Dice Exercises

forked from Aylenml/test
Last updated: September 8th, 2020

This is the final project of Intro to Probability Part 1

In [1]:
import random
import pandas as pd


Imagine that we will play a role-playing game. First, we need to know how throw a dice.

Try to do alone:

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In [2]:
def dice():
return random.randint(1,6)
dice()

Out[2]:
6

Suppose that you attack your enemy. Define a code to attack that depend on the amount of dice.

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In [3]:
def attack(number_of_dice):
return sum([dice() for _ in range(number_of_dice)])

In [4]:
attack(3)

Out[4]:
15

Now that we know how to attack, we begin a battle.

We call "enemy_armour" to number that represent the power of the armour of your enemy and "number_of_dice" to the amount of dice when attack.

Define a battle that return true if you win the battle (the sum of the dice>enemy armour) and false in otherwise.

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In [69]:
def battle(number_of_dice,enemy_armour):
'''Returns True if you beat the enemy and False otherwise'''
return attack(number_of_dice)>enemy_armour


Suppose that the enemy has an armour with power 8 and you attack with 2 dice. How is the battle?

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In [70]:
def battle_1():
return battle(2,8)


and if the enemy has an armour with power 12 and you attack with 3 dice?

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In [71]:
def battle_2():
return battle(3,12)


What previous battle will more likely than you win? We try to calculate the empirical probability.

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In [72]:
def empiric_prob(experiment,n=10000):
return sum([experiment() for _ in range(n)])/n

In [73]:
empiric_prob(battle_1)

Out[73]:
0.2781
In [74]:
empiric_prob(battle_2)

Out[74]:
0.2584

We will compare the empirical probability with the theoretical:

We begin with the battle of 2 dice and 8 in the enemy armour. Then we need that sum of the dice is more than 8.

In [75]:
amount = 0
for x in range(1,7,1):
for y in range(1,7,1):
if x+y>8:
amount=amount+1
print(amount)

10


Then we have $\frac{10}{36}=\frac{5}{18}$, that it this is:

In [76]:
5/18

Out[76]:
0.2777777777777778
In [77]:
abs(empiric_prob(battle_1)-5/18)

Out[77]:
0.002922222222222215

Is close to "empiric_prob(battle_2)", but it can be better. What will we can do to better the empirical probability?

In [78]:
emp_prob = empiric_prob(battle_1,n=100000)
emp_prob

Out[78]:
0.27883
In [79]:
abs(emp_prob- 5/18)

Out[79]:
0.0010522222222222322

We can do a table to compare the result with different n:

In [80]:
times = [10000, 100000, 1000000, 10000000]
experiments = []
for t in times:
experiments.append(empiric_prob(battle_1,t))
dif = []
for i in range(len(times)):
dif.append(abs(experiments[i]-5/18))
data = {'Times':['10000', '100000', '1000000', '10000000'], 'Empirical Probability': experiments,
'Difference': dif}
df = pd.DataFrame(data)
df


Out[80]:
Times Empirical Probability Difference
0 10000 0.270700 0.007078
1 100000 0.276150 0.001628
2 1000000 0.277570 0.000208
3 10000000 0.277962 0.000184

Try to do the compare in the battle 2

In [ ]:


In [83]:
amount = 0
for x in range(1,7,1):
for y in range(1,7,1):
for z in range(1,7,1):
if x+y+z>12:
amount=amount+1
times = [10000, 100000, 1000000, 10000000]
experiments = []
for t in times:
experiments.append(empiric_prob(battle_2,t))
dif = []
for i in range(len(times)):
dif.append(abs(experiments[i]-amount/6**3))
data = {'Times':['10000', '100000', '1000000', '10000000'], 'Empirical Probability': experiments,
'Difference': dif}
df = pd.DataFrame(data)
df


Out[83]:
Times Empirical Probability Difference
0 10000 0.258000 0.001259
1 100000 0.256750 0.002509
2 1000000 0.259447 0.000188
3 10000000 0.259383 0.000124
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