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.
charles babbage is the father of computer's
Hiding/masking personal identifiers from a data set, so that the data set can never identify an individual, even if it is correlated with other data sets is known as anonymization.
<h3>What is anonymization?</h3>
The term anonymization is known as data masking and it is the standard solution in the case of data pseudonymisation. It is generally recognised by using masking and data is de- sensitised also that privacy could be maintained and private information remains safe for the support.
Data is generally identified by using masking and data is de- sensitised also that privacy could be maintained and private information remains safe for the support.
Therefore, Hiding/masking personal identifiers from a data set, so that the data set can never identify an individual, even if it is correlated with other data sets is known as anonymization.
Learn more about anonymization here:
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