As I was looking through a bunch of the Flux.jl tutorials here: https://fluxml.ai/tutorials.html many of them use the syntax |> gpu throughout the tutorials. I understand this has something to do with the GPU but what is this special syntax doing?
The syntax you are seeing used here is not specific to Flux.jl but rather a general Julia syntax which simple wraps the statement on the left of the |> in whatever function is on the right. So in this case, it is equivalent do just doing gpu(some_statement) where the some_statement represent the code on the left of the |>.
Read more about this paradigm (Function piping and composition) in the Julia docs: https://docs.julialang.org/en/v1/manual/functions/#Function-composition-and-piping
Related
Reading back through some of my old Python code to refresh myself on the "Yield" keyword, I realized that I have not seen a similar idea in Julia. Does an analog version of Yield exist? (Note that Julia's Base library comes with a yield function but that is for tasks and does not act like the yield keyword does in Python).
There are no built-ins for yield (unfortunately, if you ask me). However, since Julia has a very advanced macro system, and the theory as well as multiple possible implementations of coroutines/generators are quite well studied, there are a couple of implementations in third-party packages.
One of them is FGenerators.jl, previously GeneratorsX.jl, which works mostly in the transducers ecosystem.
Another is ResumableFunctions.jl.
You can construct generators that yield values when iterated upon:
f(x) = x*x
g = (f(i) for i=1:5) #generator that yields values
for x in g
println(x)
end
I was reading up on the documentation of macros and ran into the following under the `Hold up: why macros' section. The reasoning given to use macros is as follows:
Macros are necessary because they execute when code is parsed,
therefore, macros allow the programmer to generate and include
fragments of customized code before the full program is run
This leads me to wonder why someone would want to use "generate and include fragments of customized code before the full program is run". Can someone provide context as to why this would be beneficial and/or other good use cases for macros?
Let me give you my view on macros.
A macro basically is a code -> code function. It takes code (a Julia expression) as input and spits out code (a different Julia expression).
Why is this useful? It has multiple purposes:
compile time copy-and-paste: You don't have to write the same piece of code multiple times but instead can define a short macro that writes it for you wherever you put it. (example)
domain specific language (DSL): You can create special syntax that after the macros code -> code transform is replaced by pure Julia constructs. This is used in many packages to define special syntax, for example here and here.
code generation: Imagine you want to write a really long piece of code which, although being long, is very simple because it has some kind of pattern that repeats itself rather trivially. Writing that code by hand can be a pain (or even practically impossible). A macro can programmatically generate the code for you. One example is for-loop unrolling (see here and here). But even the #time macro isn't doing much more than just putting a bunch of Base.time_ns() function calls around the provided Julia expression.
special string parsing: If you type the literal 3.2 in Julia it will be parsed and interpreted as a Float64. Now, imagine you want to supply a number literally that goes beyond Float64 precision but would fit into a BigFloat. Typing big(3.123124812498124812498) won't work, because the literal number is first interpreted as a Float64 and then handed to the big function. Instead you need a way to tell Julia at parse time that this should become a BigFloat. This is handled by a #big_str 3.2 macro which (for convenience) can also be written as big"3.2". The latter is just syntactic sugar.
There might be many more applications of macros, but those are the most important to me.
Let me end by referencing Steven G. Johnson's great talk at JuliaCon 2019:
Most of the time, don't do metaprogramming :)
What are all Racket procedures that mutate state?
I'm trying to create a program with as little side-effects as possible
So, I'd do something like:
#lang racket/base
(provide (except-out (all-from-out racket/base) set! …more here…))
What else should I exclude besides set! ?
Is there a complete list somewhere of all impure functions?
Oh, and the program also uses #lang racket/gui (which is mostly impure, by what I could gather). So that may be tricky...
Thank you.
There is no pre-built list of non-pure functions in Racket.
If you just refrain from using anything which has a ! in the name, you will get close.
Note that you can use mutable data structures and still be programming in a purely functional way - as long as you don't mutate them.
I would like to create a grammar for parsing a toy like formula language that resembles S-expression syntax.
I read through the "Getting Started with PyParsing" book and it included a very nice section that sort of covers a similar grammar.
Two examples of data to parse are:
sum(5,10,avg(15,20))+10
stdev(5,10)*2
Now, I have come up with a grammar that sort-of parses the formula but disregards
expanding the functions and operator precedence.
What would be the best practice to continue on with it: Should I add parseActions
for words that match oneOf the function names ( sum, avg ... ). If I build a nested
list, I could do a depth-first walking of parse results and evaluate the functions ?
It's a little difficult to advise without seeing more of your code. Still, from what you describe, it sounds like you are mostly tokenizing, to recognize the various bits of punctuation and distinguishing variable names from numeric constants from algebraic operators. nestedExpr will impart some structure, but only basic parenthetical nesting - this still leaves operator precedence handling for your post-parsing work.
If you are learning about parsing infix notation, there is a succession of pyparsing examples to look through and study (at the pyparsing wiki Examples page). Start with fourFn.py, which is actually a five function infix notation parser. Look through its BNF() method, and get an understanding of how the recursive definitions work (don't worry about the pushFirst parse actions just yet). By structuring the parser this way, operator precedence gets built right into the parsed results. If you parse 4 + 2 * 3, a mere tokenizer just gives you ['4','+','2','*','3'], and then you have to figure out how to do the 2*3 before adding the 4 to get 10, and not just brute force add 4 and 2, then multiply by 3 (which gives the wrong answer of 18). The parser in fourFn.py will give you ['4','+',['2','*','3']], which is enough structure for you to know to evaluate the 2*3 part before adding it to 4.
This whole concept of parsing infix notation with precedence of operations is so common, I wrote a helper function that does most of the hard work, called operatorPrecedence. You can see how this works in the example simpleArith.py and then move on to eval_arith.py to see the extensions need to create an evaluator of the parsed structure. simpleBool.py is another good example showing precedence for logical terms AND'ed and OR'ed together.
Finally, since you are doing something Excel-like, take a look at excelExpr.py. It tries to handle some of the crazy corner cases you get when trying to evaluate Excel cell references, including references to other sheets and other workbooks.
Good luck!
What is the smartest way to design a math parser? What I mean is a function that takes a math string (like: "2 + 3 / 2 + (2 * 5)") and returns the calculated value? I did write one in VB6 ages ago but it ended up being way to bloated and not very portable (or smart for that matter...). General ideas, psuedo code or real code is appreciated.
A pretty good approach would involve two steps. The first step involves converting the expression from infix to postfix (e.g. via Dijkstra's shunting yard) notation. Once that's done, it's pretty trivial to write a postfix evaluator.
I wrote a few blog posts about designing a math parser. There is a general introduction, basic knowledge about grammars, sample implementation written in Ruby and a test suite. Perhaps you will find these materials useful.
You have a couple of approaches. You could generate dynamic code and execute it in order to get the answer without needing to write much code. Just perform a search on runtime generated code in .NET and there are plenty of examples around.
Alternatively you could create an actual parser and generate a little parse tree that is then used to evaluate the expression. Again this is pretty simple for basic expressions. Check out codeplex as I believe they have a math parser on there. Or just look up BNF which will include examples. Any website introducing compiler concepts will include this as a basic example.
Codeplex Expression Evaluator
If you have an "always on" application, just post the math string to google and parse the result. Simple way but not sure if that's what you need - but smart in some way i guess.
I know this is old, but I came across this trying to develop a calculator as part of a larger app and ran across some issues using the accepted answer. The links were IMMENSELY helpful in understanding and solving this problem and should not be discounted. I was writing an Android app in Java and for each item in the expression "string," I actually stored a String in an ArrayList as the user types on the keypad. For the infix-to-postfix conversion, I iterated through each String in the ArrayList, then evaluated the newly arranged postfix ArrayList of Strings. This was fantastic for a small number of operands/operators, but longer calculations were consistently off, especially as the expressions started evaluating to non-integers. In the provided link for Infix to Postfix conversion, it suggests popping the Stack if the scanned item is an operator and the topStack item has a higher precedence. I found that this is almost correct. Popping the topStack item if it's precedence is higher OR EQUAL to the scanned operator finally made my calculations come out correct. Hopefully this will help anyone working on this problem, and thanks to Justin Poliey (and fas?) for providing some invaluable links.
The related question Equation (expression) parser with precedence? has some good information on how to get started with this as well.
-Adam
Assuming your input is an infix expression in string format, you could convert it to postfix and, using a pair of stacks: an operator stack and an operand stack, work the solution from there. You can find general algorithm information at the Wikipedia link.
ANTLR is a very nice LL(*) parser generator. I recommend it highly.
Developers always want to have a clean approach, and try to implement the parsing logic from ground up, usually ending up with the Dijkstra Shunting-Yard Algorithm. Result is neat looking code, but possibly ridden with bugs. I have developed such an API, JMEP, that does all that, but it took me years to have stable code.
Even with all that work, you can see even from that project page that I am seriously considering to switch over to using JavaCC or ANTLR, even after all that work already done.
11 years into the future from when this question was asked: If you don't want to re-invent the wheel, there are many exotic math parsers out there.
There is one that I wrote years ago which supports arithmetic operations, equation solving, differential calculus, integral calculus, basic statistics, function/formula definition, graphing, etc.
Its called ParserNG and its free.
Evaluating an expression is as simple as:
MathExpression expr = new MathExpression("(34+32)-44/(8+9(3+2))-22");
System.out.println("result: " + expr.solve());
result: 43.16981132075472
Or using variables and calculating simple expressions:
MathExpression expr = new MathExpression("r=3;P=2*pi*r;");
System.out.println("result: " + expr.getValue("P"));
Or using functions:
MathExpression expr = new MathExpression("f(x)=39*sin(x^2)+x^3*cos(x);f(3)");
System.out.println("result: " + expr.solve());
result: -10.65717648378352
Or to evaluate the derivative at a given point(Note it does symbolic differentiation(not numerical) behind the scenes, so the accuracy is not limited by the errors of numerical approximations):
MathExpression expr = new MathExpression("f(x)=x^3*ln(x); diff(f,3,1)");
System.out.println("result: " + expr.solve());
result: 38.66253179403897
Which differentiates x^3 * ln(x) once at x=3.
The number of times you can differentiate is 1 for now.
or for Numerical Integration:
MathExpression expr = new MathExpression("f(x)=2*x; intg(f,1,3)");
System.out.println("result: " + expr.solve());
result: 7.999999999998261... approx: 8
This parser is decently fast and has lots of other functionality.
Work has been concluded on porting it to Swift via bindings to Objective C and we have used it in graphing applications amongst other iterative use-cases.
DISCLAIMER: ParserNG is authored by me.