Recently I made available some GP code I had in Common Lisp (see previous post). Today I also put on my github account the library I did for Ant Colony Optimzation (cl-aco) and a few others like parsers for MKP and QAP, but also a very basic CFFI bindings for libLBFGS. Like before, these are mostly not to get lost since I’m not using/developing them anymore. But if someone finds this useful in any way, that’s great!
Some time ago I got an email asking for the code of one of my old papers. Unfortunately the code was lost. But that reminded me that I still had some other code that could actually be made available. Essentially, some Genetic Programming (GP) libraries that I did around seven years ago (which in turn started on some other old code that I had done before).
Fast foward some weeks and it’s now available on my github account these two GP libraries:
- mini-gp: a minimalistic library that only allows simple tree-based GP
- core-gp: a larger library that does GP, STGP, GE and GAs.
The code is released as is and it’s not even re-tested. Basically it’s there not to be lost in some old external hard drive. Still, it might be useful to someone who has some interested in GP and Common Lisp. The code it’s not particulary nice in some aspects and it even contains some over-engineering parts, but it’s was fun to do it and back then was very useful to me.
PS: I still have another library for Ant Colony Optimization that I would like to put it on Github too but it still requires a little clean-up. Hopefully in the near future it’s done.
The organizers of this year’s European Lisp Symposium made available all the videos of the talks with the respective slides, as well as the PDF containing all the accepted papers.
This is really great and everyone involved in making this happening should be congratulated! For everyone that didn’t attend (like me) it’s a good opportunity to see what happened at ELS. Even though the talks, discussions and networking between participants is the most valuable thing while attending a conference, this is very nice for everyone who was not able to be there.
Once again, kudos to the organizers!
For the past months I have spent most of my time on .NET using C# but I also would have liked to use Common Lisp on it. I do not know of a CL implementation for .NET and I read some time ago that probably doing one is not that easy, as this answer in Stack Overflow states. I wonder if it is related to floating-point exceptions (if someone knowledgeable in this area would like to comment, I thank you in advance!).
Anyway, I decided to look more carefully to see if I could find something and discovered four projects. Initially I was a bit excited that probably there is something after all. But after a more careful observation, they are far from what you would expect or want. For future reference:
The first project, ClearLisp, looked promising but it’s not even CL. Looking at the documentation you realize some differences between it and CL. Also, the project shows no sign of life since 2008 and after playing with it a little bit, it’s far from a modern CL implementation. The second one is not a proper implementation since it’s using ABCL and IKVM. The third one seems to be a way to connect ECL to .NET and uses it as a scripting language. The fourth one also looks more like an attempt than a real implementation (I didn’t explore it).
In the end, there is really no CL implementation for .NET or even one that compiles to bytecode. There is RDNZL which lets you call .NET libraries from a Common Lisp application. However, it’s not the same as having a real implementation.
Furthermore, the state of Lisp-languages on .NET is not much better since you can find lots of half-baked, abandoned languages/implementations or just without real followers (for example, L#, Yarr, dotlisp, Common Larceny, ayaka and others). The major exceptions are probably IronScheme and Clojure. And even the CLR Clojure implementation is just playing catch-up with the Java one.
The set of programmers interested in CL (or even just Lisp) and .NET is probably extremely small and thus, the current state of affairs. However, I feel that this can still be a good area of opportunity if done right.
Edit 1: zvrba on reddit explains the floats issue, which I put here:
The problem with CLR floating-point is that the virtual machine supports only one floating-point type, double (64-bit). (32-bit) float is a storage format only, so in some cases double rounding occurs, leading to results that differ from “proper” float calculations by 1 ulp.
Edit 2: clausb also on reddit pointed out to a list of Lisp projects on .NET he compiled before.
Disclaimer: the views and opinions expressed on this blog are my own and not those of my employer.
There is an interesting thread in the Clozure Common Lisp development mailing list about argument evaluation in #'<. Until now, CCL was implementing shortcircuiting when evaluating the arguments of #'<. This essentially means that when calling (< 1 3 2 4 5) it would stop before evaluating all the arguments since it wold know it would be false. Just like and. However, as reported in the initial message by Eric Marsden, it violates the Hyperspec (section 126.96.36.199.2.3 Function Forms) since it does not allow this behaviour for functions. The thread got interesting because a small discussion followed if the correct behaviour should be the one shown by CCL and not the one demanded by the Hyperspec. I must say, while reading, my initial reaction was also to consider shortcircuiting as the desired functionality but soon afterwards, you realize that you really don’t want that. The simplest argument is that you don’t want (< x y z) to have a different semantic from (funcall #'< x y z) as Tim Bradshaw rightly pointed out. Consistency is something very good to have and in the end, if you wish lazy semantics, you can always add them yourself in the true Lisp spirit. However, this shows once more how the writers of the Hyperspec got so many things right even if for a moment you might think otherwise. It was an interesting thread to read.
zsort is a library that I started working on as a simple hobby project. More or less around the same time I decided to check which algorithms the different Common Lisp implementations use. It is now part of Quicklisp so it can be easily used (thanks Zack!).
The main goal of zsort is to be a collection of portable sorting algorithms. If they can be fast, even better. Common lisp provides the sort and stable-sort functions but these can have different algorithms implemented according to each implementation, which can make an application unportable if you rely on a specific type of sorting. Also, the standard functions might not be the best for a certain situation and as such you might need a specialized sort. Even if for most situations the standard functions are more than enough, the zsort library could be a useful complement.
Right now the implemented algorithms are: insertion sort, quicksort, randomized quicksort, merge sort, heapsort and counting sort. The plan is to add more algorithms, for example, bucket sort and timsort. However, the main thing on the todo list is adding the possibility of external sorting (to handle large amounts of data) and parallel versions of some sorting algorithms. I am considering using lparallel for this but I am still undecided.
There is still a lot of work to be done, but I think the library as it is can already be a little useful. And of course, all kind of suggestions and improvements are welcome!
I was reading the thread What are some fun or useful macros? on reddit and it reminded me of another thread that appeared in the Pro mailing list. These kind of threads are always enjoyable because each time you learn something new and see really interesting things. While reading them, it crossed my mind that another variant of this question would be what are some fun or useful macros design patterns. Instead of examples of specific code macros (general or not) it would be nice to see common programming practices using macros. So, for a lack of a better expression name, let’s call them macros design patterns.
My favorite one is to configure an algorithm, especially when we want to use the correct types. Essentially, we write an algorithm using a macro that takes types or configuration arguments and then we expand it to the appropriate desired configurations. For example, if you have an algorithm that operates on different types of sequences, instead of writing several duplicate functions with the same algorithm but with the associated type declarations, just apply this pattern. A simple but contrived example: we want to compute the mean of a vector but use its proper type. In addition, we might also want the possibility of using a key to access the vector elements. We can write the following macro:
(defmacro mean-body (vector vector-type vector-ref key) (let ((size (gensym)) (i (gensym))) `(locally (declare (type ,vector-type ,vector)) (let ((,size (length ,vector))) (/ (loop for ,i from 0 below ,size sum ,(if key `(funcall ,key (,vector-ref ,vector ,i)) `(,vector-ref ,vector ,i))) ,size)))))
The macro contains the algorithm (in this simple case the mean) and the arguments allow us to configure the multiple versions we need. If we want a simple-vector, the macro will expand to use the correct type declaration and svref. If a key function is needed it will also include it. Then, we can call the macro with the several configurations value inside the main function:
(defun mean (vector &optional key) (typecase vector (simple-vector (if key (mean-body vector simple-vector svref key) (mean-body vector simple-vector svref nil))) (vector (if key (mean-body vector vector aref key) (mean-body vector vector aref nil))) (otherwise (if key (mean-body vector sequence elt key) (mean-body vector sequence elt nil)))))
This can be very useful in situations where we want to optimize code since it becomes easy to add the proper type declarations to the input arguments of an algorithm. Moreover, we keep the algorithm in a single place, making it easier to maintain. Depending on the situation, we can also define a function for each configuration. In the example we could have a mean-simple-vector and mean-vector.
I don't know if it has already a specific name but I like to call it the configurable algorithm pattern. I find it very useful. And thinking back to the reddit thread, what are your favorite macros design patterns? Which ones do you find useful and use them regularly? If you want to share, feel free to drop a line. I am interested in seing and learning other patterns!