My students and I were discussing the difference between possible and probability when it comes to lottery numbers. Is the sequence 1 2 3 4 5 6 just as likely to happen as 76 31 7 54 29 18 ?
My question is, what would be a good format to record six random numbers and what method to count the duplicates?
Simple simulation would do: run n-times random.sample on the desired range and convert result to tuple and feed to collections Counter. Then inspect results. If order doesn't matter, then before converting to tuple use sorted. I tried with n=1000000 and there was no 1, 2, 3, 4, 5, 6 in results (I used range(1, 49)).
Use a set instead of a tuple. Should be faster. But I don't think this as a possible solution.
"Simple solution" is not going to work because the numbers are staggering. I am not surprised that 1 million combinations did not produce a single 1, 2, 3, 4, 5, 6. One million is a pretty small sample size when there are almost 14 million combinations. For the kind of test you propose I would suggest 1 billion combinations. and how are you going to store the 320 MB Counter dictionary?
Hi
When speaking about duplicates for numbers, I'm alway thinking to "np.unique" => here bellow an example. Note at the same time Numpy is fast even for a huge array size.
Paul
import numpy as np
MyList=[0, 1, 10, 5, 2, 1, -1, 8, 2, 1, 5, 1, 1, 1, -1]
MyList=np.asarray(MyList)
UniqueList = np.unique(MyList, return_index=True, return_counts=True)
n = np.shape(UniqueList[0])[0]
for i in range(n):
print(f"for {UniqueList[0][i]} => {UniqueList[2][i]} occurence(s)")Provinding:
Output:
for -1 => 2 occurence(s)
for 0 => 1 occurence(s)
for 1 => 6 occurence(s)
for 2 => 2 occurence(s)
for 5 => 2 occurence(s)
for 8 => 1 occurence(s)
for 10 => 1 occurence(s)