Consider the int Array below and determine the values of A and B so that Array[0] has an integer in the range [-23, 71] Array [0] = A + rand () % B; O a. A = -23, B = 95 O b. A = -24, B = 95 Oc. A = -22, B = 94 Od. A = -23, B = 96 O e. A = -23, B = 94

Write a Java application for a grade book using arrays: Create an array to store 20 test grades, random numbers between 0-100, inclusive.  Print these 20 grades Calculate and print the average grade Calculate and print the highest and lowest grades Complete BMI arrays template BMI.java   Use two int arrays to store n numbers from user input, called height[] and weight[].  "The BMI is defined as the body mass divided by the square of the body height" Calculate BMIs and store values into a double array called bmi[].    Write a method to print an array, use an enhanced for loop in the print method.  Print the BMIs by invoking the print method.  import java.util.Scanner;public class BMI{public static void main(String[] args){Scanner key = new Scanner (System.in);int num = // use Scanner object to get an integer// declare an int array for heightint[] height = new int[num];// declare an int array for weight below...// declare an array for BMI array...for ( ){ // iterate all array…

An array int[] intArray can be initialized during its definition by   A.) It can't be initialized   B.) by writing int[] intArray = {1.0f};   C.) by writing int[] intArray = {0,1,2};   D.) by writing int[0-10] intArray = {0,1,2,3,4,5,6,7,8,9};

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()