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I have a simple code that generates a list of random numbers.
x = [random.randrange(0,11) for i in range(10)]
The problem I'm having is that, since it's random, it sometimes produces duplicate numbers right next to each other. How do I change the code so that it never happens? I'm looking for something like this:
[1, 7, 2, 8, 7, 2, 8, 2, 6, 5]
So that every time I run the code, all the numbers that are next to each other are different.
x = []
while len(x) < 10:
r = random.randrange(0,11)
if not x or x[-1] != r:
x.append(r)
x[-1] contains the last inserted element, which we check not to be the same as the new random number. With not x we check that the array is not empty, as it would generate a IndexError during the first iteration of the loop
Here's an approach that doesn't rely on retrying:
>>> import random
>>> x = [random.choice(range(12))]
>>> for _ in range(9):
... x.append(random.choice([*range(x[-1]), *range(x[-1]+1, 12)]))
...
>>> x
[6, 2, 5, 8, 1, 8, 0, 4, 6, 0]
The idea is to choose each new number by picking from a list that excludes the previously picked number.
Note that having to re-generate a new list to pick from each time keeps this from actually being an efficiency improvement. If you were generating a very long list from a relatively short range, though, it might be worthwhile to generate different pools of numbers up front so that you could then select from the appropriate one in constant time:
>>> pool = [[*range(i), *range(i+1, 3)] for i in range(3)]
>>> x = [random.choice(random.choice(pool))]
>>> for _ in range(10000):
... x.append(random.choice(pool[x[-1]]))
...
>>> x
[0, 2, 0, 2, 0, 2, 1, 0, 1, 2, 0, 1, 2, 1, 0, ...]
O(n) solution by adding to the last element randomly from [1,stop) modulo stop
import random
x = [random.randrange(0,11)]
x.extend((x[-1]+random.randrange(1,11)) % 11 for i in range(9))
x
Output
[0, 10, 4, 5, 10, 1, 4, 8, 0, 9]
from random import randrange
from itertools import islice, groupby
# Make an infinite amount of randrange's results available
pool = iter(lambda: randrange(0, 11), None)
# Use groupby to squash consecutive values into one and islice to at most 10 in total
result = [v for v, _ in islice(groupby(pool), 10)]
Function solution that doesn't iterate to check for repeats, just checks each add against the last number in the list:
import random
def get_random_list_without_neighbors(lower_limit, upper_limit, length):
res = []
# add the first number
res.append(random.randrange(lower_limit, upper_limit))
while len(res) < length:
x = random.randrange(lower_limit, upper_limit)
# check that the new number x doesn't match the last number in the list
if x != res[-1]:
res.append(x)
return res
>>> print(get_random_list_without_neighbors(0, 11, 10)
[10, 1, 2, 3, 1, 8, 6, 5, 6, 2]
def random_sequence_without_same_neighbours(n, min, max):
x = [random.randrange(min, max + 1)]
uniq_value_count = max - min + 1
next_choises_count = uniq_value_count - 1
for i in range(n - 1):
circular_shift = random.randrange(0, next_choises_count)
x.append(min + (x[-1] + circular_shift + 1) % uniq_value_count)
return x
random_sequence_without_same_neighbours(n=10, min=0, max=10)
It's not to much pythonic but you can do something like this
import random
def random_numbers_generator(n):
"Generate a list of random numbers but without two duplicate numbers in a row "
result = []
for _ in range(n):
number = random.randint(1, n)
if result and number == result[-1]:
continue
result.append(number)
return result
print(random_numbers_generator(10))
Result:
3, 6, 2, 4, 2, 6, 2, 1, 4, 7]
I have a function called prime_sieve(N) in python, and this function assigns a 0 to a number if it is not a prime number and 1 if it is a prime number - it is called mask. This function works properly. The problem is in the 2nd function below the code for prime_sieve(N) and the code is:
import numpy as np
def prime_sieve(N):
nums = np.arange(2, N + 2, 1)
mask = 1 + np.zeroes(N, dtype = int)
for n in nums:
for i in np.arange(2 * n - 2, N, n):
mask[i] = 0
return nums, mask
numbers, mask = prime_sieve(8)
print(numbers) #prints out the actual numbers starting at 2
print(mask) #prints out the 0s and 1s assigned to the values if they are a prime or not.
I have to use the above mentioned function in a function called primes_list(N) to print out only the prime numbers from the list. The code for primes_list(N) is:
def primes_list(N):
for i in range (0, N, 1):
if mask[i] == 1:
return prime_sieve(numbers[i])
print(primes_list(8))
The output I receive from the prime_sieve(N) function is:
[2, 3, 4, 5, 6, 7, 8, 9]
[1, 1, 0, 1, 0, 1, 0, 0]
The output I receive from the primes_list(N) function is:
Expected output: [2, 3, 5, 7]
My output: (array([2, 3]), array([1, 1]))
Any suggestion will be highly appreciated.
Your primes_list doesn't make sense:
def primes_list(N):
for i in range (0, N, 1):
if mask[i] == 1:
return prime_sieve(numbers[i])
mask is not defined, and even if you're using the mask from earlier, it will be true at 2, which will then call prime_sieve(numbers[2]) where numbers is also undefined. If we further accept the global numbers, that gives us prime_sieve[3], regardless of what N they put in (where N >= 3). The result of prime_sieve[3] is a tuple: ([2, 3], [1, 1]).
Try:
def primes_list(N):
nums, mask = prime_sieve(N)
return [nums[i] for i in range(len(nums)) if mask[i]]
This takes the results of prime_sieve and only returns the numbers which are prime: (where mask[i] == True)
Result: [2, 3, 5, 7]
return immediately exits the function and gives you whatever the value is at that point.
Try for example,
def f():
for i in (1, 2, 3):
return i
print(f())
This just prints 1.
You need to either return a tuple of the stuff you want, e.g.
def f():
return (1, 2, 3)
print(f())
or use the yield statement, e.g.
def f():
for i in (1, 2, 3):
yield i
for thing in f():
print(thing)
I'd like to do a random shuffle of a list but with one condition: an element can never be in the same original position after the shuffle.
Is there a one line way to do such in python for a list?
Example:
list_ex = [1,2,3]
each of the following shuffled lists should have the same probability of being sampled after the shuffle:
list_ex_shuffled = [2,3,1]
list_ex_shuffled = [3,1,2]
but the permutations [1,2,3], [1,3,2], [2,1,3] and [3,2,1] are not allowed since all of them repeat one of the elements positions.
NOTE: Each element in the list_ex is a unique id. No repetition of the same element is allowed.
Randomize in a loop and keep rejecting the results until your condition is satisfied:
import random
def shuffle_list(some_list):
randomized_list = some_list[:]
while True:
random.shuffle(randomized_list)
for a, b in zip(some_list, randomized_list):
if a == b:
break
else:
return randomized_list
I'd describe such shuffles as 'permutations with no fixed points'. They're also known as derangements.
The probability that a random permutation is a derangement is approximately 1/e (fun to prove). This is true however long the list. Thus an obvious algorithm to give a random derangement is to shuffle the cards normally, and keep shuffling until you have a derangement. The expected number of necessary shuffles is about 3, and it's rare you'll have to shuffle more than ten times.
(1-1/e)**11 < 1%
Suppose there are n people at a party, each of whom brought an umbrella. At the end of the party, each person takes an umbrella at random from the basket. What is the probability that no-one holds their own umbrella?
You could generate all possible valid shufflings:
>>> list_ex = [1,2,3]
>>> import itertools
>>> list(itertools.ifilter(lambda p: not any(i1==i2 for i1,i2 in zip(list_ex, p)),
... itertools.permutations(list_ex, len(list_ex))))
[(2, 3, 1), (3, 1, 2)]
For some other sequence:
>>> list_ex = [7,8,9,0]
>>> list(itertools.ifilter(lambda p: not any(i1==i2 for i1,i2 in zip(list_ex, p)),
... itertools.permutations(list_ex, len(list_ex))))
[(8, 7, 0, 9), (8, 9, 0, 7), (8, 0, 7, 9), (9, 7, 0, 8), (9, 0, 7, 8), (9, 0, 8, 7), (0, 7, 8, 9), (0, 9, 7, 8), (0, 9, 8, 7)]
You could also make this a bit more efficient by short-circuiting the iterator if you just want one result:
>>> list_ex = [1,2,3]
>>> i = itertools.ifilter(lambda p: not any(i1==i2 for i1,i2 in zip(list_ex, p)),
... itertools.permutations(list_ex, len(list_ex)))
>>> next(i)
(2, 3, 1)
But, it would not be a random choice. You'd have to generate all of them and choose one for it to be an actual random result:
>>> list_ex = [1,2,3]
>>> i = itertools.ifilter(lambda p: not any(i1==i2 for i1,i2 in zip(list_ex, p)),
... itertools.permutations(list_ex, len(list_ex)))
>>> import random
>>> random.choice(list(i))
(2, 3, 1)
Here is another take on this. You can pick one solution or another depending on your needs. This is not a one liner but shuffles the indices of elements instead of the elements themselves. Thus, the original list may have duplicate values or values of types that cannot be compared or may be expensive to compare.
#! /usr/bin/env python
import random
def shuffled_values(data):
list_length = len(data)
candidate = range(list_length)
while True:
random.shuffle(candidate)
if not any(i==j for i,j in zip(candidate, range(list_length))):
yield [data[i] for i in candidate]
list_ex = [1, 2, 3]
list_gen = shuffled_values(list_ex)
for i in range(0, 10):
print list_gen.next()
This gives:
[2, 3, 1]
[3, 1, 2]
[3, 1, 2]
[2, 3, 1]
[3, 1, 2]
[3, 1, 2]
[2, 3, 1]
[2, 3, 1]
[3, 1, 2]
[2, 3, 1]
If list_ex is [2, 2, 2], this method will keep yielding [2, 2, 2] over and over. The other solutions will give you empty lists. I am not sure what you want in this case.
Use Knuth-Durstenfeld to shuffle the list. As long as it is found to be in the original position during the shuffling process, a new shuffling process is started from the beginning until it returns to a qualified arrangement. The time complexity of this algorithm is the smallest constant term:
def _random_derangement(x: list, randint: Callable[[int, int], int]) -> None:
'''
Random derangement list x in place, and return None.
An element can never be in the same original position after the shuffle. provides uniform distribution over permutations.
The formal parameter randint requires a callable object such as rand_int(b, a) that generates a random integer within the specified closed interval.
'''
from collections import namedtuple
sequence_type = namedtuple('sequence_type', ('sequence_number', 'elem'))
x_length = len(x)
if x_length > 1:
for i in range(x_length):
x[i] = sequence_type(sequence_number = i, elem = x[i])
end_label = x_length - 1
while True:
for i in range(end_label, 0, -1):
random_location = randint(i, 0)
if x[random_location].sequence_number != i:
x[i], x[random_location] = x[random_location], x[i]
else:
break
else:
if x[0].sequence_number != 0: break
for i in range(x_length):
x[i] = x[i].elem
complete_shuffle
Here's another algorithm. Take cards at random. If your ith card is card i, put it back and try again. Only problem, what if when you get to the last card it's the one you don't want. Swap it with one of the others.
I think this is fair (uniformally random).
import random
def permutation_without_fixed_points(n):
if n == 1:
raise ArgumentError, "n must be greater than 1"
result = []
remaining = range(n)
i = 0
while remaining:
if remaining == [n-1]:
break
x = i
while x == i:
j = random.randrange(len(remaining))
x = remaining[j]
remaining.pop(j)
result.append(x)
i += 1
if remaining == [n-1]:
j = random.randrange(n-1)
result.append(result[j])
result[j] = n
return result
I am trying to program a standard snake draft, where team A pick, team B, team C, team C, team B, team A, ad nauseum.
If pick number 13 (or pick number x) just happened how can I figure which team picks next for n number of teams.
I have something like:
def slot(n,x):
direction = 'down' if (int(x/n) & 1) else 'up'
spot = (x % n) + 1
slot = spot if direction == 'up' else ((n+1) - spot)
return slot
I have feeling there is a simpler, more pythonic what than this solution. Anyone care to take a hack at it?
So I played around a little more. I am looking for the return of a single value, rather than the best way to count over a looped list. The most literal answer might be:
def slot(n, x): # 0.15757 sec for 100,000x
number_range = range(1, n+1) + range(n,0, -1)
index = x % (n*2)
return number_range[index]
This creates a list [1,2,3,4,4,3,2,1], figures out the index (e.g. 13 % (4*2) = 5), and then returns the index value from the list (e.g. 4). The longer the list, the slower the function.
We can use some logic to cut the list making in half. If we are counting up (i.e. (int(x/n) & 1) returns False), we get the obvious index value (x % n), else we subtract that value from n+1:
def slot(n, x): # 0.11982 sec for 100,000x
number_range = range(1, n+1) + range(n,0, -1)
index = ((n-1) - (x % n)) if (int(x/n) & 1) else (x % n)
return number_range[index]
Still avoiding a list altogether is fastest:
def slot(n, x): # 0.07275 sec for 100,000x
spot = (x % n) + 1
slot = ((n+1) - spot) if (int(x/n) & 1) else spot
return slot
And if I hold the list as variable rather than spawning one:
number_list = [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1]
def slot(n, x): # 0.03638 sec for 100,000x
return number_list[x % (n*2)]
Why not use itertools cycle function:
from itertools import cycle
li = range(1, n+1) + range(n, 0, -1) # e.g. [1, 2, 3, 4, 4, 3, 2, 1]
it = cycle(li)
[next(it) for _ in xrange(10)] # [1, 2, 3, 4, 4, 3, 2, 1, 1, 2]
Note: previously I had answered how to run up and down, as follows:
it = cycle(range(1, n+1) + range(n, 0, -1)) #e.g. [1, 2, 3, 4, 3, 2, 1, 2, 3, ...]
Here's a generator that will fulfill what you want.
def draft(n):
while True:
for i in xrange(1,n+1):
yield i
for i in xrange(n,0,-1):
yield i
>>> d = draft(3)
>>> [d.next() for _ in xrange(12)]
[1, 2, 3, 3, 2, 1, 1, 2, 3, 3, 2, 1]
from itertools import chain, cycle
def cycle_up_and_down(first, last):
up = xrange(first, last+1, 1)
down = xrange(last, first-1, -1)
return cycle(chain(up, down))
turns = cycle_up_and_down(1, 4)
print [next(turns) for n in xrange(10)] # [1, 2, 3, 4, 4, 3, 2, 1, 1, 2]
Here is a list of numbers that counts up, then down:
>>> [ -abs(5-i)+5 for i in range(0,10) ]
[0, 1, 2, 3, 4, 5, 4, 3, 2, 1]
Written out:
count_up_to = 5
for i in range( 0, count_up_to*2 ):
the_number_you_care_about = -abs(count_up_to-i) + count_up_to
# do stuff with the_number_you_care_about
Easier to read:
>>> list( range(0,5) ) + list( range( 5, 0, -1 ) )
[0, 1, 2, 3, 4, 5, 4, 3, 2, 1]
Written out:
count_up_to = 5
for i in list( range(0,5) ) + list( range(5, 0, -1) ):
# i is the number you care about
Another way:
from itertools import chain
for i in chain( range(0,5), range(5,0,-1) ):
# i is the number you care about
I have a list of lists and each list has a repeating sequence. I'm trying to count the length of repeated sequence of integers in the list:
list_a = [111,0,3,1,111,0,3,1,111,0,3,1]
list_b = [67,4,67,4,67,4,67,4,2,9,0]
list_c = [1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,23,18,10]
Which would return:
list_a count = 4 (for [111,0,3,1])
list_b count = 2 (for [67,4])
list_c count = 10 (for [1,2,3,4,5,6,7,8,9,0])
Any advice or tips would be welcome. I'm trying to work it out with re.compile right now but, its not quite right.
Guess the sequence length by iterating through guesses between 2 and half the sequence length. If no pattern is discovered, return 1 by default.
def guess_seq_len(seq):
guess = 1
max_len = len(seq) / 2
for x in range(2, max_len):
if seq[0:x] == seq[x:2*x] :
return x
return guess
list_a = [111,0,3,1,111,0,3,1,111,0,3,1]
list_b = [67,4,67,4,67,4,67,4,2,9,0]
list_c = [1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,23,18,10]
print guess_seq_len(list_a)
print guess_seq_len(list_b)
print guess_seq_len(list_c)
print guess_seq_len(range(500)) # test of no repetition
This gives (as expected):
4
2
10
1
As requested, this alternative gives longest repeated sequence. Hence it will return 4 for list_b. The only change is guess = x instead of return x
def guess_seq_len(seq):
guess = 1
max_len = len(seq) / 2
for x in range(2, max_len):
if seq[0:x] == seq[x:2*x] :
guess = x
return guess
I took Maria's faster and more stackoverflow-compliant answer and made it find the largest sequence first:
def guess_seq_len(seq, verbose=False):
seq_len = 1
initial_item = seq[0]
butfirst_items = seq[1:]
if initial_item in butfirst_items:
first_match_idx = butfirst_items.index(initial_item)
if verbose:
print(f'"{initial_item}" was found at index 0 and index {first_match_idx}')
max_seq_len = min(len(seq) - first_match_idx, first_match_idx)
for seq_len in range(max_seq_len, 0, -1):
if seq[:seq_len] == seq[first_match_idx:first_match_idx+seq_len]:
if verbose:
print(f'A sequence length of {seq_len} was found at index {first_match_idx}')
break
return seq_len
This worked for me.
def repeated(L):
'''Reduce the input list to a list of all repeated integers in the list.'''
return [item for item in list(set(L)) if L.count(item) > 1]
def print_result(L, name):
'''Print the output for one list.'''
output = repeated(L)
print '%s count = %i (for %s)' % (name, len(output), output)
list_a = [111, 0, 3, 1, 111, 0, 3, 1, 111, 0, 3, 1]
list_b = [67, 4, 67, 4, 67, 4, 67, 4, 2, 9, 0]
list_c = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2,
3, 4, 5, 6, 7, 8, 9, 0, 23, 18, 10
]
print_result(list_a, 'list_a')
print_result(list_b, 'list_b')
print_result(list_c, 'list_c')
Python's set() function will transform a list to a set, a datatype that can only contain one of any given value, much like a set in algebra. I converted the input list to a set, and then back to a list, reducing the list to only its unique values. I then tested the original list for each of these values to see if it contained that value more than once. I returned a list of all of the duplicates. The rest of the code is just for demonstration purposes, to show that it works.
Edit: Syntax highlighting didn't like the apostrophe in my docstring.