By IO I am loosely referring to the type that is typically used to wrap impure functions (IO.delay) to be invoked at a later time (my understanding after reading Grokking Functional Programming. I am aware it could also wrap values - IO.pure)
On the other hand, in the Value of Values presentation, Rich Hickey states that (besides being immutable) values are comparable. In particular, a function is equal to another function if they both return the same value given the same arguments no matter when they are called. That is, pure functions are values. A corollary being that a pure function is equal to itself.
However, an impure function, which given the same arguments may return different values depending on when you call it, is not even equal to itself according to the definition highlighted above. Hence, I conclude impure functions are not values as they are not even equal to themselves.
How could instances of the IO type which wrap impure functions be values? The wrapped impure function is not a value. How could the wrapper be a value? According to the premise made above, an instance of the IO type which wraps an impure function is not even equal to itself!
Related
In F# if I write
let p = printfn "something"
it will evaluate the expression once. Any subsequent references to p will evaluate to unit.
From a theoretical perspective, the definition of a function, this makes sense. A function should only return the same result for the same input.
However if I want the side-effect to occur (i.e. the output to the screen), then I need the pass an argument to p. Typically this argument is the unit value.
let p () = printfn "something"
But why will F# evaluate the function each time, when the argument is the same each time the function is applied? Surely the same reasoning should apply as in the first case? The input to the function p doesn't change therefore there is no need to evaluate it more than once.
The action printfn is, strictly speaking, not a function. It is, particularly, not a pure function, because it's not referentially transparent. This is possible because F# isn't a strictly functional language (it's a 'functional first' language). It makes no explicit distinction between pure functions and impure actions.
The return value of printfn "something" is () (unit), which means that p is bound to the unit value (). The fact that something is printed on the screen is a side effect of evaluating the expression.
F# is an eagerly evaluated language. That's why you see something printed on the screen as a side effect of binding printfn "something" to p. Once the expression is evaluated, p is only bound to () - the value.
F# doesn't memoize function calls, so when you change p to a function, it'll evaluate the function every time you call it with (). Since all functions can be impure, the compiler can't tell whether or not memoization would be appropriate, so it doesn't do that.
Other languages do this in different ways. Haskell, for example, is lazily evaluated, and also explicitly distinguishes between pure functions and impure actions, so it can apply different optimization in cases like these.
To expand on the answer given in the comments, the first p is an immutable value, while the second p is a function. If you refer to an immutable value multiple times, then (obviously) its value doesn't change over time. But if you invoke a function multiple times, it executes each time, even if the arguments are the same each time.
Note that this is true even for pure functional languages, such as Haskell. If you want to avoid this execution cost, there's a specific technique called memoization that can be used to return cached results when the same inputs occur again. However, memoization has its own costs, and I'm not aware of any mainstream functional language that automatically memoizes all function calls.
As I'm learning Julia, I am wondering how to properly do things I might have done in Python, Java or C++ before. For example, previously I might have used an abstract base class (or interface) to define a family of models through classes. Each class might then have a method like calculate. So to call it I might have model.calculate(), where the model is an object from one of the inheriting classes.
I get that Julia uses multiple dispatch to overload functions with different signatures such as calculate(model). The question I have is how to create different models. Do I use the type system for that and create different types like:
abstract type Model end
type BlackScholes <: Model end
type Heston <: Model end
where BlackScholes and Heston are different types of model? If so, then I can overload different calculate methods:
function calculate(model::BlackScholes)
# code
end
function calculate(model::Heston)
# code
end
But I'm not sure if this is a proper and idiomatic use of types in Julia. I will greatly appreciate your guidance!
This is a hard question to answer. Julia offers a wide range of tools to solve any given problem, and it would be hard for even a core developer of the language to assert that one particular approach is "right" or even "idiomatic".
For example, in the realm of simulating and solving stochastic differential equations, you could look at the approach taken by Chris Rackauckas (and many others) in the suite of packages under the JuliaDiffEq umbrella. However, many of these people are extremely experienced Julia coders, and what they do may be somewhat out of reach for less experienced Julia coders who just want to model something in a manner that is reasonably sensible and attainable for a mere mortal.
It is is possible that the only "right" answer to this question is to direct users to the Performance Tips section of the docs, and then assert that as long as you aren't violating any of the recommendations there, then what you are doing is probably okay.
I think the best way I can answer this question from my own personal experience is to provide an example of how I (a mere mortal) would approach the problem of simulating different Ito processes. It is actually not too far off what you have put in the question, although with one additional layer. To be clear, I make no claim that this is the "right" way to do things, merely that it is one approach that utilizes multiple dispatch and Julia's type system in a reasonably sensible fashion.
I start off with an abstract type, for nesting specific subtypes that represent specific models.
abstract type ItoProcess ; end
Now I define some specific model subtypes, e.g.
struct GeometricBrownianMotion <: ItoProcess
mu::Float64
sigma::Float64
end
struct Heston <: ItoProcess
mu::Float64
kappa::Float64
theta::Float64
xi::Float64
end
Note, in this case I don't need to add constructors that convert arguments to Float64, since Julia does this automatically, e.g. GeometricBrownianMotion(1, 2.0) will work out-of-the-box, as Julia will automatically convert 1 to 1.0 when constructing the type.
However, I might want to add some constructors for common parameterizations, e.g.
GeometricBrownianMotion() = GeometricBrownianMotion(0.0, 1.0)
I might also want some functions that return useful information about my models, e.g.
number_parameter(model::GeometricBrownianMotion) = 2
number_parameter(model::Heston) = 4
In fact, given how I've defined the models above, I could actually be a bit sneaky and define a method that works for all subtypes:
number_parameter(model::T) where {T<:ItoProcess} = length(fieldnames(typeof(model)))
Now I want to add some code that allows me to simulate my models:
function simulate(model::T, numobs::Int, stval) where {T<:ItoProcess}
# code here that is common to all subtypes of ItoProcess
simulate_inner(model, somethingelse)
# maybe more code that is common to all subtypes of ItoProcess
end
function simulate_inner(model::GeometricBrownianMotion, somethingelse)
# code here that is specific to GeometricBrownianMotion
end
function simulate_inner(model::Heston, somethingelse)
# code here that is specific to Heston
end
Note that I have used the abstract type to allow me to group all code that is common to all subtypes of ItoProcess in the simulate function. I then use multiple dispatch and simulate_inner to run any code that needs to be specific to a particular subtype of ItoProcess. For the aforementioned reasons, I hesitate to use the phrase "idiomatic", but let me instead say that the above is quite a common pattern in typical Julia code.
The one thing to be careful of in the above code is to ensure that the output type of the simulate function is type-stable, that is, the output type can be uniquely determined by the input types. Type stability is usually an important factor in ensuring performant Julia code. An easy way in this case to ensure type-stability is to always return Matrix{Float64} (if the output type is fixed for all subtypes of ItoProcess then obviously it is uniquely determined). I examine a case where the output type depends on input types below for my estimate example. Anyway, for simulate I might always return Matrix{Float64} since for GeometricBrownianMotion I only need one column, but for Heston I will need two (the first for price of the asset, the second for the volatility process).
In fact, depending on how the code is used, type-stability is not always necessary for performant code (see eg using function barriers to prevent type-instability from flowing through to other parts of your program), but it is a good habit to be in (for Julia code).
I might also want routines to estimate these models. Again, I can follow the same approach (but with a small twist):
function estimate(modeltype::Type{T}, data)::T where {T<:ItoProcess}
# again, code common to all subtypes of ItoProcess
estimate_inner(modeltype, data)
# more common code
return T(some stuff generated from function that can be used to construct T)
end
function estimate_inner(modeltype::Type{GeometricBrownianMotion}, data)
# code specific to GeometricBrownianMotion
end
function estimate_inner(modeltype::Type{Heston}, data)
# code specific to Heston
end
There are a few differences from the simulate case. Instead of inputting an instance of GeometricBrownianMotion or Heston, I instead input the type itself. This is because I don't actually need an instance of the type with defined values for the fields. In fact, the values of those fields is the very thing I am attempting to estimate! But I still want to use multiple dispatch, hence the ::Type{T} construct. Note also I have specified an output type for estimate. This output type is dependent on the ::Type{T} input, and so the function is type-stable (output type can be uniquely determined by input types). But common with the simulate case, I have structured the code so that code that is common to all subtypes of ItoProcess only needs to be written once, and code that is specific to the subtypes is separted out.
This answer is turning into an essay, so I should tie it off here. Hopefully this is useful to the OP, as well as anyone else getting into Julia. I just want to finish by emphasizing that what I have done above is only one approach, there are others that will be just as performant, but I have personally found the above to be useful from a structural perspective, as well as reasonably common across the Julia ecosystem.
In Julia, say I have an object_id for a variable but have forgotten its name, how can I retrieve the object using the id?
I.e. I want the inverse of some_id = object_id(some_object).
As #DanGetz says in the comments, object_id is a hash function and is designed not to be invertible. #phg is also correct that ObjectIdDict is intended precisely for this purpose (it is documented although not discussed much in the manual):
ObjectIdDict([itr])
ObjectIdDict() constructs a hash table where the keys are (always)
object identities. Unlike Dict it is not parameterized on its key and
value type and thus its eltype is always Pair{Any,Any}.
See Dict for further help.
In other words, it hashes objects by === using object_id as a hash function. If you have an ObjectIdDict and you use the objects you encounter as the keys into it, then you can keep them around and recover those objects later by taking them out of the ObjectIdDict.
However, it sounds like you want to do this without the explicit ObjectIdDict just by asking which object ever created has a given object_id. If so, consider this thought experiment: if every object were always recoverable from its object_id, then the system could never discard any object, since it would always be possible for a program to ask for that object by ID. So you would never be able to collect any garbage, and the memory usage of every program would rapidly expand to use all of your RAM and disk space. This is equivalent to having a single global ObjectIdDict which you put every object ever created into. So inverting the object_id function that way would require never deallocating any objects, which means you'd need unbounded memory.
Even if we had infinite memory, there are deeper problems. What does it mean for an object to exist? In the presence of an optimizing compiler, this question doesn't have a clear-cut answer. It is often the case that an object appears, from the programmer's perspective, to be created and operated on, but in reality – i.e. from the hardware's perspective – it is never created. Consider this function which constructs a complex number and then uses it for a simple computation:
julia> function f(y::Real)
z = Complex(0,y)
w = 2z*im
return real(w)
end
f (generic function with 1 method)
julia> foo(123)
-246
From the programmer's perspective, this constructs the complex number z and then constructs 2z, then 2z*im, and finally constructs real(2z*im) and returns that value. So all of those values should be inserted into the "Great ObjectIdDict in the Sky". But are they really constructed? Here's the LLVM code for this function applied to an Int:
julia> #code_llvm foo(123)
define i64 #julia_foo_60833(i64) #0 !dbg !5 {
top:
%1 = shl i64 %0, 1
%2 = sub i64 0, %1
ret i64 %2
}
No Complex values are constructed at all! Instead, all of the work is inlined and eliminated instead of actually being done. The whole computation boils down to just doubling the argument (by shifting it left one bit) and negating it (by subtracting it from zero). This optimization can be done first and foremost because the intermediate steps have no observable side effects. The compiler knows that there's no way to tell the difference between actually constructing complex values and operating on them and just doing a couple of integer ops – as long as the end result is always the same. Implicit in the idea of a "Great ObjectIdDict in the Sky" is the assumption that all objects that seem to be constructed actually are constructed and inserted into a large, permanent data structure – which is a massive side effect. So not only is recovering objects from their IDs incompatible with garbage collection, it's also incompatible with almost every conceivable program optimization.
The only other way one could conceive of inverting object_id would be to compute its inverse image on demand instead of saving objects as they are created. That would solve both the memory and optimization problems. Of course, it isn't possible since there are infinitely many possible objects but only a finite number of object IDs. You are vanishingly unlikely to actually encounter two objects with the same ID in a program, but the finiteness of the ID space means that inverting the hash function is impossible in principle since the preimage of each ID value contains an infinite number of potential objects.
I've probably refuted the possibility of an inverse object_id function far more thoroughly than necessary, but it led to some interesting thought experiments, and I hope it's been helpful – or at least thought provoking. The practical answer is that there is no way to get around explicitly stashing every object you might want to get back later in an ObjectIdDict.
I am trying to self-learn SML. Though I can write some SML code, I have not attained the ah ha.
In:
val x = 5;
how does binding a value of 5 to the name x differ from assigning a value of 5 to the memory location/variable x in imperative programming?
How does the above expression elucidate "a style that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data"?
What do I have to throw away about imperative programming to catch on FP quickly?
Please be gentle with me.
How does binding a value of 5 to the name x differ from assigning a value of 5 to the memory location/variable x in imperative programming?
The key difference between variables in functional programming and variables in imperative programming is that variables in functional programming cannot be modified.
Then why are they called “variables”?
Variables in functional programming languages don't vary in the sense that you can modify their value. However, they do vary in the mathematical sense in that they represent some unknown constant value. For example, consider the following quadratic expression:
This is a quadratic expression in one variable. The variable x varies in the sense that you can select any value for x. However, once you select a certain value for x you cannot change that value.
Now, if you have a quadractic equation then the choice of x is no longer arbitrary. For example, consider the following quadratic equation:
The only choices for x are those which satisfy the equation (i.e. x = -2 or x = -1.5).
A mathematical function on the other hand is a relation between two sets, called the domain (input set) and the codomain (output set). For example, consider the following function:
This function relates every x belonging to the set of real numbers to its corresponding 2x^2 + 7x + 6, also belonging to the set of real numbers. Again, we are free to choose any value for x. However, once we choose some value for x we are not allowed to modify it.
The point is that they are called variables because they vary in the mathematical sense that they can assume different values. Hence, they vary with instance. However, they do not vary with time.
This immutability of variables is important in functional programming because it makes your program referentially transparent (i.e. invoking a function can be thought of as simply replacing the function call with the function body).
If variables were allowed to vary with time then you wouldn't be able to simply substitute the function call with the function body because the substituted variable could change over time. Hence, your substituted function body could become invalid over time.
How does the above expression elucidate "a style that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data"?
The expression val x = 5; could be thought of as a function application (fn x => y) 5 where y is the rest of the program. Functional programming is all about functions, and immutability in the sense that variables only vary with instance and not with time.
In particular, mutability is considered bad because it is implicit. Anything that is implicit could potentially cause unforeseeable errors in your program. Hence, the Zen of Python explicitly states that:
Explicit is better than implicit.
In fact, mutable state is the primary cause of software complexity. Hence, functional programming tries to eschew mutability as much as possible. However, that doesn't mean that we only use immutable structures. We can use mutable structures. We just need to be explicit about it.
What do I have to throw away about imperative programming to catch on FP quickly?
Nothing. Functional programming and imperative programming are two ways of thinking about computation. Computation is a very abstract idea. What exactly is computation? How do we know which problems can be computed? These were some of the questions that mathematicians struggled with in the early nineteen hundreds.
To think about computation we need to have a formal model of computation (i.e. a formal system that captures the notion of computation). There are various models of computation. However, the most famous are the lambda calculus (which gave rise to functional programming) and turing machines (which gave rise to imperative programming).
Both the lambda calculus and the turing machine are equivalent in power. They both capture the notion of computing and every problem that can be computed can be expressed as either a lambda calculus expression or an equivalent turing machine. The only difference is the way in which you express your problem.
You don't have to throw away anything that you learned about imperative programming to understand functional programming. Neither one is better than the other. They are both just different ways of expressing the same problem. However, you do need to start thinking like a functional programmer to write good functional programs.
I was fooling around with some functional programming when I came across the need for this function, however I don't know what this sort of thing is called in standard nomenclature.
Anyone recognizes it?
function WhatAmIDoing(args...)
return function()
return args
end
end
Edit: generalized the function, it takes a variable amount of arguments ( or perhaps an implicit list) and returns a function that when invoked returns all the args, something like a curry or pickle, but it doesn't seem to be either.
WhatAmIDoing is a higher-order function because it is a function that returns another function.
The thing that it returns is a thunk — a closure created for delayed computation of the actual value. Usually thunks are created to lazily evaluate an expression (and possibly memoize it), but in other cases, a function is simply needed in place of a bare value, as in the case of "constantly 5", which in some languages returns a function that always returns 5.
The latter might apply in the example given, because assuming the language evaluates in applicative-order (i.e. evaluates arguments before calling a function), the function serves no other purpose than to turn the values into a function that returns them.
WhatAmIDoing is really an implementation of the "constantly" function I was describing. But in general, you don't have to return just args in the inner function. You could return "ackermann(args)", which could take a long time, as in...
function WhatAmIDoing2(args...)
return function()
return ackermann(args)
end
end
But WhatAmIDoing2 would return immediately because evaluation of the ackermann function would be suspended in a closure. (Yes, even in a call-by-value language.)
In functional programming a function that takes another function as an argument or returns another function is called a higher-order function.
I would say that XXXX returns a closure of the unnamed function bound on the values of x,y and z.
This wikipedia article may shed some light
Currying is about transforming a function to a chain of functions, each taking only one parameter and returning another such function. So, this example has no relation to currying.
Pickling is a term ususally used to denote some kind of serialization. Maybe for storing a object built from multiple values.
If the aspect interesting to you is that the returned function can access the arguments of the XXXX function, then I would go with Remo.D.
As others have said, it's a higher-order function. As you have "pattern" in your question, I thought I'd add that this feature of functional languages is often modelled using the strategy pattern in languages without higher-order functions.
Something very similar is called constantly in Clojure:
http://github.com/richhickey/clojure/blob/ab6fc90d56bfb3b969ed84058e1b3a4b30faa400/src/clj/clojure/core.clj#L1096
Only the function that constantly returns takes an arbitrary amount of arguments, making it more general (and flexible) than your pattern.
I don't know if this pattern has a name, but would use it in cases where normally functions are expected, but all I care for is that a certain value is returned:
(map (constantly 9) [1 2 3])
=> (9 9 9)
Just wondering, what do you use this for?
A delegate?
Basically you are returning a function?? or the output of a function?
Didn't understand, sorry...