A Click Submit to complete this assessment. Question 1 1- Let f(n) = 150 n4 + 10n + 5n2 and g(n) = 150n“ + 2n². Write down the best asymptotic ("big-O") characterization of the following functions: f(n) + g(n) f(n) g(n) f(n) * g(n) Attach File Browse My Computer A Click Submit to complete this assessment.

A Click Submit to complete this assessment. Question 1 1- Let f(n) = 150 n4 + 10n + 5n2 and g(n) = 150n“ + 2n². Write down the best asymptotic ("big-O") characterization of the following functions: f(n) + g(n) f(n) g(n) f(n) * g(n) Attach File Browse My Computer A Click Submit to complete this assessment.

COMPREHENSIVE MICROSOFT OFFICE 365 EXCE

1st Edition

ISBN:9780357392676

Author:FREUND, Steven

Publisher:FREUND, Steven

Chapter6: Creating, Sorting, And Querying A Table

Section: Chapter Questions

Problem 10EYK

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4. Consider the following functions: def count_larger(1, n): count = 0 for i in range(len(1)): if compare (1 [i], n): count += 1 return count (a) What are the formal parameters for compare ()? (b) What are the actual parameters for count_larger? (c) How many calls are made to compare () after calling main()? (d) What is the output after calling main()? Output: def compare (num, comp): return num > comp def main(): numbers = [21, 34, 69, 62, 82, 46, 15] print (count_larger (numbers, 50))
Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes =…
How should you write a reduce function that calculates the sum of the first 10 numbers of the geometric progression given by the following recurrence relation: an = 2 an-1 where ao = 1? Choose all that apply. To use reduce you need to import it from functools: from functools import reduce sum_ = reduce (lambda x, y: y+x, [2**x for x in range(10)]) sum_ = reduce (lambda x, y: y*x, [2*x for x in range(1,11)]) sum_ = reduce (lambda x, y: x + y, [2**i for i in range(10)]) sum_ = reduce (lambda x, y: y*x, [2 for x in range(1,11)]) sum_ = reduce (((x, x+y) for x in [2**i for i in range(10)]), [2**x for x in range(10)])
Consider the following function shoots : def shoots(x: int) -> int: if x <= 0: return 0 4 elif x == 1: return shoots(x + 2) 6 elif x == 2: return shoots(x - 1) 80 elif x == 2: 9. return -1 10 else: 11 return shoots(x - 2) 2 3
Q5. For each of the following, place the function in a Big-O set (using the smallest/lowest Big-O set possible). Just provide the Big-O set in each case – no need to show how you got to those solutions. a) n/4 + 5n2- 200 b) 3nlog,n + 5n
in python while using the libraries Pandarallel seaborn, pandas, and numpy then read in the dataset ‘geyser’ from seaborn Write some function that does a somewhat computationally complex task on a value / row of data. Using python time functions, apply this function on the dataframe and time it. Then apply this same function on the dataframe using pandarallel’s parallel_apply, and time this. Experiment with the timing / different functions Compare the run times
The Fibonacci sequence begins with 0 and then 1 follows. All subsequent values are the sum of the previous two, ex: 0, 1, 1, 2, 3, 5, 8, 13. Complete the fibonacci() function, which has an index n as parameter and returns the nth value in the sequence. Any negative index values should return -1. Ex: If the input is: 7 the output is: fibonacci (7) is 13 Note: Use a for loop and DO NOT use recursion.
Question 3.1. Write a function called simulate_estimates. It should take 4 arguments: original_df : A DataFrame from which the data should be sampled, with 1 column named 'serial_number'. • sample_size: The size of each sample, an integer. (For example, to do resampling, we would pass the number of rows in original_df for this argument.) statistic: function that computes a statistic on a sample. This argument is the name of a function that takes a Series of serial numbers as its argument and returns a number (e.g. calculate_mean_based_estimate). repetitions: The number repetitions to perform (i.e. the number of resamples to create). It should simulate repetitions samples with replacement from the given DataFrame. For each of those samples, it should compute the statistic on that sample. Then it should return an array containing the value of that statistic for each sample (this means that the length of the returned array should be equal to repetitions). The code below provides an…
The Fibonacci sequence begins with 0 and then 1 follows. All subsequent values are the sum of the previous two, ex: 0, 1, 1, 2, 3, 5, 8, 13. Complete the fibonacci() function, which has an index n as parameter and returns the nth value in the sequence. Any negative index values should return -1. Ex: If the input is: 7 the output is: fibonacci (7) is 13 Note: Use a for loop and DO NOT use recursion. 461710.3116374.qx3zqy7 LAB ACTIVITY 1 def fibonacci(n): 1234567 4 7.34.1: LAB: Fibonacci sequence 6 #Type your code here. 5 if __name__ start_num = '__main__': int(input()) main.py print(f'fibonacci ({start_num}) is {fibonacci (start_num)}') 0/10 Load default template...
The Fibonacci sequence begins with 0 and then 1 follows. All subsequent values are the sum of the previous two, ex: 0, 1, 1, 2, 3, 5, 8, 13. Complete the fibonacci() function, which has an index n as parameter and returns the nth value in the sequence. Any negative index values should return -1. Ex: If the input is: 7 the output is: fibonacci(7) is 13 Note: Use a for loop and DO NOT use recursion. python def fibonacci(n): # Type your code here. if __name__ == '__main__': start_num = int(input()) print('fibonacci({}) is {}'.format(start_num, fibonacci(start_num)))
In the figure below, B is a rigid cube whose sides are of length I. A, C, and D are square plates that are hinged to B as shown in the figure. The motion of the system is such that the angular velocity of B in reference frame G is: CoB = wzb, + @,b, + w,bz. Wy The velocity of point O of B in the reference frame G is: Gv° = vxbx + v „b, + v̟b,. yDy Note that w; and v; (i = x, y, z) are time-varying scalars. Further, 0, B, and o as shown in the figure are also time-varying orientation angles. Your tasks are: 1. Correctly identify which of the scalars are symbols or dynamicsymbols , based on the above question. You will use these scalars in all subsequent computations. Also, correctly define all the frames and reference frames using Point and ReferenceFrame from sympy. 0 2. Compute "wª: the angular velocity of A in G. This computation must be stored in the variable name G_omega_A. () 3. Compute CaD: the angular acceleration of D in G. This computation must be stored in the variable name…
Interesting, intersecting def squares_intersect(s1, s2): A square on the two-dimensional plane can be defined as a tuple (x, y, r) where (x, y) are the coordinates of its bottom left corner and r is the length of the side of the square. Given two squares as tuples (x1, y1, r1) and (x2, y2, r2), this function should determine whether these two squares intersect, that is, their areas have at least one point in common, even if that one point is merely the shared corner point when these two squares are placed kitty corner. This function should not contain any loops or list comprehensions of any kind, but should compute the result using only integer comparisons and conditional statements.This problem showcases an idea that comes up with some problems of this nature; it is actually far easier to determine that the two squares do not intersect, and negate that answer. Two squares do not intersect if one of them ends in the horizontal direction before the other one begins, or if the same…