Compute and plot the derivative of the function f(x) = x³ • Apply the above methodology to compute and plot the derivative of the function f(x) = x³, for x = [-n, n], for n = 5 and 8 In [39]: import matplotlib.pyplot as plt n = 5 r = list (range(-n, n+1)) cubes = [] ## generate the squares using a for loop for value in r: s = value**3 cubes.append(s) slopes = [] first = b = ## generate plot points from existing lists plt.plot(r, cubes, label='function line') plt.plot(r, cubes, 'ro', label = 'function points') plt.plot(r, slopes, label = 'slope line') plt.plot(r, slopes, 'go', label = 'slope points') plt.plot(r, trueslope, label 'true slope') #generate axes Lables plt.ylabel('cubes value') plt.xlabel('integer input') plt.title('cubes plot') plt.legend() # display plot plt.show()

Compute and plot the derivative of the function f(x) = x³ • Apply the above methodology to compute and plot the derivative of the function f(x) = x³, for x = [-n, n], for n = 5 and 8 In [39]: import matplotlib.pyplot as plt n = 5 r = list (range(-n, n+1)) cubes = [] ## generate the squares using a for loop for value in r: s = value**3 cubes.append(s) slopes = [] first = b = ## generate plot points from existing lists plt.plot(r, cubes, label='function line') plt.plot(r, cubes, 'ro', label = 'function points') plt.plot(r, slopes, label = 'slope line') plt.plot(r, slopes, 'go', label = 'slope points') plt.plot(r, trueslope, label 'true slope') #generate axes Lables plt.ylabel('cubes value') plt.xlabel('integer input') plt.title('cubes plot') plt.legend() # display plot plt.show()

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

Similar questions

%matplotlib inline import numpy as np from matplotlib import pyplot as plt import math
Prompt: In python langauge, write a function that adds a random noise to all elements of a NumPy array Code: import numpy as np import math import matplotlib.pyplot as plt import pandas as pd def addNoise(inputArray): modifiedArray = np.zeros(len(inputArray)) #YOUR CODE HERE: return(modifiedArray) def test(): inputs = np.arange(-5, 5,0.2) outputs = addNoise(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()
Python Complete the get_2_dimensional_list() function that takes two parameters: 1. An integer num_rows. 2. An integer num_columns. The function should return a 2D list with num_rows rows and num_columns columns. Each element is set to a random value between 1 and 99 included. If both parameters num_rows and num_columns are set 0, the function returns an empty list. You MUST use a for...in.range loop to create the 2D list. You do not need to import the random module as it has already been done for you. Some examples of the function being called are shown below. For example: Test random.seed (15) num_rows - 2 num columns 5 matrix = get_2_dimensional_list(num_rows, num_columns) print(matrix) random.seed (132) num_rows=2 num_columns 2 matrix = get_2_dimensional_list(num_rows, num_columns) print(matrix) Result [[27, 2, 67, 95, 5], [21, 31, 3, 8, 88]] [[53, 20], [53, 8]]
An array is special if every even index contains an even number and every odd index contains an odd number. Create a function that returns true if an array is special, and false otherwise. Examples isSpecialArray([2, 7, 4, 9, 6, 1, 6, 3]) → true // Even indices: [2, 4, 6, 6]; Odd indices: [7, 9, 1, 3] isSpecialArray([2, 7, 9, 1, 6, 1, 6, 3]) → false // Index 2 has an odd number 9. isSpecialArray ([2, 7, 8, 8, 6, 1, 6, 3]) → false // Index 3 has an even number 8.
Python please Use map(s) to program this formula: Σ [ (xi – x)(yi – y) ] / Σ [ (xi – x)^2] Note the Σ symbol means "SUM" where the sum goes from 0 to a limit. And the ^2 at the end of the equation means 'squared'.
Write in java
python How to convert the first and the last character of each element n _student to uppercase? Hint :use the function map0 that return a map object(which is an iterator) of the results after applyingthe given function to each item of a qiven iterable (list, tuple etc.) Syntax : map(a function towhich map passes each element of given iterable, terable which is to be mapped)n_student=pd.Series(['Justine', 'nora', 'Ugur', 'lana', 'hiba', 'lola'])
Modify the quick sort implementation in the textbook to sort the array using pivot as the median of the first, last, and middle elements of the array. Add the modified quick sort implementation to the arrayListType class provided (arrayListType.h). Ask the user to enter a list of positive integers ending with -999, sort the integers, and display the pivots for each iteration and the sorted array. Main Function #include <iostream>#include "arrayListType.h"using namespace std; int main(){ arrayListType<int> list;int num;cout << "Line 8: Enter numbers ending with -999" << endl;cin >> num; while (num != -999){list.insert(num);cin >> num;} cout << "Line 15: The list before sorting:" << endl;list.print();cout << endl; list.selectionSort();cout << "Line 19: The list after sorting:" << endl;list.print();cout << endl; return 0;} Header File (arrayList.h) Including images #include <iostream>#include <cassert>…
Let A[-4:3, -3:2], element size 4, start position = 100,Use Row-major method to store, please calculate the location of A[1, 1] a. 301 b. 262 c. 359 d. 145 e. 236 f. 289 g. 209 h. 178 i. 228 j. 134
import numpy as np a = np.array([1,2,3]) b = np.array([4,5,6]) You may need to use NumPy methods to answer the following questions. What is the cross product of the two vectors a and b given above? What is the dot product of the two vectors a and b given above?
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()
In C language.