CSCI 335 HW 2: Lists and Trees solution

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Learning Outcome: The goal of this assignment is to become familiar with lists and trees and compare the performance of the self-balancing AVL tree. You will also work with a real world data set and construct a generic test routine for comparing several different implementations of the tree container class. You are encouraged to use the book’s implementation for AVL tree. Acknowledge the sources you use in the README file. There are 3 extar credit questions EC1, EC2, EC3, each for 5 points. The answer for each extra credit question should be completely correct to get the 5 points. It is either full credit or no credit for extra credit questions. AVL Trees 85 points When you accept the assignment on GitHub Classrooms, you will be provided with the following starter code: avl_tree.h, query_tree.cc, test_tree.cc, test_tree_mod.cc, dsexceptions.h, a README.md, a Makefile and *.txt files for input and expected output. Part 1 (15 points + 5 extra credit points) First, create a class object named SequenceMap that has as private data members the following two: string recognition_sequence_ ; vector enzyme_acronyms_; This file should have the name sequence_map.h Other than the big-five (note that you can use the defaults for all of them), you have to add the following: a) A constructor SequenceMap(const string &a_rec_seq, const string &an_enz_acro), that constructs a SequenceMap from two strings (note that after the constructor is called the vector enzyme_acronyms_ will contain just one element, the an_enz_acro). b) Overloaded operators bool operator<(const SequenceMap &rhs) const and bool operator>(const SequenceMap &rhs) const that operates based on the regular string comparison between the recognition_sequence_ strings (this will be a one line function). c) EC1: Overloaded operators operator<< and operator>> for SequenceMaps. (Extracredit 5 points) d) A member function void Merge(const SequenceMap &other_sequence). This function assumes that the object’s recognition_sequence_ and other_sequence.recognition_sequence_ are equal to each other. The function Merge() merges the other_sequence.enzyme_acronym_ with the object’s enzyme_acronym_. The other_sequence object will not be affected. This class (which is non-templated) will be used in the following programs. First test it with your own test functions to make sure that it operates correctly. Part 2 Introduction to the problem For this assignment you will receive as input two text files, rebase210.txt and sequences.txt. After the header, each line of the database file rebase210.txt contains the name of a restriction enzyme and possible DNA sites the enzyme may cut (cut location is indicated by a ‘) in the following format: enzyme_acronym/recognition_sequence/…/recognition_sequence// For instance the first few lines of rebase210.txt are: AanI/TTA’TAA// AarI/CACCTGCNNNN’NNNN/’NNNNNNNNGCAGGTG// AasI/GACNNNN’NNGTC// AatII/GACGT’C// AbsI/CC’TCGAGG// AccI/GT’MKAC// AccII/CG’CG// AccIII/T’CCGGA// Acc16I/TGC’GCA// Acc36I/ACCTGCNNNN’NNNN/’NNNNNNNNGCAGGT// … You must not change this file. That means that each line contains one enzyme acronym associated with one or more recognition sequences. For example on line 2: The enzyme acronym AarI corresponds to the two recognition sequences CACCTGCNNNN’NNNN and ‘NNNNNNNNGCAGGTG. Part 2(a) (30 points) You will create a parser to read in this database and construct an AVL tree. For each line of the database and for each recognition sequence in that line, you will create a new SequenceMap object that contains the recognition sequence as its recognition_sequence_ and the enzyme acronym as the only string of its enzyme_acronyms_, and you will insert this object into the tree. This is explained with the following pseudo code: Tree a_tree; string db_line; // Read the file line-by-line: while (GetNextLineFromDatabaseFile(db_line)) { // Get the first part of the line: string an_enz_acro = GetEnzymeAcronym(db_line); string a_reco_seq; while (GetNextRecognitionSequence(db_line, a_rego_seq){ SequenceMap new_sequence_map(a_reco_seq, an_enz_acro); a_tree.insert(new_sequence_map); } // End second while. } // End first while. In the case that the new_sequence_map.recognition_sequence_ equals the recognition_sequence_ of a node X in the tree, then the search tree’s insert() function will call the X.Merge(new_sequence_map) function of the existing element. This will have the effect of updating the enzyme_acronym_ of X. Note, that this will be part of the functionality of the insert() function. The Merge() will only be called in case of duplicates as described above. Otherwise, no Merge() is required and the new_sequence_map will be inserted into the tree. To implement the above, write a test program named query_tree which will use your parser to create a search tree and then allow the user to query it using a recognition sequence. If that sequence exists in the tree then this routine should print all the corresponding enzymes that correspond to that recognition sequence. Your programs should run from the terminal as follows: query_tree For example you can write on the terminal: ./query_tree rebase210.txt The user should enter for instance THREE strings (supposed to be recognition sequences): CC’TCGAGG TTA’TAA TC’C Your program should print in the standard output their associated enzyme acronyms. In the above example the output will be AbsI AanI PsiI Not Found We will test it with a file containing arbitrary number of strings and run your code like that: ./query_trees rebase210.txt < input_part2a.txt Part2(b) (25 points) Next, create a test routine named test_tree that does the following in the sequence described below: 1. Parses the database and construct a search tree (this is the same as in Part2(a)). 2. Prints the number of nodes in your tree 𝑛. 3. Computes the average depth of your search tree, i.e. the internal path length divided by 𝑛. a. Prints the average depth. b. Prints the ratio of the average depth to 𝑛 . E.g., if average depth is 6.9 and 𝑛 = 5. 0, then you should print . 6.9 5.0 = 1. 38 4. Searches (find()) the tree for each string in the sequences.txt file and counts the total number of recursive calls for all executions of find(). a. Prints the total number of successful queries (number of strings found). b. Prints the average number of recursion calls, i.e. #total number of recursion calls / number of queries. 5. Removes every other sequence in sequences.txt from the tree and counts the total number of recursion calls for all executions of remove(). a. Prints the total number successful removes. b. Prints the average number of recursion calls, i.e. #total number of recursion calls / number of remove calls. 6. Redo steps 2 and 3: a. Prints number of nodes in your tree. b. Prints the average depth. c. Prints the ratio of the average depth to 𝑛 . The output of Part2(b) should be of the exact form: 2: 3a: 3b: 4a: 4b: 5a: 5b: 6a: 6b: 6c: If you didn’t complete a step, just print after the step number: Not Done Your program should run from the terminal as follows: test_tree For example you can write on terminal ./test_tree rebase210.txt sequences.txt Part2(c) (15 points) You will use the avl_tree.h code you have written for Part2(b) and you will modify it in order to implement double rotations directly instead of calling the two single rotations. Name your modified implementation avl_tree_p2c.h. Run the exact same routines as in Part2(b), but now with your modified AVL implementation. The executable should be named test_tree_mod. The results should be the same as in Part2(b). For example you can write on terminal ./test_tree_mod rebase210.txt sequences.txt You will be given a mandatory Makefile, along with some code to start (start of main functions query_tree.cc test_tree.cc test_tree_mod.cc) Submission On or before the due date listed on Bb, using GitHub Classrooms, submit (and only submit) the following files to Gradescope: ● Part 1: ○ sequence_map.h: Your original sequence_map.h will be reused for all further sections. ● Part 2A: (Modify avl_tree & code by adding functions) ○ query_tree.cc ○ avl_tree.h ● Part 2B: ○ test_tree.cc ● Part 2C: ○ test_tree_mod.cc ○ avl_tree_p2c.h ● README file as described in Assignment 1 requirements and Programming Rules document. ● The following are 2 extracredit questions. The response should be in the README under the title Extra Credit with question #. ○ EC2 : Show that via AVL single rotations, any binary search tree (with greater than 3 nodes) T1 can be transformed to another search tree T2 (with the same items). (5 extra credit points) ○ EC3: Give an algorithm to perform this transformation using O(NlogN) rotations on average. (5 extra credit points) In summary, submit your assignment by following these two steps: 1. Push all materials to GitHub Classrooms. The material includes (and only includes) the following files: ○ README ○ sequence_map.h ○ avl_tree.h ○ avl_tree_p2c.h ○ query_tree.cc ○ test_tree.cc ○ test_tree_mod.cc 2. Do not change their names in any way—do not capitalize any letter. Do not delete any files. Submit ancillary files like .gitignore or any files that exist in the starter code is OK. 3. Once you have your code on GitHub Classrooms, upload it to Gradescope using GitHub Classrooms. Instructions for all this can be found on YouTube. Make sure you run and test your code without Gradescope.