Answer:
there should be an option to attach an item. if not try pasting it
Explanation:
Answer:
Harley-Davidson Motor Company
Explanation:
Harley-Davidson produces and sells custom-made, touring as well as cruiser motorcycles that feature elegant styling, modern innovative design, and high quality performance with the ability to customize to ones taste. Harley-Davidson moved 95% of their x86 server workloads to virtualized environments utilizing VMware infrastructures as of 2018. They report to have being able to have higher system availability, effective and simple disaster recovery capabilities, improved operation efficiencies, and cloud-ready infrastructure. One of the major challenges of virtualization is Security, Virtual systems can easily get compromised.
I believe the answer is font.
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.
Answer:
The answer is option (4) Maximize redundancy as normalization minimizes redundancy of data.
Explanation:
Normalization of databases leads to minimization of data redundancy in databases. It doesn't maximize data redundancy. Data redundancy leads to wastage of resources. Normalization of databases minimizes insertion anomolies. Normalization of databases minimizes deletion anomolies. Normalization of databases minimizes updation anomolies. So , the answer to the question is option (4) maximize redundancy.
