simply at a high level. The individual steps and parts are QUITE complicated and DCE is only a small piece of that.
Hope this helps! I will be working with this code myself so if I do end up using it in C++ in some way that might help you I will let you know.
I'm not exactly sure what you mean by Discrete Curve evolutionary algorithm, but if you mean a Symbolic regression algorithm, you can start by reading about symbolic regression (or genetic programming in general):
http://en.wikipedia.org/wiki/Symbolic_Regression
There's also some nice existing programs. The Eureqa one has an open API:
http://code.google.com/p/eureqa-api/