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Given a number M and a list A which contains N elements (A1, A2,...)
Find the all the numbers k so that:
1=<k=<M which satisfied gcd(Ai, k) is always equal to 1
Here's my code, the only problem for it is that it uses loops in each other, which slow the process if my inputs are big, how can I fix it so that it requires less time?
N, M = [int(v) for v in input().split()]
A = [int(v) for v in input().split()]
from math import gcd
cnt = 0
print(N)
for k in range(1, M+1):
for i in range(N):
if gcd(k, A[i]) == 1:
cnt += 1
if cnt == N:
print(k)
cnt = 0
inputs example: (first line contains N and M, second contains the list A1, A2,...)
3 12
6 1 5
Here's a fast version that eliminates the nested loops:
N, M = [int(v) for v in input().split()]
A = [int(v) for v in input().split()]
from math import gcd
print(N)
l = 1
for v in A:
l = l*v//gcd(l, v)
for k in range(1, M+1):
if gcd(l, k) == 1:
print(k)
It works by first taking the LCM, l, of the values in A. It then suffices to check if the GCD of k and l is 1, which means there are no common factors with any of the values in A.
Note: If you're using a newer version of Python than I am (3.9 or later), you can import lcm from math and replace l = l*v//gcd(l, v) with l = lcm(l, v).
Or, as Kelly Bundy pointed out, lcm accepts an arbitrary number of arguments, so the first loop can be replaced with l = lcm(*A) if you're using 3.9 or later.
Just another approach using sympy.theory, factorint and Python sets which from the point of view of speed has on my machine no advantage compared to the math.lcm() or the math.gcd() based solutions if applied to small sizes of lists and numbers, but excels at very large size of randomized list:
M = 12
lstA = (6, 1, 5)
from sympy.ntheory import factorint
lstAfactors = []
for a in lstA:
lstAfactors += factorint(a)
setA = set(lstAfactors)
for k in range(1, M+1):
if not (set(factorint(k)) & setA):
print(k)
The code above implements the idea described in the answer of Yatisi coded by Tom Karzes using math.gcd(), but is using sympy.ntheory factorint() and set() instead of math gcd().
In terms of speed the factorint() solution seems to be fastest on the below tested data:
# ======================================================================
from time import perf_counter as T
from math import gcd, lcm
from sympy import factorint
from random import choice
#M = 3000
#lstA = 100 * [6, 12, 18, 121, 256, 1024, 361, 2123, 39]
M = 8000
lstA = [ choice(range(1,8000)) for _ in range(8000) ]
# ----------------------------------------------------------------------
from sympy.ntheory import factorint
lstResults = []
lstAfactors = []
sT=T()
for a in lstA:
lstAfactors += factorint(a)
setA = set(lstAfactors)
for k in range(1, M+1):
if not (set(factorint(k)) & setA):
lstResults += [k]
print("factorint:", T()-sT)
#print(lstResults)
print("---")
# ----------------------------------------------------------------------
lstResults = []
sT=T()
#l = 1
#for a in lstA:
# l = (l*a)//gcd(l, a) # can be replaced by:
l = lcm(*lstA) # least common multiple divisible by all lstA items
# ^-- which runs MAYBE a bit faster than the loop with gcd()
for k in range(1, M+1):
if gcd(l, k) == 1:
lstResults += [k]
print("lcm() :", T()-sT)
#print(lstResults)
print("---")
# ----------------------------------------------------------------------
lstResults = []
sT=T()
l = 1
for a in lstA:
l = (l*a)//gcd(l, a) # can be replaced by:
#l = lcm(*lstA) # least common multiple divisible by all lstA items
# ^-- which runs MAYBE a bit faster than the loop with gcd()
for k in range(1, M+1):
if gcd(l, k) == 1:
lstResults += [k]
print("gcd() :", T()-sT)
#print(lstResults)
print("---")
# ----------------------------------------------------------------------
import numpy as np
A = np.array(lstA)
def find_gcd_np(M, A, to_gcd=1):
vals = np.arange(1, M + 1)
return vals[np.all(np.gcd(vals, np.array(A)[:, None]) == to_gcd, axis=0)]
sT=T()
lstResults = find_gcd_np(M, A, 1).tolist()
print("numpy :", T()-sT)
#print(lstResults)
print("---")
printing
factorint: 0.09754624799825251
---
lcm() : 0.10102138598449528
---
gcd() : 0.10236155497841537
---
numpy : 6.923375226906501
---
The timing results change extremely for the second data variant in the code provided above printing:
factorint: 0.021642255946062505
---
lcm() : 0.0010238440008834004
---
gcd() : 0.0013772319070994854
---
numpy : 0.19953695288859308
---
where the factorint based approach is 20x and the numpy based approach 200x times slower than the gcd/lcm based one.
Run the timing test yourself online. It won't run the case of large data, but it can at least demonstrate that the numpy approach is 100x times slower than the gcd one:
factorint: 0.03271647123619914
---
lcm() : 0.003286922350525856
---
gcd() : 0.0029655308462679386
---
numpy : 0.41759901121258736
1 https://ato.pxeger.com/run?1=3VXBitswED0W9BXDGoq16-zaSbtsAzmk0GN6KLmUkAbFkdcismQkZbc-9Et62Uv7Uf2ajiwnNt1Du7ClUIORpXmaefNmLH39Xjeu1Orh4dvBFaObHy--RDB7locURlfgRMUBQFS1Ng5qbopNrg_KcQPMwjKAKubKHnSb7xKQeRVstqnq5mQrWO60EcoFo2Fqh0NnzEstck6iBfqCGUzSNCWRtG6OkyxN4RxW1wlkY3xv_JglMH7tV9LxqwQm136ejSf4-WZNhk46H6suQoxhb3mcJTdpSikU2sAGhIKw7BdhTUgEo2d5BjJcKldybZrHaiDDD9wepLOe9GrdUg5mGxbscraMKfFkmSfrAVOCOQ6RF7PeZ8wosbxNHId4AAte9n3KKNziIqOtOxAFKO0g9pt6Z3sU6qV3NO9gEEIfWWPk1X5N6hZ8dto3PUsAaY_skpIoGPtN9AhHlc7oMyo-4DXULpK-kUj0i4ZRmwqaYnnO6NUV9m8sE2AUIsiZgi0Hw2vJcr6DbTMF4rHY3_G53-9RkjOL7aurSiuoMK6oJYeduBNWbPFr2wCTsg0HwvHKYsxPoxHclyIvwRyUhcX849t3yGorfFtY_3-5EoNjw4DUuoZ7gf-Yp_bb6nX89xQPAsj-oFo-F-oRT6nW3y5WqNXjdn9SpaL_rVSt139Wqu7YUgd_pOPxr2rijxdVXzJjWNOeMZTseAGFULsNkt2oOl4kME_AaT-fHZO_Y9Iet56kgQvIaGs23B2MalErj5EyxsFn75eSPuScrqYJvNeKr1sRQxjsic_CzlLat9Owyx6zy-il01JYF5-0C1k-UepwC3eX8fFS_gk)
This is probably more a math question than a programming question, however, here comes my take: Depending on M and A, it might be better to
Find the prime divisors of the Ai (have a look at this) and put them in a set.
Either remove (sieve) all multiples of these primes from list(range(1,M+1)), which you can do (more) efficiently by smart ordering, or find all primes smaller or equal to M (which could even be pre-computed) that are not divisors of any Ai and compute all multiples up to M.
Explanation: Since gcd(Ai,k)=1 if and only if Ai and k have no common divisors, they also have no prime divisors. Thus, we can first find all prime divisors of the Ai and then make sure our k don't have any of them as divisors, too.
Using numpy with vectorised operations will be a good alternative when your input range M goes up to hundreds and higher and A is stably small (is about as your current A):
import numpy as np
def find_gcd_np(M, A, to_gcd=1):
vals = np.arange(1, M + 1)
return vals[np.all(np.gcd(vals, np.array(A)[:, None]) == to_gcd, axis=0)]
Usage:
print(find_gcd_np(100, [6, 1, 5], 1))
Is there a pythonic way to select N consecutive elements from a list or numpy array.
So Suppose:
Choice = [1,2,3,4,5,6]
I would like to create a new list of length N by randomly selecting element X in Choice along with the N-1 consecutive elements following choice.
So if:
X = 4
N = 4
The resulting list would be:
Selection = [5,6,1,2]
I think something similar to the following would work.
S = []
for i in range(X,X+N):
S.append(Selection[i%6])
But I was wondering if there is a python or numpy function that can select the elements at once that was more efficient.
Use itertools, specifically islice and cycle.
start = random.randint(0, len(Choice) - 1)
list(islice(cycle(Choice), start, start + n))
cycle(Choice) is an infinite sequence that repeats your original list, so that the slice start:start + n will wrap if necessary.
You could use a list comprehension, using modulo operations on the index to keep it in range of the list:
Choice = [1,2,3,4,5,6]
X = 4
N = 4
L = len(Choice)
Selection = [Choice[i % L] for i in range(X, X+N)]
print(Selection)
Output
[5, 6, 1, 2]
Note that if N is less than or equal to len(Choice), you can greatly simplify the code:
Choice = [1,2,3,4,5,6]
X = 4
N = 4
L = len(Choice)
Selection = Choice[X:X+N] if X+N <= L else Choice[X:] + Choice[:X+N-L]
print(Selection)
Since you are asking for the most efficient way I created a little benchmark to test the solutions proposed in this thread.
I rewrote your current solution as:
def op(choice, x):
n = len(choice)
selection = []
for i in range(x, x + n):
selection.append(choice[i % n])
return selection
Where choice is the input list and x is the random index.
These are the results if choice contains 1_000_000 random numbers:
chepner: 0.10840400000000017 s
nick: 0.2066781999999998 s
op: 0.25887470000000024 s
fountainhead: 0.3679908000000003 s
Full code
import random
from itertools import cycle, islice
from time import perf_counter as pc
import numpy as np
def op(choice, x):
n = len(choice)
selection = []
for i in range(x, x + n):
selection.append(choice[i % n])
return selection
def nick(choice, x):
n = len(choice)
return [choice[i % n] for i in range(x, x + n)]
def fountainhead(choice, x):
n = len(choice)
return np.take(choice, range(x, x + n), mode='wrap')
def chepner(choice, x):
n = len(choice)
return list(islice(cycle(choice), x, x + n))
results = []
n = 1_000_000
choice = random.sample(range(n), n)
x = random.randint(0, n - 1)
# Correctness
assert op(choice, x) == nick(choice,x) == chepner(choice,x) == list(fountainhead(choice,x))
# Benchmark
for f in op, nick, chepner, fountainhead:
t0 = pc()
f(choice, x)
t1 = pc()
results.append((t1 - t0, f))
for t, f in sorted(results):
print(f'{f.__name__}: {t} s')
If using a numpy array as the source, we could of course use numpy "fancy indexing".
So, if ChoiceArray is the numpy array equivalent of the list Choice, and if L is len(Choice) or len(ChoiceArray):
Selection = ChoiceArray [np.arange(X, N+X) % L]
Here's a numpy approach:
import numpy as np
Selection = np.take(Choice, range(X,N+X), mode='wrap')
Works even if Choice is a Python list rather than a numpy array.
I need to convert some data to base 29 before processing and I'm using this:
import string
def datatobase(data, base):
digs = string.digits + string.lowercase + string.uppercase
if base > len(digs):
return None
digits = []
x = int(data.encode("hex"), 16)
while x:
digits.append(digs[x % base])
x /= base
digits.reverse()
return ''.join(digits)
Problem is that this small code is slowing my program too much so what would you do to replace it?
A custom answer for base 29 only would be great too!
If you are not averse to using a third-party package, numpy.base_repr() is a very convenient way to do your conversions:
import os
import numpy
def datatobase(data, base):
n = int(data.encode('hex'), 16)
return numpy.base_repr(n, base)
>>> data = os.urandom(32)
>>> data
'\xfcBs\x82\xa8&\x18\xaaK\x8c$\x0fZ\x95\xc0aA%\x93\x91\xcc\x8a\xa8\xfdbk\xeb\x14\x15\x06\xbag'
>>> datatobase(data, 29)
'A8FB42CHLNEIOOE75AG773EKGBA69QP89PANAF8ROH2GA1LF3CC5H'
>>> datatobase(data, 16)
'FC427382A82618AA4B8C240F5A95C06141259391CC8AA8FD626BEB141506BA67'
You'd need to profile to see whether this provides adequate performance for your application.
Update
Profiling shows that numpy.base_repr() is slower than the OP's implementation. This is because the numpy implementation is basically the same algorithm implemented in Python, with the addition of optional zero padding.
If you take care of runtime... This version is 2.8 times faster then yours and 7% faster then #wwii.
def bin2base29(n):
s = '0123456789ABCDEFGHIJKLMNOPQRS'
return s[n] if n < 29 else bin2base29(n / 29) + s[n % 29]
Here is my final iterative and fastest solution of my approaches adapted from #wwii.
def bin2base29(n):
s = '0123456789ABCDEFGHIJKLMNOPQRS'
x = ''
while n > 0:
x = s[n % 29] + x
n /= 29
return x
Base 29, only, solution for an int argument.
Recursive:
s = '0123456789ABCDEFGHIJKLMNOPQRS'
def foo(n, s=s):
if n < 29:
return s[n]
a, b = divmod(n, 29)
return foo(a) + s[b]
Regular:
def foo(n, s=s):
x = ''
while n >= 29:
n, b = divmod(n, 29)
x += s[b]
x += s[n]
return x[::-1]
I know there is nothing wrong with writing with proper function structure, but I would like to know how can I find nth fibonacci number with most Pythonic way with a one-line.
I wrote that code, but It didn't seem to me best way:
>>> fib = lambda n:reduce(lambda x, y: (x[0]+x[1], x[0]), [(1,1)]*(n-2))[0]
>>> fib(8)
13
How could it be better and simplier?
fib = lambda n:reduce(lambda x,n:[x[1],x[0]+x[1]], range(n),[0,1])[0]
(this maintains a tuple mapped from [a,b] to [b,a+b], initialized to [0,1], iterated N times, then takes the first tuple element)
>>> fib(1000)
43466557686937456435688527675040625802564660517371780402481729089536555417949051
89040387984007925516929592259308032263477520968962323987332247116164299644090653
3187938298969649928516003704476137795166849228875L
(note that in this numbering, fib(0) = 0, fib(1) = 1, fib(2) = 1, fib(3) = 2, etc.)
(also note: reduce is a builtin in Python 2.7 but not in Python 3; you'd need to execute from functools import reduce in Python 3.)
A rarely seen trick is that a lambda function can refer to itself recursively:
fib = lambda n: n if n < 2 else fib(n-1) + fib(n-2)
By the way, it's rarely seen because it's confusing, and in this case it is also inefficient. It's much better to write it on multiple lines:
def fibs():
a = 0
b = 1
while True:
yield a
a, b = b, a + b
I recently learned about using matrix multiplication to generate Fibonacci numbers, which was pretty cool. You take a base matrix:
[1, 1]
[1, 0]
and multiply it by itself N times to get:
[F(N+1), F(N)]
[F(N), F(N-1)]
This morning, doodling in the steam on the shower wall, I realized that you could cut the running time in half by starting with the second matrix, and multiplying it by itself N/2 times, then using N to pick an index from the first row/column.
With a little squeezing, I got it down to one line:
import numpy
def mm_fib(n):
return (numpy.matrix([[2,1],[1,1]])**(n//2))[0,(n+1)%2]
>>> [mm_fib(i) for i in range(20)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181]
This is a closed expression for the Fibonacci series that uses integer arithmetic, and is quite efficient.
fib = lambda n:pow(2<<n,n+1,(4<<2*n)-(2<<n)-1)%(2<<n)
>> fib(1000)
4346655768693745643568852767504062580256466051737178
0402481729089536555417949051890403879840079255169295
9225930803226347752096896232398733224711616429964409
06533187938298969649928516003704476137795166849228875L
It computes the result in O(log n) arithmetic operations, each acting on integers with O(n) bits. Given that the result (the nth Fibonacci number) is O(n) bits, the method is quite reasonable.
It's based on genefib4 from http://fare.tunes.org/files/fun/fibonacci.lisp , which in turn was based on an a less efficient closed-form integer expression of mine (see: http://paulhankin.github.io/Fibonacci/)
If we consider the "most Pythonic way" to be elegant and effective then:
def fib(nr):
return int(((1 + math.sqrt(5)) / 2) ** nr / math.sqrt(5) + 0.5)
wins hands down. Why use a inefficient algorithm (and if you start using memoization we can forget about the oneliner) when you can solve the problem just fine in O(1) by approximation the result with the golden ratio? Though in reality I'd obviously write it in this form:
def fib(nr):
ratio = (1 + math.sqrt(5)) / 2
return int(ratio ** nr / math.sqrt(5) + 0.5)
More efficient and much easier to understand.
This is a non-recursive (anonymous) memoizing one liner
fib = lambda x,y=[1,1]:([(y.append(y[-1]+y[-2]),y[-1])[1] for i in range(1+x-len(y))],y[x])[1]
fib = lambda n, x=0, y=1 : x if not n else fib(n-1, y, x+y)
run time O(n), fib(0) = 0, fib(1) = 1, fib(2) = 1 ...
I'm Python newcomer, but did some measure for learning purposes. I've collected some fibo algorithm and took some measure.
from datetime import datetime
import matplotlib.pyplot as plt
from functools import wraps
from functools import reduce
from functools import lru_cache
import numpy
def time_it(f):
#wraps(f)
def wrapper(*args, **kwargs):
start_time = datetime.now()
f(*args, **kwargs)
end_time = datetime.now()
elapsed = end_time - start_time
elapsed = elapsed.microseconds
return elapsed
return wrapper
#time_it
def fibslow(n):
if n <= 1:
return n
else:
return fibslow(n-1) + fibslow(n-2)
#time_it
#lru_cache(maxsize=10)
def fibslow_2(n):
if n <= 1:
return n
else:
return fibslow_2(n-1) + fibslow_2(n-2)
#time_it
def fibfast(n):
if n <= 1:
return n
a, b = 0, 1
for i in range(1, n+1):
a, b = b, a + b
return a
#time_it
def fib_reduce(n):
return reduce(lambda x, n: [x[1], x[0]+x[1]], range(n), [0, 1])[0]
#time_it
def mm_fib(n):
return (numpy.matrix([[2, 1], [1, 1]])**(n//2))[0, (n+1) % 2]
#time_it
def fib_ia(n):
return pow(2 << n, n+1, (4 << 2 * n) - (2 << n)-1) % (2 << n)
if __name__ == '__main__':
X = range(1, 200)
# fibslow_times = [fibslow(i) for i in X]
fibslow_2_times = [fibslow_2(i) for i in X]
fibfast_times = [fibfast(i) for i in X]
fib_reduce_times = [fib_reduce(i) for i in X]
fib_mm_times = [mm_fib(i) for i in X]
fib_ia_times = [fib_ia(i) for i in X]
# print(fibslow_times)
# print(fibfast_times)
# print(fib_reduce_times)
plt.figure()
# plt.plot(X, fibslow_times, label='Slow Fib')
plt.plot(X, fibslow_2_times, label='Slow Fib w cache')
plt.plot(X, fibfast_times, label='Fast Fib')
plt.plot(X, fib_reduce_times, label='Reduce Fib')
plt.plot(X, fib_mm_times, label='Numpy Fib')
plt.plot(X, fib_ia_times, label='Fib ia')
plt.xlabel('n')
plt.ylabel('time (microseconds)')
plt.legend()
plt.show()
The result is usually the same.
Fiboslow_2 with recursion and cache, Fib integer arithmetic and Fibfast algorithms seems the best ones. Maybe my decorator not the best thing to measure performance, but for an overview it seemed good.
Another example, taking the cue from Mark Byers's answer:
fib = lambda n,a=0,b=1: a if n<=0 else fib(n-1,b,a+b)
I wanted to see if I could create an entire sequence, not just the final value.
The following will generate a list of length 100. It excludes the leading [0, 1] and works for both Python2 and Python3. No other lines besides the one!
(lambda i, x=[0,1]: [(x.append(x[y+1]+x[y]), x[y+1]+x[y])[1] for y in range(i)])(100)
Output
[1,
2,
3,
...
218922995834555169026,
354224848179261915075,
573147844013817084101]
Here's an implementation that doesn't use recursion, and only memoizes the last two values instead of the whole sequence history.
nthfib() below is the direct solution to the original problem (as long as imports are allowed)
It's less elegant than using the Reduce methods above, but, although slightly different that what was asked for, it gains the ability to to be used more efficiently as an infinite generator if one needs to output the sequence up to the nth number as well (re-writing slightly as fibgen() below).
from itertools import imap, islice, repeat
nthfib = lambda n: next(islice((lambda x=[0, 1]: imap((lambda x: (lambda setx=x.__setitem__, x0_temp=x[0]: (x[1], setx(0, x[1]), setx(1, x0_temp+x[1]))[0])()), repeat(x)))(), n-1, None))
>>> nthfib(1000)
43466557686937456435688527675040625802564660517371780402481729089536555417949051
89040387984007925516929592259308032263477520968962323987332247116164299644090653
3187938298969649928516003704476137795166849228875L
from itertools import imap, islice, repeat
fibgen = lambda:(lambda x=[0,1]: imap((lambda x: (lambda setx=x.__setitem__, x0_temp=x[0]: (x[1], setx(0, x[1]), setx(1, x0_temp+x[1]))[0])()), repeat(x)))()
>>> list(islice(fibgen(),12))
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]
def fib(n):
x =[0,1]
for i in range(n):
x=[x[1],x[0]+x[1]]
return x[0]
take the cue from Jason S, i think my version have a better understanding.
Starting Python 3.8, and the introduction of assignment expressions (PEP 572) (:= operator), we can use and update a variable within a list comprehension:
fib = lambda n,x=(0,1):[x := (x[1], sum(x)) for i in range(n+1)][-1][0]
This:
Initiates the duo n-1 and n-2 as a tuple x=(0, 1)
As part of a list comprehension looping n times, x is updated via an assignment expression (x := (x[1], sum(x))) to the new n-1 and n-2 values
Finally, we return from the last iteration, the first part of the x
To solve this problem I got inspired by a similar question here in Stackoverflow Single Statement Fibonacci, and I got this single line function that can output a list of fibonacci sequence. Though, this is a Python 2 script, not tested on Python 3:
(lambda n, fib=[0,1]: fib[:n]+[fib.append(fib[-1] + fib[-2]) or fib[-1] for i in range(n-len(fib))])(10)
assign this lambda function to a variable to reuse it:
fib = (lambda n, fib=[0,1]: fib[:n]+[fib.append(fib[-1] + fib[-2]) or fib[-1] for i in range(n-len(fib))])
fib(10)
output is a list of fibonacci sequence:
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
I don't know if this is the most pythonic method but this is the best i could come up with:->
Fibonacci = lambda x,y=[1,1]:[1]*x if (x<2) else ([y.append(y[q-1] + y[q-2]) for q in range(2,x)],y)[1]
The above code doesn't use recursion, just a list to store the values.
My 2 cents
# One Liner
def nthfibonacci(n):
return long(((((1+5**.5)/2)**n)-(((1-5**.5)/2)**n))/5**.5)
OR
# Steps
def nthfibonacci(nth):
sq5 = 5**.5
phi1 = (1+sq5)/2
phi2 = -1 * (phi1 -1)
n1 = phi1**(nth+1)
n2 = phi2**(nth+1)
return long((n1 - n2)/sq5)
Why not use a list comprehension?
from math import sqrt, floor
[floor(((1+sqrt(5))**n-(1-sqrt(5))**n)/(2**n*sqrt(5))) for n in range(100)]
Without math imports, but less pretty:
[int(((1+(5**0.5))**n-(1-(5**0.5))**n)/(2**n*(5**0.5))) for n in range(100)]
import math
sqrt_five = math.sqrt(5)
phi = (1 + sqrt_five) / 2
fib = lambda n : int(round(pow(phi, n) / sqrt_five))
print([fib(i) for i in range(1, 26)])
single line lambda fibonacci but with some extra variables
Similar:
def fibonacci(n):
f=[1]+[0]
for i in range(n):
f=[sum(f)] + f[:-1]
print f[1]
A simple Fibonacci number generator using recursion
fib = lambda x: 1-x if x < 2 else fib(x-1)+fib(x-2)
print fib(100)
This takes forever to calculate fib(100) in my computer.
There is also closed form of Fibonacci numbers.
fib = lambda n: int(1/sqrt(5)*((1+sqrt(5))**n-(1-sqrt(5))**n)/2**n)
print fib(50)
This works nearly up to 72 numbers due to precision problem.
Lambda with logical operators
fibonacci_oneline = lambda n = 10, out = []: [ out.append(i) or i if i <= 1 else out.append(out[-1] + out[-2]) or out[-1] for i in range(n)]
here is how i do it ,however the function returns None for the list comprehension line part to allow me to insert a loop inside ..
so basically what it does is appending new elements of the fib seq inside of a list which is over two elements
>>f=lambda list,x :print('The list must be of 2 or more') if len(list)<2 else [list.append(list[-1]+list[-2]) for i in range(x)]
>>a=[1,2]
>>f(a,7)
You can generate once a list with some values and use as needed:
fib_fix = []
fib = lambda x: 1 if x <=2 else fib_fix[x-3] if x-2 <= len(fib_fix) else (fib_fix.append(fib(x-2) + fib(x-1)) or fib_fix[-1])
fib_x = lambda x: [fib(n) for n in range(1,x+1)]
fib_100 = fib_x(100)
than for example:
a = fib_fix[76]
Here's the very dumb way:
def divisorGenerator(n):
for i in xrange(1,n/2+1):
if n%i == 0: yield i
yield n
The result I'd like to get is similar to this one, but I'd like a smarter algorithm (this one it's too much slow and dumb :-)
I can find prime factors and their multiplicity fast enough.
I've an generator that generates factor in this way:
(factor1, multiplicity1)
(factor2, multiplicity2)
(factor3, multiplicity3)
and so on...
i.e. the output of
for i in factorGenerator(100):
print i
is:
(2, 2)
(5, 2)
I don't know how much is this useful for what I want to do (I coded it for other problems), anyway I'd like a smarter way to make
for i in divisorGen(100):
print i
output this:
1
2
4
5
10
20
25
50
100
UPDATE: Many thanks to Greg Hewgill and his "smart way" :)
Calculating all divisors of 100000000 took 0.01s with his way against the 39s that the dumb way took on my machine, very cool :D
UPDATE 2: Stop saying this is a duplicate of this post. Calculating the number of divisor of a given number doesn't need to calculate all the divisors. It's a different problem, if you think it's not then look for "Divisor function" on wikipedia. Read the questions and the answer before posting, if you do not understand what is the topic just don't add not useful and already given answers.
Given your factorGenerator function, here is a divisorGen that should work:
def divisorGen(n):
factors = list(factorGenerator(n))
nfactors = len(factors)
f = [0] * nfactors
while True:
yield reduce(lambda x, y: x*y, [factors[x][0]**f[x] for x in range(nfactors)], 1)
i = 0
while True:
f[i] += 1
if f[i] <= factors[i][1]:
break
f[i] = 0
i += 1
if i >= nfactors:
return
The overall efficiency of this algorithm will depend entirely on the efficiency of the factorGenerator.
To expand on what Shimi has said, you should only be running your loop from 1 to the square root of n. Then to find the pair, do n / i, and this will cover the whole problem space.
As was also noted, this is a NP, or 'difficult' problem. Exhaustive search, the way you are doing it, is about as good as it gets for guaranteed answers. This fact is used by encryption algorithms and the like to help secure them. If someone were to solve this problem, most if not all of our current 'secure' communication would be rendered insecure.
Python code:
import math
def divisorGenerator(n):
large_divisors = []
for i in xrange(1, int(math.sqrt(n) + 1)):
if n % i == 0:
yield i
if i*i != n:
large_divisors.append(n / i)
for divisor in reversed(large_divisors):
yield divisor
print list(divisorGenerator(100))
Which should output a list like:
[1, 2, 4, 5, 10, 20, 25, 50, 100]
I think you can stop at math.sqrt(n) instead of n/2.
I will give you example so that you can understand it easily. Now the sqrt(28) is 5.29 so ceil(5.29) will be 6. So I if I will stop at 6 then I will can get all the divisors. How?
First see the code and then see image:
import math
def divisors(n):
divs = [1]
for i in xrange(2,int(math.sqrt(n))+1):
if n%i == 0:
divs.extend([i,n/i])
divs.extend([n])
return list(set(divs))
Now, See the image below:
Lets say I have already added 1 to my divisors list and I start with i=2 so
So at the end of all the iterations as I have added the quotient and the divisor to my list all the divisors of 28 are populated.
Source: How to determine the divisors of a number
Although there are already many solutions to this, I really have to post this :)
This one is:
readable
short
self contained, copy & paste ready
quick (in cases with a lot of prime factors and divisors, > 10 times faster than the accepted solution)
python3, python2 and pypy compliant
Code:
def divisors(n):
# get factors and their counts
factors = {}
nn = n
i = 2
while i*i <= nn:
while nn % i == 0:
factors[i] = factors.get(i, 0) + 1
nn //= i
i += 1
if nn > 1:
factors[nn] = factors.get(nn, 0) + 1
primes = list(factors.keys())
# generates factors from primes[k:] subset
def generate(k):
if k == len(primes):
yield 1
else:
rest = generate(k+1)
prime = primes[k]
for factor in rest:
prime_to_i = 1
# prime_to_i iterates prime**i values, i being all possible exponents
for _ in range(factors[prime] + 1):
yield factor * prime_to_i
prime_to_i *= prime
# in python3, `yield from generate(0)` would also work
for factor in generate(0):
yield factor
An illustrative Pythonic one-liner:
from itertools import chain
from math import sqrt
def divisors(n):
return set(chain.from_iterable((i,n//i) for i in range(1,int(sqrt(n))+1) if n%i == 0))
But better yet, just use sympy:
from sympy import divisors
I like Greg solution, but I wish it was more python like.
I feel it would be faster and more readable;
so after some time of coding I came out with this.
The first two functions are needed to make the cartesian product of lists.
And can be reused whnever this problem arises.
By the way, I had to program this myself, if anyone knows of a standard solution for this problem, please feel free to contact me.
"Factorgenerator" now returns a dictionary. And then the dictionary is fed into "divisors", who uses it to generate first a list of lists, where each list is the list of the factors of the form p^n with p prime.
Then we make the cartesian product of those lists, and we finally use Greg' solution to generate the divisor.
We sort them, and return them.
I tested it and it seem to be a bit faster than the previous version. I tested it as part of a bigger program, so I can't really say how much is it faster though.
Pietro Speroni (pietrosperoni dot it)
from math import sqrt
##############################################################
### cartesian product of lists ##################################
##############################################################
def appendEs2Sequences(sequences,es):
result=[]
if not sequences:
for e in es:
result.append([e])
else:
for e in es:
result+=[seq+[e] for seq in sequences]
return result
def cartesianproduct(lists):
"""
given a list of lists,
returns all the possible combinations taking one element from each list
The list does not have to be of equal length
"""
return reduce(appendEs2Sequences,lists,[])
##############################################################
### prime factors of a natural ##################################
##############################################################
def primefactors(n):
'''lists prime factors, from greatest to smallest'''
i = 2
while i<=sqrt(n):
if n%i==0:
l = primefactors(n/i)
l.append(i)
return l
i+=1
return [n] # n is prime
##############################################################
### factorization of a natural ##################################
##############################################################
def factorGenerator(n):
p = primefactors(n)
factors={}
for p1 in p:
try:
factors[p1]+=1
except KeyError:
factors[p1]=1
return factors
def divisors(n):
factors = factorGenerator(n)
divisors=[]
listexponents=[map(lambda x:k**x,range(0,factors[k]+1)) for k in factors.keys()]
listfactors=cartesianproduct(listexponents)
for f in listfactors:
divisors.append(reduce(lambda x, y: x*y, f, 1))
divisors.sort()
return divisors
print divisors(60668796879)
P.S.
it is the first time I am posting to stackoverflow.
I am looking forward for any feedback.
Here is a smart and fast way to do it for numbers up to and around 10**16 in pure Python 3.6,
from itertools import compress
def primes(n):
""" Returns a list of primes < n for n > 2 """
sieve = bytearray([True]) * (n//2)
for i in range(3,int(n**0.5)+1,2):
if sieve[i//2]:
sieve[i*i//2::i] = bytearray((n-i*i-1)//(2*i)+1)
return [2,*compress(range(3,n,2), sieve[1:])]
def factorization(n):
""" Returns a list of the prime factorization of n """
pf = []
for p in primeslist:
if p*p > n : break
count = 0
while not n % p:
n //= p
count += 1
if count > 0: pf.append((p, count))
if n > 1: pf.append((n, 1))
return pf
def divisors(n):
""" Returns an unsorted list of the divisors of n """
divs = [1]
for p, e in factorization(n):
divs += [x*p**k for k in range(1,e+1) for x in divs]
return divs
n = 600851475143
primeslist = primes(int(n**0.5)+1)
print(divisors(n))
If your PC has tons of memory, a brute single line can be fast enough with numpy:
N = 10000000; tst = np.arange(1, N); tst[np.mod(N, tst) == 0]
Out:
array([ 1, 2, 4, 5, 8, 10, 16,
20, 25, 32, 40, 50, 64, 80,
100, 125, 128, 160, 200, 250, 320,
400, 500, 625, 640, 800, 1000, 1250,
1600, 2000, 2500, 3125, 3200, 4000, 5000,
6250, 8000, 10000, 12500, 15625, 16000, 20000,
25000, 31250, 40000, 50000, 62500, 78125, 80000,
100000, 125000, 156250, 200000, 250000, 312500, 400000,
500000, 625000, 1000000, 1250000, 2000000, 2500000, 5000000])
Takes less than 1s on my slow PC.
Adapted from CodeReview, here is a variant which works with num=1 !
from itertools import product
import operator
def prod(ls):
return reduce(operator.mul, ls, 1)
def powered(factors, powers):
return prod(f**p for (f,p) in zip(factors, powers))
def divisors(num) :
pf = dict(prime_factors(num))
primes = pf.keys()
#For each prime, possible exponents
exponents = [range(i+1) for i in pf.values()]
return (powered(primes,es) for es in product(*exponents))
Old question, but here is my take:
def divs(n, m):
if m == 1: return [1]
if n % m == 0: return [m] + divs(n, m - 1)
return divs(n, m - 1)
You can proxy with:
def divisorGenerator(n):
for x in reversed(divs(n, n)):
yield x
NOTE: For languages that support, this could be tail recursive.
Assuming that the factors function returns the factors of n (for instance, factors(60) returns the list [2, 2, 3, 5]), here is a function to compute the divisors of n:
function divisors(n)
divs := [1]
for fact in factors(n)
temp := []
for div in divs
if fact * div not in divs
append fact * div to temp
divs := divs + temp
return divs
Here's my solution. It seems to be dumb but works well...and I was trying to find all proper divisors so the loop started from i = 2.
import math as m
def findfac(n):
faclist = [1]
for i in range(2, int(m.sqrt(n) + 2)):
if n%i == 0:
if i not in faclist:
faclist.append(i)
if n/i not in faclist:
faclist.append(n/i)
return facts
If you only care about using list comprehensions and nothing else matters to you!
from itertools import combinations
from functools import reduce
def get_devisors(n):
f = [f for f,e in list(factorGenerator(n)) for i in range(e)]
fc = [x for l in range(len(f)+1) for x in combinations(f, l)]
devisors = [1 if c==() else reduce((lambda x, y: x * y), c) for c in set(fc)]
return sorted(devisors)
My solution via generator function is:
def divisor(num):
for x in range(1, num + 1):
if num % x == 0:
yield x
while True:
yield None
Try to calculate square root a given number and then loop range(1,square_root+1).
number = int(input("Enter a Number: "))
square_root = round(number ** (1.0 / 2))
print(square_root)
divisor_list = []
for i in range(1,square_root+1):
if number % i == 0: # Check if mod return 0 if yes then append i and number/i in the list
divisor_list.append(i)
divisor_list.append(int(number/i))
print(divisor_list)
def divisorGen(n): v = n last = [] for i in range(1, v+1) : if n % i == 0 : last.append(i)
I don´t understand why there are so many complicated solutions to this problem.
Here is my take on it:
def divisors(n):
lis =[1]
s = math.ceil(math.sqrt(n))
for g in range(s,1, -1):
if n % g == 0:
lis.append(g)
lis.append(int(n / g))
return (set(lis))
return [x for x in range(n+1) if n/x==int(n/x)]