Past two Weeks
How to implement language constructs?
- Local variables and scope
- Environments and Closures
Next two Weeks
Modern language features for structuring programs
- Type classes
- Monads
Overloading Operators: Arithmetic
The +
operator works for a bunch of different types.
For Integer
:
for Double
precision floats:
Overloading Comparisons
Similarly we can compare different types of values
λ> 2 == 3
False
λ> [2.9, 3.5] == [2.9, 3.5]
True
λ> ("cat", 10) < ("cat", 2)
False
λ> ("cat", 10) < ("cat", 20)
True
Ad-Hoc Overloading
Seems unremarkable?
Languages since the dawn of time have supported “operator overloading”
To support this kind of ad–hoc polymorphism
Ad-hoc: “created or done for a particular purpose as necessary.”
You really need to add and compare values of multiple types!
Haskell has no caste system
No distinction between operators and functions
- All are first class citizens!
But then, what type do we give to functions like +
and ==
?
QUIZ
Which of the following would be appropriate types for (+)
?
(A) (+) :: Integer -> Integer -> Integer
(B) (+) :: Double -> Double -> Double
(C) (+) :: a -> a -> a
(D) All of the above
(E) None of the above
Integer -> Integer -> Integer
is bad because?
- Then we cannot add
Double
s!
Double -> Double -> Double
is bad because?
- Then we cannot add
Double
s!
a -> a -> a
is bad because?
- That doesn’t make sense, e.g. to add two
Bool
or two[Int]
or two functions!
Type Classes for Ad Hoc Polymorphism
Haskell solves this problem with an insanely slick mechanism called typeclasses, introduced by Wadler and Blott
BTW: The paper is one of the clearest examples of academic writing I have seen. The next time you hear a curmudgeon say all the best CS was done in the 60s, just point them to the above.
Qualified Types
To see the right type, lets ask:
We call the above a qualified type. Read it as +
- takes in two
a
values and returns ana
value
for any type a
that
- is a
Num
or - implements the
Num
interface or - is an instance of a
Num
.
The name Num
can be thought of as a predicate or constraint over types.
Some types are Num
s
Examples include Integer
, Double
etc
- Any such values of those types can be passed to
+
.
Other types are not Num
s
Examples include Char
, String
, functions etc,
- Values of those types cannot be passed to
+
.
λ> True + False
<interactive>:15:6:
No instance for (Num Bool) arising from a use of ‘+’
In the expression: True + False
In an equation for ‘it’: it = True + False
Aha! Now those no instance for
error messages should make sense!
- Haskell is complaining that
True
andFalse
are of typeBool
- and that
Bool
is not an instance ofNum
.
Type Class is a Set of Operations
A typeclass is a collection of operations (functions) that must exist for the underlying type.
The Eq
Type Class
The simplest typeclass is perhaps, Eq
A type a
is an instance of Eq
if there are two functions
==
and/=
That determine if two a
values are respectively equal or disequal.
The Show
Type Class
The typeclass Show
requires that instances be convertible to String
(which can then be printed out)
Indeed, we can test this on different (built-in) types
(Hey, whats up with the funny \"
?)
When we type an expression into ghci, it computes the value and then calls show
on the result. Thus, if we create a new type by
and then create values of the type,
but then we cannot view them
λ> x
<interactive>:1:0:
No instance for (Show Unshowable)
arising from a use of `print' at <interactive>:1:0
Possible fix: add an instance declaration for (Show Unshowable)
In a stmt of a 'do' expression: print it
and we cannot compare them!
λ> x == x
<interactive>:1:0:
No instance for (Eq Unshowable)
arising from a use of `==' at <interactive>:1:0-5
Possible fix: add an instance declaration for (Eq Unshowable)
In the expression: x == x
In the definition of `it': it = x == x
Again, the previously incomprehensible type error message should make sense to you.
Creating Instances
Tell Haskell how to show or compare values of type Unshowable
By creating instances of Eq
and Show
for that type:
instance Eq Unshowable where
(==) A A = True -- True if both inputs are A
(==) B B = True -- ...or B
(==) C C = True -- .. or C
(==) _ _ = False -- otherwise
(/=) x y = not (x == y) -- Test if `x == y` and negate result!
EXERCISE Lets create an instance
for Show Unshowable
Automatic Derivation
This is silly: we should be able to compare and view Unshowble
“automatically”!
Haskell lets us automatically derive functions for some classes in the standard library.
To do so, we simply dress up the data type definition with
data Showable = A' | B' | C'
deriving (Eq, Show) -- tells Haskell to automatically generate instances
Now we have
Standard Typeclass Hierarchy
Let us now peruse the definition of the Num
typeclass.
λ> :info Num
class (Eq a, Show a) => Num a where
(+) :: a -> a -> a
(*) :: a -> a -> a
(-) :: a -> a -> a
negate :: a -> a
abs :: a -> a
signum :: a -> a
fromInteger :: Integer -> a
A type a
is an instance of (i.e. implements) Num
if
- The type is also an instance of
Eq
andShow
, and - There are functions for adding, multiplying, subtracting, negating etc values of that type.
In other words in addition to the “arithmetic” operations, we can compare two Num
values and we can view them (as a String
.)
Haskell comes equipped with a rich set of built-in classes.
In the above picture, there is an edge from Eq
and Show
to Num
because for something to be a Num
it must also be an Eq
and Show
.
The Ord
Typeclass
Another typeclass you’ve used already is the one for Ord
ering values:
For example:
QUIZ
Recall the datatype:
What is the result of:
(A) True
(B) False
(C) Type error (D) Run-time exception
Using Typeclasses
Typeclasses integrate with the rest of Haskell’s type system.
Lets build a small library for Environments mapping keys k
to values v
data Env k v
= Def v -- default value `v` to be used for "missing" keys
| Bind k v (Env k v) -- bind key `k` to the value `v`
deriving (Show)
An API for Env
Lets write a small API for Env
-- >>> let env0 = add "cat" 10.0 (add "dog" 20.0 (Def 0))
-- >>> get "cat" env0
-- 10
-- >>> get "dog" env0
-- 20
-- >>> get "horse" env0
-- 0
Ok, lets implement!
-- | 'add key val env' returns a new env that additionally maps `key` to `val`
add :: k -> v -> Env k v -> Env k v
add key val env = ???
-- | 'get key env' returns the value of `key` and the "default" if no value is found
get :: k -> Env k v -> v
get key env = ???
Oops, y u no check?
Constraint Propagation
Lets delete the types of add
and get
and see what Haskell says their types are!
Haskell tells us that we can use any k
value as a key as long as the value is an instance of the Eq
typeclass.
How, did GHC figure this out?
- If you look at the code for
get
you’ll see that we check if two keys are equal!
Exercise
Write an optimized version of
add
that ensures the keys are in increasing order,get
that gives up and returns the “default” the moment we see a key thats larger than the one we’re looking for.
(How) do you need to change the type of Env
?
(How) do you need to change the types of get
and add
?
Explicit Signatures
While Haskell is pretty good about inferring types in general, there are cases when the use of type classes requires explicit annotations (which change the behavior of the code.)
For example, Read
is a built-in typeclass, where any instance a
of Read
has a function
which can parse a string and turn it into an a
.
That is, Read
is the opposite of Show
.
Quiz
What does the expression read "2"
evaluate to?
(A) compile time error
(B) "2" :: String
(C) 2 :: Integer
(D) 2.0 :: Double
(E) run-time exception
Haskell is foxed!
- Doesn’t know what type to convert the string to!
- Doesn’t know which of the
read
functions to run!
Did we want an Int
or a Double
or maybe something else altogether?
Thus, here an explicit type annotation is needed to tell Haskell what to convert the string to:
Note the different results due to the different types.
Creating Typeclasses
Typeclasses are useful for many different things.
We will see some of those over the next few lectures.
Lets conclude today’s class with a quick example that provides a small taste.
JSON
JavaScript Object Notation or JSON is a simple format for transferring data around. Here is an example:
{ "name" : "Ranjit"
, "age" : 41.0
, "likes" : ["guacamole", "coffee", "bacon"]
, "hates" : [ "waiting" , "grapefruit"]
, "lunches" : [ {"day" : "monday", "loc" : "zanzibar"}
, {"day" : "tuesday", "loc" : "farmers market"}
, {"day" : "wednesday", "loc" : "harekrishna"}
, {"day" : "thursday", "loc" : "faculty club"}
, {"day" : "friday", "loc" : "coffee cart"} ]
}
In brief, each JSON object is either
a base value like a string, a number or a boolean,
an (ordered) array of objects, or
a set of string-object pairs.
A JSON Datatype
We can represent (a subset of) JSON values with the Haskell datatype
data JVal
= JStr String
| JNum Double
| JBool Bool
| JObj [(String, JVal)]
| JArr [JVal]
deriving (Eq, Ord, Show)
Thus, the above JSON value would be represented by the JVal
js1 =
JObj [("name", JStr "Ranjit")
,("age", JNum 41.0)
,("likes", JArr [ JStr "guacamole", JStr "coffee", JStr "bacon"])
,("hates", JArr [ JStr "waiting" , JStr "grapefruit"])
,("lunches", JArr [ JObj [("day", JStr "monday")
,("loc", JStr "zanzibar")]
, JObj [("day", JStr "tuesday")
,("loc", JStr "farmers market")]
, JObj [("day", JStr "wednesday")
,("loc", JStr "hare krishna")]
, JObj [("day", JStr "thursday")
,("loc", JStr "faculty club")]
, JObj [("day", JStr "friday")
,("loc", JStr "coffee cart")]
])
]
Serializing Haskell Values to JSON
Lets write a small library to serialize Haskell values as JSON.
We could write a bunch of functions like
doubleToJSON :: Double -> JVal
doubleToJSON = JNum
stringToJSON :: String -> JVal
stringToJSON = JStr
boolToJSON :: Bool -> JVal
boolToJSON = JBool
Serializing Collections
But what about collections, namely lists of things?
doublesToJSON :: [Double] -> JVal
doublesToJSON xs = JArr (map doubleToJSON xs)
boolsToJSON :: [Bool] -> JVal
boolsToJSON xs = JArr (map boolToJSON xs)
stringsToJSON :: [String] -> JVal
stringsToJSON xs = JArr (map stringToJSON xs)
This is getting rather tedious
- We are rewriting the same code :(
Serializing Collections (refactored with HOFs)
You could abstract by making the individual-element-converter a parameter
xsToJSON :: (a -> JVal) -> [a] -> JVal
xsToJSON f xs = JArr (map f xs)
xysToJSON :: (a -> JVal) -> [(String, a)] -> JVal
xysToJSON f kvs = JObj [ (k, f v) | (k, v) <- kvs ]
But this is *still rather tedious** as you have to pass in the individual data converter (yuck)
λ> doubleToJSON 4
JNum 4.0
λ> xsToJSON stringToJSON ["coffee", "guacamole", "bacon"]
JArr [JStr "coffee",JStr "guacamole",JStr "bacon"]
λ> xysToJSON stringToJSON [("day", "monday"), ("loc", "zanzibar")]
JObj [("day",JStr "monday"),("loc",JStr "zanzibar")]
This gets more hideous when you have richer objects like
lunches = [ [("day", "monday"), ("loc", "zanzibar")]
, [("day", "tuesday"), ("loc", "farmers market")]
]
because we have to go through gymnastics like
λ> xsToJSON (xysToJSON stringToJSON) lunches
JArr [ JObj [("day",JStr "monday") ,("loc",JStr "zanzibar")]
, JObj [("day",JStr "tuesday") ,("loc",JStr "farmers market")]
]
Yikes. So much for readability
Is it too much to ask for a magical toJSON
that just works?
Typeclasses To The Rescue
Lets define a typeclass that describes types a
that can be converted to JSON.
Now, just make all the above instances of JSON
like so
instance JSON Double where
toJSON = JNum
instance JSON Bool where
toJSON = JBool
instance JSON String where
toJSON = JStr
This lets us uniformly write
Bootstrapping Instances
The real fun begins when we get Haskell to automatically bootstrap the above functions to work for lists and key-value lists!
The above says, if a
is an instance of JSON
, that is, if you can convert a
to JVal
then here’s a generic recipe to convert lists of a
values!
λ> toJSON [True, False, True]
JArr [JBln True, JBln False, JBln True]
λ> toJSON ["cat", "dog", "Mouse"]
JArr [JStr "cat", JStr "dog", JStr "Mouse"]
or even lists-of-lists!
λ> toJSON [["cat", "dog"], ["mouse", "rabbit"]]
JArr [JArr [JStr "cat",JStr "dog"],JArr [JStr "mouse",JStr "rabbit"]]
We can pull the same trick with key-value lists
after which, we are all set!
λ> toJSON lunches
JArr [ JObj [ ("day",JStr "monday"), ("loc",JStr "zanzibar")]
, JObj [("day",JStr "tuesday"), ("loc",JStr "farmers market")]
]
It is also useful to bootstrap the serialization for tuples (upto some fixed size) so we can easily write “non-uniform” JSON objects where keys are bound to values with different shapes.
instance (JSON a, JSON b) => JSON ((String, a), (String, b)) where
toJSON ((k1, v1), (k2, v2)) =
JObj [(k1, toJSON v1), (k2, toJSON v2)]
instance (JSON a, JSON b, JSON c) => JSON ((String, a), (String, b), (String, c)) where
toJSON ((k1, v1), (k2, v2), (k3, v3)) =
JObj [(k1, toJSON v1), (k2, toJSON v2), (k3, toJSON v3)]
instance (JSON a, JSON b, JSON c, JSON d) => JSON ((String, a), (String, b), (String, c), (String,d)) where
toJSON ((k1, v1), (k2, v2), (k3, v3), (k4, v4)) =
JObj [(k1, toJSON v1), (k2, toJSON v2), (k3, toJSON v3), (k4, toJSON v4)]
instance (JSON a, JSON b, JSON c, JSON d, JSON e) => JSON ((String, a), (String, b), (String, c), (String,d), (String, e)) where
toJSON ((k1, v1), (k2, v2), (k3, v3), (k4, v4), (k5, v5)) =
JObj [(k1, toJSON v1), (k2, toJSON v2), (k3, toJSON v3), (k4, toJSON v4), (k5, toJSON v5)]
Now, we can simply write
hs = (("name" , "Ranjit")
,("age" , 41.0)
,("likes" , ["guacamole", "coffee", "bacon"])
,("hates" , ["waiting", "grapefruit"])
,("lunches", lunches)
)
which is a Haskell value that describes our running JSON example, and can convert it directly like so
Serializing Environments
To wrap everything up, lets write a routine to serialize our Env
and presto! our serializer just works
λ> env0
Bind "cat" 10.0 (Bind "dog" 20.0 (Def 0))
λ> toJSON env0
JObj [ ("cat", JNum 10.0)
, ("dog", JNum 20.0)
, ("def", JNum 0.0)
]
Thats it for today.
We will see much more typeclass awesomeness in the next few lectures…