Answer:
Extremal hard drive is not form of communication.
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.
Common examples are ATMs, POS terminals, and airport ticket vending machines. Cashierless checkout technologies like “Scan and Go,” inventory scanning, corporate-owned work profile devices, self-ordering kiosks, mobile devices, and POS/m POS are some of the most common examples of dedicated devices in use today.
Answer:
divide it into smaller pieces
Explanation:
Usually, the main reason a programmer does not understand a problem fully is that there is too much information. The best way to try and fully understand a problem is to divide it into smaller pieces. Start off by answering the question what is the expected output for an input. Then divide each individual issue within the problem. This will allow you as a programmer/developer to create an individual solution for each individual issue within the problem and then test to make sure that the input gives the correct output.
