True or False: K means clustering method can handle both continuous and categorical data.
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True or False: K means clustering method can handle both continuous and categorical data.
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- What about predicting the price of a stock 30 days from now? unsupervised ML supervised ML reinforcement learningDefine the term " random srand " .struct insert_into_hash_table { // Function takes a constant Book as a parameter, inserts that book indexed by // the book's ISBN into a hash table, and returns nothing. void operator()(const Book& book) { ///// /// TO-DO (8) ||||. // Write the lines of code to insert the key (book's ISBN) and value // ("book") pair into "my_hash_table". /// END-TO-DO (8) // } std::unordered_map& my_hash_table; };
- What collection-type data type is most useful with data values that are heterogeneous and may be processed differently? record data types arrays associative arrays hashesAssignment Apex: Add a new method: //Name: getcontactInformationUsingInlinesoQL //Return Type: Map> //args: none // Logic: //1. Apply a soQL query and using this get the account record (Use Inline SOQL : Get Contacts): Map>: Id = Account Id, ListCase study: An event company asks you to design the data structure and program prototype for their running event system. The participant for the running event is expected to reach more than 1000 in various categories. You decided to use hashing function method to store the information for each participant's BIB number in the hash table. To begin the problem solving, suppose that 10 registered runners need to be stored in a hash table, HT, with a size of 13. The sample BIB number of the runners are: 101, 102, 103, 104, 107, 111, 121, 217, 157, and 185. Furthermore, you set the hash function to determine the index of the participant in the HT as: hash (BIB) = BIB % table size (or 13) However, if the hash index given by hash(BIB) is already occupied (collision), the linear probing hash function will be used as: hash (BIB) = (Hash (BIB)+1) % 17 and, further collision with hash function: hash (BIB) = (Hash(BIB)+2) % 17 or, hash(BIB) = (Hash (BIB) + n) % 17, where n is probe increment. Refer…
- Iterating over a dictionary example: Gradebook statistics. Write a program that uses the keys(), values(), and/or items() dict methods to find statistics about the student_grades dictionary. Find the following: Print the name and grade percentage of the student with the highest total of points. Find the average score of each assignment. Find and apply a curve to each student's total score, such that the best student has 100% of the total points. The language is in Python. # student_grades contains scores (out of 100) for 5 assignmentsstudent_grades = {'Andrew': [56, 79, 90, 22, 50],'Nisreen': [88, 62, 68, 75, 78],'Alan': [95, 88, 92, 85, 85],'Chang': [76, 88, 85, 82, 90],'Tricia': [99, 92, 95, 89, 99]} So far I have the first part of the question: for student, scores in student_grades.items(): print(student, sum(scores)/len(scores))The Set<T> API has the following methods: void add(T) boolean contains(T) boolean remove(T) The data structure to use for best average case performance for time complexity for random inputs, with answers always guaranteed to be correct.Adjacency lists have benefits over adjacency matrices in a number of ways.
- Horizontal sequence :VIRL Vertical sequence:MKF Scoring rules: g/o = -3, g/e = -1, match or mismatch - from PAM250 substitution matrix below. NW algorithm. 1. Complete the scoring matrix. Scoring matrix with PAM250 scores: V I R L M K F 2. Set up, initialize and complete the NW matrix. 3. Retrace, align and score alignment(s). Use the arrows and circles for the matrix and path(s). V I R L M K F Align and score all optimal alignments here.Function Name: odd_even_diagParameters: a 2D list (list of lists)Returns: list of lists Description: Given a 2-dimensional matrix (list of lists) with any size (n*n), modify it according to the following rules: Find the sum of the main diagonal. If the sum is an odd number, change all the values of the given matrix (except the main diagonal) to 0. If the sum is an even number, change all the values of the given matrix (except the main diagonal) to 1. Return the resulting matrix. Example 1:If argument is: [[1, 2], [4, 3]] odd_even_diag should return: [[1, 1], [1, 3]] because the sum 1 + 3 is even. Example 2:If argument is: [[1, 2, 3], [4, 5, 6], [7, 8, 9]] odd_even_diag should return: [[1, 0, 0], [0, 5, 0], [0, 0, 9]] because the sum 1 + 5 + 9 is odd.Horizontal sequence :VIRL Vertical sequence:MKF Scoring rules: g/o = -3, g/e = -1, match or mismatch - from PAM250 substitution matrix below. NW algorithm. 1. Complete the scoring matrix. Scoring matrix with PAM250 scores: V I R L M K F 2. Set up, initialize and complete the NW matrix. 3. Retrace, align and score alignment(s). Use the arrows and circles for the matrix and path(s). V I R L M K F Align and score all optimal alignments here. PLZ the arrows and circles for the matrix and path(s) AND SHOW ALL possible Alignment