Question

The input list can be more than 1 million numbers. When I run the following code with smaller ‘repeats’, its fine;

def sample(x):
    length = 1000000 
    new_array = random.sample((list(x)),length)
    return (new_array)

def repeat_sample(x):    
    i = 0
    repeats = 100
    list_of_samples = []
    for i in range(repeats):
       list_of_samples.append(sample(x))
    return(list_of_samples)

repeat_sample(large_array)

However, using high repeats such as the 100 above, results in MemoryError. Traceback is as follows;

Traceback (most recent call last):
  File "C:\Python31\rnd.py", line 221, in <module>
    STORED_REPEAT_SAMPLE = repeat_sample(STORED_ARRAY)
  File "C:\Python31\rnd.py", line 129, in repeat_sample
    list_of_samples.append(sample(x))
  File "C:\Python31\rnd.py", line 121, in sample
    new_array = random.sample((list(x)),length)
  File "C:\Python31\lib\random.py", line 309, in sample
    result = [None] * k
MemoryError

I am assuming I’m running out of memory. I do not know how to get around this problem.

Thank you for your time!

Answer 1

Expanding on my comment:

Let’s say the processing you do to each sample is calculate its mean.

def mean(samplelists):
    means = []
    n = float(len(samplelists[0]))
    for sample in samplelists:
        mean = sum(sample)/n
        means.append(mean)
    return means

calc_means(repeat_sample(large_array))

This is going to make you sweat holding all those lists in memory. You can get it much lighter like this:

def mean(sample, n):
    n = float(n)
    mean = sum(sample)/n
    return mean

def sample(x):
    length = 1000000 
    new_array = random.sample(x, length)
    return new_array

def repeat_means(x):    
    repeats = 100
    list_of_means = []
    for i in range(repeats):
        list_of_means.append(mean(sample(x)))
    return list_of_means    

repeat_means(large_array)

But that’s still not good enough… You can do it all with only ever constructing your list of results:

import random

def sampling_mean(population, k, times):
    # Part of this is lifted straight from random.py
    _int = int
    _random = random.random

    n = len(population)
    kf = float(k)
    result = []

    if not 0 <= k <= n:
        raise ValueError, "sample larger than population"

    for t in range(times):
        selected = set()
        sum_ = 0
        selected_add = selected.add

        for i in xrange(k):
            j = _int(_random() * n)
            while j in selected:
                j = _int(_random() * n)
            selected_add(j)
            sum_ += population[j]

        mean = sum_/kf
        result.append(mean)
    return result

sampling_mean(x, 1000000, 100)

Now, can your algorithm be streamlined like this?