Related
I am currently trying to make a comparable object and working on the compareTo() function, for which I wrote the following code
class InfoAcad(e: String, m: String, c: Int): Comparable<InfoAcad> {
override operator fun compareTo(other: InfoAcad): Int {
if (this.e < other.e) return -1
if (this.e > other.e) return 1
if (this.e == other.e && this.m < other.m) return -1
if (this.e == other.e && this.m > other.m) return 1
return 0
}
}
The idea is that e is an ID number inputted as a string, which always follows the format XX-XXX where every X character is an integer between 0 and 9, and m is a course code following the format LL-XXX where each L character is a capital letter between A and Z and the X characters are integers between 0 and 9 like in the ID numbers. The objects are first compared by their ID number, and if the ID numbers are equal they are then compared by the course code, if both values are the same then the objects are equal, the c parameter is not taken into account in the comparison. I found out yesterday that I could compare strings directly in Kotlin in < and > relations, so I decided to try using that to make the task of comparing the InfoAcad objects a bit easier on myself, however when I make a main function to test the comparisons, the equality always returns a false value independently of what is in the string values of the InfoAcad objects. Here's said main function:
fun main() {
var A = InfoAcad("18-10125", "CI-2526", 3)
var B = InfoAcad("18-10125", "CI-2526", 5)
println("A = B: " + (A == B).toString()) //true
println("A < B: " + (A < B).toString()) //false
ptintln("A > B: " + (A > B).toString()) //false
}
When I change characters in the ID and course code values the inequality relations work just as intended, so what could be causing the equality relation to always return false? I appreciate and thank any responses in advance.
Note: I have also tried giving A and B the same c value, the equality part still returned false.
Override the equals function as well, or use a data class.
compareTo is only used for the < and > operators. The == operator is implemented by the separate equals function.
You can find the available operators, and the functions you need to override for each, in the Operator overloading section of the Kotlin docs.
If you don't override the equals function, the default behaviour is for it to use object identity. That means that two different objects, even if they contain the same fields, will never be considered equal.
There is however a nice shortcut for what you want to do! Kotlin will automatically generate an equals function for you if you make your class a data class. It's a good fit for classes like yours, whose main purpose is to hold data.
Because == and != translates to a call to equals(other: Any?): Boolean method, from kotlinlang
Expression Translated to
a == b a?.equals(b) ?: (b === null)
a != b !(a?.equals(b) ?: (b === null))
These operators only work with the function equals(other: Any?): Boolean, which can be overridden to provide custom equality check
implementation. Any other function with the same name (like
equals(other: Foo)) will not be called.
So I have a custom programming language, and in it I am doing some math formalization/modeling. In this instance I am doing basically this (a pseudo-javascript representation):
isIntersection([1, 2, 3], [1, 2], [2, 3]) // => true
isIntersection([1, 2, 3], [1, 2, 3], [3, 4, 5]) // => false
function isIntersection(setTest, setA, setB) {
i = 0
while (i < setTest.length) {
let t = setTest[i]
if (includes(setA, t) || includes(setB, t)) {
i++
} else {
return false
}
}
return true
}
function includes(set, element) {
for (x in set) {
if (isEqual(element, x)) {
return true
}
}
return false
}
function isEqual(a, b) {
if (a is Set && b is Set) {
return isSetEqual(a, b)
} else if (a is X... && b is X...) {
return isX...Equal(a, b)
} ... {
...
}
}
function isSetEqual(a, b) {
i = 0
while (i < a.length) {
let x = a[i]
let y = b[i]
if (!isEqual(x, y)) {
return false
}
i++
}
return true
}
The isIntersection is checking isEqual, and isEqual is configured to be able to handle all kinds of cases of equality check, from sets compared to sets, objects to objects, X's to X's, etc..
The question is, how can we make the isEqual somehow ignorant of the implementation details? Right now you have to have one big if/else/switch statement for every possible type of object. If we add a new type, we have to modify this gigantic isEqual method to add support for it. How can we avoid this, and just define them separately and cleanly?
I was thinking initially of making the objects be "instances of classes" so to speak, with class methods. But I like the purity of having everything just be functions and structs (objects without methods). Is there any way to implement this sort of thing without using classes with methods, instead keeping it just functions and objects?
If not, then how would you implement it with classes? Would it just be something like this?
class Set {
isEqual(set) {
i = 0
while (i < this.length) {
let x = this[i]
let y = set[i]
if (!x.isEqual(y)) {
return false
}
i++
}
return true
}
}
This would mean every object would have to have an isEqual defined on it. How does Haskell handle such a system? Basically looking for inspiration on how this can be most cleanly done. I want to ideally avoid having classes with methods.
Note: You can't just delegate to == native implementation (like assuming this is in JavaScript). We are using a custom programming language and are basically trying to define the meaning of == in the first place.
Another approach is to pass around an isEqual function along with everything somehow, though I don't really see how to do this and if it were possible it would be clunky. So not sure what the best approach is.
Haskell leverages its type and type-class system to deal with polymorphic equality.
The relevant code is
class Eq a where
(==) :: a -> a -> Bool
The English translation is: a type a implements the Eq class if, and only if, it defines a function (==) which takes two inputs of type a and outputs a Bool.
Generally, we declare certain "laws" that type-classes should abide by. For example, x == y should be identical to y == x in all cases, and x == x should never be False. There's no way for the compiler to check these laws, so one typically just writes them into the documentation.
Once we have defined the typeclass Eq in the above manner, we have access to the (==) function (which can be called using infix notation - ie, we can either write (==) x y or x == y). The type of this function is
(==) :: forall a . Eq a => a -> a -> Bool
In other words, for every a that implements the typeclass Eq, (==) is of type a -> a -> Bool.
Consider an example type
data Boring = Dull | Uninteresting
The type Boring has two proper values, Dull and Uninteresting. We can define the Eq implementation as follows:
instance Eq Boring where
Dull == Dull = True
Dull == Uninteresting = False
Uninteresting == Uninteresting = True
Uninteresting == Dull = False
Now, we will be able to evaluate whether two elements of type Boring are equal.
ghci> Dull == Dull
True
ghci> Dull == Uninteresting
False
Note that this is very different from Javascript's notion of equality. It's not possible to compare elements of different types using (==). For example,
ghci> Dull == 'w'
<interactive>:146:9: error:
* Couldn't match expected type `Boring' with actual type `Char'
* In the second argument of `(==)', namely 'w'
In the expression: Dull == 'w'
In an equation for `it': it = Dull == 'w'
When we try to compare Dull to the character 'w', we get a type error because Boring and Char are different types.
We can thus define
includes :: Eq a => [a] -> a -> Bool
includes [] _ = False
includes (x:xs) element = element == x || includes xs element
We read this definition as follows:
includes is a function that, for any type a which implements equality testing, takes a list of as and a single a and checks whether the element is in the list.
If the list is empty, then includes list element will evaluate to False.
If the list is not empty, we write the list as x : xs (a list with the first element as x and the remaining elements as xs). Then x:xs includes element iff either x equals element, or xs includes element.
We can also define
instance Eq a => Eq [a] where
[] == [] = True
[] == (_:_) = False
(_:_) == [] = False
(x:xs) == (y:ys) = x == y && xs == ys
The English translation of this code is:
Consider any type a such that a implements the Eq class (in other words, so that (==) is defined for type a). Then [a] also implements the Eq type class - that is, we can use (==) on two values of type [a].
The way that [a] implements the typeclass is as follows:
The empty list equals itself.
An empty list does not equal a non-empty list.
To decide whether two non-empty lists (x:xs) and (y:ys) are equal, check whether their first elements are equal (aka whether x == y). If the first elements are equal, check whether the remaining elements are equal (whether xs == ys) recursively. If both of these are true, the two lists are equal. Otherwise, they're not equal.
Notice that we're actually using two different ==s in the implementation of Eq [a]. The equality x == y is using the Eq a instance, while the equality xs == ys is recursively using the Eq [a] instance.
In practice, defining Eq instances is typically so simple that Haskell lets the compiler do the work. For example, if we had instead written
data Boring = Dull | Uninteresting deriving (Eq)
Haskell would have automatically generated the Eq Boring instance for us. Haskell also lets us derive other type classes like Ord (where the functions (<) and (>) are defined), show (which allows us to turn our data into Strings), and read (which allows us to turn Strings back into our data type).
Keep in mind that this approach relies heavily on static types and type-checking. Haskell makes sure that we only ever use the (==) function when comparing elements of the same type. The compiler also always knows at compile type which definition of (==) to use in any given situation because it knows the types of the values being compared, so there is no need to do any sort of dynamic dispatch (although there are situations where the compiler will choose to do dynamic dispatch).
If your language uses dynamic typing, this method will not work and you'll be forced to use dynamic dispatch of some variety if you want to be able to define new types. If you use static typing, you should definitely look into Haskell's type class system.
Is it bad practice to use mutable objects as Hashmap keys? What happens when you try to retrieve a value from a Hashmap using a key that has been modified enough to change its hashcode?
For example, given
class Key
{
int a; //mutable field
int b; //mutable field
public int hashcode()
return foo(a, b);
// setters setA and setB omitted for brevity
}
with code
HashMap<Key, Value> map = new HashMap<Key, Value>();
Key key1 = new Key(0, 0);
map.put(key1, value1); // value1 is an instance of Value
key1.setA(5);
key1.setB(10);
What happens if we now call map.get(key1)? Is this safe or advisable? Or is the behavior dependent on the language?
It has been noted by many well respected developers such as Brian Goetz and Josh Bloch that :
If an object’s hashCode() value can change based on its state, then we
must be careful when using such objects as keys in hash-based
collections to ensure that we don’t allow their state to change when
they are being used as hash keys. All hash-based collections assume
that an object’s hash value does not change while it is in use as a
key in the collection. If a key’s hash code were to change while it
was in a collection, some unpredictable and confusing consequences
could follow. This is usually not a problem in practice — it is not
common practice to use a mutable object like a List as a key in a
HashMap.
This is not safe or advisable. The value mapped to by key1 can never be retrieved. When doing a retrieval, most hash maps will do something like
Object get(Object key) {
int hash = key.hashCode();
//simplified, ignores hash collisions,
Entry entry = getEntry(hash);
if(entry != null && entry.getKey().equals(key)) {
return entry.getValue();
}
return null;
}
In this example, key1.hashcode() now points to the wrong bucket of the hash table, and you will not be able to retrieve value1 with key1.
If you had done something like,
Key key1 = new Key(0, 0);
map.put(key1, value1);
key1.setA(5);
Key key2 = new Key(0, 0);
map.get(key2);
This will also not retrieve value1, as key1 and key2 are no longer equal, so this check
if(entry != null && entry.getKey().equals(key))
will fail.
Hash maps use hash code and equality comparisons to identify a certain key-value pair with a given key. If the has map keeps the key as a reference to the mutable object, it would work in the cases where the same instance is used to retrieve the value. Consider however, the following case:
T keyOne = ...;
T keyTwo = ...;
// At this point keyOne and keyTwo are different instances and
// keyOne.equals(keyTwo) is true.
HashMap myMap = new HashMap();
myMap.push(keyOne, "Hello");
String s1 = (String) myMap.get(keyOne); // s1 is "Hello"
String s2 = (String) myMap.get(keyTwo); // s2 is "Hello"
// because keyOne equals keyTwo
mutate(keyOne);
s1 = myMap.get(keyOne); // returns "Hello"
s2 = myMap.get(keyTwo); // not found
The above is true if the key is stored as a reference. In Java usually this is the case. In .NET for instance, if the key is a value type (always passed by value), the result will be different:
T keyOne = ...;
T keyTwo = ...;
// At this point keyOne and keyTwo are different instances
// and keyOne.equals(keyTwo) is true.
Dictionary myMap = new Dictionary();
myMap.Add(keyOne, "Hello");
String s1 = (String) myMap[keyOne]; // s1 is "Hello"
String s2 = (String) myMap[keyTwo]; // s2 is "Hello"
// because keyOne equals keyTwo
mutate(keyOne);
s1 = myMap[keyOne]; // not found
s2 = myMap[keyTwo]; // returns "Hello"
Other technologies might have other different behaviors. However, almost all of them would come to a situation where the result of using mutable keys is not deterministic, which is very very bad situation in an application - a hard to debug and even harder to understand.
If key’s hash code changes after the key-value pair (Entry) is stored in HashMap, the map will not be able to retrieve the Entry.
Key’s hashcode can change if the key object is mutable. Mutable keys in HahsMap can result in data loss.
This will not work. You are changing the key value, so you are basically throwing it away. Its like creating a real life key and lock, and then changing the key and trying to put it back in the lock.
As others explained, it is dangerous.
A way to avoid that is to have a const field giving explicitly the hash in your mutable objects (so you would hash on their "identity", not their "state"). You might even initialize that hash field more or less randomly.
Another trick would be to use the address, e.g. (intptr_t) reinterpret_cast<void*>(this) as a basis for hash.
In all cases, you have to give up hashing the changing state of the object.
There are two very different issues that can arise with a mutable key depending on your expectation of behavior.
First Problem: (probably most trivial--but hell it gave me problems that I didn't think about!)
You are attempting to place key-value pairs into a map by updating and modifying the same key object. You might do something like Map<Integer, String> and simply say:
int key = 0;
loop {
map.put(key++, newString);
}
I'm reusing the "object" key to create a map. This works fine in Java because of autoboxing where each new value of key gets autoboxed to a new Integer object. What would not work is if I created my own (mutable) Integer object:
MyInteger {
int value;
plusOne(){
value++;
}
}
Then tried the same approach:
MyInteger key = new MyInteger(0);
loop{
map.put(key.plusOne(), newString)
}
My expectation is that, for instance, I map 0 -> "a" and 1 -> "b". In the first example, if I change int key = 0, the map will (correctly) give me "a". For simplicity let's assume MyInteger just always returns the same hashCode() (if you can somehow manage to create unique hashCode values for all possible states of an object, this will not be an issue, and you deserve an award). In this case, I call 0 -> "a", so now the map holds my key and maps it to "a", I then modify key = 1 and try to put 1 -> "b". We have a problem! The hashCode() is the same, and the only key in the HashMap is my MyInteger key object which has just been modified to be equal to 1, so It overwrites that key's value so that now, instead of a map with 0 -> "a" and 1 -> "b", I have 1 -> "b" only! Even worse, if I change back to key = 0, the hashCode points to 1 -> "b", but since the HashMap's only key is my key object, it satisfied the equality check and returns "b", not "a" as expected.
If, like me, you fall prey to this type of issue, it's incredibly difficult to diagnose. Why? Because if you have a decent hashCode() function it will generate (mostly) unique values. The hash value will largely take care of the inequality problem when structuring the map but if you have enough values, eventually you'll get a collision on the hash value and then you get unexpected and largely inexplicable results. The resultant behavior is that it works for small runs but fails for larger ones.
Advice:
To find this type of issue, modify the hashCode() method, even trivially (i.e. = 0--obviously when doing this, keep in mind that the hash values should be the same for two equal objects*), and see if you get the same results--because you should and if you don't, there's likely a semantic error with your implementation that's using a hash table.
*There should be no danger (if there is--you have a semantic problem) in always returning 0 from a hashCode() (although it would defeat the purpose of a Hash Table). But that's sort of the point: the hashCode is a "quick and easy" equality measure that's not exact. So two very different objects could have the same hashCode() yet not be equal. On the other hand, two equal objects must always have the same hashCode() value.
p.s. In Java, from my understanding, if you do such a terrible thing (as have many hashCode() collisions), it will start using a red-black-tree as opposed to ArrayList. So when you expect O(1) lookup, you'll get O(log(n))--which is better than the ArrayList which would give O(n).
Second Problem:
This is the one that most others seem to be focusing on, so I'll try to be brief. In this use case, I try to map a key-value pair and then I do some work on the key and then want to come back and get my value.
Expectation: key -> value is mapped, I then modify key and try to get(key). I expect that will give me value.
It seems kind of obvious to me that this wouldn't work but I'm not above having tried to use things like Collections as a key before (and quite quickly realizing it doesn't work). It doesn't work because it's quite likely that the hash value of key has changed so you won't even be looking in the correct bucket.
This is why it's very inadvisable to use collections as keys. I would assume, if you were doing this, you're trying to establish a many-to-one relationship. So I have a class (as in teaching) and I want two groups to do two different projects. What I want is that given a group, what is their project? Simple, I divide the class in two, and I have group1 -> project1 and group2 -> project2. But wait! A new student arrives so I place them in group1. The problem is that group1 has now been modified and likely its hash value has changed, therefore trying to do get(group1) is likely to fail because it will look in a wrong or non-existent bucket of the HashMap.
The obvious solution to the above is to chain things--instead of using the groups as keys, give them labels (that don't change) that point to the group and therefore the project: g1 -> group1 and g1 -> project1, etc.
p.s.
Please make sure to define a hashCode() and equals(...) method for any object you expect to use as a key (eclipse and, I'm assuming, most IDE's can do this for you).
Code Example:
Here is a class which exhibits the two different "problem" behaviors. In this case, I attempt to map 0 -> "a", 1 -> "b", and 2 -> "c" (in each case). In the first problem, I do that by modifying the same object, in the second problem, I use unique objects, and in the second problem "fixed" I clone those unique objects. After that I take one of the "unique" keys (k0) and modify it to attempt to access the map. I expect this will give me a, b, c and null when the key is 3.
However, what happens is the following:
map.get(0) map1: 0 -> null, map2: 0 -> a, map3: 0 -> a
map.get(1) map1: 1 -> null, map2: 1 -> b, map3: 1 -> b
map.get(2) map1: 2 -> c, map2: 2 -> a, map3: 2 -> c
map.get(3) map1: 3 -> null, map2: 3 -> null, map3: 3 -> null
The first map ("first problem") fails because it only holds a single key, which was last updated and placed to equal 2, hence why it correctly returns "c" when k0 = 2 but returns null for the other two (the single key doesn't equal 0 or 1). The second map fails twice: the most obvious is that it returns "b" when I asked for k0 (because it's been modified--that's the "second problem" which seems kind of obvious when you do something like this). It fails a second time when it returns "a" after modifying k0 = 2 (which I would expect to be "c"). This is more due to the "first problem": there's a hash code collision and the tiebreaker is an equality check--but the map holds k0, which it (apparently for me--could theoretically be different for someone else) checked first and thus returned the first value, "a" even though had it kept checking, "c" would have also been a match. Finally, the 3rd map works perfectly because I'm enforcing that the map holds unique keys no matter what else I do (by cloning the object during insertion).
I want to make clear that I agree, cloning is not a solution! I simply added that as an example of why a map needs unique keys and how enforcing unique keys "fixes" the issue.
public class HashMapProblems {
private int value = 0;
public HashMapProblems() {
this(0);
}
public HashMapProblems(final int value) {
super();
this.value = value;
}
public void setValue(final int i) {
this.value = i;
}
#Override
public int hashCode() {
return value % 2;
}
#Override
public boolean equals(final Object o) {
return o instanceof HashMapProblems
&& value == ((HashMapProblems) o).value;
}
#Override
public Object clone() {
return new HashMapProblems(value);
}
public void reset() {
this.value = 0;
}
public static void main(String[] args) {
final HashMapProblems k0 = new HashMapProblems(0);
final HashMapProblems k1 = new HashMapProblems(1);
final HashMapProblems k2 = new HashMapProblems(2);
final HashMapProblems k = new HashMapProblems();
final HashMap<HashMapProblems, String> map1 = firstProblem(k);
final HashMap<HashMapProblems, String> map2 = secondProblem(k0, k1, k2);
final HashMap<HashMapProblems, String> map3 = secondProblemFixed(k0, k1, k2);
for (int i = 0; i < 4; ++i) {
k0.setValue(i);
System.out.printf(
"map.get(%d) map1: %d -> %s, map2: %d -> %s, map3: %d -> %s",
i, i, map1.get(k0), i, map2.get(k0), i, map3.get(k0));
System.out.println();
}
}
private static HashMap<HashMapProblems, String> firstProblem(
final HashMapProblems start) {
start.reset();
final HashMap<HashMapProblems, String> map = new HashMap<>();
map.put(start, "a");
start.setValue(1);
map.put(start, "b");
start.setValue(2);
map.put(start, "c");
return map;
}
private static HashMap<HashMapProblems, String> secondProblem(
final HashMapProblems... keys) {
final HashMap<HashMapProblems, String> map = new HashMap<>();
IntStream.range(0, keys.length).forEach(
index -> map.put(keys[index], "" + (char) ('a' + index)));
return map;
}
private static HashMap<HashMapProblems, String> secondProblemFixed(
final HashMapProblems... keys) {
final HashMap<HashMapProblems, String> map = new HashMap<>();
IntStream.range(0, keys.length)
.forEach(index -> map.put((HashMapProblems) keys[index].clone(),
"" + (char) ('a' + index)));
return map;
}
}
Some Notes:
In the above it should be noted that map1 only holds two values because of the way I set up the hashCode() function to split odds and evens. k = 0 and k = 2 therefore have the same hashCode of 0. So when I modify k = 2 and attempt to k -> "c" the mapping k -> "a" gets overwritten--k -> "b" is still there because it exists in a different bucket.
Also there are a lot of different ways to examine the maps in the above code and I would encourage people that are curious to do things like print out the values of the map and then the key to value mappings (you may be surprised by the results you get). Do things like play with changing the different "unique" keys (i.e. k0, k1, and k2), try changing the single key k. You could also see how even the secondProblemFixed isn't actually fixed because you could also gain access to the keys (for instance via Map::keySet) and modify them.
I won't repeat what others have said. Yes, it's inadvisable. But in my opinion, it's not overly obvious where the documentation states this.
You can find it on the JavaDoc for the Map interface:
Note: great care must be exercised if mutable objects are used as map
keys. The behavior of a map is not specified if the value of an object
is changed in a manner that affects equals comparisons while the
object is a key in the map
Behaviour of a Map is not specified if value of an object is changed in a manner that affects equals comparision while object(Mutable) is a key. Even for Set also using mutable object as key is not a good idea.
Lets see a example here :
public class MapKeyShouldntBeMutable {
/**
* #param args
*/
public static void main(String[] args) {
// TODO Auto-generated method stub
Map<Employee,Integer> map=new HashMap<Employee,Integer>();
Employee e=new Employee();
Employee e1=new Employee();
Employee e2=new Employee();
Employee e3=new Employee();
Employee e4=new Employee();
e.setName("one");
e1.setName("one");
e2.setName("three");
e3.setName("four");
e4.setName("five");
map.put(e, 24);
map.put(e1, 25);
map.put(e2, 26);
map.put(e3, 27);
map.put(e4, 28);
e2.setName("one");
System.out.println(" is e equals e1 "+e.equals(e1));
System.out.println(map);
for(Employee s:map.keySet())
{
System.out.println("key : "+s.getName()+":value : "+map.get(s));
}
}
}
class Employee{
String name;
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
#Override
public boolean equals(Object o){
Employee e=(Employee)o;
if(this.name.equalsIgnoreCase(e.getName()))
{
return true;
}
return false;
}
public int hashCode() {
int sum=0;
if(this.name!=null)
{
for(int i=0;i<this.name.toCharArray().length;i++)
{
sum=sum+(int)this.name.toCharArray()[i];
}
/*System.out.println("name :"+this.name+" code : "+sum);*/
}
return sum;
}
}
Here we are trying to add mutable object "Employee" to a map. It will work good if all keys added are distinct.Here I have overridden equals and hashcode for employee class.
See first I have added "e" and then "e1". For both of them equals() will be true and hashcode will be same. So map sees as if the same key is getting added so it should replace the old value with e1's value. Then we have added e2,e3,e4 we are fine as of now.
But when we are changing the value of an already added key i.e "e2" as one ,it becomes a key similar to one added earlier. Now the map will behave wired. Ideally e2 should replace the existing same key i.e e1.But now map takes this as well. And you will get this in o/p :
is e equals e1 true
{Employee#1aa=28, Employee#1bc=27, Employee#142=25, Employee#142=26}
key : five:value : 28
key : four:value : 27
key : one:value : 25
key : one:value : 25
See here both keys having one showing same value also. So its unexpected.Now run the same programme again by changing e2.setName("diffnt"); which is e2.setName("one"); here ...Now the o/p will be this :
is e equals e1 true
{Employee#1aa=28, Employee#1bc=27, Employee#142=25, Employee#27b=26}
key : five:value : 28
key : four:value : 27
key : one:value : 25
key : diffnt:value : null
So by adding changing the mutable key in a map is not encouraged.
To make the answer compact:
The root cause is that HashMap calculates an internal hash of the user's key object hashcode only once and stores it inside for own needs.
All other operations for data navigation inside the map are doing by this pre-calculated internal hash.
So if you change the hashcode of the key object (mutate) it will be still stored nicely inside the map with the changed key object's hashcode (you could even observe it via HashMap.keySet() and see the altered hashcode).
But HashMap internal hash will not be recalculated of course and it will be the old stored one and the map won't be able to locate your data by the provided mutated key object new hashcode. (e.g. by HashMap.get() or HashMap.containsKey()).
Your key-value pairs will be still inside the map but to get it back you will need that old hash code value that was given when you put your data into the map.
Notice that you also will be unable to get data back by the mutated key object taken right from the HashMap.keySet().
I am pretty confused with both functions fold() and reduce() in Kotlin, can anyone give me a concrete example that distinguishes both of them?
fold takes an initial value, and the first invocation of the lambda you pass to it will receive that initial value and the first element of the collection as parameters.
For example, take the following code that calculates the sum of a list of integers:
listOf(1, 2, 3).fold(0) { sum, element -> sum + element }
The first call to the lambda will be with parameters 0 and 1.
Having the ability to pass in an initial value is useful if you have to provide some sort of default value or parameter for your operation. For example, if you were looking for the maximum value inside a list, but for some reason want to return at least 10, you could do the following:
listOf(1, 6, 4).fold(10) { max, element ->
if (element > max) element else max
}
reduce doesn't take an initial value, but instead starts with the first element of the collection as the accumulator (called sum in the following example).
For example, let's do a sum of integers again:
listOf(1, 2, 3).reduce { sum, element -> sum + element }
The first call to the lambda here will be with parameters 1 and 2.
You can use reduce when your operation does not depend on any values other than those in the collection you're applying it to.
The major functional difference I would call out (which is mentioned in the comments on the other answer, but may be hard to understand) is that reduce will throw an exception if performed on an empty collection.
listOf<Int>().reduce { x, y -> x + y }
// java.lang.UnsupportedOperationException: Empty collection can't be reduced.
This is because .reduce doesn't know what value to return in the event of "no data".
Contrast this with .fold, which requires you to provide a "starting value", which will be the default value in the event of an empty collection:
val result = listOf<Int>().fold(0) { x, y -> x + y }
assertEquals(0, result)
So, even if you don't want to aggregate your collection down to a single element of a different (non-related) type (which only .fold will let you do), if your starting collection may be empty then you must either check your collection size first and then .reduce, or just use .fold
val collection: List<Int> = // collection of unknown size
val result1 = if (collection.isEmpty()) 0
else collection.reduce { x, y -> x + y }
val result2 = collection.fold(0) { x, y -> x + y }
assertEquals(result1, result2)
Another difference that none of the other answers mentioned is the following:
The result of a reduce operation will always be of the same type (or a super type) as the data that is being reduced.
We can see that from the definition of the reduce method:
public inline fun <S, T : S> Iterable<T>.reduce(operation: (acc: S, T) -> S): S {
val iterator = this.iterator()
if (!iterator.hasNext()) throw UnsupportedOperationException("Empty collection can't be reduced.")
var accumulator: S = iterator.next()
while (iterator.hasNext()) {
accumulator = operation(accumulator, iterator.next())
}
return accumulator
}
On the other hand, the result of a fold operation can be anything, because there are no restrictions when it comes to setting up the initial value.
So, for example, let us say that we have a string that contains letters and digits. We want to calculate the sum of all the digits.
We can easily do that with fold:
val string = "1a2b3"
val result: Int = string.fold(0, { currentSum: Int, char: Char ->
if (char.isDigit())
currentSum + Character.getNumericValue(char)
else currentSum
})
//result is equal to 6
reduce - The reduce() method transforms a given collection into a single result.
val numbers: List<Int> = listOf(1, 2, 3)
val sum: Int = numbers.reduce { acc, next -> acc + next }
//sum is 6 now.
fold - What would happen in the previous case of an empty list? Actually, there’s no right value to return, so reduce() throws a RuntimeException
In this case, fold is a handy tool. You can put an initial value by it -
val sum: Int = numbers.fold(0, { acc, next -> acc + next })
Here, we’ve provided initial value. In contrast, to reduce(), if the collection is empty, the initial value will be returned which will prevent you from the RuntimeException.
Simple Answer
Result of both reduce and fold is "a list of items will be transformed into a single item".
In case of fold,we provide 1 extra parameter apart from list but in case of reduce,only items in list will be considered.
Fold
listOf("AC","Fridge").fold("stabilizer") { freeGift, itemBought -> freeGift + itemBought }
//output: stabilizerACFridge
In above case,think as AC,fridge bought from store & they give stabilizer as gift(this will be the parameter passed in the fold).so,you get all 3 items together.Please note that freeGift will be available only once i.e for the first iteration.
Reduce
In case of reduce,we get items in list as parameters and can perform required transformations on it.
listOf("AC","Fridge").reduce { itemBought1, itemBought2 -> itemBought1 + itemBought2 }
//output: ACFridge
The difference between the two functions is that fold() takes an initial value and uses it as the accumulated value on the first step, whereas the first step of reduce() uses the first and the second elements as operation arguments on the first step.
This morning, I was reading Steve Yegge's: When Polymorphism Fails, when I came across a question that a co-worker of his used to ask potential employees when they came for their interview at Amazon.
As an example of polymorphism in
action, let's look at the classic
"eval" interview question, which (as
far as I know) was brought to Amazon
by Ron Braunstein. The question is
quite a rich one, as it manages to
probe a wide variety of important
skills: OOP design, recursion, binary
trees, polymorphism and runtime
typing, general coding skills, and (if
you want to make it extra hard)
parsing theory.
At some point, the candidate hopefully
realizes that you can represent an
arithmetic expression as a binary
tree, assuming you're only using
binary operators such as "+", "-",
"*", "/". The leaf nodes are all
numbers, and the internal nodes are
all operators. Evaluating the
expression means walking the tree. If
the candidate doesn't realize this,
you can gently lead them to it, or if
necessary, just tell them.
Even if you tell them, it's still an
interesting problem.
The first half of the question, which
some people (whose names I will
protect to my dying breath, but their
initials are Willie Lewis) feel is a
Job Requirement If You Want To Call
Yourself A Developer And Work At
Amazon, is actually kinda hard. The
question is: how do you go from an
arithmetic expression (e.g. in a
string) such as "2 + (2)" to an
expression tree. We may have an ADJ
challenge on this question at some
point.
The second half is: let's say this is
a 2-person project, and your partner,
who we'll call "Willie", is
responsible for transforming the
string expression into a tree. You get
the easy part: you need to decide what
classes Willie is to construct the
tree with. You can do it in any
language, but make sure you pick one,
or Willie will hand you assembly
language. If he's feeling ornery, it
will be for a processor that is no
longer manufactured in production.
You'd be amazed at how many candidates
boff this one.
I won't give away the answer, but a
Standard Bad Solution involves the use
of a switch or case statment (or just
good old-fashioned cascaded-ifs). A
Slightly Better Solution involves
using a table of function pointers,
and the Probably Best Solution
involves using polymorphism. I
encourage you to work through it
sometime. Fun stuff!
So, let's try to tackle the problem all three ways. How do you go from an arithmetic expression (e.g. in a string) such as "2 + (2)" to an expression tree using cascaded-if's, a table of function pointers, and/or polymorphism?
Feel free to tackle one, two, or all three.
[update: title modified to better match what most of the answers have been.]
Polymorphic Tree Walking, Python version
#!/usr/bin/python
class Node:
"""base class, you should not process one of these"""
def process(self):
raise('you should not be processing a node')
class BinaryNode(Node):
"""base class for binary nodes"""
def __init__(self, _left, _right):
self.left = _left
self.right = _right
def process(self):
raise('you should not be processing a binarynode')
class Plus(BinaryNode):
def process(self):
return self.left.process() + self.right.process()
class Minus(BinaryNode):
def process(self):
return self.left.process() - self.right.process()
class Mul(BinaryNode):
def process(self):
return self.left.process() * self.right.process()
class Div(BinaryNode):
def process(self):
return self.left.process() / self.right.process()
class Num(Node):
def __init__(self, _value):
self.value = _value
def process(self):
return self.value
def demo(n):
print n.process()
demo(Num(2)) # 2
demo(Plus(Num(2),Num(5))) # 2 + 3
demo(Plus(Mul(Num(2),Num(3)),Div(Num(10),Num(5)))) # (2 * 3) + (10 / 2)
The tests are just building up the binary trees by using constructors.
program structure:
abstract base class: Node
all Nodes inherit from this class
abstract base class: BinaryNode
all binary operators inherit from this class
process method does the work of evaluting the expression and returning the result
binary operator classes: Plus,Minus,Mul,Div
two child nodes, one each for left side and right side subexpressions
number class: Num
holds a leaf-node numeric value, e.g. 17 or 42
The problem, I think, is that we need to parse perentheses, and yet they are not a binary operator? Should we take (2) as a single token, that evaluates to 2?
The parens don't need to show up in the expression tree, but they do affect its shape. E.g., the tree for (1+2)+3 is different from 1+(2+3):
+
/ \
+ 3
/ \
1 2
versus
+
/ \
1 +
/ \
2 3
The parentheses are a "hint" to the parser (e.g., per superjoe30, to "recursively descend")
This gets into parsing/compiler theory, which is kind of a rabbit hole... The Dragon Book is the standard text for compiler construction, and takes this to extremes. In this particular case, you want to construct a context-free grammar for basic arithmetic, then use that grammar to parse out an abstract syntax tree. You can then iterate over the tree, reducing it from the bottom up (it's at this point you'd apply the polymorphism/function pointers/switch statement to reduce the tree).
I've found these notes to be incredibly helpful in compiler and parsing theory.
Representing the Nodes
If we want to include parentheses, we need 5 kinds of nodes:
the binary nodes: Add Minus Mul Divthese have two children, a left and right side
+
/ \
node node
a node to hold a value: Valno children nodes, just a numeric value
a node to keep track of the parens: Parena single child node for the subexpression
( )
|
node
For a polymorphic solution, we need to have this kind of class relationship:
Node
BinaryNode : inherit from Node
Plus : inherit from Binary Node
Minus : inherit from Binary Node
Mul : inherit from Binary Node
Div : inherit from Binary Node
Value : inherit from Node
Paren : inherit from node
There is a virtual function for all nodes called eval(). If you call that function, it will return the value of that subexpression.
String Tokenizer + LL(1) Parser will give you an expression tree... the polymorphism way might involve an abstract Arithmetic class with an "evaluate(a,b)" function, which is overridden for each of the operators involved (Addition, Subtraction etc) to return the appropriate value, and the tree contains Integers and Arithmetic operators, which can be evaluated by a post(?)-order traversal of the tree.
I won't give away the answer, but a
Standard Bad Solution involves the use
of a switch or case statment (or just
good old-fashioned cascaded-ifs). A
Slightly Better Solution involves
using a table of function pointers,
and the Probably Best Solution
involves using polymorphism.
The last twenty years of evolution in interpreters can be seen as going the other way - polymorphism (eg naive Smalltalk metacircular interpreters) to function pointers (naive lisp implementations, threaded code, C++) to switch (naive byte code interpreters), and then onwards to JITs and so on - which either require very big classes, or (in singly polymorphic languages) double-dispatch, which reduces the polymorphism to a type-case, and you're back at stage one. What definition of 'best' is in use here?
For simple stuff a polymorphic solution is OK - here's one I made earlier, but either stack and bytecode/switch or exploiting the runtime's compiler is usually better if you're, say, plotting a function with a few thousand data points.
Hm... I don't think you can write a top-down parser for this without backtracking, so it has to be some sort of a shift-reduce parser. LR(1) or even LALR will of course work just fine with the following (ad-hoc) language definition:
Start -> E1
E1 -> E1+E1 | E1-E1
E1 -> E2*E2 | E2/E2 | E2
E2 -> number | (E1)
Separating it out into E1 and E2 is necessary to maintain the precedence of * and / over + and -.
But this is how I would do it if I had to write the parser by hand:
Two stacks, one storing nodes of the tree as operands and one storing operators
Read the input left to right, make leaf nodes of the numbers and push them into the operand stack.
If you have >= 2 operands on the stack, pop 2, combine them with the topmost operator in the operator stack and push this structure back to the operand tree, unless
The next operator has higher precedence that the one currently on top of the stack.
This leaves us the problem of handling brackets. One elegant (I thought) solution is to store the precedence of each operator as a number in a variable. So initially,
int plus, minus = 1;
int mul, div = 2;
Now every time you see a a left bracket increment all these variables by 2, and every time you see a right bracket, decrement all the variables by 2.
This will ensure that the + in 3*(4+5) has higher precedence than the *, and 3*4 will not be pushed onto the stack. Instead it will wait for 5, push 4+5, then push 3*(4+5).
Re: Justin
I think the tree would look something like this:
+
/ \
2 ( )
|
2
Basically, you'd have an "eval" node, that just evaluates the tree below it. That would then be optimized out to just being:
+
/ \
2 2
In this case the parens aren't required and don't add anything. They don't add anything logically, so they'd just go away.
I think the question is about how to write a parser, not the evaluator. Or rather, how to create the expression tree from a string.
Case statements that return a base class don't exactly count.
The basic structure of a "polymorphic" solution (which is another way of saying, I don't care what you build this with, I just want to extend it with rewriting the least amount of code possible) is deserializing an object hierarchy from a stream with a (dynamic) set of known types.
The crux of the implementation of the polymorphic solution is to have a way to create an expression object from a pattern matcher, likely recursive. I.e., map a BNF or similar syntax to an object factory.
Or maybe this is the real question:
how can you represent (2) as a BST?
That is the part that is tripping me
up.
Recursion.
#Justin:
Look at my note on representing the nodes. If you use that scheme, then
2 + (2)
can be represented as
.
/ \
2 ( )
|
2
should use a functional language imo. Trees are harder to represent and manipulate in OO languages.
As people have been mentioning previously, when you use expression trees parens are not necessary. The order of operations becomes trivial and obvious when you're looking at an expression tree. The parens are hints to the parser.
While the accepted answer is the solution to one half of the problem, the other half - actually parsing the expression - is still unsolved. Typically, these sorts of problems can be solved using a recursive descent parser. Writing such a parser is often a fun exercise, but most modern tools for language parsing will abstract that away for you.
The parser is also significantly harder if you allow floating point numbers in your string. I had to create a DFA to accept floating point numbers in C -- it was a very painstaking and detailed task. Remember, valid floating points include: 10, 10., 10.123, 9.876e-5, 1.0f, .025, etc. I assume some dispensation from this (in favor of simplicty and brevity) was made in the interview.
I've written such a parser with some basic techniques like
Infix -> RPN and
Shunting Yard and
Tree Traversals.
Here is the implementation I've came up with.
It's written in C++ and compiles on both Linux and Windows.
Any suggestions/questions are welcomed.
So, let's try to tackle the problem all three ways. How do you go from an arithmetic expression (e.g. in a string) such as "2 + (2)" to an expression tree using cascaded-if's, a table of function pointers, and/or polymorphism?
This is interesting,but I don't think this belongs to the realm of object-oriented programming...I think it has more to do with parsing techniques.
I've kind of chucked this c# console app together as a bit of a proof of concept. Have a feeling it could be a lot better (that switch statement in GetNode is kind of clunky (it's there coz I hit a blank trying to map a class name to an operator)). Any suggestions on how it could be improved very welcome.
using System;
class Program
{
static void Main(string[] args)
{
string expression = "(((3.5 * 4.5) / (1 + 2)) + 5)";
Console.WriteLine(string.Format("{0} = {1}", expression, new Expression.ExpressionTree(expression).Value));
Console.WriteLine("\nShow's over folks, press a key to exit");
Console.ReadKey(false);
}
}
namespace Expression
{
// -------------------------------------------------------
abstract class NodeBase
{
public abstract double Value { get; }
}
// -------------------------------------------------------
class ValueNode : NodeBase
{
public ValueNode(double value)
{
_double = value;
}
private double _double;
public override double Value
{
get
{
return _double;
}
}
}
// -------------------------------------------------------
abstract class ExpressionNodeBase : NodeBase
{
protected NodeBase GetNode(string expression)
{
// Remove parenthesis
expression = RemoveParenthesis(expression);
// Is expression just a number?
double value = 0;
if (double.TryParse(expression, out value))
{
return new ValueNode(value);
}
else
{
int pos = ParseExpression(expression);
if (pos > 0)
{
string leftExpression = expression.Substring(0, pos - 1).Trim();
string rightExpression = expression.Substring(pos).Trim();
switch (expression.Substring(pos - 1, 1))
{
case "+":
return new Add(leftExpression, rightExpression);
case "-":
return new Subtract(leftExpression, rightExpression);
case "*":
return new Multiply(leftExpression, rightExpression);
case "/":
return new Divide(leftExpression, rightExpression);
default:
throw new Exception("Unknown operator");
}
}
else
{
throw new Exception("Unable to parse expression");
}
}
}
private string RemoveParenthesis(string expression)
{
if (expression.Contains("("))
{
expression = expression.Trim();
int level = 0;
int pos = 0;
foreach (char token in expression.ToCharArray())
{
pos++;
switch (token)
{
case '(':
level++;
break;
case ')':
level--;
break;
}
if (level == 0)
{
break;
}
}
if (level == 0 && pos == expression.Length)
{
expression = expression.Substring(1, expression.Length - 2);
expression = RemoveParenthesis(expression);
}
}
return expression;
}
private int ParseExpression(string expression)
{
int winningLevel = 0;
byte winningTokenWeight = 0;
int winningPos = 0;
int level = 0;
int pos = 0;
foreach (char token in expression.ToCharArray())
{
pos++;
switch (token)
{
case '(':
level++;
break;
case ')':
level--;
break;
}
if (level <= winningLevel)
{
if (OperatorWeight(token) > winningTokenWeight)
{
winningLevel = level;
winningTokenWeight = OperatorWeight(token);
winningPos = pos;
}
}
}
return winningPos;
}
private byte OperatorWeight(char value)
{
switch (value)
{
case '+':
case '-':
return 3;
case '*':
return 2;
case '/':
return 1;
default:
return 0;
}
}
}
// -------------------------------------------------------
class ExpressionTree : ExpressionNodeBase
{
protected NodeBase _rootNode;
public ExpressionTree(string expression)
{
_rootNode = GetNode(expression);
}
public override double Value
{
get
{
return _rootNode.Value;
}
}
}
// -------------------------------------------------------
abstract class OperatorNodeBase : ExpressionNodeBase
{
protected NodeBase _leftNode;
protected NodeBase _rightNode;
public OperatorNodeBase(string leftExpression, string rightExpression)
{
_leftNode = GetNode(leftExpression);
_rightNode = GetNode(rightExpression);
}
}
// -------------------------------------------------------
class Add : OperatorNodeBase
{
public Add(string leftExpression, string rightExpression)
: base(leftExpression, rightExpression)
{
}
public override double Value
{
get
{
return _leftNode.Value + _rightNode.Value;
}
}
}
// -------------------------------------------------------
class Subtract : OperatorNodeBase
{
public Subtract(string leftExpression, string rightExpression)
: base(leftExpression, rightExpression)
{
}
public override double Value
{
get
{
return _leftNode.Value - _rightNode.Value;
}
}
}
// -------------------------------------------------------
class Divide : OperatorNodeBase
{
public Divide(string leftExpression, string rightExpression)
: base(leftExpression, rightExpression)
{
}
public override double Value
{
get
{
return _leftNode.Value / _rightNode.Value;
}
}
}
// -------------------------------------------------------
class Multiply : OperatorNodeBase
{
public Multiply(string leftExpression, string rightExpression)
: base(leftExpression, rightExpression)
{
}
public override double Value
{
get
{
return _leftNode.Value * _rightNode.Value;
}
}
}
}
Ok, here is my naive implementation. Sorry, I did not feel to use objects for that one but it is easy to convert. I feel a bit like evil Willy (from Steve's story).
#!/usr/bin/env python
#tree structure [left argument, operator, right argument, priority level]
tree_root = [None, None, None, None]
#count of parethesis nesting
parenthesis_level = 0
#current node with empty right argument
current_node = tree_root
#indices in tree_root nodes Left, Operator, Right, PRiority
L, O, R, PR = 0, 1, 2, 3
#functions that realise operators
def sum(a, b):
return a + b
def diff(a, b):
return a - b
def mul(a, b):
return a * b
def div(a, b):
return a / b
#tree evaluator
def process_node(n):
try:
len(n)
except TypeError:
return n
left = process_node(n[L])
right = process_node(n[R])
return n[O](left, right)
#mapping operators to relevant functions
o2f = {'+': sum, '-': diff, '*': mul, '/': div, '(': None, ')': None}
#converts token to a node in tree
def convert_token(t):
global current_node, tree_root, parenthesis_level
if t == '(':
parenthesis_level += 2
return
if t == ')':
parenthesis_level -= 2
return
try: #assumption that we have just an integer
l = int(t)
except (ValueError, TypeError):
pass #if not, no problem
else:
if tree_root[L] is None: #if it is first number, put it on the left of root node
tree_root[L] = l
else: #put on the right of current_node
current_node[R] = l
return
priority = (1 if t in '+-' else 2) + parenthesis_level
#if tree_root does not have operator put it there
if tree_root[O] is None and t in o2f:
tree_root[O] = o2f[t]
tree_root[PR] = priority
return
#if new node has less or equals priority, put it on the top of tree
if tree_root[PR] >= priority:
temp = [tree_root, o2f[t], None, priority]
tree_root = current_node = temp
return
#starting from root search for a place with higher priority in hierarchy
current_node = tree_root
while type(current_node[R]) != type(1) and priority > current_node[R][PR]:
current_node = current_node[R]
#insert new node
temp = [current_node[R], o2f[t], None, priority]
current_node[R] = temp
current_node = temp
def parse(e):
token = ''
for c in e:
if c <= '9' and c >='0':
token += c
continue
if c == ' ':
if token != '':
convert_token(token)
token = ''
continue
if c in o2f:
if token != '':
convert_token(token)
convert_token(c)
token = ''
continue
print "Unrecognized character:", c
if token != '':
convert_token(token)
def main():
parse('(((3 * 4) / (1 + 2)) + 5)')
print tree_root
print process_node(tree_root)
if __name__ == '__main__':
main()