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## Pseudocode:

Pseudocode is an english like way to state an algorithm.

Simple problem:

1) Determine the sum, average and product of three numbers.

2) We can solve this by writing out steps

3) Then we can then convert these steps to code

 Pseudo CodeDo forever [while (true is true)]{ While () key not pressed) { if (character received)   ( getcharacter     display pattern }}send 'Y'while ( key pressed){ if (character received) { getcharacter  display pattern } } send 'N'}

### Optimal_encoding algorithm:

- The degree of compression (size of compressed data is) - Where f( ch ) is the frequency of ‘ch’ and d( ch ) is the number of bits in its code

Example : Illustration of codeword generation in Huffman coding Message Code probabilities a1 0 P1 = 5 / 8 a2 110 P2  = 3 / 32 a3 110 P3 = 3 / 32 a4 1110 P4 = 1 / 32 a5 101 P5 = 1 / 8 a6 1111 P6 = 1 / 32

This figure show the Huffman coding here L = 6 having the probability for every message possibility noted at each node.

 MESSAGE CODEWORD PROBABILITY a1 0 P1 = 5 / 8 a2 100 P2 = 3 / 32 a3 110 P3 = 3 /32 a4 1110 P4 = 1 / 32 a5 101 P5 = 1 / 8 a6 1111 P6 = 1 / 32

### Huffman coding:

(5 / 8)1 + (3 / 32)3 + (3 / 32)3 + (1 / 32) - 4 + (1 / 8)3 + (1 / 32) - 4

= (29 / 16) bits / message

= (1.813) bits / message

### Application of Huffman tree:

1) To obtain an optimal set of codes for symbols

2) Which constitute messages. Each code is a binary string (combinations of 0's and 1's) which will be used for transmission of messages.

3) Fixed length coding

4) Variable length coding

#### Huffman Code Data Structures

Binary (Huffman) Tree

- Represents Huffman code

- Edge = code(‘0’ or ‘1’)

- Leaf =  symbol

- Path to leaf = encoding

- Example: A = ( 110 ), B = ( 10 ), C= ( 0 ) Priority Queue

- To efficiently build binary tree