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Please help me write the following function (please see the attached photos for context of the problem): find_treasure(start_map_num): Takes an integer between 0 and 9 as input. Loads the corresponding map file, and starts following the trail (at position 0, 0 of that file) as described above. Continues following the trail through other map files as needed. Places an 'X' at the conclusion of the trail and saves the updated treasure map to a new file with 'new_' prepended to the current map filename. Returns a tuple of the row and column index where the 'X' was placed in that file.

Please help me write the following function (please see the attached photos for context of the problem): find_treasure(start_map_num): Takes an integer between 0 and 9 as input. Loads the corresponding map file, and starts following the trail (at position 0, 0 of that file) as described above. Continues following the trail through other map files as needed. Places an 'X' at the conclusion of the trail and saves the updated treasure map to a new file with 'new_' prepended to the current map filename. Returns a tuple of the row and column index where the 'X' was placed in that file.

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
Problem 1PE
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Please help me write the following function (please see the attached photos for context of the problem):

find_treasure(start_map_num):

Takes an integer between 0 and 9 as input. Loads the corresponding map file, and starts following the trail (at position 0, 0 of that file) as described above. Continues following the trail through other map files as needed. Places an 'X' at the conclusion of the trail and saves the updated treasure map to a new file with 'new_' prepended to the current map filename. Returns a tuple of the row and column index where the 'X' was placed in that file.

such deceptions, we must also acquire the knowiedge benind this type of algorithnm. Background In the 1948 landmark paper 'A Mathematical Theory of Communication', Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today's Information Age. In the paper, Shannon proposed using a Markov chain to create a statistical model of the sequences of letters in a piece of English text. Markov chains are now widely used in speech recognition, handwriting recognition, information retrieval, data compression, and spam filtering. They also have many scientific computing applications including the genemark algorithm for gene prediction, the Metropolis algorithm for measuring thermodynamical properties, and Google's PageRank algorithm for Web search. For this assignment question, we consider a variant of Markov chains to generate stylized pseudo-random text. Shannon approximated the statistical structure of a piece of text using a simple mathematical model known as a Markov model. A Markov model of order 0 predicts that each letter in the alphabet will occur with a fixed probability. For instance, it might predict that each letter occurs % of the time, that is, entirely at random. Or, we might base its prediction on a particular piece of text, counting the number of occurrences of each letter in that text, and using those ratios as our probabilities. Page 11 For example, if the input text is 'gagggagaggcgagaaa', the Markov model of order 0 predicts that, in future, 'a' will occur with probability 7/17, 'c' will occur with probability 1/17, and 'g' will occur with probability 9/17, because these are the fractions of times each letter occurs in the input text. If we were to then use these predictions in order to generate a new piece of text, we might obtain the following: g agg cg ag a ag aga aga a a gag agaga a ag ag a ag ... Note how, in this generated piece of text, there are very few c's (since we predict they will occur with probability 1/17), whereas there are many more a's and g's. A Markov model of order 0 assumes that each letter is chosen independently. That is, each letter occurs with the given probabilities, no matter what letter came before it. This independence makes things simple but does not translate to real language. In English, for example, there is a very high correlation among successive characters in a word or sentence. For example, 'w' is more likely to be followed with 'e' than with 'u', while 'q' is more likely to be followed with 'u' than with 'e'. We obtain a more refined model by allowing the probability of choosing each successive letter to depend on the preceding letter or letters. A Markov model of order k predicts that each letter occurs with a fixed probability, but that probability can depend on the previous k consecutive characters. Let a k-gram mean any string of k characters. Then for example, if the text has 100 occurrences of 'th', with 60 occurrences of 'the', 25 occurrences of 'thi', 10 occurrences of 'tha', and 5 occurrences of 'tho', the Markov model of order 2 predicts that the next letter following the 2-gram 'th' will be 'e' with probability 3/5, 'i' with probability 1/4, 'a' with probability 1/10, and 'o' with probability 1/20. Once we have such a model, we can then use it to generate text. some particular k characters, and then ask it what it predicts will come next. We can repeat asking for its predictions until we have a large corpus of generated text. The generated text will, by definition, resemble the text that was used to create the model. We can base our model on any kind of text (fiction, poetry, news articles, song lyrics, plays, etc.), and the text generated from that model will have similar That is, we can start it off with characteristics.
Transcribed Image Text:such deceptions, we must also acquire the knowiedge benind this type of algorithnm. Background In the 1948 landmark paper 'A Mathematical Theory of Communication', Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today's Information Age. In the paper, Shannon proposed using a Markov chain to create a statistical model of the sequences of letters in a piece of English text. Markov chains are now widely used in speech recognition, handwriting recognition, information retrieval, data compression, and spam filtering. They also have many scientific computing applications including the genemark algorithm for gene prediction, the Metropolis algorithm for measuring thermodynamical properties, and Google's PageRank algorithm for Web search. For this assignment question, we consider a variant of Markov chains to generate stylized pseudo-random text. Shannon approximated the statistical structure of a piece of text using a simple mathematical model known as a Markov model. A Markov model of order 0 predicts that each letter in the alphabet will occur with a fixed probability. For instance, it might predict that each letter occurs % of the time, that is, entirely at random. Or, we might base its prediction on a particular piece of text, counting the number of occurrences of each letter in that text, and using those ratios as our probabilities. Page 11 For example, if the input text is 'gagggagaggcgagaaa', the Markov model of order 0 predicts that, in future, 'a' will occur with probability 7/17, 'c' will occur with probability 1/17, and 'g' will occur with probability 9/17, because these are the fractions of times each letter occurs in the input text. If we were to then use these predictions in order to generate a new piece of text, we might obtain the following: g agg cg ag a ag aga aga a a gag agaga a ag ag a ag ... Note how, in this generated piece of text, there are very few c's (since we predict they will occur with probability 1/17), whereas there are many more a's and g's. A Markov model of order 0 assumes that each letter is chosen independently. That is, each letter occurs with the given probabilities, no matter what letter came before it. This independence makes things simple but does not translate to real language. In English, for example, there is a very high correlation among successive characters in a word or sentence. For example, 'w' is more likely to be followed with 'e' than with 'u', while 'q' is more likely to be followed with 'u' than with 'e'. We obtain a more refined model by allowing the probability of choosing each successive letter to depend on the preceding letter or letters. A Markov model of order k predicts that each letter occurs with a fixed probability, but that probability can depend on the previous k consecutive characters. Let a k-gram mean any string of k characters. Then for example, if the text has 100 occurrences of 'th', with 60 occurrences of 'the', 25 occurrences of 'thi', 10 occurrences of 'tha', and 5 occurrences of 'tho', the Markov model of order 2 predicts that the next letter following the 2-gram 'th' will be 'e' with probability 3/5, 'i' with probability 1/4, 'a' with probability 1/10, and 'o' with probability 1/20. Once we have such a model, we can then use it to generate text. some particular k characters, and then ask it what it predicts will come next. We can repeat asking for its predictions until we have a large corpus of generated text. The generated text will, by definition, resemble the text that was used to create the model. We can base our model on any kind of text (fiction, poetry, news articles, song lyrics, plays, etc.), and the text generated from that model will have similar That is, we can start it off with characteristics.
In this question, we will go on a hunt for treasure!! We must follow the trail in a treasure map, which has been split across multiple files. Along with this PDF we provide you three examples of treasure map files: map0.txt, map1.txt and map8.txt. Here is the contents of one such treasure map file, map8.txt: >>v......V ^v^. You will observe that there is what could be considered a 'trail' in the file. A trail, which in this case starts in the top-left corner, can be composed of the following characters: '>', '<', 'v' and '^'. You must follow this trail (by looking at the character to the right, left, below or above, respectively), until you reach a .' character at the end. You will then place an 'X' at said location to mark that you have found the location of the treasure, and save a new file containing the updated treasure map. E.g., for the file above, after following the trail, you would create a new file called new_map8.txt containing the following: >>v. ..v...... V ..>>>>X.. .^v^ You will also observe that there can be other trails in the file, leading perhaps to dead-ends or even off the grid. These false trails must be ignored. There are other special characters that can appear in a file. For example, here is the file map0.txt provided to you with this PDF: >>>v...... ...v... v..v. v..>>* You will observe that there is a trail beginning in the upper-left corner and ending at row 4, column 5, where there is a '*' character. This character is a 'fall-through' character. It means that you should continue the trail at the same row and column (row 4, column 5) of the next map file. Since this is mapo.txt, you must thus check in map1.txt and start looking at the trail at row 4, column 5 (instead of the top-left corner). Similarly, the '|' character is a ladder character: if you encounter this character in a particular row and column position, then you must look into the preceding map file and start the trail at that same row and column position.
Transcribed Image Text:In this question, we will go on a hunt for treasure!! We must follow the trail in a treasure map, which has been split across multiple files. Along with this PDF we provide you three examples of treasure map files: map0.txt, map1.txt and map8.txt. Here is the contents of one such treasure map file, map8.txt: >>v......V ^v^. You will observe that there is what could be considered a 'trail' in the file. A trail, which in this case starts in the top-left corner, can be composed of the following characters: '>', '<', 'v' and '^'. You must follow this trail (by looking at the character to the right, left, below or above, respectively), until you reach a .' character at the end. You will then place an 'X' at said location to mark that you have found the location of the treasure, and save a new file containing the updated treasure map. E.g., for the file above, after following the trail, you would create a new file called new_map8.txt containing the following: >>v. ..v...... V ..>>>>X.. .^v^ You will also observe that there can be other trails in the file, leading perhaps to dead-ends or even off the grid. These false trails must be ignored. There are other special characters that can appear in a file. For example, here is the file map0.txt provided to you with this PDF: >>>v...... ...v... v..v. v..>>* You will observe that there is a trail beginning in the upper-left corner and ending at row 4, column 5, where there is a '*' character. This character is a 'fall-through' character. It means that you should continue the trail at the same row and column (row 4, column 5) of the next map file. Since this is mapo.txt, you must thus check in map1.txt and start looking at the trail at row 4, column 5 (instead of the top-left corner). Similarly, the '|' character is a ladder character: if you encounter this character in a particular row and column position, then you must look into the preceding map file and start the trail at that same row and column position.
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