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Simple Data Compression CS 10C Programming Assignment Huffman coding is used to compress data. The idea is straightforward: represent more common longer strings with shorter ones via a basic translation matrix. The translation matrix is easily computed from the data itself by counting and sorting by frequency. For example, in a well-known corpus used in Natural Language Processing called the "Brown" corpus (see nitk.org), the top-20 most frequent tokens, which are words or punctuation marks are listed below associated with frequency and code. The word "and" for example requires writing three characters. However, if I encoded it differently, say, using the word "5" (yes, I called "5" a word on purpose), then I save having to write two extra characters! Note, the word "and" is so frequent, I save those two extra characters many times over! Token Frequency Code the 62713 58334 2 49346 of 36080 4 and 27932 5 25732 21881 to 6 a 7 in 19536 8 that 10237 is 10011 10 was 9777 11 for 8841 12 8837 13 8789 14 The 7258 15 with 7012 16 it 6723 17 as 6706 18 6566 6466 he 19 his 20 So the steps of Huffman coding are relatively straightforward: 1. Pass through the data once, collecting a list of token-frequency counts. 2. Sort the token-frequency counts by frequency, in descending order. 3. Assign codes to tokens using a simple counter, for example by incrementing over the integers; this is just to keep things simple.

Simple Data Compression CS 10C Programming Assignment Huffman coding is used to compress data. The idea is straightforward: represent more common longer strings with shorter ones via a basic translation matrix. The translation matrix is easily computed from the data itself by counting and sorting by frequency. For example, in a well-known corpus used in Natural Language Processing called the "Brown" corpus (see nitk.org), the top-20 most frequent tokens, which are words or punctuation marks are listed below associated with frequency and code. The word "and" for example requires writing three characters. However, if I encoded it differently, say, using the word "5" (yes, I called "5" a word on purpose), then I save having to write two extra characters! Note, the word "and" is so frequent, I save those two extra characters many times over! Token Frequency Code the 62713 58334 2 49346 of 36080 4 and 27932 5 25732 21881 to 6 a 7 in 19536 8 that 10237 is 10011 10 was 9777 11 for 8841 12 8837 13 8789 14 The 7258 15 with 7012 16 it 6723 17 as 6706 18 6566 6466 he 19 his 20 So the steps of Huffman coding are relatively straightforward: 1. Pass through the data once, collecting a list of token-frequency counts. 2. Sort the token-frequency counts by frequency, in descending order. 3. Assign codes to tokens using a simple counter, for example by incrementing over the integers; this is just to keep things simple.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Simple Data Compression CS 10C Programming Assignment Huffman coding is used to compress data. The idea is straightforward: represent more common longer strings with shorter ones via a basic translation matrix. The translation matrix is easily computed from the data itself by counting and sorting by frequency. For example, in a well-known corpus used in Natural Language Processing called the "Brown" corpus (see nltk.org), the top-20 most frequent tokens, which are words or punctuation marks are listed below associated with frequency and code. The word "and" for example requires writing three characters. However, if I encoded it differently, say, using the word "5" (yes, I called "5" a word on purpose), then I save having to write two extra characters! Note, the word "and" is so frequent, I save those two extra characters many times over! Token Frequency Code the 62713 1 58334 2 49346 3 of 36080 4 and 27932 5 to 25732 6 a 21881 7 in 19536 that 10237 9 is 10011 10 was 9777 11 for 8841 12 8837 13 8789 14 The 7258 15 with 7012 16 6723 6706 it 17 as 18 he 6566 19 his 6466 20 So the steps of Huffman coding are relatively straightforward: 1. Pass through the data once, collecting a list of token-frequency counts. 2. Sort the token-frequency counts by frequency, in descending order. 3. Assign codes to tokens using a simple counter, for example by incrementing over the integers; this is just to keep things simple.
Transcribed Image Text:Simple Data Compression CS 10C Programming Assignment Huffman coding is used to compress data. The idea is straightforward: represent more common longer strings with shorter ones via a basic translation matrix. The translation matrix is easily computed from the data itself by counting and sorting by frequency. For example, in a well-known corpus used in Natural Language Processing called the "Brown" corpus (see nltk.org), the top-20 most frequent tokens, which are words or punctuation marks are listed below associated with frequency and code. The word "and" for example requires writing three characters. However, if I encoded it differently, say, using the word "5" (yes, I called "5" a word on purpose), then I save having to write two extra characters! Note, the word "and" is so frequent, I save those two extra characters many times over! Token Frequency Code the 62713 1 58334 2 49346 3 of 36080 4 and 27932 5 to 25732 6 a 21881 7 in 19536 that 10237 9 is 10011 10 was 9777 11 for 8841 12 8837 13 8789 14 The 7258 15 with 7012 16 6723 6706 it 17 as 18 he 6566 19 his 6466 20 So the steps of Huffman coding are relatively straightforward: 1. Pass through the data once, collecting a list of token-frequency counts. 2. Sort the token-frequency counts by frequency, in descending order. 3. Assign codes to tokens using a simple counter, for example by incrementing over the integers; this is just to keep things simple.
4. Store the new mapping (token -> code) in a hashtable called "encoder". 5. Store the reverse mapping (code -> token) in a hashtable called "decoder". 6. Pass through the data a second time. This time, replace all tokens with their codes. Now, be amazed at how much you've shrunk your data! Delivery Notes: (1) Implement your own hashtable from scratch, you are not allowed to use existing hash table libraries. (2) To be useful, your output should include the coded data as well as the decoder (code -> token) mapping file. Now GZIP all that and watch it shrink immensely!
Transcribed Image Text:4. Store the new mapping (token -> code) in a hashtable called "encoder". 5. Store the reverse mapping (code -> token) in a hashtable called "decoder". 6. Pass through the data a second time. This time, replace all tokens with their codes. Now, be amazed at how much you've shrunk your data! Delivery Notes: (1) Implement your own hashtable from scratch, you are not allowed to use existing hash table libraries. (2) To be useful, your output should include the coded data as well as the decoder (code -> token) mapping file. Now GZIP all that and watch it shrink immensely!
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