Implement a main method that profiles the performance of insert and outputs: ving the average time per insert as the length of the array increases. code: Setting to allow fine-tuning the granularity of the readings = NUM_READINGS = 60 =INSERTS_PER_READING = 1000 Start with an array containing 1 element new array containing one element having value 0 Take NUM_READINGS readings op NUM_READINGS times // Each reading will be taken after INSERTS_PER_READING inserts Let startTime = current time Loop INSERTS_PER_READING times Let index = random integer in range [0. array, length)

In Ocaml: transFresh nfa qs   Type: ('q, 's) nfa_t -> 'q list -> ('q list, 's) transition listNO IMPERATIVES.THIS INCLUDES REFERENCES, FOR AND WHILE LOOPS, AND HASHMAPS      Description: The inputs are an NFA and a list of states. The output is a transition list. Each element in the returned list is a tuple in the form of (src, char, dest), where dest is the list of states you can get to after starting from any state in the inputted list and moving on one character of the alphabet (sigma), followed by any number of epsilon transitions. This may seem a little confusing at first, so make sure you look at the examples below!     Examples: type ('q, 's) transition = 'q * 's option * 'q type ('q, 's) nfa_t = { sigma : 's list; qs : 'q list; q0 : 'q; fs : 'q list; delta : ('q, 's) transition list; }   let nfa_ex = {   sigma = ['a'];   qs = [0; 1; 2];   q0 = 0;   fs = [2];   delta = [(0, Some 'a', 1); (1, None, 2)]   } let dfa_ex = {   sigma = ['a'; 'b'; 'c'];   qs = [0; 1; 2];   q0 =…

Prompt: In Python language, write a function that applies the logistic sigmoid function to all elements of a NumPy array. Code:   import numpy as np import math import matplotlib.pyplot as plt import pandas as pd   def sigmoid(inputArray):               modifiedArray = np.zeros(len(inputArray))               #YOUR CODE HERE:               return(modifiedArray) def test(): inputs = np.arange(-100, 100, 0.5) outputs = sigmoid(inputs) plt.figure(1) plt.plot(inputs) plt.title('Input') plt.xlabel('Index') plt.ylabel('Value') plt.show() plt.figure(2) plt.plot(outputs,'Black') plt.title('Output') plt.xlabel('Index') plt.ylabel('Value') plt.show() test()

Write a program that reads student scores into an array, gets the best score, and then assigns grades based on the following scheme: • Grade is A if score is • Grade is B if score is • Grade is C if score is • Grade is D if score is • Grade is F otherwise. best - 10 best - 20; best - 30; best - 40; The program prompts the user to enter the total number of students, then prompts the user to enter all of the scores, and concludes by displaying the grades