# 蒙特卡落法扔骰子概率游戏_帮国外留学生做的作业 ## 如本文章标有价格,需议价或其它事情商议请加微信pythonxia

```"""
蒙特卡落法扔骰子概率游戏_帮国外留学生做的作业
"""
import random

def throwDice(m, k, R):

a = []
for _ in range(m):
a.append(R.randrange(1,k+1))
return tuple(a)

def main(SEED=237):

m = int(input('请输入骰子数量:'))
while m<1:
print('骰子数量最少要是1')
m = int(input('请输入骰子数量: '))

k = int(input('请输入每枚骰子的面数: '))
while k<2:
print('骰子面数最少要是2')
k = int(input('请输入每枚骰子的面数:: '))

numberOfTrials = int(input('请输入测试次数: '))
while numberOfTrials<1:
print('测试次数最少要是1')
numberOfTrials = int(input('请再次输入测试次数: '))

# m  骰子数
# k 面数

# 随机数产生器
rng = random.Random(SEED)

# 记录投骰子后每面的和的列表，由于和最少是2，所以0和1这两个索引号用不到。
frequency = ((m*k)+1)*  #  [0,0,0,0,0,0,0,0,0,0,0,0,0]
for i in range(numberOfTrials):
t = throwDice(m,k,rng)
frequency[sum(t)] += 1;

print()

# 计算概率等
relativeFrequency = [0, 0]
Experimental = [0,0]
for i in range(2, len(frequency)):
relativeFrequency.append(frequency[i]/numberOfTrials)
Experimental.append(int(relativeFrequency[i] * 100))

print()

# 格式化打印结果
f1 = "{0:<10}{1:<22}{2:<22}{3:<22}"
f2 = 71*"-"
f3 = "{0:>3}       {1:<22}{2:<22.6f}{3:<2d} %"
print(f1.format("Sum","Frequency","Relative Frequency","Experimental"))
print(f2)
for i in range(2, len(frequency)):
print(f3.format(i, frequency[i],relativeFrequency[i], Experimental[i]))

print()

if __name__ == '__main__':

main()

```

# Beginning Programming in Python Programming Assignment 7

In this project you will write a Python program that simulates a dice game. The number of sides on each die, the number of dice, and the number of simulations to perform will all be taken from user input. After each simulation, your program will calculate the sum of the numbers on the dice. Then, after the specified number of simulations, your program will produce an estimate of the probability of each possible sum. This is a simple version of a well-known computational technique known as Monte Carlo. Begin by carefully studying the example DiceProbabilities.py posted on the class webpage under /Examples/pa7. Your program will be a direct generalization of that example, and will be called Probability.py.

The Monte Carlo method was invented by scientists working on the atomic bomb in the 1940s. They named their technique for the city in Monaco famed for its casinos. The core idea is to use randomly chosen inputs to explore the behavior of a complex dynamical system. These scientists faced difficult problems of mathematical physics, such as neutron diffusion, that were too complex for a direct analytical solution, and must therefore be evaluated numerically. They had access to one of the earliest computers (ENIAC), but their models involved so many dimensions that exhaustive numerical evaluation was prohibitively slow. Monte Carlo simulation proved to be surprisingly effective at finding solutions to these problems. Since that time, Monte Carlo methods have been applied to an incredibly diverse range of problems in science, engineering, and finance. In our case, a pure analytical solution is possible for the probabilities that we seek, but since this is not a class in probability theory, we will take the computational/experimental approach. You can find a very interesting history of early computing machines, the Monte Carlo Method, and the development of the atomic bomb in the book Turing's Cathedral by George Dyson. Follow the link

https://en.wikipedia.org/wiki/Monte_Carlo_method

for an article on Monte Carlo methods.

A normal six-sided die is a symmetrical cube that, when thrown, is equally likely to land with any of its six faces up (provided its mass distribution is uniform.) By labeling its faces with the numbers 1-6, we have a physical device capable generating random numbers in the set {1, 2, 3, 4, 5, 6}. It is possible to make perfectly symmetrical dice in the shape of any of the so-called Platonic Solids, whose number of sides are 4 (Tetrahedron), 6 (Cube), 8 (Octahedron), 12 (dodecahedron), and 20 (Icosahedron). See https://www.mathsisfun.com/geometry/platonic-solids-why-five.html for a nice explanation as to why these are the only perfectly symmetrical shapes possible for dice. For purposes of this project however, we shall assume it is possible to make dice with any number of faces in such a way that each face is equally likely to land in the up position. To simulate a throw of a k-sided die in Python, use the randrange() function belonging to the random module, which will be discussed in class and illustrated in the example DiceProbability.py.

def throwDice(m, k, R):

that uses a random number generator R to simulate a throw of independent, symmetrical k-sided dice, then returns the result as an m-tuple. Your program will also a program called main() with heading

def main(SEED=237):

The heading for main() indicates that it takes either one or zero arguments. If called with no arguments, the parameter SEED is assigned the default value 237. Otherwise SEED is assigned the value of the argument. The parameter SEED will be used to seed the random number generator. Function main() will prompt for, and read three quantities: the number of dice, the number of sides on each die, and the number of simulations (or throws) to perform. These prompts will be robust, in that, if the user enters an integer less than 1 for the number of dice, or an integer less than 2 for the number of sides on each die, or an integer less than 1 for the number of simulations, then your program will continue to prompt until adequate values are entered. Your program is not required to handle non-integer input like floats or general strings.

Once these values have been entered by the user, your program will perform the specified number of simulations, recording the frequency of each possible sum as it goes. To do this you must first calculate the range of possible sums, and create a list of appropriate length. If you call this list frequency[], for instance, then by the time the simulations are complete, frequency[i] will be the number of simulations in which the sum of the dice was i. Again, emulate the example DiceProbability.py to accomplish this. Calculate the relative frequency for each possible sum (the number of simulations resulting in that sum, divided by the total number of simulations). Also calculate the experimental probability for each sum (the relative frequency expressed as a percent.) Print out these quantities in a table formatted as in the sample runs below.

\$ python Probability.py Enter the number of dice: 3

Enter the number of sides on each die: 6 Enter the number of trials to perform: 10000

Sum Frequency Relative Frequency Experimental Probability

----------------------------------------------------------------------

 3 45 0.0045 0.45 % 4 126 0.0126 1.26 % 5 281 0.0281 2.81 % 6 494 0.0494 4.94 % 7 677 0.0677 6.77 % 8 968 0.0968 9.68 % 9 1191 0.1191 11.91 % 10 1257 0.1257 12.57 % 11 1257 0.1257 12.57 %
 12 1164 0.1164 11.64 % 13 932 0.0932 9.32 % 14 683 0.0683 6.83 % 15 469 0.0469 4.69 % 16 282 0.0282 2.82 % 17 122 0.0122 1.22 % 18 52 0.0052 0.52 %

\$

As usual, represents the command line prompt. The output table begins with the heading shown, then a line of 70 dashes "-". The body of the table presents the sum right justified in a field of width 4, the frequency right justified in a field of width 11, the relative frequency accurate to 5 decimal digits and right justified in field of width 18, and the experimental probability accurate to 2 decimal digits and right justified in a field of width 21. Note the blank lines before, after and within program output. The following sample run shows what happens when the user enters invalid parameters.

\$ python Probability.py Enter the number of dice: -1

The number of dice must be at least 1 Please enter the number of dice: 4

Enter the number of sides on each die: 1

The number of sides on each die must be at least 2 Please enter the number of sides on each die: 7

Enter the number of trials to perform: -1 The number of trials must be at least 1

Please enter the number of trials to perform: 10000

Sum Frequency Relative Frequency Experimental Probability

----------------------------------------------------------------------

 4 6 0.0006 0.06 % 5 18 0.0018 0.18 % 6 52 0.0052 0.52 % 7 83 0.0083 0.83 % 8 166 0.0166 1.66 % 9 273 0.0273 2.73 % 10 346 0.0346 3.46 % 11 469 0.0469 4.69 % 12 630 0.063 6.3 % 13 738 0.0738 7.38 % 14 836 0.0836 8.36 % 15 930 0.093 9.3 % 16 930 0.093 9.3 % 17 985 0.0985 9.85 % 18 844 0.0844 8.44 % 19 737 0.0737 7.37 % 20 589 0.0589 5.89 % 21 526 0.0526 5.26 % 22 326 0.0326 3.26 % 23 238 0.0238 2.38 % 24 124 0.0124 1.24 % 25 86 0.0086 0.86 % 26 49 0.0049 0.49 % 27 13 0.0013 0.13 % 28 6 0.0006 0.06 %

\$

To get full credit, your output must be formatted exactly as above. See the examples FormatNumbers1.py and FormatNumbers2.py on the webpage in /Examples/pa7 to see how this can be accomplished. If main() is called with no arguments, so the random number generator is seeded with the integer 237, then

your numbers should exactly match those above. Here is a rough skeleton of the logic of function main()

and the conditional that calls main()def main(SEED=237):

# get number of dice, number of sides on each die,

# and number of trials

# Create a random number generator rng = random.Random(SEED)

# perform simulation, record frequencies

# calculate relative frequencies

# tabulate results

# end main()

if name =='__main ': main()

# end if

You can experiment with other seeds by importing Probability in interactive mode, then running main() on a single integer argument. However, main() in the conditional if name =='__main ': must be called with no arguments so as to use the seed 237, which will facilitate automated grading of your project.

## What to turn in

Submit Probability.py to the assignment pa7 on Gradescope before the due date. As always, start early and ask plenty of questions.

scratch 