minor

Author: sanjarcode

home/4_resource_itineraries/2_CN_Data_Structures_and_OOP/5._Data_Structures/3._Auxilary_Data_Structures/3._Tries/4._Huffman_Encoding.md

@@ -9,7 +9,7 @@ BTW, we do concatenation here, not frequency (number) and code.
 
 Note:
 - There are versions of thinking about encoding:
-1. Fixed size + concatenated. ineffecient.
+1. Fixed size + concatenated. inefficient.
 2. Fixed size code + frequency (not concatenating the same char). equally inefficient as above, asymptotically.
 3. Variable sized + frequency (not possible) ❌
 4. Variable sized + concatenation. ✅
@@ -21,7 +21,7 @@ Note:
 This is a technique for text compression. **Remember that this compression(text) has to be lossless.**
 
 ## Huffman algorithm (generating prefix tree)
-- Huffman encoding tries to reduce the space requirement for the most ocurring characters. If some rare character(s) need to space greater than 1 bytes, it will be okay, since they are so less.
+- Huffman encoding tries to reduce the space requirement for the most occurring characters. If some rare character(s) need to space greater than 1 bytes, it will be okay, since they are so less.
 - Basically we are using different length of bits for each letter. i.e not 8 bits for each letter.
 - Note that prefixes for a code cannot be the code for some other character. This happens because, in case of codes with same prefixes, when we reach a letter, the next node **is** a number, **not** a letter and vice-versa. i.e all parents are numbers(sum of frequencies of children).