I'm implementing an FRP framework in Scala and I seem to have run into a problem. Motivated by some thinking, this question I decided to restrict the public interface of my framework so Behaviours could only be evaluated in the 'present' i.e.:
behaviour.at(now)
This also falls in line with Conal's assumption in the Fran paper that Behaviours are only ever evaluated/sampled at increasing times. It does restrict transformations on Behaviours but otherwise we find ourselves in huge problems with Behaviours that represent some input:
val slider = Stepper(0, sliderChangeEvent)
With this Behaviour, evaluating future values would be incorrect and evaluating past values would require an unbounded amount of memory (all occurrences used in the 'slider' event would have to be stored).
I am having trouble with the specification for the 'snapshot' operation on Behaviours given this restriction. My problem is best explained with an example (using the slider mentioned above):
val event = mouseB // an event that occurs when the mouse is pressed
val sampler = slider.snapshot(event)
val stepper = Stepper(0, sampler)
My problem here is that if the 'mouseB' Event has occurred when this code is executed then the current value of 'stepper' will be the last 'sample' of 'slider' (the value at the time the last occurrence occurred). If the time of the last occurrence is in the past then we will consequently end up evaluating 'slider' using a past time which breaks the rule set above (and your original assumption). I can see a couple of ways to solve this:
We 'record' the past (keep hold of all past occurrences in an Event) allowing evaluation of Behaviours with past times (using an unbounded amount of memory)
We modify 'snapshot' to take a time argument ("sample after this time") and enforce that that time >= now
In a more wacky move, we could restrict creation of FRP objects to the initial setup of a program somehow and only start processing events/input after this setup is complete
I could also simply not implement 'sample' or remove 'stepper'/'switcher' (but I don't really want to do either of these things). Has anyone any thoughts on this? Have I misunderstood anything here?
Oh I see what you mean now.
Your "you can only sample at 'now'" restriction isn't tight enough, I think. It needs to be a bit stronger to avoid looking into the past. Since you are using an environmental conception of now, I would define the behavior construction functions in terms of it (so long as now cannot advance by the mere execution of definitions, which, per my last answer, would get messy). For example:
Stepper(i,e) is a behavior with the value i in the interval [now,e1] (where e1 is the
time of first occurrence of e after now), and the value of the most recent occurrence of e afterward.
With this semantics, your prediction about the value of stepper that got you into this conundrum is dismantled, and the stepper will now have the value 0. I don't know whether this semantics is desirable to you, but it seems natural enough to me.
From what I can tell, you are worried about a race condition: what happens if an event occurs while the code is executing.
Purely functional code does not like to have to know that it gets executed. Functional techniques are at their finest in the pure setting, such that it does not matter in what order code is executed. A way out of this dilemma is to pretend that every change happened in one sensitive (internal, probably) piece of imperative code; pretend that any functional declarations in the FRP framework happen in 0 time so it is impossible for something to change during their declaration.
Nobody should ever sleep, or really do anything time sensitive, in a section of code that is declaring behaviors and things. Essentially, code that works with FRP objects ought to be pure, then you don't have any problems.
This does not necessarily preclude running it on multiple threads, but to support that you might need to reorganize your internal representations. Welcome to the world of FRP library implementation -- I suspect your internal representation will fluctuate many times during this process. :-)
I'm confused about your confusion. The way I see is that Stepper will "set" the behavior to a new value whenever the event occurs. So, what happens is the following:
The instant in which the event mouseB occurs, the value of the slider behavior will be read (snapshot). This value will be "set" into the behavior stepper.
So, it is true that the Stepper will "remember" values from the past; the point is that it only remembers the latest value from the past, not everything.
Semantically, it is best to model Stepper as a function like luqui proposes.
Related
I'm having hard time understanding what value effect systems, like ZIO or Cats Effect.
It does not make code readable, e.g.:
val wrappedB = for {
a <- getA() // : ZIO[R, E, A]
b <- getB(a) // : ZIO[R, E, B]
} yield b
is no more readable to me than:
val a = getA() // : A
val b = getB(a) // : B
I could even argue, that the latter is more straight forward, because calling a function executes it, instead of just creating an effect or execution pipeline.
Delayed execution does not sound convincing, because all examples I've encountered so far are just executing the pipeline right away anyways. Being able to execute effects in parallel or multiple time can be achieved in simpler ways IMHO, e.g. C# has Parallel.ForEach
Composability. Functions can be composed without using effects, e.g. by plain composition.
Pure functional methods. In the end the pure instructions will be executed, so it seems like it's just pretending DB access is pure. It does not help to reason, because while construction of the instructions is pure, executing them is not.
I may be missing something or just downplaying the benefits above or maybe benefits are bigger in certain situations (e.g. complex domain).
What are the biggest selling points to use effect systems?
Because it makes it easy to deal with side effects. From your example:
a <- getA() // ZIO[R, E, A] (doesn't have to be ZIO btw)
val a = getA(): A
The first getA accounts in the effect and the possibility of returning an error, a side effect. This would be like getting an A from some db where the said A may not exist or that you lack permission to access it. The second getA would be like a simple def getA = "A".
How do we put these methods together ? What if one throws an error ? Should we still proceed to the next method or just quit it ? What if one blocks your thread ?
Hopefully that addresses your second point about composability. To quickly address the rest:
Delayed execution. There are probably two reasons for this. The first is you actually don't want to accidentally start an execution. Or just because you write it it starts right away. This breaks what the cool guys refer to as referential transparency. The second is concurrent execution requires a thread pool or execution context. Normally we want to have a centralized place where we can fine tune it for the whole app. And when building a library we can't provide it ourselves. It's the users who provide it. In fact we can also defer the effect. All you do is define how the effect should behave and the users can use ZIO, Monix, etc, it's totally up to them.
Purity. Technically speaking wrapping a process in a pure effect doesn't necessarily mean the underlying process actually uses it. Only the implementation knows if it's really used or not. What we can do is lift it to make it compatible with the composition.
what makes programming with ZIO or Cats great is when it comes to concurrent programming. They are also other reasons but this one is IMHO where I got the "Ah Ah! Now I got it".
Try to write a program that monitor the content of several folders and for each files added to the folders parse their content but not more than 4 files at the same time. (Like the example in the video "What Java developpers could learn from ZIO" By Adam Fraser on youtube https://www.youtube.com/watch?v=wxpkMojvz24 .
I mean this in ZIO is really easy to write :)
The all idea behind the fact that you combine data structure (A ZIO is a data structure) in order to make bigger data structure is so easy to understand that I would not want to code without it for complex problems :)
The two examples are not comparable since an error in the first statement will mark as faulty the value equal to the objectified sequence in the first form while it will halt the whole program in the second. The second form shall then be a function definition to properly encapsulate the two statements, followed by an affectation of the result of its call.
But more than that, in order to completely mimic the first form, some additional code has to be written, to catch exceptions and build a true faulty result, while all these things are made for free by ZIO...
I think that the ability to cleanly propagate the error state between successive statements is the real value of the ZIO approach. Any composite ZIO program fragment is then fully composable itself.
That's the main benefit of any workflow based approach, anyway.
It is this modularity which gives to effect handling its real value.
Since an effect is an action which structurally may produce errors, handling effects like this is an excellent way to handle errors in a composable way. In fact, handling effects consists in handling errors !
I'm trying to code a priority queue in MATLAB, I know there is the SIMULINK toolbox for priority queue, but I'm trying to code it in MATLAB. I have a pseudo code that uses priority queue for a method called BEST First Search with Branch and Bound. The branch and bound algorithm design strategy is a state space tree and it is used to solve optimization problems. simple explanation of what is branch and bound
I have read chapter 5: Branch and Bound from a book called 'FOUNDATIONS OF ALGORITHMS', it's the 4th edition by Richard Neapolitan and Kumarss Naimipour , and the text is about designing algorithms, complexity analysis of algorithms, and computational complexity (analysis of problems), very interesting book, and I came across this pseudocode:
Void BeFS( state_space_tree T, number& best)
{
priority _queue-of_node PQ;
node(u,v);
initialize (PQ) % initialize PQ to be empty
u=root of T;
best=value(v);
insert(PQ,v) insert(PQ,v) is a procedure that adds v to the priority queue PQ
while(!empty(PQ){ % remove node with best bound
remove(PQ,v);
remove(PQ,v) is a procedure that removes the node with the best bound and it assigns its value to v
if(bound(v) is better than best) % check if node is still promising
for (each child of u of v){
if (value (u) is better than best)
(best=value(u);
if (bound(u) is better than best)
insert(PQ,u)
}
}
}
I don't know how to code it in matlab, and branch and bound is an interesting general algorithm for finding optimal solutions of various optimization problems, especially in discrete and combinatorial optimization, instead of using heuristics to find an optimal solution, since branch and bound reduces calculation time and finds the optimal solution faster.
EDIT:
I have checked everywhere whether a solution already has been implemented , before posting a question here. And I came here to get ideas of how I can get started to implement this code
I have included this in your post so people can know better what you expect of them. However, 'ideas to get started to implement' is still not much more specific than 'how to write code in matlab'.
However, I will still try to answer:
Make the structure of the code, write the basic loops and fill them with comments of what you want to do
Pick (the easiest or first) one of those comments, and see whether you can make it happen in a few lines, you can test it by generating some dummy input for that piece of code
Keep repeating step 2 untill all comments have the required code
If you get stuck in one of the blocks, and have searched but not found the answer to a specific question. Then this is not a bad place to ask.
This is a very naive question about input validation in general.
I learned about input validation techniques such as parse and validatestring. In fact, MATLAB built-in functions are full of those validations and parsers. So, I naturally thought this is the professional way of code development. With these techniques, you can be sure of data format of input variables. Otherwise your codes will reject the inputs and return an error.
However, some people argue that if there is a problem in input variable, codes will cause errors and stop. You'll notice the problem anyway, and then what's the point of those complicated validations? Given that codes for validation itself take some efforts and time, often with quite complicated flow controls, I had to admit this opinion has its point. With massive input validations, readability of codes may be compromised.
I would like hear about opinions from advanced users on this issue.
Here is my experience, I hope it matches best practice.
First of all, let me mention that I typically work in situations where I have full control, and won't build my own UI as #tom mentioned. In general, if there is at any point a large probability that your program gets junk inputs it will be worth checking for them.
Some tradeoffs that I typically make to decide whether I should check my inputs:
Development time vs debug time
If erronious inputs are hard to debug (for example because they don't cause errors but just undesirable outcomes) the balance will typically be in favor of checking, otherwise not.
If you are not sure where you will end up (re)using the code, it may help to enforce any assumptions that are required on the input.
Development time vs runtime experience
If your code takes an hour to run, and will break in the end when an invalid input value occurs, you would want to check of this at the beginning of the code
If the code runs into an error whilst opening a file, the user may not understand immediately, if you mention that no valid filename is specified this may be easier to deal with.
The really (really) short story:
Break your design down into user interface, business logic and data - (see MVC pattern)
In your UI layer, do "common sense" validation, e.g. if the input is a $ cost value then it should be >= 0, be able to be parsed into a decimal etc.
In your business logic layer, validate the value, e.g. the $ cost value might not be allowed to be greater than the profit margin (etc.)
In your data layer, validate the data operation, e.g. that insert operation succeeded
The extra really short story: YES! Validate all inputs.
For extra reading credits see: this!
For example, if I implement some simple object caching, which method is faster?
1. return isset($cache[$cls]) ? $cache[$cls] : $cache[$cls] = new $cls;
2. return #$cache[$cls] ?: $cache[$cls] = new $cls;
I read somewhere # takes significant time to execute (and I wonder why), especially when warnings/notices are actually being issued and suppressed. isset() on the other hand means an extra hash lookup. So which is better and why?
I do want to keep E_NOTICE on globally, both on dev and production servers.
I wouldn't worry about which method is FASTER. That is a micro-optimization. I would worry more about which is more readable code and better coding practice.
I would certainly prefer your first option over the second, as your intent is much clearer. Also, best to keep away edge condition problems by always explicitly testing variables to make sure you are getting what you are expecting to get. For example, what if the class stored in $cache[$cls] is not of type $cls?
Personally, if I typically would not expect the index on $cache to be unset, then I would also put error handling in there rather than using ternary operations. If I could reasonably expect that that index would be unset on a regular basis, then I would make class $cls behave as a singleton and have your code be something like
return $cls::get_instance();
The isset() approach is better. It is code that explicitly states the index may be undefined. Suppressing the error is sloppy coding.
According to this article 10 Performance Tips to Speed Up PHP, warnings take additional execution time and also claims the # operator is "expensive."
Cleaning up warnings and errors beforehand can also keep you from
using # error suppression, which is expensive.
Additionally, the # will not suppress the errors with respect to custom error handlers:
http://www.php.net/manual/en/language.operators.errorcontrol.php
If you have set a custom error handler function with
set_error_handler() then it will still get called, but this custom
error handler can (and should) call error_reporting() which will
return 0 when the call that triggered the error was preceded by an #.
If the track_errors feature is enabled, any error message generated by
the expression will be saved in the variable $php_errormsg. This
variable will be overwritten on each error, so check early if you want
to use it.
# temporarily changes the error_reporting state, that's why it is said to take time.
If you expect a certain value, the first thing to do to validate it, is to check that it is defined. If you have notices, it's probably because you're missing something. Using isset() is, in my opinion, a good practice.
I ran timing tests for both cases, using hash keys of various lengths, also using various hit/miss ratios for the hash table, plus with and without E_NOTICE.
The results were: with error_reporting(E_ALL) the isset() variant was faster than the # by some 20-30%. Platform used: command line PHP 5.4.7 on OS X 10.8.
However, with error_reporting(E_ALL & ~E_NOTICE) the difference was within 1-2% for short hash keys, and up 10% for longer ones (16 chars).
Note that the first variant executes 2 hash table lookups, whereas the variant with # does only one lookup.
Thus, # is inferior in all scenarios and I wonder if there are any plans to optimize it.
I think you have your priorities a little mixed up here.
First of all, if you want to get a real world test of which is faster - load test them. As stated though suppressing will probably be slower.
The problem here is if you have performance issues with regular code, you should be upgrading your hardware, or optimize the grand logic of your code rather than preventing proper execution and error checking.
Suppressing errors to steal the tiniest fraction of a speed gain won't do you any favours in the long run. Especially if you think that this error may keep happening time and time again, and cause your app to run more slowly than if the error was caught and fixed.
when reading Ada.Real_Time.Clock right after power-up it shows a value that isn't close to zero and sometimes even negative.
As far as I know Ada.Real_Time.Clock suppose to reset on power-up.
How can I reset Ada.Real_Time.Clock?
Thanks.
The Ada 2005 LRM declares that "real time is defined to be the physical time as observed in the external environment. [emphasis added--MC]
"It is not specified by the language whether the time values are synchronized with any standard time reference. For example, E can correspond to the time of system initialization or it can correspond to the epoch of some time standard." (D.8[18-19])
As it states, Ada does not require that "E", the start of the epoch serving as the "zero time" for real-time Time values, correspond to any particular starting point; it's left up to the compiler implementer.
Whatever specific numeric values you observe for the instances of Time you're seeing, whether near or far from zero, positive or negative, are dependent solely on the compiler implementer's choice of E, how it represents times values, and how it correspondingly implements the real-time capability.
Therefore you should avoid writing code that depends on specific, knowable values of Time, nor code that requires Time values to be intimately manipulable.
Real_Time.Time values should be considered abstract quantities.
Agreeing with Marc. While I have seen some platforms that use time since boot (particularly on Intel platforms, where I think they like to use the processor's iteration counter), that is entirely up to the compiler vendor.
If you need something like "time since startup" and your platform isn't giving you that, then the thing to do would be to grab Real_Time.Clock when you start up, and subtract that value from all further reads from Real_Time.Clock.
You can look at exactly what facilites are defined for the Real_Time package, including all the LRM sections Marc was quoting you, at its LRM page here.
It was long ago but if it helps someone...
I reseted the clock by writing 0 to the time base registers of the MCU.
That's a lovely explanation, but what if someone is trying to write unit tests against code which implements the real_Time clock? For instance, I know that my function foo does an internal comparison against Ada.Real_Time.Clock to check for time spans. Before executing foo with the appropriate inputs I want to reset the clock to force foo down a specific path internally and verify the resulting out parameter has changed.
return_value := foo;
assert (return_value = path1, "tested foo path1");
Reset_Clock;
return_value := foo;
assert (return_value = path2, "tested foo path2");