Function 1load_treasure_map(filename): Takes a string as input corresponding to a filename. Opens the treasure map at that filename and loads the treasure map into a list of lists, then returns said list of lists. You can assume that the file will exist. Note that the treasure map could have any number of rows and columns. If there is any issue with the format of the file (e.g., it is not a matrix, or contains any invalid characters), then raise an AssertionError with an appropriate error message. Note that an 'X' in a file is valid. Note: In the example below, we show each row of the resulting list on its own line, but when you use doctest, you must put all the rows of the list into one line of code, as doctest checks for any improper whitespace Function 2write_treasure_map(treasure_map, filename): Takes as inputs a list of lists corresponding to a treasure map and a string corresponding to a filename. Writes the treasure map to a file at the given filename, with a newline after each row of the map. Does not return anything. Function 3write_X_to_map(filename, row, col): Takes as inputs a string corresponding to a filename, and two non-negative integers representing a row and column index. Reads in the map at the given filename, inserts an X into the given row and column position, then saves the map to a new file with 'new_' prepended to the given filename. You can assume the filename given to the function as argument refers to a file that exists. In this question, we will go on a hunt for treasure!! We must follow the trail in a treasure map, whichhas been split across multiple files.Along with this PDF we provide you three examples of treasure map files: map0.txt, map1.txt andmap8.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 casestarts in the top-left corner, can be composed of the following characters: '>', '<', 'v' and '^'. Youmust follow this trail (by looking at the character to the right, left, below or above, respectively), untilyou reach a .' character at the end. You will then place an 'X' at said location to mark that you havefound the location of the treasure, and save a new file containing the updated treasure map. E.g., forthe file above, after following the trail, you would create a new file called new_map8.txt containing thefollowing:>>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 offthe 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.txtprovided 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 shouldcontinue the trail at the same row and column (row 4, column 5) of the next map file. Since this ismapo.txt, you must thus check in map1.txt and start looking at the trail at row 4, column 5 (instead ofthe top-left corner). Similarly, the '|' character is a ladder character: if you encounter this characterin a particular row and column position, then you must look into the preceding map file and start thetrail at that same row and column position. such deceptions, we must also acquire the knowiedge benind this type of algorithnm.BackgroundIn the 1948 landmark paper 'A Mathematical Theory of Communication', Claude Shannon founded thefield of information theory and revolutionized the telecommunications industry, laying the groundworkfor today's Information Age. In the paper, Shannon proposed using a Markov chain to create a statisticalmodel of the sequences of letters in a piece of English text. Markov chains are now widely used in speechrecognition, handwriting recognition, information retrieval, data compression, and spam filtering. Theyalso have many scientific computing applications including the genemark algorithm for gene prediction,the Metropolis algorithm for measuring thermodynamical properties, and Google's PageRank algorithmfor Web search. For this assignment question, we consider a variant of Markov chains to generate stylizedpseudo-random text.Shannon approximated the statistical structure of a piece of text using a simple mathematical modelknown as a Markov model. A Markov model of order 0 predicts that each letter in the alphabet willoccur 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 thenumber of occurrences of each letter in that text, and using those ratios as our probabilities.Page 11For example, if the input text is 'gagggagaggcgagaaa', the Markov model of order 0 predicts that, infuture, 'a' will occur with probability 7/17, 'c' will occur with probability 1/17, and 'g' will occurwith 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 thefollowing: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 withprobability 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 occurswith the given probabilities, no matter what letter came before it. This independence makes thingssimple but does not translate to real language. In English, for example, there is a very high correlationamong 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 dependon the preceding letter or letters. A Markov model of order k predicts that each letter occurs witha fixed probability, but that probability can depend on the previous k consecutive characters. Let ak-gram mean any string of k characters. Then for example, if the text has 100 occurrences of 'th', with60 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' withprobability 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 forits 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 similarThat is, we can start it off withcharacteristics.

Answer: our guideline is answer the first three question form the first question

Answered: Function 1…

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