The Environment Model Semanticsenv :: n  n
env :: e1 + e2  n
if env :: e1  n1 and env :: e2  n2
and n is the result of applying
primitive operation + to n1 and n2
env :: (e1, e2)  (v1, v2)
if env :: e1  v1 and env :: e2  v2
env :: fst e  v1
if env :: e  (v1,v2)
env :: Left e  Left v
if env :: e  v
env :: match e with Left x > e1
 Right y > e2  v1
if env :: e  Left v and
env+{x=v} :: e1  v1
env :: let x = e1 in e2  v2
if env :: e1  v1 and
env+{x=v1} :: e2  v2
env :: (fun x > e)  <<fun x > e, env>>
env :: e1 e2  v
if env :: e1  <<fun x > e, env'>>
and env :: e2  v2
and env' + {x=v2} :: e  v
env :: let rec f x = e1 in e2  v
if env + {f = <<f, fun x > e1, env>>}
:: e2  v
env :: e1 e2  v
if env :: e1  <<f, fun x > e, env'>>
and env :: e2  v2
and env' + {x=v2,f=<<f, fun x > e, env'>>}
:: e  v

Environment Model Semantics Rule with Lexical Scoping
Technique to Generalize Folding1. Write a recursive fold function that takes in one argument for each variant of the datatype.
2. That fold function matches against the datatype variants, calling itself recursively on any instance of the datatype that it encounters.
3. When a variant carries data of a type other than the datatype being folded, use the appropriate argument to fold to incorporate that data.
4. When a variant carries no data, use the appropriate argument to fold to produce an accumulator.
let rec fold_left (f : 'a > 'b >'a) (acc : 'a) (lst : 'b list): 'a =
match lst with
[] > acc
 x :: xs > fold_left f (f acc x) xs
fold_left : 'a > 'b > 'a > 'a > 'b list > 'a
let rec fold_right (f : 'a > 'b > 'b) (l : 'a list) (acc : 'b) : 'b =
match l with
[] > acc
 x :: xs > f x (List.fold_right f xs acc)
fold_right: 'a > 'b > 'b > 'a list > 'b >'b
Example of Generalized fold:
type
'a exprTree =
 Val of 'a
 Unop of ('a > 'a) * 'a exprTree
 Binop of ('a > 'a > 'a) 'a exprTree 'a exprTree
let rec exprTree_fold (foldVal) (foldUnop) (foldBinop) = function
 Val x > foldVal x
 Unop (f, t) > foldUnop f (exprTree_fold foldVal foldUnop foldBinop t)  Binop (f, t1, t2) > foldBinop f (exprTree_fold foldVal foldUnop foldBinop t1) (exprTree_fold foldVal foldUnop foldBinop t2)
;;

Generalized fold and List folding functions
Function Type InferrenceInfer the type of functions from operations nested within the function. Start off by labeling all of the bindings and parameters with a random type Tn. And, then find out the type for each of them. Use patterns like the branches of an if and else statements are the same type and same goes for match statements.
Points to note are that the failure ("blah") and Exception Not_found have type 'a (just something random), so they can be restricted to whatever the other type is in a match expression. Also, let rec f x= f x in f has type 'a > 'b 
Documenting AbstractionsA specification is a contract between an implementer of an abstraction and a client of an abstraction. An implementation satisfies a specification if it provides the described behavior.
Locality: abstraction can be understood without needing to examine implementation
Modifiability: abstraction can be reimplemented without changing implementation of other abstractions
Good Specs:
Sufficiently restrictive: rule out implementations that wouldn’t be useful to clients
Sufficiently general: do not rule out implementations that would be useful to clients
Sufficiently clear: easy for clients to understand behavior
Abstraction function (AF) captures designer’s intent in choosing a particular representation of a data abstraction. Not actually OCaml function but an abstract function. Maps concrete values to abstract values. Think about Set example, where implementer sees Set as 'a list [1;2] but user sees it as {1,2}.
Manytoone: many values of concrete type can map to same value of abstract type. [1;2] & [2;1] both map to {1,2}
Partial: some values of concrete type do not map to any value of abstract type
[1;1;2] because no duplicates
opA(AF(c)) = AF(opC(c)). AF commutes with op!
You might write:
– Abstraction Function: comment – AF: comment
– comment
Representation invariant characterizes which concrete values are valid and which are invalid.
Valid concrete values will be mapped by AF to abstract values
Invalid concrete value will not be mapped by AF to abstract values 
  Substitution Model of Evaluatione1 + e2 > e1' + e2
if e1 > e1'
v1 + e2 > v1 + e2'
if e2 > e2'
n1 + n2 > n3
where n3 is the result of applying primitive operation +
to n1 and n2
(e1, e2) > (e1', e2)
if e1 > e1'
(v1, e2) > (v1, e2')
if e2 > e2'
fst (v1,v2) > v1
Left e > Left e'
if e > e'
match e with Left x > e1  Right y > e2
> match e' with Left x > e1  Right y > e2
if e > e'
match Left v with Left x > e1  Right y > e2
> e1{v/x}
match Right v with Left x > e1  Right y > e2
> e2{v/y}
let x = e1 in e2 > let x = e1' in e2
if e1 > e1'
let x = v in e2 > e2{v/x}
e1 e2 > e1' e2
if e1 > e1'
v e2 > v e2'
if e2 > e2'
Capture Avoiding Substitution
(fun x > e) v2 > e{v2/x}
(Left e'){e/x} = Left e'{e/x}
(Right e'){e/x} = Right e'{e/x}
(match e' with Left y > e1  Right z > e2){e/x}
= match e'{e/x} with Left y > e1{e/x}  Right z > e2{e/x}
(match e' with Left x > e1  Right z > e2){e/x}
= match e'{e/x} with Left x > e1  Right z > e2{e/x}
(match e' with Left y > e1  Right x > e2){e/x}
= match e'{e/x} with Left y > e1{e/x}  Right x > e2
(match e' with Left x > e1  Right x > e2){e/x}
= match e'{e/x} with Left x > e1  Right x > e2
(let x = e1 in e2){v/x} = let x = e1{v/x} in e2
(let y = e1 in e2){v/x} = let y = e1{v/x} in e2{v/x}
(e1,e2){e/x} = (e1{e/x}, e2{e/x})
(fst e'){e/x} = fst e'{e/x}

Substitution Model Evaluation Captureavoiding substitution
Example Module & Functor exampleStart off with this functor for Intervals.
module Make_interval :
functor (Endpoint : Comparable) >
sig
type t = Interval of Endpoint.t * Endpoint.t  Empty
val create : Endpoint.t > Endpoint.t > t
val is_empty : t > bool
val contains : t > Endpoint.t > bool
val intersect : t > t > t
end
Now, the functor does not have an abstract type. Because, the user can see the type in the functor. So, we have to hid that type t impelementation. There's a problem with Make_interval. The invariant is enforced by the create function, but because Interval.t is not abstract, we can bypass the create function. So you do something like this with sharing constraints: :
module Make_interval(Endpoint : Comparable) : Interval_intf with type endpoint = int struct
type endpoint = Endpoint.t
type t =  Interval of Endpoint.t * Endpoint.t
 Empty

Modules Signatures, Structures and FunctorsBasically, signature is the interface that we must follow for a certain module. The Structure of a module is the implementation of the given signature of the module. Furthermore, the functors go ahead and parameterize modules: that is, they will take in a module or multiple modules as inputs and return a new module that is parameterized with the input module. So, suppose you have a given Set module and you want this module to applicable to all types not only ints. So, you will need the notion of equality in your module, but this notion of equality is different between Ints and Strings, so you can parameterize by having a functor that has a type sig of EQUAL as its input. With functors remember to do the sharing constraints. 
Matching Mechanics & Type DeclarationsA type synonym is a new kind of declaration. The type and the name are interchangeable in every way.
Matching: Given a pattern p and a value v, decide
– Does pattern match value?
– If so, what variable bindings are introduced?
If p is a variable x, the match succeeds and x is bound to v.
If p is _, the match succeeds and no bindings are introduced
If p is a constant c, the match succeeds if v is c. No bindings are introduced
If p is C p1, the match succeeds if v is C v1 (i.e., the same constructor) and p1 matches v1. The bindings are the bindings from the submatch.
If p is (p1,..., pn) and v is (v1,..., vn), the match succeeds if p1 matches v1, and ..., and pn matches vn. The bindings are the union of all bindings from the submatches.
1. If Expressions are just pattern matches
2. Lists and options are just datatypes
3. Let expressions are also pattern matches.
4. A function argument can also be a pattern. 
  Type Checking RulesSyntax:
e1 + e2
Typechecking:
If e1 and e2 have type int, then e1 + e2 has type int
Syntax: e1 < e2
Typechecking: if e1 has type int and e2 has type
int then e1<e2 has type bool
Syntax: if e1 then e2 else e3
Typechecking: if e1 has type bool and, for some type t, both e2 and e3 have type t, then if e1 then e2 else e3 has type t
Simplified syntax:
let x = e1 in e2
Typechecking:
If e1:t1, and if e2:t2 under the assumption that
x:t1, then let x = e1 in e2 : t2
Syntax: e0 (e1,...,en)
Typechecking:
If: e0 has some type (t1 ... tn) > t
and e1 has type t1, ..., en has type tn
Then e0 (e1,...,en) has type t
Syntax: {f1=e1;...;fn=en}
Typechecking:
If e1:t1ande2:t2 and ... en:tn, and if t is a declared type of the form {f1:t1, ..., fn:tn} , then{f1 = e1; ...; fn = en}:t
Syntax: e.f
Typechecking:
If e:t1 and if t1 is a declared type of the form {f:t2, ...} , then e.f: t2
None has type ‘a option
– much like [] has type ‘a list – None is a value
Some e :t option ife:t
– much like e::[] has type t list if e:t – If e>v then Some e>Some v
Note Datatype VS Records Table

Type Checking Rules part of Semantics
Key Points about ModulesOther key points with modules:
1. Difference between include and open is that include just sort of extends a module/ signature when its called. In general, opening a module adds the contents of that module to the environment that the compiler looks at to find the definition of various identifiers.While opening a module affects the environment used to search for identifiers, including a module is a way of actually adding new identifiers to a module proper. The difference between include and open is that we've done more than change how identifiers are searched for: we've changed what's in the module. Opening modules is usuallly not a good thing in top level as you are getting rid of the advantage of a new namespace and if you want to do it, do it locally.
2. Don't expose the type of module especially in the signature, it is smart to hid from your user as they may abuse your invariant and don't have any idea on the implementation. So, you can also change the implementation without them knowing.
3. We can also use sharing constraints in the context of a functor. The most common use case is where you want to expose that some of the types of the module being generated by the functor are related to the types in the module fed to the functor 
Data Types VS Record VS Tuple  Declare  Build/Construct  Access/ Destruct  DataType  type  Constructor name  Pattern matching with match  Record  type  Record expression with {...}  Pattern matching with let OR field selection with dot operator .  Tuple  N/A  Tuple expression with (...)  Pattern matching with let OR fst or snd 
Records are used to store this AND that. Datatypes represent this OR that. Also, a tuple is just a record with its fields referred to by position, where as with records it is by name.
Algebraic Dataypes of form <Datatype: Name Student> of String
Dynamic VS Lexical ScopingRule of dynamic scope: The body of a function is evaluated in the current dynamic environment at the time the function is called, not the old dynamic environment that existed at the time the function was defined.
Rule of lexical scope: The body of a function is evaluated in the old dynamic environment that existed at the time the function was defined, not the current environment when the function is called. 
Functions as First Class CitizensFunctions are values
Can use them anywhere we use values
Firstclass citizens of language, afforded all the “rights” of any other values
– Functions can take functions as arguments – Functions can return functions as results
...functions can be higherorder
Map: let rec map f xs = match xs with
[] > []
 x::xs’ > (f x)::(map f xs’)
map: ('a>'b)>'alist>'blist
Filter, Map, folds are iterators basically. They can iterate through structures just like normal loops can. 

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