Find an element in a sorted array. """ import math def jump_search(arr,target): """ Worst-case Complexity: O(√n) (root(n)) All items in list must be sorted like binary search Find block that contains target value and search it linearly in that block It returns a first target value in array reference: https://en.wikipedia.org/wiki/Jump_search """ length = len(arr) block_size = int(math.sqrt(length)) block_prev = 0 block= block_size # return -1 means that array doesn't contain target value # find block that contains target value if arr[length - 1] < target: return -1 while block <= length and arr[block - 1] < target: block_prev = block block += block_size # find target value in block while arr[block_prev] < target : block_prev += 1 if block_prev == min(block, length) : return -1.

Exercise - Collection Functions Using the code below, use the map function to create an array of Int values, whose values are equal to the original integer value, plus 1. Use 0ドル as you iterate through the values of the array. Print the resulting collection. 5 let testScores = [65, 80, 88, 90, 47] Using the code below, use the filter function to create a new array of String values. The new array should only include Strings longer than four characters. Use 0ドル as you iterate through the values of the array. Print the resulting collection. Olet schoolSubjects = ["Math" , "Computer Science", "Gym", "English", "Biology"] Using the code below, use the reduce function to subtract all of the values within the array from the starting value 100. Print the resulting value. let damageTaken = [25, 10, 15, 30, 20]

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()

What does each line of code mean?

TASK 1. Re-write this code using Lambda. S TASK 2. Create an array2 that consists of 30987888 digits. Write a code using Lambda function to display only even numbers from array2. int[] array = { 2, 6, 4, 12, 7, 8, 9, 13, 2 }; var orderedFilteredArray = from element in array where element < 7 orderby element select element; PrintArray(orderedFilteredArray, "All values less than 7 and sorted:");

Python Please **Bold is code** Recap of two-dimensional arrays and their numpy implementation import numpy as np my_2d_array = np.array([[1,2],[3,4],[5,6]]) print("This is my_2d_array:") print(my_2d_array) print("This array has shape " + str(my_2d_array.shape) + " as there are " + str(my_2d_array.shape[0]) + " rows and " + str(my_2d_array.shape[1]) + " columns.") print("The entry of the array with row index " + str(1) + " and column index " + str(1) + " has value " + str(my_2d_array[1,1])) 1) In this problem, implement a TwoDArray class that is meant to mimic (some of) the functionality of a two-dimensional numpy array. DO NOT use numpy at any point. 1A) Give the class an __init__ method Make sure that the variable array is a valid input. This means you should a) Make sure that array is a list of lists. and b) Make sure that each of the inner lists has the same length. You do not need to check for anything else. Rise the appropriate error(s) if these conditions are not met. It is up...

Write an array that matches the following requirement, and provide explanation of how you got your answer.--> Using a insertion sort on the array compared to using a selection sort requires FEWER comparisons of elements.