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MarkovHodel MarkovModel (text, k) constructs a Markov model = of order x from text returns the order of = returns the number of occurrences of kgran in = returns the number of times character e follows kgra in = using =, finds and returns a random character following kgram using =, builds and returns a string of length n, the first characters of which is kgram 2.order () z. kgran freq (kgran) 2. char treq(kgran, e) z.rand (kgraz) z. gen (kgran, n) • Constructor To implement the data type, define two instance variables: an integer k that stores the order of the Markov model, and a symbol table _at whose keys are all the k-grams from the given text. The value corresponding to each key (say kgram) in et is a symbol table whose keys are the characters that follow kgram in the text, and the corresponding values are their frequencies. You may assume that the input text is a sequence of characters over the ASCII alphabet so that all values are between 0 and 127. The frequencies should be tallied as if the text were circular (i.e., as if it repeated the first k characters at the end). For example, if the text is 'gaggagaggegagaaa' and k = 2, then the symbol table _at should store the following information: 'aa': {'a': 1, 'g': 1}, 'ag': {'a': 3, 'g': 2}, 'cg': {'a': 1}, 'ga': {'a': 1. '8': 4}, 'ge': {'g'i 1}. '88': {'a': 1, 'c': 1, 'g': 1} If you are careful enough, the entire symbol table can be built in just one pass through the circular text. Note that there is no reason to save the original text or the circular text as an attribute of the data type. That would be a grossly

MarkovHodel MarkovModel (text, k) constructs a Markov model = of order x from text returns the order of = returns the number of occurrences of kgran in = returns the number of times character e follows kgra in = using =, finds and returns a random character following kgram using =, builds and returns a string of length n, the first characters of which is kgram 2.order () z. kgran freq (kgran) 2. char treq(kgran, e) z.rand (kgraz) z. gen (kgran, n) • Constructor To implement the data type, define two instance variables: an integer k that stores the order of the Markov model, and a symbol table _at whose keys are all the k-grams from the given text. The value corresponding to each key (say kgram) in et is a symbol table whose keys are the characters that follow kgram in the text, and the corresponding values are their frequencies. You may assume that the input text is a sequence of characters over the ASCII alphabet so that all values are between 0 and 127. The frequencies should be tallied as if the text were circular (i.e., as if it repeated the first k characters at the end). For example, if the text is 'gaggagaggegagaaa' and k = 2, then the symbol table _at should store the following information: 'aa': {'a': 1, 'g': 1}, 'ag': {'a': 3, 'g': 2}, 'cg': {'a': 1}, 'ga': {'a': 1. '8': 4}, 'ge': {'g'i 1}. '88': {'a': 1, 'c': 1, 'g': 1} If you are careful enough, the entire symbol table can be built in just one pass through the circular text. Note that there is no reason to save the original text or the circular text as an attribute of the data type. That would be a grossly

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Problem 1. (Markov Model Data Type) Define a data type called xarkovModel in zarkov _zodel.py to represent a Markov model of order k from a given text string. The data type must support the following API: Markovlodel MarkovModel (text, k) constructs a Markov model = of order z from text 2.order () returns the order of = returns the number of occurrences of kgram in = returns the number of times character e follows kgra in = using =, finds and returns a random character following kgraz using =, builds and returns a string of length n, the first x characters of which is zgraz z. kgran freq(kgram) z. char_treq(kgran, c) z.rand (kgraz) z. gen (kgran, n) • Constructor To implement the data type, define two instance variables: an integer k that stores the order of the Markov model, and a symbol table _at whose keys are all the k-grams from the given text. The value corresponding to each key (say kgram) in _at is a symbol table whose keys are the characters that follow kgram in the text, and the corresponding values are their frequencies. You may assume that the input text is a sequence of characters over the ASCII alphabet so that all values are between 0 and 127. The frequencies should be tallied as if the text were circular (i.e., as if it repeated the first k characters at the end). For example, if the text is 'gagggageggegagaaa' and k = 2, then the symbol table _at should store the following information: 'aa': {'a': 1, 'g': 1}, 'ag': {'a': 3. '8': 2}. cg': {'a': 1}, 'ga': {'a': 1. '8': 43, 'ge': {'g': 1}. 'gg': {'a': 1, 'c': 1, 'g': 1} If you are careful enough, the entire symbol table can be built in just one pass through the circular text. Note that there is no reason to save the original text or the circular text as an attribute of the data type. That would be a grossly inefficient waste of space. Your Markovkodel object does not need either of these strings after the symbol table is built. • Order. Return the order k of the Markov Model. 3 /8 Project 6 (Markov Model) Frequency. There are two frequency methods. - kgraz treq (kgran) returns the number of times kgram was found in the original text. Returns 0 when kgram is not found. Raises an error if kgram is not of length k. - char_treq(kgran, c) returns the number of times kgram was followed by the character e in the original text. Returns O when kgram or e is not found. Raises an error if kgram is not of length k. • Randomly generate a character. Return a character. It must be a character that followed the kgram in the original text. The character should be chosen randomly, but the results of calling rand (kgran) several times should mirror the frequencies of characters that followed the kgram in the original text. Raise an error if kgram is not of length k or if kgram is unknown. • Generate pseudo-random text. Return a string of length n that is a randomly generated stream of characters whose first k characters are the argument kgram. Starting with the argument kgram, repeatedly call rand() to generate the next character. Successive k-grams should be formed by using the most recent k characters in the newly generated text. To avoid dead ends, treat the input text as a circular string: the last character is considered to precede the first character. For example, if k = 2 and the text is the 17-character string 'geggagaggegagaaa', then the salient features of the Markov model are captured in the table below:
Transcribed Image Text:Problem 1. (Markov Model Data Type) Define a data type called xarkovModel in zarkov _zodel.py to represent a Markov model of order k from a given text string. The data type must support the following API: Markovlodel MarkovModel (text, k) constructs a Markov model = of order z from text 2.order () returns the order of = returns the number of occurrences of kgram in = returns the number of times character e follows kgra in = using =, finds and returns a random character following kgraz using =, builds and returns a string of length n, the first x characters of which is zgraz z. kgran freq(kgram) z. char_treq(kgran, c) z.rand (kgraz) z. gen (kgran, n) • Constructor To implement the data type, define two instance variables: an integer k that stores the order of the Markov model, and a symbol table _at whose keys are all the k-grams from the given text. The value corresponding to each key (say kgram) in _at is a symbol table whose keys are the characters that follow kgram in the text, and the corresponding values are their frequencies. You may assume that the input text is a sequence of characters over the ASCII alphabet so that all values are between 0 and 127. The frequencies should be tallied as if the text were circular (i.e., as if it repeated the first k characters at the end). For example, if the text is 'gagggageggegagaaa' and k = 2, then the symbol table _at should store the following information: 'aa': {'a': 1, 'g': 1}, 'ag': {'a': 3. '8': 2}. cg': {'a': 1}, 'ga': {'a': 1. '8': 43, 'ge': {'g': 1}. 'gg': {'a': 1, 'c': 1, 'g': 1} If you are careful enough, the entire symbol table can be built in just one pass through the circular text. Note that there is no reason to save the original text or the circular text as an attribute of the data type. That would be a grossly inefficient waste of space. Your Markovkodel object does not need either of these strings after the symbol table is built. • Order. Return the order k of the Markov Model. 3 /8 Project 6 (Markov Model) Frequency. There are two frequency methods. - kgraz treq (kgran) returns the number of times kgram was found in the original text. Returns 0 when kgram is not found. Raises an error if kgram is not of length k. - char_treq(kgran, c) returns the number of times kgram was followed by the character e in the original text. Returns O when kgram or e is not found. Raises an error if kgram is not of length k. • Randomly generate a character. Return a character. It must be a character that followed the kgram in the original text. The character should be chosen randomly, but the results of calling rand (kgran) several times should mirror the frequencies of characters that followed the kgram in the original text. Raise an error if kgram is not of length k or if kgram is unknown. • Generate pseudo-random text. Return a string of length n that is a randomly generated stream of characters whose first k characters are the argument kgram. Starting with the argument kgram, repeatedly call rand() to generate the next character. Successive k-grams should be formed by using the most recent k characters in the newly generated text. To avoid dead ends, treat the input text as a circular string: the last character is considered to precede the first character. For example, if k = 2 and the text is the 17-character string 'geggagaggegagaaa', then the salient features of the Markov model are captured in the table below:
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