Yampa AFRP Introduction

Introduction to FRP with a Haskell Implemented, Quake-like Game

Functional Thursday #[email protected]

Link to this slide: http://bit.ly/sntw-ypai

http://bit.ly/sntw-ypai

Frag

HaskellYampaOpenGLQuake-like FPS Game

Mun Hon Cheong2005License: GPL

GitHub Mirrorbit.ly/sntw-frag

http://bit.ly/sntw-frag

http://bit.ly/sntw-frag

Snowmantw

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GitHub Profilegithub.com/snowmantw

http://snowmantw.github.io/Fluorine/document/introduce/index.html

http://snowmantw.github.io/Fluorine/document/introduce/index.html

http://github.com/snowmantw




Outline

Yampa & FRP

SignalSignal FunctionSpace-Time LeakArrowArrow SyntaxEventSwitchProgramming withYampa

Yampawww.haskell.org/haskellwiki/Yampa

(not official logo)

http://www.haskell.org/haskellwiki/Yampa




Functional Reactive Programming

Signal vs. Event

It's all about time & value


H0 1

E L2 3 4 5 6 6.5

L O

Signal a ≈ Time → a

( 0, 'H') ( 1, 'E') ( 2, 'L') ( 6, 'L') ( 6.5, 'O')

Time will be an implicit input in FRP program:


x = (1/2) * ((integral (vr + vl)) * cos θ)θ = (1/l) * (integral (vr - vl))

function program() {

};



draw(mouse.x, mouse.y)

function program() {

};



draw(mouse.x, mouse.y)

The abstraction absorbed all explicit "event" handling



xintegral, +, cos...

a ( t0 )a ( t1 )a ( t2 )a ( t3 )

a ( tN )a ( .... )

b ( t0 )b ( t1 )b ( t2 )b ( t3 )b ( .... )

x:: Time → Doublex = (1/2) * ( (integral (vr + vl)) * cos θ)

b ( tN )

Note that the ìntegral` is stateful (rely on time):


x:: Time → Doublex = (1/2) * ( (integral (vr + vl)) * cos θ)

t = N, integral (vr + vl)t from 0 to N...

t = 2, integral (vr + vl)t from 0 to 2t = 1, integral (vr + vl)t from 0 to 1t = 0, integral (vr + vl)t from 0 to 0

In order to perform stateful computations, we must represent our signals in FRP as streams:


newtype S a = S ([DTime] → [a])newtype C a = C (a, DTime → C a)

integralS :: Double → S Double → S DoubleintegralC :: Double → C Double → C Double

x = integralS 0 (vr + vl), where vr & vl are Signals x = integralC 0 (vr + vl), where vr & vl are Signals

In fact we can't touch any signal in Yampa. We can only compose Signal Functions

f[ state (t) ]

a ( t ) b ( t )

Signal Function

SF:: Signal a → Signal b

f:: SF a b

f:: SF a b

The `state (t)` summarizes input history:same `f` handle every à` in different time

SF:: Signal a → Signal b

f[ state (t) ]

a ( t0 )a ( t1 )a ( t2 )a ( t3 )

a ( tN )a ( .... )

b ( t0 )b ( t1 )b ( t2 )b ( t3 )

b ( tN )b ( .... )

Signal Function

"Space-Time Leak"

Why Signal Functions ?

Space-Time Leak in FRP

It means that old records got heaped up and occurs the memory due to our expressions had been expanded improperly.

An analogous example

repeat x = λx → x : repeat x repeat x = λx → let xs = x:xs in xs

Leaks No-Leaks


repeat x = λx → x : repeat xrepeat 3

↪ (λx → x : repeat x) (3)

↪ 3 : repeat 3

↪ 3 : (λx → x : repeat x) (3)

↪ 3 : 3 : repeat 3

↪ 3 : 3 : (λx → x : repeat x) (3)

↪ 3 : 3 : 3 : repeat 3

↪ ...

It must create new nodes for every `repeat 3`


repeat x = λx → let xs = x:xs in xsrepeat 3

↪ (λx → let xs = x:xs in xs) (3)

↪ xs, where `xs` was defined as above:

↪ 3: xs, where `xs` was defined as above:

↪ 3: 3: xs, where `xs` was defined as above:

↪ 3: 3: 3: xs, where `xs` was defined as above:

↪ ...

The `let` bind the same `xs` "reference" in every expanded expression, thus there is no need to create new nodes in memory.


Even though this is just an analogous example, but a similar idea is in Arrow:

class Arrow a ⇒ ArrowLoop a where loop :: a (b,d) (c,d) → a b c

instance ArrowLoop (→ ) where

loop f b = let (c,d) = f (b,d) in c

And Yampa uses Arrow to prevent space- time leaks, especially the ArrowLoop.


RECURSIVE CODES MAY DESTRUCT YOUR MIND


A practical example:

This example will show that space-time leak may happen when a computation is stateful ( based on time )

The Exponential Function


Because in practical systems we only care about how to compute on continuous streams of values, we can represent our Signals as streams in two forms:

newtype S a = S ([DTime] → [a])newtype C a = C (a, DTime → C a)

-- delta time, for samplingtype DTime = Double

The later one will expand its second (C a) to make a continuing stream.


And the integral functions:

integralS :: Double → S Double → S DoubleintegralS i (S f) = S (λdts → scanl (+) i (zipWith (∗) dts (f dts)))

integralC :: Double → C Double → C DoubleintegralC i (C p) = C (i, λdt→integralC (i + fst p ∗ dt) (snd p dt))

We assume that delta time is fixed, which is impractical in real system, where it will depends on processor speed, computational load, interrupts, and so on


Remember that the integral need to accumulate each values at every DTime:

DTime

That's why it need to expand the stream while evaluation:

e = integralC 1 e = integralC i:1 (C p):e=integralC 1 e=...↪ C (1, λdt→integralC (1 + fst p ∗ dt) (snd p dt) )

↪ C (1, λdt→integralC (1 + 1 ∗ dt) (snd p dt) )


p

fst P




↪ C (1, λdt→integralC (1 + 1 ∗ dt) (snd p dt) )


psnd p



↪ C (1, λdt→integralC (1 + 1 ∗ dt) (sndp dt) )


psnd p



↪ C (1, λdt→integralC (1 + 1 ∗ dt) (sndp dt) )

↪ C (1, q )


psnd p

Now we may want to "run" the stream to see what will happen:

run :: C Double → [Double]run (C p) = fst p : run (snd p dt)


Now we may want to "run" the stream to see what will happen:

run e, where run (C p) = fst p : run (snd p dt)

↪ run C (1, q ), because e = C (1, q ); then evaluate the run:

↪ 1 : run (q dt)

↪ 1 : run ((λdt→integralC (1 + 1 ∗ dt) (q dt) ) dt)

↪ 1 : run (integralC (1 + 1 ∗ dt) (q dt) )

↪ let's expand the horrible q now...




↪ 1 : run (q dt)

↪ 1 : run ((λdt→integralC (1 + 1 ∗ dt) (qdt) ) dt)


↪ 1 : run (C ( (1 + 1 ∗ dt),

(λdt→integralC (1 + fst p ∗ dt) (snd p dt) ) (dt) )




↪ 1 : run (q dt)

↪ 1 : run ((λdt→integralC (1 + 1 ∗ dt) (qdt) ) dt


↪ 1 : run (C ( (1 + 1 ∗ dt),


↪ 1 : run (C ( (1 + 1 ∗ dt),



new P new Q

↪ 1 : run (C ( (1 + 1 ∗ dt),


↪ 1 : run (C ( (1 + 1 ∗ dt), q ))↪ 1 : run ((1 + 1 ∗ dt): run (q dt)) ↪ ...


This leads to O(n) space ,and O(n2) time similar to the simplifier `repeat` example

We hope to reduce it to O(Const) space, and O(n) time to compute it


The main issue here is the progress of evaluation can't recognize this:

Is the same with:

Just like the `repeat` example

f = λdt → integralC (1 + dt) (f dt)

f = λdt → let x = integralC (1 + dt) x in x


And we can compare them by diagrams:

f = λdt → integralC (1 + dt) (f dt)

f = λdt → let x = integralC (1 + dt) x in x


In the `repeat` example:

repeat x = λx → x : repeat x

repeat x = λx → let xs = x:xs in xs


So, in Yampa, we can't touch and handle signals directly, and need to use predefined combinators to compose our program.

This can greatly reduce possible leaks, and keep our program is still similar with the most intuitive version.

e = integralC 1 e e = proc () → do rec e ← integral 1 ↢e returnA ↢ e


integralSF :: Double → SF Double DoubleintegralSF i = SF (λx → (i, λdt → integralSF (i + dt ∗ x)))


Versus the previous version:

Our integral function now become:

Signal handling become implicit. We now raise customized functions to SFs.


integralSF :: Double → SF Double DoubleintegralSF i = SF (λx → (i, λdt → integralSF (i + dt ∗ x)))

Now integralSF need be embedded in Arrow structure to feed input in & run it:

e = proc () → do rec e ← integral 1 ↢e returnA ↢ e

runSF:: SF () Double → [Double]runSF e

Back to Yampa

Yampa & AFRP


Yeah ! Just escaped from the black hole !

Yampa constructs the whole system by combining Signal Functions

Combine Signal Functions

gfa b c

h:: SF a c



gfa b c

... and there's already a natural mechanism to achieve this in Haskell



gfa b c

( ⋙):: SF a b → SF b c → SF a c

"Composition" in Control.Arrow

So Yampa is actually an AFRP framework, not only a FRP framework.


gfa b c

( ⋙):: SF a b → SF b c → SF a c

"Composition" in Control.Arrow

Arrow

We're here:

Typeclassopediawww.haskell.org/haskellwiki/

Typeclassopedia

http://www.haskell.org/haskellwiki/Typeclassopedia






Arrow Basics

"Unlike Monad and Applicative, whose types only reflect their output, the type of an Arrow computation reflects both its input and output"

( ≫= ):: M a → (a → M b) → M b

( ⋙ ):: A a b → A b c → A a c

Arrow Basics

In Monad the binding generates a contexted value like ÌO String` from an opaque function , and it can output such values without feeding any input

( ≫= ):: M a → (a → M b) → M b

Arrow Basics

In Monad the binding generates a contexted value like ÌO String` from an opaque function , and it can output such values without feeding any input

( ≫= ):: M a → (a → M b) → M b

When you got a composed ÌO String`, you don't need to feed it any input to get the output. The "input" is already encapsulated in the Monad.

Ex: readFile "/tmp/foo.txt" :: IO String

Arrow Basics

Arrows, on the other hand, can only be composed by other arrow combinators, and keep it still crystal clear after the composition

( ⋙ ):: A a b → A b c → A a c

Arrow Basics

Arrows, on the other hand, can only be composed by other arrow combinators, and keep it still crystal clear after the composition

( ⋙ ):: A a b → A b c → A a cYou can't get any meaningful value without executing the composed Arrow, which will also require you to feed it an input value.

Ex: let area = runF ( pow ⋙ mulpi ) r

Arrow Basics

So Arrows are just like pipelines, and Monads are more like pipeline plus self-contained pumps

There are many combinators in Control.Arrow

Arrow Basics

Compose First

f g f

Split

f

g

f

Loop ( self-feedback )

They will make your application like circuits

Arrow Basics

Back to Yampa

Yampa & AFRP


Yampawww.haskell.org/haskellwiki/Yampa

(not official logo)





Using Arrows without some syntax sugars will make programs a bit very ugly

Arrow Syntax

proc x → do y ← f ↢ x+1 g ← 2*y let z = x+y t ← h ↢ x*z returnA ↢ t+z

arr (λ x → (x, x)) ⋙ first (arr (\ x → x+1) ⋙ f) ⋙ arr (\ (y, x) → (y, (x, y))) ⋙ first (arr (\ y → 2*y) ⋙ g) ⋙ arr snd ⋙ arr (\ (x, y) → let z = x+y in ((x, z), z)) ⋙ first (arr (\ (x, z) → x*z) ⋙ h) ⋙ arr (\ (t, z) → t+z) ⋙ returnA

http://www.haskell.org/ghc/docs/6.12.3/html/users_guide/arrow-notation.html





Using Arrows without some syntax sugars will make programs a bit very ugly

Arrow Syntax

proc x → do y ← f ↢ x+1 g ← 2*y let z = x+y t ← h ↢ x*z returnA ↢ t+z

proc <pattern> = λ→ <pattern>

<pattern> ← <arrf> ↢<expr> ≈let <pattern> = <arrf> <expr>

rec <do block> = ArrowLoop

http://stackoverflow.com/questions/5405850/how-does-the-haskell-rec-keyword-work




In Yampa, Event is just a Maybe-like type, representing discrete signals

Event

data Event a = NoEvent | Event atag:: Event a → b → Event b

We usually use events to trigger switchers, which will change the structure of our system dynamically.

Arrows will make your application like circuits. But you own a dynamic system now, not a static one.

Switches

Time 0Time 1Time 2Time 3Time 4Time 5Time 6Time 7Time ...Time N

This means circuits won't change unless we introduce Switches

Switches

Normal signals will be SFs' input; only events will go through the line toward the `k` function with data `mng`. If so the continuation function `k` will spawn a new SF or kill old SFs in the system.

A pseudo example shows how switches works

Switches

f Kin out System @ T01 SF inside a Switch. Switch will let normal signal pass

f Kin out System @ T1`f` generate an Event Spawn,rather than a Signal with data

Spawn

f Kin out System @ T1`K` captured the event, and generate a new SF `g`

g


Switches

f Kin out System @ T1Then `K` put the new SF in our system.g

System @ T2Now we've 2 SFs and inputs will be dispatched to them all

f Kin out

g


Switches

f Kin out System @ T3`g` trigger a Kill event with id 'f'

g Kill

Kin out System @ T3`K` will kill the SF `f`

gf

Kin out System @ T4Done. This shows our system will change by time.

g

Note: we omitted many details to keep this example clean enough. For instance, we can't kill named SFs after all. They're actually IL Objects.

Switches, more switches... ( cry )

Switches


Switches


Switches

http://www.haskell.org/haskellwiki/Yampa/switch





How is it possible to use these stuff making a 3D game ?

Switches

Programming withYampa

Game ObjectsRouteKill or SpawndpSwitch

Yampa Acradehttp://bit.ly/sntw-ypa

http://bit.ly/sntw-ypa

http://bit.ly/sntw-ypa

Game objects, like dragons, weapons, players and NPCs are just SFs which receive inputs like the position and velocity, and output the new, updated GameStates

Game Objects

fin out

g

Game objects, like dragons, weapons, players and NPCs are just SFs which receive inputs like the position and velocity, and output the new, updated GameStates

Game Objects

type Object = SF ObjInput ObjOutputFrom the file "Object.hs" in the game Frag.

Object inputs in Frag:

Game Objects

data ObjInput = ObjInput { oiHit :: !(Event [(ILKey,ObsObjState)]), oiMessage :: !(Event [(ILKey,Message)]), oiCollision :: !Camera, oiCollisionPos :: !(Double,Double,Double), oiOnLand :: !Bool, oiGameInput :: !GameInput, oiVisibleObjs :: !(Event [(ILKey,ObsObjState)])}

Object outputs in Frag:

Game Objects

data ObjOutput = ObjOutput { ooObsObjState :: !ObsObjState, ooSendMessage :: !(Event [(ILKey,(ILKey,Message))]),

ooKillReq :: (Event ()), ooSpawnReq :: (Event [ILKey->Object])}

A simple game object from Yampa Arcade

Game Objects

data SimpleGunState = SimpleGunState { sgsPos :: Position2, sgsVel :: Velocity2, sgsFired :: Event ()}

type SimpleGun = SF GameInput SimpleGunState

Source code:http://hackage.haskell.org/package/SpaceInvaders

http://haskell.cs.yale.edu/wp-content/uploads/2011/01/yampa-arcade.pdf

http://hackage.haskell.org/package/SpaceInvaders

http://hackage.haskell.org/package/SpaceInvaders

A simple game object from Yampa Arcade

Game Objects

gun

GameInput

Mouse PositionMouse ButtonKeyboard Input

Calculate new position and velocity of the gun

SimpleGunState

sgsPossgsVelsgsFired Event


In fact we don't define a SF. We define a SF generator :

Game Objects

simpleGun :: Position2 → SimpleGun

simpleGun (Point2 x0 y0) = proc gi → do (Point2 xd _) ← ptrPos ↢ gi rec let ad = 10 * (xd - x) - 5 * v v ← integral ↢ clampAcc v ad {- ...... -} returnA -< SimpleGunState { sgsPos = (Point2 x y0), sgsVel = (vector2 v 0), sgsFired = fire }

This allow us to embed it into our circuits and change the initial value as we need to:

Game Objects

game g nAliens vydAlien score0 = proc gi -> do rec oos <- game' objs0 -< (gi, oos {- oosp -}) {- ...... -} returnA -< ((score, map ooObsObjState (elemsIL oos)), (newRound `tag` (Left score)) `lMerge` (gameOver `tag` (Right score))) where objs0 = listToIL (gun (Point2 0 50))

Now we have game objects. Then we've to use a `route` function to pair each object with an input.

Route

route:: BSPMap → (GameInput, IL ObjOutput) → IL sf → IL (ObjInput, sf)

From the file "Game.hs" in the game Frag.


Route


Route will broadcast GameInput to all game object which are like keyboard & mouse state , and handle outputs from game objects to send messages or detect collisions, then only pair it with those related objects.


Route

gun

NPC#1

PC

Wall

RGameInput

ObjOutput

Decide which objects should be dispatched with current ObjOutputs


Route


ÌL a` means a "identity list", which is actually a associate list contains `(ILKey , a)`. Frag use it to store named game objects ( just SFs ) .


Route


Finally the route generate a associate list, pairing each SF with an object input. Note that the original ObjOutput may be converted inside the function.

The function `KillOrSpawn` will capture all events generated by game objects, and find if any kill or spawn events occured.

Kill or Spawn

killOrSpawn :: (a, IL ObjOutput)→ (Event (IL Object→IL Object))

From the file "Game.hs" in the game Frag.


Kill or Spawn

killOrSpawn :: (a, IL ObjOutput)→ (Event (IL Object→IL Object))It'll receive lots of object outputs, then generate:

1. NoEvent: do nothing ( rem: Event can be NoEvent )2. Kill: with a function that will kill some SFs3. Spawn: with a function that will spawn new SFs

The function will apply on object collections then change it


Kill or Spawn

f Kin out

g

ooKillReqWill generate an Event to kill some SFs in the collection

f Kin out

g

ooSpawnReq

Will generate an Event to spawn new SFs in the collection

Our `route` and `killOrSpwan` are prepared for the `dpSwitch`, which maintain whole dynamic structure in our program

dpSwitch

Our `route` and `killOrSpwanàre prepared for the `dpSwitch`, which maintain whole dynamic structure in our program

dpSwitch

dpSwitch :: Functor col ⇒(∀ sf . (a → col sf → col (b, sf)))→ col (SF b c)→ SF (a, col c) (Event d)→ (col (SF b c) -> d -> SF a (col c))→ SF a (col c)


The first argument is our routing function.

dpSwitch


The second one is the initial collection of game objects.

dpSwitch


And the third argument is our `killOrSpawn`.

dpSwitch


The fourth function will be invoked while switching event occurs, yielding a new switch function and switch into, based on the collection previous transformed.

dpSwitch


This allows the collection to be updated and then switched back in, typically by employing `dpSwitch` again.

dpSwitch

Now we have a dynamic structure can compose every piece in our game, so the Quake warrior can roar with firing guns, right ?

Some Other Stuff

Still Missing

NPC & AILevelsOpenGL Binding...

There is no royal road to Haskell. —Euclid ( typeclassopedia )

Maybe we're still not apt to create a 3D game with the AFRP framework, Yampa, but ideas in FRP & Arrow still inspire us.

Conclusion

And FRP is also a generic way to construct any "Event-Driven" like system, from 3D FPS games to HTTP servers.

Conclusion

http://stackoverflow.com/questions/13486293/is-frp-a-proper-way-to-implement-most-event-driven-things





Some differences between FRP and so-called "Event-Driven" pattern:

Conclusion

FRP based on continuing Streams of values, and we compute on them as we compute on a single value.

dt1 dt2 dt3 dt4 dt5

sf

sf can be primitive SF, composed SF or Switch

Some differences between FRP and so-called "Event-Driven" pattern:

Conclusion

Event-Driven use individual callbacks to react at every moment the event got triggered.

T1 T2 T3 T4 T5

cb

cb is a simple function, closure,or class method

cb cb cb cb

Conclusion

This can be obvious in JavaScript and Node.js, which use Event-Driven as their reactive pattern

DOM.addEventListener('click', function(e){ // do something})

fs.readFile( '/tmp/test.txt', function(err, data){ // do something})

Conclusion

But some libraries also try to implement FRP paradigm in JavaScript:

// from Bacon.js

var plus = $("#plus").asEventStream("click").map(1)var minus = $("#minus").asEventStream("click").map(-1)var both = plus.merge(minus)

https://github.com/raimohanska/bacon.js?utm_source=javascriptweekly&utm_medium=email

Conclusion


// from Flapjax.js, compiler mode

<div><h1> You caught up <span style="color: white; background-color: black"> {! caughtUpB.toString() !} </span>times</h1>hit up with your mouse</div>

http://www.flapjax-lang.org/try/index.html?edit=catchup.fx

Conclusion


http://elm-lang.org/learn/What-is-FRP.elm





Conclusion

And you may notice that in FRP, events/signals are handled "globally", but in JavaScript, they're handled "locally":

DOM → click, mouse hover...

click, mouse hover → Event DOM// FRP

// Event-Driven

References

There's a lot of resources in Haskell Wikihttp://www.haskell.org/haskellwiki/Yampa



References

Two useful articles about Yampa and game

http://www.cse.unsw.edu.au/~pls/thesis/munc-thesis.pdfhttp://haskell.cs.yale.edu/wp-content/uploads/2011/01/yampa-arcade.pdf

Yampa and robot control

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.5.2886&rep=rep1&type=pdf

http://www.cse.unsw.edu.au/~pls/thesis/munc-thesis.pdf

http://www.cse.unsw.edu.au/~pls/thesis/munc-thesis.pdf








References

Space-Time Leak:

http://conal.net/blog/posts/trimming-inputs-in-functional-reactive-programming

http://cs-www.cs.yale.edu/homes/hl293/download/leak.pdf

http://www.cs.ox.ac.uk/ralf.hinze/WG2.8/24/slides/paul.pdf








Presentations & Public Speaking

Yampa AFRP Introduction