Algorithm goal

Count number of changes needed to make. This problem is similar to:

• On HackerRank: You must split it [a string] into two contiguous substrings, then determine the minimum number of characters to change to make the two substrings into anagrams of one another.
• Find if two Strings are k-anagrams of each other (in this case, the two sides are the split of the string).

One string is an anagram of another if they have the same number of each character, not necessarily in the same order - for example abcc is an anagram of accb and vice-versa.

ab is composed of a and b, and exchanging a to b is enough to create an anagram - ie 1 change of letter is enough.

abc is not possible to create anagrams of because it cannot be split.

poeq requires 2 changes, to create an anagram, for example to pqpq, popo, eoeo and so forth.

Test cases in Scala

assert(anagramChanges("aaabbb") == Some(3))
assert(anagramChanges("ab") == Some(1))
assert(anagramChanges("mnop") == Some(2))
assert(anagramChanges("xyyx") == Some(0))
assert(anagramChanges("xaxbbbxx") == Some(1))
assert(anagramChanges("abc") == None)

Algorithm in Scala

23 lines of Scala (compatible versions 2.13 & 3.0), showing how concise Scala can be!

Explanation

Once we split a string into 2 parts, we are left with 2 strings. Number of changes required is effectively the number of characters that are not in common.

Originally, this solution was written with a MultiSet, but there is a cleaner, more efficient approach: (this is © from www.scala-algorithms.com)

Because the set of Char is finite, we can use a static array-like structure, such as a Vector, to do the counting, rather than a HashMap.

If the letter 'a' is in one half of the string, and is also in the other half, that means the total number of changes needed would be '+ 1 - 1 = 0'. So, we can say that any letter on the left part of the string has a score of +1, and any side on the right has -1.

Scala concepts & Hints

1. foldLeft and foldRight

   A 'fold' allows you to perform the equivalent of a for-loop, but with a lot less code.

   def foldMutable[I, O](initialState: O)(items: List[I])(f: (O, I) => O): O =
     items.foldLeft(initialState)(f)
   

   ▶ Run code in the IDE

2. Option Type

   The 'Option' type is used to describe a computation that either has a result or does not. In Scala, you can 'chain' Option processing, combine with lists and other data structures. For example, you can also turn a pattern-match into a function that return an Option, and vice-versa!

   assert(Option(1).flatMap(x => Option(x + 2)) == Option(3))
   
   assert(Option(1).flatMap(x => None) == None)
   

   ▶ Run code in the IDE

3. Pattern Matching

   Pattern matching in Scala lets you quickly identify what you are looking for in a data, and also extract it.

   assert("Hello World".collect {
     case character if Character.isUpperCase(character) => character.toLower
   } == "hw")
   

   ▶ Run code in the IDE

4. Stack Safety

   Stack safety is present where a function cannot crash due to overflowing the limit of number of recursive calls.

   This function will work for n = 5, but will not work for n = 2000 (crash with java.lang.StackOverflowError) - however there is a way to fix it :-)

   In Scala Algorithms, we try to write the algorithms in a stack-safe way, where possible, so that when you use the algorithms, they will not crash on large inputs. However, stack-safe implementations are often more complex, and in some cases, overly complex, for the task at hand.

   def sum(from: Int, until: Int): Int =
     if (from == until) until else from + sum(from + 1, until)
   
   def thisWillSucceed: Int = sum(1, 5)
   
   def thisWillFail: Int = sum(1, 300)
   

   ▶ Run code in the IDE

5. View

   The .view syntax creates a structure that mirrors another structure, until "forced" by an eager operation like .toList, .foreach, .forall, .count.