pythongeneratorinfinite-loop

# How can I make an infinite generator in Python to all prime numbers?

I was trying to make this infinite generator in Python:

``````import math
def all_primes():
count = 2
while True:
flag = True
for x in range(2, int(math.sqrt(count) + 1)):
if count % x == 0:
flag = False

if flag:
yield count
else:
count += 1

for i in all_primes():
print(i)
``````

but in the output it always gives me 2. Why is that?

Solution

• You don't augment count after finding a prime, so you're always returning the same value (3).

Your prime generator is going to take longer and longer between each prime as you move forward.

Here's an infinite prime generator that is more efficient. It is inspired from the sieve of Eratosthenes, but it uses a dictionary to only propagate multiples as it reaches a non-prime number and moves the prime multiple to the next multiple that hasn't been flagged as non-prime yet:

``````def genPrimes():
yield 2                      # get the first prime out of the way
skips      = dict()          # multiples to skip {Multiple:2xPrime}
multiples  = ((p*p,2*p) for p in genPrimes()) # multiples of primes
skipMark,_ = next(multiples)                  # skipping coverage
N = 1                        # prime candidate (odd numbers)
while True:
N += 2                        # next prime candidate
if N >= skipMark:                     # extend skips coverage
skipMark,stride = next(multiples) # 1st multiple and stride
skips[skipMark] = stride
if N in skips:                # not a prime (multiple of a prime)
stride   = skips.pop(N)   # get prime multiple steps
while multiple in skips:
multiple += stride    # not already skipped
skips[multiple] = stride
else:                         # N is prime
yield N                   # return it
``````

Output:

``````for p in genPrimes(): print(p)
``````
``````2
3
5
7
11
13
17
19
23
29
31
37
41
43
47
53
59
...
``````

The skips dictionary contains roughly one entry per √P (where P is the number of primes found so far) and doesn't require pre-allocating memory. This approach trades space for a gain in time.