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Python代写 | FE-520 Assignment 3 Sprint 2020

这个作业是练习使用面向对象编程
FE-520 Assignment 3
Sprint 2020
Submission Requirement:
For all the problems in this assignment you need to design and use Python 3, output
and present the results in nicely format. Please submit a written report (pdf), where
your results and copy your code should be readable, and 4 python files (main.py, generator.py, point.py and MCTest.py). Your grade will be evaluated by combination of
report and code. You are strongly encouraged to write comment for your code, because
it is a convention to have your code documented all the time. In your python file, you
need contain both function and test part of function. Python script must be a ‘.py’
script, Jupyter notebook ‘.ipynb is not allowed. Do NOT copy and paste from others,
all homework will be firstly checked by plagiarism detection tool.
1 Class practice 30 pts
1.1 Define class (20pts)
Create a class called Rectangular, with two data attributes: length and width. They
should be assigned in constructor ( init ()) It should also have two function attributes
called area() and perimeter() which return area and perimeter of this rectangular respectively. Here is an example of the class structure:
>>> class Rectangular:



>>> myRec = Rectangular(10,20)
>>> print(myRec.area())
200
>>> print(myRec.perimeter())
60
1.2 Numpy applying on class (10 pts)
1. define two numpy array with size 10, named with length and width.
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2. test your your class Rectangular with input as np array. (Here you should have
10 output for area and perimeter).
2 Display Time (20pts)
Create a Time class and initialize it with hours, minutes and seconds.
1. Make a method addTime which should take two time object and add them. E.g.
if your original initial parameter is (2 hour and 50 min and 10 seconds), then
you could call this method with another input parameters (1 hr and 20 min and 5
seconds) , then output is (4 hr and 10 min and 15 seconds)
2. Make a method displayTime which should print the time (the initial parameter).
3. Make a method DisplayMinute which should display the total seconds in the
Time. E.g.- (1 hr 2 min) should display 3720 seconds.
3 Uniform Distributed Random Number Generator (50pt)
You can not use any random number generators in packages like Numpy, because
you are required to build your own generator in this question.
Almost all the random numbers generated by computer are pseudo random numbers, because they are generated by a formula. A good random number generator is
very important for financial simulations, such as Monte-Carlo method.
The goal of this assignment is to let you understand how computer generate random numbers and create pseudo random number generators which can generate 0-1
uniform-distributed random numbers by yourselves.
The probability density function (PDF) of 0-1 uniform distribution is
f(x) =



1 f or x ∈ [0, 1]
0 otherwise
It means each point in interval [0, 1] has the same probability to be chosen.
1. For implementation purpose, people use different algorithms to generate pseudorandom numbers. One of the most famous algorithms is called Linear Congruential Generator(LCG). This generator can yield a sequence of random numbers {Xi}. The recurrence relation of this algorithm is:
Xn+1 = (aXn + c) mod M
here a, c, M are all parameters. a is called multiplier which satisfies 0 < a < M,
c is called increment which satisfy 0 6 c < M, M is the modulus which is also
2
stands for the maximum range of this sequence. You can find more information
about the common parameters setting in the link above. There is another parameter X0 which is so called seed. You can generate totally different sequence by
different seeds. In the formula above mod stands for the modulus operation, for
example: 7 mod 3 = 1 due to 7 = 2 × 3 + 1.
As you can guess, this generator can generate a sequence of integers within
[0, M). To create a uniform distributed sequence, you only need to divide the
sequence above by M.
For example, now I have a setting: X0 = 1, and a = 1103515245, M = 2
32
, c =
12345.
X1 =(a · X0 + c) mod M = 1103527590
X2 =(a · X1 + c) mod M = 25248852323

Then the uniform distributed sequence will be
{
1
2
32 ,
1103527590
2
32 ,
25248852323
2
32 , · · · } ∼ {2.328 × 10−10
, 0.2569, 0.5879, · · · }
There are some variants of LCG, one of them has such recurrence relation:
Xn+1 = (Xn(Xn + 1)) mod M
The seed of this generator has to satisfy X0 mod 4 = 2.
Now it’s your turn to implement it in Python 3.
• (15 pt) Create a file called generator.py. In this file, create a class called
LCG whose instance can generate random numbers by Linear Conguential Generator algorithm. It should have these data attributes: self.seed,
self.multiplier, self.increment, self.modulus which are assigned in initializer ( init ()). The parameters should be passed by argument from outside of the class. This class’s instance should also at least have function
attributes allow me: 1) get the seed; 2) set the seed; 3) initialize the generator (start from the seed); 4) give me the next random number; 5) give me
a sequence (list) of random number, the length should be passed by argument.
• (10 pt) In generator.py, create an inherited class called SCG from class
LCG you have created. You are required to implement the variant of LCG
I mentioned above in this class. It should have the same interfaces (attributes) with LCG, but different recurrence relation. Remember the seed
of this generator has to satisfy X0 mod 4 = 2, so you need to check this
condition in initializer, raise error with customized error information if the
seed doesn’t satisfy this condition.
Hint: To better reuse your code, your class should have a function attribute
to specify the recurrence relation. You can only override this function in
your inherited class.
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• (Bonus 3pt) Specify next () or iter () attributes in your class to make
the instance of your class compatible with iter() or next(), this kind of generator can yield me one random number for each call.
• A good code style like following good conventions, writing comment and
doc string for your code will earn additional bonus.
2. To test the performance of your generator, one way is to use a simple MonteCarlo method to test it. Consider a square and its inscribed circle in a 2-D rectangular coordinate. The length of square and the diameter of the circle are 2, and
the center of them are both origin of this coordinate. See 2.
Figure 1: x
2 +y
2 = 1, and its circumscribed square. Blue points are uniform distributed
random points
You can populated the points in this 2-D coordinate within this square, and count
the number of points which fall in the circle. Then dividing this number by the
number of all points will give you an estimate of ratio of area between inscribed
circle and its square . Since you have already have theoretical result of this ratio:
ratio =
πr
2
4r
2
=
π
4
∼ 0.78539816339
where r is the radius of the circle, thus the length of square is 2r. The closer your
result is to this ratio, the better your random generator performs.
Now you need to use Python to do this:
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• (15 pt) Create a file called point.py. Write a class to represent the points
in rectangular coordinate. It should have the data attributes that store its
x coordinate and y coordinate, and also a function attribute distance to
calculate its distance from origin point.
• (10 pt) Create a file called MCTest.py. Import the classes you created above
(generators and point) from Python modules. Populate 10,000,000 random
points within this square. To do this, you only need to populate 2 × 107 0-1
uniform distributed random numbers firstly, then re-scale them into [−1, 1],
and pair them into 1 × 107 points. Test both generators.
Determine whether points are within the circle. Hint: You can do this by
computing its distance from origin, if it is smaller than 1 (the radius), it is
within the circle.
Give the estimate of above ratio, and the difference between yours and
theoretical one.
• You are also encouraged to record the time consumed by your program.
Hint: use time package.
For this question, you need to submit 3 file: generator.py, point.py and MCTest.py.
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