Consider the two pairs of functions below. Between the two functions, which runs the fastest (i.e. uses the fewest number of operations), and which function runs the slowest? Does the size or order of the input change the order ranking? i.e. if the input was an array of ten million items instead of 100 or if the data was sorted (or unsorted) does the efficiency of each function change? Pair #1 def func_one(n): if (n <= 0): return 0 elif (n == 1): else: return 1 res1 = func_one(n-1) res2 = func_one(n - 2) return res1 + res2 def func_two(n): if (n <= 0): return 0 num1 = 0 num2 = 1 for i in range(1,n): temp = num1 + num2 num1 = num2 num2 = temp return num2

Consider the two pairs of functions below. Between the two functions, which runs the fastest (i.e. uses the fewest number of operations), and which function runs the slowest? Does the size or order of the input change the order ranking? i.e. if the input was an array of ten million items instead of 100 or if the data was sorted (or unsorted) does the efficiency of each function change? Pair #1 def func_one(n): if (n <= 0): return 0 elif (n == 1): else: return 1 res1 = func_one(n-1) res2 = func_one(n - 2) return res1 + res2 def func_two(n): if (n <= 0): return 0 num1 = 0 num2 = 1 for i in range(1,n): temp = num1 + num2 num1 = num2 num2 = temp return num2

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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Consider the pair of functions below. Between the two functions, which runs the fastest (i.e. uses the fewest number of operations), and which function runs the slowest? Does the size or order of the input change the order ranking? i.e. if the input was an array of ten million items instead of 100 or if the data was sorted (or unsorted) does the efficiency of each function change? Pair #1 def func_one(my_array): if len(my_array) > 1: mid= len(my_array)//2 L = my_array[:mid] R = my_array[mid:] func_one(L) func_one(R) i=j= k = 0 while i my_array[j+1] : my_array[j], my_array[j+1] = my_array[j+1], my_array[j] swapped = True
Algorithm Analysis1. Revises the function quickSort so that it always chooses the first item in the array as the pivot. Add a counter to the function partition that counts the number of comparisons that are made. Compare the behavior of the revised function with the original one (pivot should be selected using sortFirstMiddleLast algorithm), using arrays of various sizes. At what size array does the difference in the number of comparisons become significant? For which pivot selection strategy does the difference in the number of comparisons become significant? Compare your analysis with the actual running times and counter as a function of the input size n = 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 8192, 16384 <time.h> and clock() function.). For comparison, you need to create two tables for execution times and a counter for both algorithms.2. Revises the function quickSort so that it always chooses the last item in the array as the pivot. Add a counter to the function…
I need the problem really step by step, careful solving with all comments in every line
A set of data is given with the rebound height of tennis balls in centimeters from height 250cm (data.txt). Write three MatLab user-defined functions: usersort, GroupData and MeanStD. The function xo = usesort(x) should be able to sort an array in ascending order, the function [f, X] = GroupData(x) should create labels of the data and should return the labels X and the number of data for each label f and the function [A,s] = MeanStD(x) should return the mean value and the standard deviation as defined by the last equations in the slides 11 and 12. The labels X should start from the smallest round integer (use fix in MatLab) and should go until the largest round integer (use fix) with interval equal to 1. Each label should count the points that are smaller by half interval until larger by half interval. Calculate the density function of the Gauss distribution and define the following probabilities that a randomly chosen value of x will be: (i) no greater than 174cm, (ii) between 174cm…
Answer the given question with a proper explanation and step-by-step solution. Please meet this requirement: All partition values are given for the three (3) ranges, optionally with a reflection about the proper handling of rightAscension.
Write an interactive modular program that uses a user- defined function to accept a M x N matrix. Then write a function to compute column averages and row averages, and print the entire matrix with averages shown in the respective rows and columns.
Using the linear_predict function that you previously wrote, write a function linear_residuals that given the learned weights in the linear_regression function calculates the residuals vector. Your functions takes the following arguments as input: 1. X:A numpy array of the shape (N,d) where N is the number of data points, and d is the data dimension. Do not assume anything about N or d other than being a positive integer. 2. beta : Anumpy array of the shape (d+1,1) where d is the data dimension Bo Ba 3. Y: A numpy array of the shape (N,) where N is the number of data points. Your function should produce the e numpy array with the shape of (N,), whose i" element is defined as el) = yl) – (Bo + Bix? + Bzx + + Bax) In [ ]: def linear_residuals (X,beta,Y): assert X.ndim==2 N = X.shape[0] х.shape[1] assert beta.shape assert Y.shape d = (d+1,1) (N,) == == #3 your code here e e.reshape(-1) %3D assert e.size return e (np.arange ( 35).reshape ( 7,5) ** 13) % 20 2.** (-np.arange (6).reshape…
What you need to Implement? Your task is to implement two versions of the example reducer from MDN, so that these new functions can handle non-numeric values in the array in a graceful manner. Here are the 2 functions you need to implement: reducer1 o If this function is passed to reduce, any non-numeric values in the array will be skipped and the sum of only the numeric values in the array will be returned. o If all the values in the array are non-numeric, then the value O will be returned. reducer2 ○ If this function is passed to reduce and the array contains any non-numeric values, this function will throw the exception TypeError. o Otherwise, the sum of all the values in the array will be returned. you can use o To throw the exception TypeError with the message 'My error message' the statement throw new TypeError('My error message'). See TypeError on MDN for more discussion of this exception.
Please I need Bsupport quick.
If array A consists only of values of 0, 1, and 2, design a function that sorts A. However, sorting should be done without comparing the records' keys. We approach array A by dividing the area into three places and placing 0, 1, 2 in each place. O goes from index 0, and 2 goes from the largest index. 0 The results before and after alignment are shown below. Before alignment = {0, 1, 1, 0, 1, 2, 1, 2, 0, 0, 0, 1} After alignment = {0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2}
please provide step by step solution with explanation. In matlab, create a function or two functions that reads dat files and organizes into the array or matrix such that you have a designated array, or row in a matrix, that handles the x coordinates for the points describing the top half of the airfoil. Do this same thing for the y coordinates of the top half, do this same thing for the x and y coordinates of the bottom half.
Using truth table and functions please complete task 3. Schematic truth table should match). Must have 3 ins and 7 outs. Will upvote!(: