Equivalence of Functional dependency set - database-theory

Fd1 = {AB --> C, D --> E, E --> C}
Fd2 = { AB --> C, D --> E, AB --> E, E --> C}
are these two FD's are equivalent or not, i think they are. But in the answer it's shown as not equivalent.

You cannot produce AB → E from dependencies in the first set.
To mathematically prove their (in)equivalence, you should build closures for both sets and compare the closures.
There are a few simple induction rules to build the a closure. Quoting Wikipedia on Functional Dependency, the axioms are:
Reflexivity: If Y is a subset of X, then X → Y
Augmentation: If X → Y, then XZ → YZ
Transitivity: If X → Y and Y → Z, then X → Z
with by a few rules that follow from them:
Union: If X → Y and X → Z, then X → YZ
Decomposition: If X → YZ, then X → Y and X → Z
Pseudotransitivity: If X → Y and WY → Z, then WX → Z
Composition: If X → Y and Z → W, then XZ → YW
Using these rules and axioms, one can build a closure for a FDS.
Omitting trivial dependencies (the ones where right side is included into left side), first set { AB → C (1), D → E (2), E → C (3) } gives:
AB → C (1)
ABD → CE, ABD → C, ABD → E (composition 1+2, decomposition)
ABDE → CE, ABDE → C (composition 1+2+3, decomposition)
ABE → C (composition 1+3)
D → E, D → C, D → CE (2, transitivity 2+3, union)
DE → CE, DE → C (composition 2+3, decomposition)
E → C (3)
And the second set { AB → C (1), D → E (2), E → C (3), AB → E (4) } gives:
AB → C, AB → E, AB → CE (1, 4, union 1+4)
ABD → CE, ABD → C, ABD → E (composition 1+2, decomposition)
ABDE → CE, ABDE → C (composition 1+2+3, decomposition)
ABE → C (composition 1+3)
D → E, D → C, D → CE (2, transitivity 2+3, union)
DE → CE, DE → C (composition 2+3, decomposition)
E → C (3)
The second closure has AB → E, AB → CE, which is not present in the first closure, therefore original sets are different.

Related

Is (A -> B) /\ (C -> D) <: (A /\ C) -> (B /\ D)?

Is (A -> B) /\ (C -> D) a subtype of (A /\ C) -> (B /\ D)?
It seems like it shouldn't be, simply on account of -> being contravariant, but I can't find a good counterexample.
If it is, how can I derive this?
If not, what would a counterexample be?
(To clarify, I'm using /\ for intersection here.)
These types are in a subtype relation, precisely because of contravariance. The union would be a supertype of A and C, so would violate contravariance.
Recall the subtyping rule for functions, which is contravariant in the domain type:
T → U <: T' → U' iff T' <: T and U <: U'
For intersection types, you also have a distributivity rule over arrow types:
(T → U) ∧ (T → V) = T → (U ∧ V)
And of course we have the usual elimination rules for intersection types:
T ∧ U <: T
T ∧ U <: U
Plugging these four rules together, you can easily derive the subtyping you're asking about:
(A → B) ∧ (C → D)
<: (by contravariance and left elimination)
((A ∧ C) → B) ∧ (C → D)
<: (by contravariance and right elimination)
((A ∧ C) → B) ∧ ((A ∧ C) → D)
<: (by distributivity)
(A ∧ C) → (B ∧ D)
FWIW, with union types, you also have the dual distributivity rule:
(U → T) ∨ (V → T) = (U ∨ V) → T
With that, you can analogously derive:
(A → B) ∨ (C → D) <: (A ∨ C) → (B ∨ D)

Generic programming via effects

In the Idris Effects library effects are represented as
||| This type is parameterised by:
||| + The return type of the computation.
||| + The input resource.
||| + The computation to run on the resource given the return value.
Effect : Type
Effect = (x : Type) -> Type -> (x -> Type) -> Type
If we allow resources to be values and swap the first two arguments, we get (the rest of the code is in Agda)
Effect : Set -> Set
Effect R = R -> (A : Set) -> (A -> R) -> Set
Having some basic type-context-membership machinery
data Type : Set where
nat : Type
_⇒_ : Type -> Type -> Type
data Con : Set where
ε : Con
_▻_ : Con -> Type -> Con
data _∈_ σ : Con -> Set where
vz : ∀ {Γ} -> σ ∈ Γ ▻ σ
vs_ : ∀ {Γ τ} -> σ ∈ Γ -> σ ∈ Γ ▻ τ
we can encode lambda terms constructors as follows:
app-arg : Bool -> Type -> Type -> Type
app-arg b σ τ = if b then σ ⇒ τ else σ
data TermE : Effect (Con × Type) where
Var : ∀ {Γ σ } -> σ ∈ Γ -> TermE (Γ , σ ) ⊥ λ()
Lam : ∀ {Γ σ τ} -> TermE (Γ , σ ⇒ τ ) ⊤ (λ _ -> Γ ▻ σ , τ )
App : ∀ {Γ σ τ} -> TermE (Γ , τ ) Bool (λ b -> Γ , app-arg b σ τ)
In TermE i r i′ i is an output index (e.g. lambda abstractions (Lam) construct function types (σ ⇒ τ) (for ease of description I'll ignore that indices also contain contexts besides types)), r represents a number of inductive positions (Var doesn't (⊥) receive any TermE, Lam receives one (⊤), App receives two (Bool) — a function and its argument) and i′ computes an index at each inductive position (e.g. the index at the first inductive position of App is σ ⇒ τ and the index at the second is σ, i.e. we can apply a function to a value only if the type of the first argument of the function equals the type of the value).
To construct a real lambda term we must tie the knot using something like a W data type. Here is the definition:
data Wer {R} (Ψ : Effect R) : Effect R where
call : ∀ {r A r′ B r′′} -> Ψ r A r′ -> (∀ x -> Wer Ψ (r′ x) B r′′) -> Wer Ψ r B r′′
It's the indexed variant of the Oleg Kiselyov's Freer monad (effects stuff again), but without return. Using this we can recover the usual constructors:
_<∨>_ : ∀ {B : Bool -> Set} -> B true -> B false -> ∀ b -> B b
(x <∨> y) true = x
(x <∨> y) false = y
_⊢_ : Con -> Type -> Set
Γ ⊢ σ = Wer TermE (Γ , σ) ⊥ λ()
var : ∀ {Γ σ} -> σ ∈ Γ -> Γ ⊢ σ
var v = call (Var v) λ()
ƛ_ : ∀ {Γ σ τ} -> Γ ▻ σ ⊢ τ -> Γ ⊢ σ ⇒ τ
ƛ b = call Lam (const b)
_·_ : ∀ {Γ σ τ} -> Γ ⊢ σ ⇒ τ -> Γ ⊢ σ -> Γ ⊢ τ
f · x = call App (f <∨> x)
The whole encoding is very similar to the corresponding encoding in terms of indexed containers: Effect corresponds to IContainer and Wer corresponds to ITree (the type of Petersson-Synek Trees). However the above encoding looks simpler to me, because you don't need to think about things you have to put into shapes to be able to recover indices at inductive positions. Instead, you have everything in one place and the encoding process is really straightforward.
So what am I doing here? Is there some real relation to the indexed containers approach (besides the fact that this encoding has the same extensionality problems)? Can we do something useful this way? One natural thought is to built an effectful lambda calculus as we can freely mix lambda terms with effects, since a lambda term is itself just an effect, but it's an external effect and we either need other effects to be external as well (which means that we can't say something like tell (var vz), because var vz is not a value — it's a computation) or we need to somehow internalize this effect and the whole effects machinery (which means I don't know what).
The code used.
Interesting work! I don't know much about effects and i have only a basic understanding of indexed containers, but i am doing stuff with generic programming so here's my take on it.
The type of TermE : Con × Type → (A : Set) → (A → Con × Type) → Set reminds me of the type of descriptions used to formalize indexed induction recursion in [1]. The second chapter of that paper tells us that there is an equivalence between Set/I = (A : Set) × (A → I) and I → Set. This means that the type of TermE is equivalent to Con × Type → (Con × Type → Set) → Set or (Con × Type → Set) → Con × Type → Set. The latter is an indexed functor, which is used in the polynomial functor ('sum-of-products') style of generic programming, for instance in [2] and [3]. If you have not seen it before, it looks something like this:
data Desc (I : Set) : Set1 where
`Σ : (S : Set) → (S → Desc I) → Desc I
`var : I → Desc I → Desc I
`ι : I → Desc I
⟦_⟧ : ∀{I} → Desc I → (I → Set) → I → Set
⟦ `Σ S x ⟧ X o = Σ S (λ s → ⟦ x s ⟧ X o)
⟦ `var i xs ⟧ X o = X i × ⟦ xs ⟧ X o
⟦ `ι o′ ⟧ X o = o ≡ o′
data μ {I : Set} (D : Desc I) : I → Set where
⟨_⟩ : {o : I} → ⟦ D ⟧ (μ D) o → μ D o
natDesc : Desc ⊤
natDesc = `Σ Bool (λ { false → `ι tt ; true → `var tt (`ι tt) })
nat-example : μ natDesc tt
nat-example = ⟨ true , ⟨ true , ⟨ false , refl ⟩ , refl ⟩ , refl ⟩
finDesc : Desc Nat
finDesc = `Σ Bool (λ { false → `Σ Nat (λ n → `ι (suc n))
; true → `Σ Nat (λ n → `var n (`ι (suc n)))
})
fin-example : μ finDesc 5
fin-example = ⟨ true , 4 , ⟨ true , 3 , ⟨ false , 2 , refl ⟩ , refl ⟩ , refl ⟩
So the fixpoint μ corresponds directly to your Wer datatype, and the interpreted descriptions (using ⟦_⟧) correspond to your TermE. I'm guessing that some of the literature on this topic will be relevant for you. I don't remember whether indexed containers and indexed functors are really equivalent but they are definitely related. I do not entirely understand your remark about tell (var vz), but could that be related to the internalization of fixpoints in these kinds of descriptions? In that case maybe [3] can help you with that.
[1]: Peter Hancock, Conor McBride, Neil Ghani, Lorenzo Malatesta, Thorsten Altenkirch - Small Induction Recursion (2013)
[2]: James Chapman, Pierre-Evariste Dagand, Conor McBride, Peter Morris - The gentle art of levitation (2010)
[3]: Andres Löh, José Pedro Magalhães - Generic programming with indexed functors

How can I generalise defaulting across Maybe and Either?

In Haskell, if I have two functions like this:
defEither ∷ Either l r → r → r
defEither eith defVal = either (const defVal) id eith
and
defMaybe ∷ Maybe a → a → a
defMaybe m d = fromMaybe d m
How do I write a type class (or something to similar effect) such that I can generalise the concept of "defaultable" across both Either and Maybe?
Something like
class Defaultable ???? where
def ∷ a b → b → b
Turns out it was the syntax around creating the instance for Either that was confusing me.
Here is what I finished up with:
class Defaultable a where
def ∷ a b → b → b
instance Defaultable (Either m) where
def e d = either (const d) id e
instance Defaultable Maybe where
def m d = fromMaybe d m
And some tests
def (Just 1) 2
>> 1
def Nothing 2
>> 2
def (Right 2) 5
>> 2
def (Left 3) 5
>> 5
def (Left "Arrghh") 5
>> 5

Haskell's Arrow-Class in Agda and -> in Agda

I have two closely related questions:
First, how can the Haskell's Arrow class be modeled / represented in Agda?
class Arrow a where
arr :: (b -> c) -> a b c
(>>>) :: a b c -> a c d -> a b d
first :: a b c -> a (b,d) (c,d)
second :: a b c -> a (d,b) (d,c)
(***) :: a b c -> a b' c' -> a (b,b') (c,c')
(&&&) :: a b c -> a b c' -> a b (c,c')
(the following Blog Post states that it should be possible...)
Second, in Haskell, the (->) is a first-class citizen and just another higher-order type and its straightforward to define (->) as one instance of the Arrow class. But how is that in Agda? I could be wrong, but I feel, that Agdas -> is a more integral part of Agda, than Haskell's -> is. So, can Agdas -> be seen as a higher-order type, i.e. a type function yielding Set which can be made an instance of Arrow?
Type classes are usually encoded as records in Agda, so you can encode the Arrow class as something like this:
open import Data.Product -- for tuples
record Arrow (A : Set → Set → Set) : Set₁ where
field
arr : ∀ {B C} → (B → C) → A B C
_>>>_ : ∀ {B C D} → A B C → A C D → A B D
first : ∀ {B C D} → A B C → A (B × D) (C × D)
second : ∀ {B C D} → A B C → A (D × B) (D × C)
_***_ : ∀ {B C B' C'} → A B C → A B' C' → A (B × B') (C × C')
_&&&_ : ∀ {B C C'} → A B C → A B C' → A B (C × C')
While you can't refer to the function type directly (something like _→_ is not valid syntax), you can write your own name for it, which you can then use when writing an instance:
_=>_ : Set → Set → Set
A => B = A → B
fnArrow : Arrow _=>_ -- Alternatively: Arrow (λ A B → (A → B)) or even Arrow _
fnArrow = record
{ arr = λ f → f
; _>>>_ = λ g f x → f (g x)
; first = λ { f (x , y) → (f x , y) }
; second = λ { f (x , y) → (x , f y) }
; _***_ = λ { f g (x , y) → (f x , g y) }
; _&&&_ = λ f g x → (f x , g x)
}
While hammar's answer is a correct port of the Haskell code, the definition of _=>_ is too limited compared to ->, since it doesn't support dependent functions. When adapting code from Haskell, that's a standard necessary change if you want to apply your abstractions to the functions you can write in Agda.
Moreover, by the usual convention of the standard library, this typeclass would be called RawArrow because to implement it you do not need to provide proofs that your instance satisfies the arrow laws; see RawFunctor and RawMonad for other examples (note: definitions of Functor and Monad are nowhere in sight in the standard library, as of version 0.7).
Here's a more powerful variant, which I wrote and tested with Agda 2.3.2 and the 0.7 standard library (should also work on version 0.6). Note that I only changed the type declaration of RawArrow's parameter and of _=>_, the rest is unchanged. When creating fnArrow, though, not all alternative type declarations work as before.
Warning: I only checked that the code typechecks and that => can be used sensibly, I didn't check whether examples using RawArrow typecheck.
module RawArrow where
open import Data.Product --actually needed by RawArrow
open import Data.Fin --only for examples
open import Data.Nat --ditto
record RawArrow (A : (S : Set) → (T : {s : S} → Set) → Set) : Set₁ where
field
arr : ∀ {B C} → (B → C) → A B C
_>>>_ : ∀ {B C D} → A B C → A C D → A B D
first : ∀ {B C D} → A B C → A (B × D) (C × D)
second : ∀ {B C D} → A B C → A (D × B) (D × C)
_***_ : ∀ {B C B' C'} → A B C → A B' C' → A (B × B') (C × C')
_&&&_ : ∀ {B C C'} → A B C → A B C' → A B (C × C')
_=>_ : (S : Set) → (T : {s : S} → Set) → Set
A => B = (a : A) -> B {a}
test1 : Set
test1 = ℕ => ℕ
-- With → we can also write:
test2 : Set
test2 = (n : ℕ) → Fin n
-- But also with =>, though it's more cumbersome:
test3 : Set
test3 = ℕ => (λ {n : ℕ} → Fin n)
--Note that since _=>_ uses Set instead of being level-polymorphic, it's still
--somewhat limited. But I won't go the full way.
--fnRawArrow : RawArrow _=>_
-- Alternatively:
fnRawArrow : RawArrow (λ A B → (a : A) → B {a})
fnRawArrow = record
{ arr = λ f → f
; _>>>_ = λ g f x → f (g x)
; first = λ { f (x , y) → (f x , y) }
; second = λ { f (x , y) → (x , f y) }
; _***_ = λ { f g (x , y) → (f x , g y) }
; _&&&_ = λ f g x → (f x , g x)
}

Non commutative intersection over Maps

How can I define function, that for each key of first map lookup a value of second map, apply some function to these 2 values and generate third map?
∷ (α → Maybe β → γ) → Map k α → Map k β → Map k γ
I played a little bit with some combinations of unionWith, differenceWith and intersectionWith, but stuck on mixing them with lookup.
Is
foo :: (α → Maybe β → γ) → Map k α → Map k β → Map k γ
foo comb ma mb = Map.mapWithKey (\k a -> comb a (Map.lookup k mb)) ma
what you want?

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