Answer:
It helps to put the code in to your working memory and you will have a greater ability to problem solve.
Explanation:
If your just a beginner, then writing the code out your self will help you learn what each line means. Also, you will see the effects that each line has. Of course, this is really dependent on how much code you've written before and how much code your dealing with.
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:
False.
Explanation:
The description provided matches better with Software performance testing, and shouldn't be confused with a benchmark.
In computing, a benchmark is a tool or software designed to measure the average performance of another program, by running several tests and trials against it.
A performance testing is designed to measure the performance and responsiveness of a computer system (not a program) under a heavy workload.
