Hoping this is a dumb question and not a language limitation - but does anyone know how I can use trig functions in ksqldb queries? I need to calculate bearing between latitudes and longitudes, which doesn't seem to have a standard implementation. I can write the func myself no problem, but it requires sin, cos, and atan2, which I can't for the life of me find anywhere in the reference or defined keywords. Anyone know any tricks to handle that?
As of ksqldb 0.23, there are no trig functions available. (An entire list of functions is here: https://docs.ksqldb.io/en/latest/developer-guide/ksqldb-reference/scalar-functions/.
In terms of adding support, there are a few options. If you are managing ksqldb yourself, you can add UDFs by following this guide: https://docs.ksqldb.io/en/latest/how-to-guides/create-a-user-defined-function/.
If you are interested in contributing trig functions, let me know and we can work through that on GitHub!
Related
I have a Gurobi licence and I am after a good MILP/LP modelling language, which should be
free/open source
intuitive, i.e. something that looks like (taken from MiniZinc)
var int: x;
constraint x >= 0.5;
solve minimize x;
fast: the time to build the model and send it to Gurobi should be of similar order to the best ones (AMPL GAMS etc.)
flexible/powerful (ability to deal with 3D+ arrays, activate/deactivate constraints easily, provide initial solutions to the solver, etc.)
Of course, and correct me if I'm wrong, AMPL GAMS fail at 1), Python and R fail at 2) (and perhaps at 3)?).
How about GLPK, Minizinc, ZIMPL etc.? They satisfy 1) and 2) but what about 3) and 4)? Are they as good as AMPL in this regard? If not, is there a modelling language satisfying 1-4?
I've used AMPL with Gurobi for mid-sized MIPs (~ 100k-1m variables?) and MiniZinc, mostly with Gecode, for smaller combinatorial problems. I've seen some Gurobi work done with R and Python, but haven't used it that way myself.
I'm less familiar with the other options. My understanding is that GAMS is quite similar to AMPL and much of what I have to say about AMPL may also be valid for GAMS, but I can't vouch for it.
Of course, and correct me if I'm wrong, AMPL GAMS fail at 1),
Yes, generally. There is an exception which probably isn't helpful for your specific requirements but might be useful to others: you can get free use of AMPL, Gurobi, and many other optimisation products, by using the NEOS web service. This is restricted to academic non-commercial purposes and you have to grant NEOS certain rights in relation to the problems you send them; definitely read those terms of service before using it. It also requires waiting for an available server, so if speed is a high priority this probably isn't the solution for you.
Python and R fail at 2) (and perhaps at 3)?).
In my limited experience, yes for (2). AMPL, GAMS, and MiniZinc are designed specifically for defining optimisation problems, so it's unsurprising that their syntax is more user-friendly for that purpose than languages like Python and R.
The flip-side to this is that if you want to do just about anything other than defining an optimisation problem with these languages, Python/R/etc. will probably be better for that purpose.
On speed: for the problems I usually work with, AMPL takes maybe a couple of seconds to build and presolve a MIP model which takes Gurobi a couple of minutes to solve. Obviously this is going to vary somewhat with hardware and details of the problem, but in general I would expect build time to be small compared to solve time for any of the solutions under discussion. Even with a good solver like Gurobi, big MIPs are hard. Many of the serious optimisation programmers I've met do use Python, so I presume the performance side is good enough.
However, that doesn't mean the choice of language/platform is irrelevant to speed. One of the nice features of AMPL (and also GAMS) is presolve, which attempts to reduce the problem size before sending it to the solver. My standard problems have a lot of redundant variables and constraints; AMPL identifies and eliminates many of these, reducing the problem size by about 80% and giving a noticeable improvement in solver time (as compared to runs where I switch off presolve, which I sometimes do for debugging-related reasons). This might be a consideration if you expect a lot of redundancy.
flexible/powerful (ability to deal with 3D+ arrays, activate/deactivate constraints easily, provide initial solutions to the solver, etc.)
MiniZinc handles up to 6D arrays, which may or may not be enough depending on your applications.
It's more flexible than AMPL in some areas and less so in others. AMPL has a lot of set-based functionality that I find useful (e.g. I can define a variable whose index set is something like "pairs of non-identical cities separated by no more than 500 km") and MiniZinc doesn't have this. OTOH, MiniZinc seems to be better than AMPL for solver-hopping, e.g. if I write a MZ model with a combinatorial constraint like "alldifferent" but then try to run it on a solver that doesn't recognise such constraints, MZ will translate it into something the solver can deal with.
I haven't tried deactivating constraints in MZ other than by commenting them out, so I can't help there, and similarly on providing initial solutions.
Overall, MiniZinc is a good choice to consider. Some pluses and minuses relative to AMPL ("free" being a big plus!) but it fills a similar niche.
IMHO, there is no such system if you consider the Python interfaces/modeling environments to SCIP or Gurobi too complicated:
x = model.addVar()
y = model.addVar(vtype="INTEGER")
model.setObjective(x + y)
model.addCons(2*x - y*y >= 0)
model.optimize()
To me this looks quite natural and straight forward. The immense benefit of using an actual programming language instead of modeling language is that you can do anything in there, while there will always be boundaries in the latter.
If you are a looking for a modeling GUI, you should check out LITIC. It can be used almost entirely with drag-and-drop operations: https://litic.com/showcase.html
I've used a lot of the options mentioned, and some not yet mentioned
GAMS
GAMS' Python API
GAMS' MATLAB API
AMPL
FICO Xpress Mosel
FICO Xpress Model's Python API
IBM ILOG OPL
Gurobi's Python API
PuLP (Python)
Pyomo (Python)
Python-MIP
JuMP (Julia)
MATLAB Optimization Toolbox
Google OR-Tools
Based on your requirements, I'd suggest trying Python-MIP, PuLP or JuMP. They are free and have easy syntax with no limit on array dimensionality.
Take a look at Google or-tools. I’m not sure if getting initial solution to the solver is available in all of its interfaces, but if you use it in python, it should probably satisfy all 1-4.
Does webassembly have support for trig functions? I mean like built in support because it seems we have to import those from javascript. It would be great if we had things like:
f32.sin
f32.cos
If it makes any sense. If they dont exist I assume its because the implementation is very system dependent.
The problem:
Imagine we had some really complex and computationally expensive formula which involves these math functions. I would like to compute everything in webassembly without relying on imports or subdividing my code where one part is run in webassembly and the other in javascript.
Apart from that I do believe semantically things look neater if trig functions were built in.
Trigonometric functions aren't available in WebAssembly though it's been discussed before 1, 2, 3. In general, WebAssembly provides opcodes for things that exist efficiently as CPU instructions, and trigonometric functions just don't have efficient CPU versions nowadays (even the x86 sin / cos is slow).
Further, we don't want to mandate specific precision bounds at this point in time. It's an art-form to specify trigonometric functions with the right precision and there hasn't been strong interest thus far.
In the future, we'd like to see code sharing of something like libm.wasm, which would remove the code download burden.
An argument could be made that implementations could be more efficient if one had ISA information, but we'd need someone championing that usecase with data to standardize it. I expect the first such case to be fast sqrt and inverse sqrt approximations, which are often used in games and do have good ISA support. I haven't heard of real performance-sensitive trigonometric uses otherwise in the context of WebAssembly. Not that I don't think they'd exist: it's a simple question of priorities for the Community Group.
I know, I can define new functions with the help of the primitives that the language provides me, but I want to know if the built-in functions are also defined by the same primitives or there is something else that powers these functions?
Often, yes. In many cases, the libraries provided are implementations of the language's given primitives, and someone has made various functions out of them to make your life easier.
I have to code in APL. Since the code is going to be maintained for a long time, I am wondering if there are some papers/books which contain heuristics/tips/samples to help in designing clean and readable APL programs.
It is a different experience than coding in other programming language. Making a function, for example. Small will not help: such a function can contain one line of code, which is completely incomprehensible.
First, welcome to the wonderful world of APL.
Writing readable and maintainable APL code is not much different than writing readable and maintainable code in any language. Any good book on writing clean code is as applicable to APL as any other language, perhaps even more so. I recommend Clean Code by Robert C. Martin.
Consider the guideline in this book that all code in a function should be at the same level of abstraction. This applies to APL 100 times over. For example, if you have a function named DoThisBigTask it should have very few APL primitive symbols in it, and certainly no long complex one-liners. It should just be series of calls to other, lower level functions. If these higher-level functions are all well-named and well-defined, the general drift should be easily determined by someone who does not even know APL. The lowest level functions will be nothing but primitives and will be inscrutable to the non-APLer. Depending on how they are written they may even initially appear inscrutable to a seasoned APLer. However, these low level functions should be short, have no side effects, and can easily be re-written rather than modified if the maintaining programmer is unable to understand the original coding technique.
In general, keep your functions short, well-named, well-defined, and to the point. And keep the lines of code even shorter. It is much more important to have well-defined and well-documented functions than it is to have well-written or well document lines of code.
Since you asked for books and other references, I can suggest:
APL2 in Depth by Norman D. Thomson and Raymond P. Polivka. I worked with Ray Polivka for years and he was one of the best APL teachers I
have ever known.
The classic A. P. L.: An Interactive Approach by
Leonard Gilman and Allen J. Rose is good for the core language, but
is rather outdated and doesn't contain much that is truly relevant on
readability.
APL 2 at a Glance by James A. Brown and Sandra Pakin serves in some ways as an update to Gilman and Rose. It covers nested operations and other updates to APL, but has not much specifically directed at readability. Still, if you follow the examples here you will be writing readable code.
APL is Easy by STSC and Jerry R. Turner is an intro directed specifically at the APL*Plus line. Again, not much specifically on readability, but the models are generally well-designed readable code.
Mastering Dyalog APL: A Complete Introduction to Dyalog APL by Bernard Legrand is quite good if you are specifically workign in Dyalog APL, not so much if you are working in one of the other versions such as APL*Plus (from APL2000)
It is my view that the reputation of APL as a "write-only language" is much overstated. One does need to get used to the primitives and the symbols used to represent them. But then one needs to get used to the syntax and the various library functions in many other language environments. I have seen convoluted code in C, C++, and Java as hard to follow as any APL. Of course, it isn't good C, C++, or Java, even if it is clever.
Some advice:
Writing 'one-liners' is a way to test one's mastery of the language,
but is very poor practice for production code.
Comment to make the algorithm and especially the data structure being used clear. As with any code, comments should add something
that cannot be easily read from the code itself, or call attention to
complex or obscure code.
If possible avoid obscure code so there is no need to explain it. It is usually possible.
Make each function do one and only one job, with a clear interface.
Avoid global variables for the most part, and document any that are needed.
Document the interface, purpose, and efect of any function at the
top. Make utilities black boxes without side-effects if possible. If
side-effects are essential, document those as part of the interface.
Develop a standard header comment structure.
Dynamic code built on-the-fly can add flexabiliy to a solution, but
is often much harder to debug if problems occur. Make such code
bullet-proof to the extent you can, and build in optional logging to
help when it turns out to have problems anyway.
You can use an OOP-like style if you wish. But there is no need to do so. If you do, it should IMO be used fairly pervasively through an application, except perhaps for low-level utilities. But OOP-style code can be at least as convoluted as non-OOP code, and APL doesn't have built-in inheritance or other OOP-supporting syntax.
(I'll use here "A" instead of comment, "'" instead of symbol sign.)
Well, I was developing APL for a year, I have only used Aplusdev.org.
You don't even need more. The trick is to try to think OOP-like. You should have -- if I remember well -- structured fields used as class data, sth like {'attribute1 'attribute2, {value,value2}}, so you can easily pick them out like obj.attribute1 in c++.
(here 'attribute Pick object, use only in class functions :) )
Moreover, use namespaced functions:
namespace_classname.method(this, arg1)
namespace_classname._private_method(this, arg1, arg2)
and lots of simple tool functions instead of nifty, long lines. The performance drop is not substantial, you can optimize later for say arrays once you see something could be faster.
And before anything: think matlab and mathematica without for loops! :) It helps a lot.
My suggestions for robust, maintainable code:
use extensive set of utility functions instead of trickery with those unreadable symbols to make your code always to the point.
try-catch blocks there is a built in exception handling, which can be utilized here,
try_begin();
A tried code, maybe in extra brackets not to forget try_end() at the end.
try_end();
catch(sth, function_here);
can be nicely implemented. (You'll see, catching errors is very important)
crude type checking : implement a standard and use for not-so-many times called functions... (you can put a function with flexible parameters right after a function definition)
Syntax:
function(point2i, ch):
{
typecheck({{'int, [1 2]}, 'char}); A do some assertions in typecheck...
// your function goes here
}
lambda functions can be very effective, you can do some reflections to achieve lambdas.
always declare returns with saying "return"!
Unit tests based on try-catch testing each and every function you write.
I also used a lot of 'apply' and 'map' from mathematica, implementing my own version, they are very-very effective here.
I wrote matlab thinking since you can here have a list of structured fields (=class data) in a variable. You will write lots of those if you wanna keep things for-loop-less (and you wanna, trust me). For that you need to have a standard naming convention say indicate with plurals:
namespace_class.method(objects, arg1, arg2)
To the end: also, I wrote inputBox and messageBox like the ones in Javascript or VisualBasic, they will make very easy hacking together simple tools or checking states. The only catch of messageBox, that it can't put the function-flow on hold,
so you need
AA documentation of f1
f1():
{
A do sth
msgbox.call("Hi there",{'Ok, {'f2}});
}
f2():
{
A continue doing stuff
}
You can write auto-docs in bash with a gawk/sed combination to put it into a webpage.
Also creating HTML formatted code helps in printing. ;)
I hope this was good outline for a proper build-up. Before writing own tools, try to dig up the available tools from the legacy codebase... functions are often even 4 times implemented with different names due to the mess that time.
Why do most languages seem to only exhibit fairly basic control structures from a logic point of view? Stuff like If ... then, Else..., loops, For each, switch statement, etc. The standard list seems fairly basic from a logic point of view.
Why is there not much more in the way of logic syntactical sugar? Perhaps something like a proposition engine, where you could feed an array of premises or functions that return complicated self referential interdependent functions and results. Something where you could chain together a complex array of conditions, but represented in a way that was easy and clear to read in the code.
Premise 1
Premise 2 if and only if Premise 1
Premise 3
Premise 4 if Premise 2 and Premise 3
Premise 5 if and only if Premise 4
etc...
Conclusion
I realize that this kind of logic this can be constructed in functions and/or nested conditional statements. But why are there not generally more syntax options for structuring these kind of logical propositions without resulting in hairy looking conditional statements that can be hard to read and debug?
Is there an explanation for the kinds of control structures we typically see in mainstream programming languages? Are there specific control structures you would like to see directly supported by a language's syntax? Does this just add unnecessary complexity to the language?
Have you looked a Prolog? A Prolog program is basically a set of rules that is turned into one big evaluation engine.
From my personal experience Prolog is a bit too weird and I actually prefer ifs, whiles and so on but YMMV.
Boolean algebra is not difficult, and provides a solution for any conditionals you can think of, plus an infinite number of other variants.
You might as well ask for special syntax for "commonly-used" arithmetic expressions. Who is to say what qualifies as commonly-used? And where do you stop adding special-case syntax?
Adding to the complexity of a language parser is not preferable to using constructive expression syntax, combined with extensibility through defining functions.
It's been a long time since my Logic class in college but I would guess it's a mixture of difficulty in writing them into the language vs. the frequency with which they'd be used. I can't say I've ever had the need for them (not that I can recall). For those times that you would require something of that ilk the language designers probably figure you can work out the logic yourself using just the basic structures.
Just my wild guess though.
Because most programming languages don't provide sufficient tools for users to implement them, it is not seen as an important enough feature for the implementer to provide as an extension, and it isn't demanded enough or used enough to be added to the standard.
If you really want it, use a language that provides it, or provides the tools to implement it (for instance, lisp macros).
It sounds as though you are describing a rules engine.
The basic control algorithms we use mirror what processor can do efficiently. Basicly this boils down to simple test-and-branches.
It may seem limiting to you, but many people don't like the idea of writing a simple-looking line of code that requires hundreds or thousands (or millions) of processor cycles to complete. Among these people are systems software folks, who write things like Operating Systems and compilers. Naturally most compilers are going to reflect their own writer's concerns.
It relates to the concern regarding atomicity. If you can express A,B,C,D in simpler structures Y, Z, why not simply not supply A,B,C,D but supply Y, Z instead?
The existing languages reflect 60 years of the tension between atomicity and usability. The modern approach is "small language, large libraries". (C#, Java, C++, etc).
Because computers are binary, all decisions must come down to a 1/0, yes/no, true/false, etc.
To be efficient, the language constructs must reflect this.
Eventually all your code goes down to a micro-code that is executed one instruction at a time. Until the micro-code and accompanying CPU can describe something more colorful, we are stuck with a very plain language.