public static int addGrade(int[] allGrades, int grade, int listSize) { allGrades[listSize] = grade; ++listSize; return listSize; } public static void main(String[] args) { final int MAX_GRADES = 100; int[] courseGrades = new int[MAX_GRADES]; int numGrades = 0; } numGrades = addGrade(csGrades, 74, numGrades); numGrades = addGrade(csGrades, 74, numGrades); numGrades = addGrade (csGrades, 74, numGrades);

Use a one-dimensional array to solve the following problem. Read in 20 numbers, each of which is between 20 and 50, inclusive. As each number is read, validate it and check its value. Numbers greater than 40 are to be stored as they are. Numbers that are less than or equal to 40 should be doubled and then stored. After reading all the values, display all the values which were stored.

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()

The contents of the array below represent a maxHeap. What would be the contents of the array after a deletion. Briefly explain how the deletion is done. 60 20 30 5 10 15 25

In [ ]: We obtained an array with N = 10 values, scattered about mean of 25 (mm). Let's calculate the mean, standard deviation, standard deviation of the mean and 20-uncertainty SL of these numbers. In the following code, do these steps: • In the first cell, add a brief comment that explains what each statement does. Start each comment with a # sign. • In the second cell, delete the raise Not ImplementedError() code line and print the standard deviation and 20-uncertainty on the screen (follow the example for mean_L) mean_L = np.mean (L) print("Mean length: ", mean_L) stddev_L = np. std (L, ddof=1) stddev_mean_L = stddev_L/np.sqrt(N) uncertainty_L = 2*stddev_mean_L In [ ] V #YOUR CODE HERE raise NotImplementedError() #calculates the mean of L In the above code, the green expressions on the right hand side, such as np.mean, np. std and np.sqrt are functions built into the numpy package of Python. The names on the left hand side are variable names invented by me.

se for loops on the above 2D array to: •Find how many ZEROsand how many negative numbersare in the matrixand display results*HELP: declare a variable “count_zero= 0and count_neg=0” and increment the count each time you findzero and negative

If an array variable is declared as score[7] then the valid range of array indexes for score is 0...7 Select one: True False