Generation of products of power











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Given positive integers a,b,c and limit, I want to generate all products having the form
$$p^aq^br^c leq limit$$ less than limit (where p,q,r are distinct primes). a,b and c are not necessary distinct.



I tried something like:



primes= [ 2,3,5,...] # primes up to 10**8
[ (p**a)*(q**b)*(r**c) for p in primes for q in primes for r in primes if (p**a)*(q**b)*(r**c) <= limit ]


But it is very slow because len(primes)(=5761455) is high.



Then I tried a very ugly code for printing values.
It generates all values for p < q < r (p, q, r primes)



def generate3(a,b,c,limit):
global primes
i1 = 0
i2 = 1
i3 = 2
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c < limit:
print(str(i1)+" "+str(i2)+str(i3))
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c< limit:
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c< limit:
print(str(primes[i1])+" "+str(primes[i2])+" "+str(primes[i3])+" "+str((primes[i1]**a)*(primes[i2]**b)*(primes[i3]**c)))
i3 = i3 + 1
i2 = i2 +1
i3 = i2 +1
i1 = i1 + 1
i2 = i1 + 1
i3 = i2 + 1


Is there a more pythonic way to do this?
Is there a better method for generating these products of powers quickly?










share|improve this question









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montardon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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  • Is the constraint p < q < r a coincidence, or one of the do they just need to be distinct?
    – Maarten Fabré
    2 days ago










  • They just need to be distinct but the second example I gave only generates cases for which p < q < r.
    – montardon
    2 days ago















up vote
2
down vote

favorite
1












Given positive integers a,b,c and limit, I want to generate all products having the form
$$p^aq^br^c leq limit$$ less than limit (where p,q,r are distinct primes). a,b and c are not necessary distinct.



I tried something like:



primes= [ 2,3,5,...] # primes up to 10**8
[ (p**a)*(q**b)*(r**c) for p in primes for q in primes for r in primes if (p**a)*(q**b)*(r**c) <= limit ]


But it is very slow because len(primes)(=5761455) is high.



Then I tried a very ugly code for printing values.
It generates all values for p < q < r (p, q, r primes)



def generate3(a,b,c,limit):
global primes
i1 = 0
i2 = 1
i3 = 2
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c < limit:
print(str(i1)+" "+str(i2)+str(i3))
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c< limit:
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c< limit:
print(str(primes[i1])+" "+str(primes[i2])+" "+str(primes[i3])+" "+str((primes[i1]**a)*(primes[i2]**b)*(primes[i3]**c)))
i3 = i3 + 1
i2 = i2 +1
i3 = i2 +1
i1 = i1 + 1
i2 = i1 + 1
i3 = i2 + 1


Is there a more pythonic way to do this?
Is there a better method for generating these products of powers quickly?










share|improve this question









New contributor




montardon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.




















  • Is the constraint p < q < r a coincidence, or one of the do they just need to be distinct?
    – Maarten Fabré
    2 days ago










  • They just need to be distinct but the second example I gave only generates cases for which p < q < r.
    – montardon
    2 days ago













up vote
2
down vote

favorite
1









up vote
2
down vote

favorite
1






1





Given positive integers a,b,c and limit, I want to generate all products having the form
$$p^aq^br^c leq limit$$ less than limit (where p,q,r are distinct primes). a,b and c are not necessary distinct.



I tried something like:



primes= [ 2,3,5,...] # primes up to 10**8
[ (p**a)*(q**b)*(r**c) for p in primes for q in primes for r in primes if (p**a)*(q**b)*(r**c) <= limit ]


But it is very slow because len(primes)(=5761455) is high.



Then I tried a very ugly code for printing values.
It generates all values for p < q < r (p, q, r primes)



def generate3(a,b,c,limit):
global primes
i1 = 0
i2 = 1
i3 = 2
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c < limit:
print(str(i1)+" "+str(i2)+str(i3))
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c< limit:
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c< limit:
print(str(primes[i1])+" "+str(primes[i2])+" "+str(primes[i3])+" "+str((primes[i1]**a)*(primes[i2]**b)*(primes[i3]**c)))
i3 = i3 + 1
i2 = i2 +1
i3 = i2 +1
i1 = i1 + 1
i2 = i1 + 1
i3 = i2 + 1


Is there a more pythonic way to do this?
Is there a better method for generating these products of powers quickly?










share|improve this question









New contributor




montardon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











Given positive integers a,b,c and limit, I want to generate all products having the form
$$p^aq^br^c leq limit$$ less than limit (where p,q,r are distinct primes). a,b and c are not necessary distinct.



I tried something like:



primes= [ 2,3,5,...] # primes up to 10**8
[ (p**a)*(q**b)*(r**c) for p in primes for q in primes for r in primes if (p**a)*(q**b)*(r**c) <= limit ]


But it is very slow because len(primes)(=5761455) is high.



Then I tried a very ugly code for printing values.
It generates all values for p < q < r (p, q, r primes)



def generate3(a,b,c,limit):
global primes
i1 = 0
i2 = 1
i3 = 2
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c < limit:
print(str(i1)+" "+str(i2)+str(i3))
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c< limit:
while i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and (primes[i1]**a)*(primes[i2]**b)*primes[i3]**c< limit:
print(str(primes[i1])+" "+str(primes[i2])+" "+str(primes[i3])+" "+str((primes[i1]**a)*(primes[i2]**b)*(primes[i3]**c)))
i3 = i3 + 1
i2 = i2 +1
i3 = i2 +1
i1 = i1 + 1
i2 = i1 + 1
i3 = i2 + 1


Is there a more pythonic way to do this?
Is there a better method for generating these products of powers quickly?







python performance primes






share|improve this question









New contributor




montardon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











share|improve this question









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Check out our Code of Conduct.









share|improve this question




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edited Nov 15 at 13:51









200_success

127k15148410




127k15148410






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asked Nov 15 at 10:37









montardon

133




133




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montardon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.





New contributor





montardon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






montardon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.












  • Is the constraint p < q < r a coincidence, or one of the do they just need to be distinct?
    – Maarten Fabré
    2 days ago










  • They just need to be distinct but the second example I gave only generates cases for which p < q < r.
    – montardon
    2 days ago


















  • Is the constraint p < q < r a coincidence, or one of the do they just need to be distinct?
    – Maarten Fabré
    2 days ago










  • They just need to be distinct but the second example I gave only generates cases for which p < q < r.
    – montardon
    2 days ago
















Is the constraint p < q < r a coincidence, or one of the do they just need to be distinct?
– Maarten Fabré
2 days ago




Is the constraint p < q < r a coincidence, or one of the do they just need to be distinct?
– Maarten Fabré
2 days ago












They just need to be distinct but the second example I gave only generates cases for which p < q < r.
– montardon
2 days ago




They just need to be distinct but the second example I gave only generates cases for which p < q < r.
– montardon
2 days ago










1 Answer
1






active

oldest

votes

















up vote
2
down vote



accepted










global



there is no need to make primes a global. You only read from it, but don't assign to it, you can use it as is. It will be even faster if you make primes a local variable by passing it in as a parameter, so Python uses the LOAD_FAST bytecode instead of LOAD_GLOBAL. Since primes is called, indexed and sliced a lot, this can make a difference.



while condition



i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and .... Since i1<i2<i3, only i3 < len(primes) is needed. If you use len(primes) so often, it pays to make it a local variable.



return, don't print



your method immediately prints the results. In general it is better to split the calculation and presentation, so to let the method return or yield the values, and another method do the presentation



looping



I suggest you watch the talk Loop like a Pro by David Baumgold. It's recommended material for every python programmer.



Instead of looping over the indices, you can loop over the primes-list immediately.



For p, and the first index (i), you can loop over enumerate(primes)



Then you can use this index i to slice primes to only include the elements with an index larger than p and go on to r, so you arrive at the following, naive implementation:



def generate4_naive(a, b, c, limit):
for i, p in enumerate(primes):
for j, q in enumerate(primes[i+1:], i+1):
for r in primes[j+1:]:
product = p**a * q**b * r**c
if product < limit:
yield p, q, r, product


If you include the early breaks, you arrive at something like this:



def generate4(a, b, c, limit):
for i, p in enumerate(primes):
for j, q in enumerate(primes[i+1:], i+1):
sub_product = p**a * q**b
for r in primes[j+1:]:
product = sub_product * r**c
if product > limit:
break
yield p, q, r, product
if sub_product * primes[j+1]**c > limit:
break
if p ** a * primes[i+1] **b * primes[i+2] ** c > limit:
return


then I also used primes as a local variable by changing the signature: def generate4b(a, b, c, limit, primes):



Performance:



limit = 1_000_000
%timeit tuple(generate3(2,3,4, limit=limit))



16.6 µs ± 166 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



%timeit tuple(generate3b(2,3,4, limit=limit, primes=primes))



16.2 µs ± 138 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



%timeit tuple(generate4(2,3,4, limit=limit))



14.2 µs ± 643 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



%timeit tuple(generate4b(2,3,4, limit=limit, primes=primes))



7.72 ms ± 516 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)



%timeit list(generate4_naive(2,3,4, 1_000)) # primes also only to 1000



940 ms ± 84.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)






share|improve this answer





















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    up vote
    2
    down vote



    accepted










    global



    there is no need to make primes a global. You only read from it, but don't assign to it, you can use it as is. It will be even faster if you make primes a local variable by passing it in as a parameter, so Python uses the LOAD_FAST bytecode instead of LOAD_GLOBAL. Since primes is called, indexed and sliced a lot, this can make a difference.



    while condition



    i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and .... Since i1<i2<i3, only i3 < len(primes) is needed. If you use len(primes) so often, it pays to make it a local variable.



    return, don't print



    your method immediately prints the results. In general it is better to split the calculation and presentation, so to let the method return or yield the values, and another method do the presentation



    looping



    I suggest you watch the talk Loop like a Pro by David Baumgold. It's recommended material for every python programmer.



    Instead of looping over the indices, you can loop over the primes-list immediately.



    For p, and the first index (i), you can loop over enumerate(primes)



    Then you can use this index i to slice primes to only include the elements with an index larger than p and go on to r, so you arrive at the following, naive implementation:



    def generate4_naive(a, b, c, limit):
    for i, p in enumerate(primes):
    for j, q in enumerate(primes[i+1:], i+1):
    for r in primes[j+1:]:
    product = p**a * q**b * r**c
    if product < limit:
    yield p, q, r, product


    If you include the early breaks, you arrive at something like this:



    def generate4(a, b, c, limit):
    for i, p in enumerate(primes):
    for j, q in enumerate(primes[i+1:], i+1):
    sub_product = p**a * q**b
    for r in primes[j+1:]:
    product = sub_product * r**c
    if product > limit:
    break
    yield p, q, r, product
    if sub_product * primes[j+1]**c > limit:
    break
    if p ** a * primes[i+1] **b * primes[i+2] ** c > limit:
    return


    then I also used primes as a local variable by changing the signature: def generate4b(a, b, c, limit, primes):



    Performance:



    limit = 1_000_000
    %timeit tuple(generate3(2,3,4, limit=limit))



    16.6 µs ± 166 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



    %timeit tuple(generate3b(2,3,4, limit=limit, primes=primes))



    16.2 µs ± 138 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



    %timeit tuple(generate4(2,3,4, limit=limit))



    14.2 µs ± 643 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



    %timeit tuple(generate4b(2,3,4, limit=limit, primes=primes))



    7.72 ms ± 516 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)



    %timeit list(generate4_naive(2,3,4, 1_000)) # primes also only to 1000



    940 ms ± 84.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)






    share|improve this answer

























      up vote
      2
      down vote



      accepted










      global



      there is no need to make primes a global. You only read from it, but don't assign to it, you can use it as is. It will be even faster if you make primes a local variable by passing it in as a parameter, so Python uses the LOAD_FAST bytecode instead of LOAD_GLOBAL. Since primes is called, indexed and sliced a lot, this can make a difference.



      while condition



      i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and .... Since i1<i2<i3, only i3 < len(primes) is needed. If you use len(primes) so often, it pays to make it a local variable.



      return, don't print



      your method immediately prints the results. In general it is better to split the calculation and presentation, so to let the method return or yield the values, and another method do the presentation



      looping



      I suggest you watch the talk Loop like a Pro by David Baumgold. It's recommended material for every python programmer.



      Instead of looping over the indices, you can loop over the primes-list immediately.



      For p, and the first index (i), you can loop over enumerate(primes)



      Then you can use this index i to slice primes to only include the elements with an index larger than p and go on to r, so you arrive at the following, naive implementation:



      def generate4_naive(a, b, c, limit):
      for i, p in enumerate(primes):
      for j, q in enumerate(primes[i+1:], i+1):
      for r in primes[j+1:]:
      product = p**a * q**b * r**c
      if product < limit:
      yield p, q, r, product


      If you include the early breaks, you arrive at something like this:



      def generate4(a, b, c, limit):
      for i, p in enumerate(primes):
      for j, q in enumerate(primes[i+1:], i+1):
      sub_product = p**a * q**b
      for r in primes[j+1:]:
      product = sub_product * r**c
      if product > limit:
      break
      yield p, q, r, product
      if sub_product * primes[j+1]**c > limit:
      break
      if p ** a * primes[i+1] **b * primes[i+2] ** c > limit:
      return


      then I also used primes as a local variable by changing the signature: def generate4b(a, b, c, limit, primes):



      Performance:



      limit = 1_000_000
      %timeit tuple(generate3(2,3,4, limit=limit))



      16.6 µs ± 166 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



      %timeit tuple(generate3b(2,3,4, limit=limit, primes=primes))



      16.2 µs ± 138 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



      %timeit tuple(generate4(2,3,4, limit=limit))



      14.2 µs ± 643 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



      %timeit tuple(generate4b(2,3,4, limit=limit, primes=primes))



      7.72 ms ± 516 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)



      %timeit list(generate4_naive(2,3,4, 1_000)) # primes also only to 1000



      940 ms ± 84.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)






      share|improve this answer























        up vote
        2
        down vote



        accepted







        up vote
        2
        down vote



        accepted






        global



        there is no need to make primes a global. You only read from it, but don't assign to it, you can use it as is. It will be even faster if you make primes a local variable by passing it in as a parameter, so Python uses the LOAD_FAST bytecode instead of LOAD_GLOBAL. Since primes is called, indexed and sliced a lot, this can make a difference.



        while condition



        i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and .... Since i1<i2<i3, only i3 < len(primes) is needed. If you use len(primes) so often, it pays to make it a local variable.



        return, don't print



        your method immediately prints the results. In general it is better to split the calculation and presentation, so to let the method return or yield the values, and another method do the presentation



        looping



        I suggest you watch the talk Loop like a Pro by David Baumgold. It's recommended material for every python programmer.



        Instead of looping over the indices, you can loop over the primes-list immediately.



        For p, and the first index (i), you can loop over enumerate(primes)



        Then you can use this index i to slice primes to only include the elements with an index larger than p and go on to r, so you arrive at the following, naive implementation:



        def generate4_naive(a, b, c, limit):
        for i, p in enumerate(primes):
        for j, q in enumerate(primes[i+1:], i+1):
        for r in primes[j+1:]:
        product = p**a * q**b * r**c
        if product < limit:
        yield p, q, r, product


        If you include the early breaks, you arrive at something like this:



        def generate4(a, b, c, limit):
        for i, p in enumerate(primes):
        for j, q in enumerate(primes[i+1:], i+1):
        sub_product = p**a * q**b
        for r in primes[j+1:]:
        product = sub_product * r**c
        if product > limit:
        break
        yield p, q, r, product
        if sub_product * primes[j+1]**c > limit:
        break
        if p ** a * primes[i+1] **b * primes[i+2] ** c > limit:
        return


        then I also used primes as a local variable by changing the signature: def generate4b(a, b, c, limit, primes):



        Performance:



        limit = 1_000_000
        %timeit tuple(generate3(2,3,4, limit=limit))



        16.6 µs ± 166 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



        %timeit tuple(generate3b(2,3,4, limit=limit, primes=primes))



        16.2 µs ± 138 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



        %timeit tuple(generate4(2,3,4, limit=limit))



        14.2 µs ± 643 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



        %timeit tuple(generate4b(2,3,4, limit=limit, primes=primes))



        7.72 ms ± 516 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)



        %timeit list(generate4_naive(2,3,4, 1_000)) # primes also only to 1000



        940 ms ± 84.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)






        share|improve this answer












        global



        there is no need to make primes a global. You only read from it, but don't assign to it, you can use it as is. It will be even faster if you make primes a local variable by passing it in as a parameter, so Python uses the LOAD_FAST bytecode instead of LOAD_GLOBAL. Since primes is called, indexed and sliced a lot, this can make a difference.



        while condition



        i1 < len(primes) and i2 < len(primes) and i3 < len(primes) and .... Since i1<i2<i3, only i3 < len(primes) is needed. If you use len(primes) so often, it pays to make it a local variable.



        return, don't print



        your method immediately prints the results. In general it is better to split the calculation and presentation, so to let the method return or yield the values, and another method do the presentation



        looping



        I suggest you watch the talk Loop like a Pro by David Baumgold. It's recommended material for every python programmer.



        Instead of looping over the indices, you can loop over the primes-list immediately.



        For p, and the first index (i), you can loop over enumerate(primes)



        Then you can use this index i to slice primes to only include the elements with an index larger than p and go on to r, so you arrive at the following, naive implementation:



        def generate4_naive(a, b, c, limit):
        for i, p in enumerate(primes):
        for j, q in enumerate(primes[i+1:], i+1):
        for r in primes[j+1:]:
        product = p**a * q**b * r**c
        if product < limit:
        yield p, q, r, product


        If you include the early breaks, you arrive at something like this:



        def generate4(a, b, c, limit):
        for i, p in enumerate(primes):
        for j, q in enumerate(primes[i+1:], i+1):
        sub_product = p**a * q**b
        for r in primes[j+1:]:
        product = sub_product * r**c
        if product > limit:
        break
        yield p, q, r, product
        if sub_product * primes[j+1]**c > limit:
        break
        if p ** a * primes[i+1] **b * primes[i+2] ** c > limit:
        return


        then I also used primes as a local variable by changing the signature: def generate4b(a, b, c, limit, primes):



        Performance:



        limit = 1_000_000
        %timeit tuple(generate3(2,3,4, limit=limit))



        16.6 µs ± 166 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



        %timeit tuple(generate3b(2,3,4, limit=limit, primes=primes))



        16.2 µs ± 138 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



        %timeit tuple(generate4(2,3,4, limit=limit))



        14.2 µs ± 643 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)



        %timeit tuple(generate4b(2,3,4, limit=limit, primes=primes))



        7.72 ms ± 516 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)



        %timeit list(generate4_naive(2,3,4, 1_000)) # primes also only to 1000



        940 ms ± 84.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)







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        answered 2 days ago









        Maarten Fabré

        4,264417




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