Explanation:
Object Oriented Programming (OOPS or OOPS for Object Oriented Programming System) is the most widely used programming paradigm today. While most of the popular programming languages are multi-paradigm, the support to object-oriented programming is fundamental for large projects. Of course OOP has its share of critics. Nowadays functional programming seems the new trendy approach. Still, criticism is due mostly to misuse of OOP.
Answer:
Entities are - Students, CourseList, Advisor and CourseSelection.
Explanation:
The database is structure is designed using Crow Foot Database Notation as attached
Md command, (make directory) creates a directory. It's a subdirectory when you md under a directory.
Answer:
Answered below.
Explanation:
#Answer is written in Python programming language
#Get inputs
radius = float(input("Enter radius in inches: "))
height = float(input("Enter height in feet: "))
#Convert height in feet to height in inches
height_in_inches = height * 12
#calculate volume in cubic inches
volume = 3.14 * (radius**2) * height_to_inches
#convert volume in cubic inches to volume in gallons
volume_in_gallons = volume * 0.00433
#output result
print (volume_in_gallons)
Answer:
import numpy as np
import matplotlib.pyplot as plt
def calculate_pi(x,y):
points_in_circle=0
for i in range(len(x)):
if np.sqrt(x[i]**2+y[i]**2)<=1:
points_in_circle+=1
pi_value=4*points_in_circle/len(x)
return pi_value
length=np.power(10,6)
x=np.random.rand(length)
y=np.random.rand(length)
pi=np.zeros(7)
sample_size=np.zeros(7)
for i in range(len(pi)):
xs=x[:np.power(10,i)]
ys=y[:np.power(10,i)]
sample_size[i]=len(xs)
pi_value=calculate_pi(xs,ys)
pi[i]=pi_value
print("The value of pi at different sample size is")
print(pi)
plt.plot(sample_size,np.abs(pi-np.pi))
plt.xscale('log')
plt.yscale('log')
plt.xlabel('sample size')
plt.ylabel('absolute error')
plt.title('Error Vs Sample Size')
plt.show()
Explanation:
The python program gets the sample size of circles and the areas and returns a plot of one against the other as a line plot. The numpy package is used to mathematically create the circle samples as a series of random numbers while matplotlib's pyplot is used to plot for the visual statistics of the features of the samples.
