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I know how to find the 1st highest value but don't know the rest. Keep in mind i need to print the position of the 1st 2nd and 3rd highest value.Thank You and try to keep it simple as i have only been coding for 2 months. Also they can be joint ranks
def linearSearch(Fscore_list):
pos_list = []
target = (max(Fscore_list))
for i in range(len(Fscore_list)):
if Fscore_list[i] >= target:
pos_list.append(i)
return pos_list
This will create a list of the 3 largest items, and a list of the corresponding indices:
lst = [9,7,43,2,4,7,8,5,4]
values = []
values = zip(*sorted( [(x,i) for (i,x) in enumerate(f_test)],
reverse=True )[:3] )[0]
posns = []
posns = zip(*sorted( [(x,i) for (i,x) in enumerate(f_test)],
reverse=True )[:3] )[1]
Things are a bit more complicated if the same value can appear multiple times (this will show the highest position for a value):
lst = [9,7,43,2,4,7,8,5,4]
ranks = sorted( [(x,i) for (i,x) in enumerate(lst)], reverse=True
)
values = []
for x,i in ranks:
if x not in values:
values.append( x )
posns.append( i )
if len(values) == 3:
break
print zip( values, posns )
Use heapq.nlargest:
>>> import heapq
>>> [i
... for x, i
... in heapq.nlargest(
... 3,
... ((x, i) for i, x in enumerate((0,5,8,7,2,4,3,9,1))))]
[7, 2, 3]
Add all the values in the list to a set. This will ensure you have each value only once.
Sort the set.
Find the index of the top three values in the set in the original list.
Make sense?
Edit
thelist = [1, 45, 88, 1, 45, 88, 5, 2, 103, 103, 7, 8]
theset = frozenset(thelist)
theset = sorted(theset, reverse=True)
print('1st = ' + str(theset[0]) + ' at ' + str(thelist.index(theset[0])))
print('2nd = ' + str(theset[1]) + ' at ' + str(thelist.index(theset[1])))
print('3rd = ' + str(theset[2]) + ' at ' + str(thelist.index(theset[2])))
Edit
You still haven't told us how to handle 'joint winners' but looking at your responses to other answers I am guessing this might possibly be what you are trying to do, maybe? If this is not the output you want please give us an example of the output you are hoping to get.
thelist = [1, 45, 88, 1, 45, 88, 5, 2, 103, 103, 7, 8]
theset = frozenset(thelist)
theset = sorted(theset, reverse=True)
thedict = {}
for j in range(3):
positions = [i for i, x in enumerate(thelist) if x == theset[j]]
thedict[theset[j]] = positions
print('1st = ' + str(theset[0]) + ' at ' + str(thedict.get(theset[0])))
print('2nd = ' + str(theset[1]) + ' at ' + str(thedict.get(theset[1])))
print('3rd = ' + str(theset[2]) + ' at ' + str(thedict.get(theset[2])))
Output
1st = 103 at [8, 9]
2nd = 88 at [2, 5]
3rd = 45 at [1, 4]
BTW : What if all the values are the same (equal first) or for some other reason there is no third place? (or second place?). Do you need to protect against that? If you do then I'm sure you can work out appropriate safety shields to add to the code.
Jupyter image of the code working
This question was on my Udemy machine learning course way too soon. Scott Hunter helped me the most on this problem, but didn't get me to a pass on the site. Having to really think about the issue deeper on my own. Here is my solution, since couldn't find it anywhere else online--in terms that I understood everything that was going on*:
lst = [9,7,43,2,4,7,8,9,4]
ranks = sorted( [(x,i) for (i,x) in enumerate(lst)], reverse=True )
box = []
for x,i in ranks:
if i&x not in box:
box.append( x )
if len(box) == 3:
break
print(box)
So we have a list of numbers. To rank the numbers we sort the value with its position for every position that has a value when we enumerate/iterate the list. Then we put the highest values on top by reversing it. Now we need a box to put our information in to pull out of later, so we build that box []. Now for every value with a position put that in the box, if the value and position isn't already in the box--meaning if the value is already in the box, but the position isn't, still put in the box. And we only want three answers. Finally tell me what is in the variable called box.
*Many of these answers, on this post, will most likely work.
Input : [4, 5, 1, 2, 9]
N = 2
Output : [9, 5]
Input : [81, 52, 45, 10, 3, 2, 96]
N = 3
Output : [81, 96, 52]
# Python program to find N largest
# element from given list of integers
l = [1000,298,3579,100,200,-45,900]
n = 4
l.sort()
print(l[-n:])
Output:
[298, 900, 1000, 3579]
lst = [9,7,43,2,4,7,8,9,4]
temp1 = lst
print(temp1)
#First Highest value:
print(max(temp1))
temp1.remove(max(temp1))
#output: 43
# Second Highest value:
print(max(temp1))
temp1.remove(max(temp1))
#output: 9
# Third Highest Value:
print(max(temp1))
#output: 7
There's a complicated O(n) algorithm, but the simplest way is to sort it, which is O(n * log n), then take the top. The trickiest part here is to sort the data while keeping the indices information.
from operator import itemgetter
def find_top_n_indices(data, top=3):
indexed = enumerate(data) # create pairs [(0, v1), (1, v2)...]
sorted_data = sorted(indexed,
key=itemgetter(1), # sort pairs by value
reversed=True) # in reversed order
return [d[0] for d in sorted_data[:top]] # take first N indices
data = [5, 3, 6, 3, 7, 8, 2, 7, 9, 1]
print find_top_n_indices(data) # should be [8, 5, 4]
Similarly, it can be done with heapq.nlargest(), but still you need to pack the initial data into tuples and unpack afterwards.
To have a list filtered and returned in descending order with duplicates removed try using this function.
You can pass in how many descending values you want it to return as keyword argument.
Also a side note, if the keyword argument (ordered_nums_to_return) is greater than the length of the list, it will return the whole list in descending order. if you need it to raise an exception, you can add a check to the function. If no args is passed it will return the highest value, again you can change this behaviour if you need.
list_of_nums = [2, 4, 23, 7, 4, 1]
def find_highest_values(list_to_search, ordered_nums_to_return=None):
if ordered_nums_to_return:
return sorted(set(list_to_search), reverse=True)[0:ordered_nums_to_return]
return [sorted(list_to_search, reverse=True)[0]]
print find_highest_values(list_of_nums, ordered_nums_to_return=4)
If values can appear in your list repeatedly you can try this solution.
def search(Fscore_list, num=3):
l = Fscore_list
res = dict([(v, []) for v in sorted(set(l), reverse=True)[:num]])
for index, val in enumerate(l):
if val in res:
res[val].append(index)
return sorted(res.items(), key=lambda x: x[0], reverse=True)
First it find num=3 highest values and create dict with empty list for indexes for it. Next it goes over the list and for every of the highest values (val in res) save it's indexes. Then just return sorted list of tuples like [(highest_1, [indexes ...]), ..]. e.g.
>>> l = [9, 7, 43, 2, 4, 7, 43, 8, 5, 8, 4]
>>> print(search(l))
[(43, [2, 6]), (9, [0]), (8, [7, 9])]
To print the positions do something like:
>>> Fscore_list = [9, 7, 43, 2, 4, 7, 43, 8, 5, 8, 4, 43, 43, 43]
>>> result = search(Fscore_list)
>>> print("1st. %d on positions %s" % (result[0][0], result[0][1]))
1st. 43 on positions [2, 6, 11, 12, 13]
>>> print("2nd. %d on positions %s" % (result[1][0], result[1][1]))
2nd. 9 on positions [0]
>>> print("3rd. %d on positions %s" % (result[2][0], result[2][1]))
3rd. 8 on positions [7, 9]
In one line:
lst = [9,7,43,2,8,4]
index = [i[1] for i in sorted([(x,i) for (i,x) in enumerate(lst)])[-3:]]
print(index)
[2, 0, 1]
None is always considered smaller than any number.
>>> None<4
True
>>> None>4
False
Find the highest element, and its index.
Replace it by None. Find the new highest element, and its index. This would be the second highest in the original list. Replace it by None. Find the new highest element, which is actually the third one.
Optional: restore the found elements to the list.
This is O(number of highest elements * list size), so it scales poorly if your "three" grows, but right now it's O(3n).
Good[ Morning : Evening ]
Im trying to make a small programme that :
-reads a text file containing numbers -assigned each line into an element of int_list
-takes a given list (max_quantity) and divides all the elements by 2 resulting in (max_q_in_half)
takes (int_list) and compares each element to its counterpart in (max_q_in_half) to eliminate each element that is above 50% of its maximum value (max_quantity)
-then make all the elements that are below 50% of their maximum value
into a new list (new_list)
-then return the smallest number in the (new_list)
-then uses that number to get it's index in the original (int_list)
-then uses that index to match the number to it's element in (metal_list)
to return the metal that is about to run out from the stock so the it could be restocked
HERE IS THE PROBLME
As you see in the output of this code the numbers below 50% are [41, 1]
the lowest value is 1 which has the index 6
but when i search int_list for this value it returns the firt occurence of 1 which is at index 3 which coresponds to BRASS but the correct answer should be Titanium
im triying to work around this issue by assigning a weight to each line in the text file so when i search the index of a value that value would be unique
take into account that the lines vary in number in the original text file
could somebody help me make a programe that assignes wheights of X.01 to line one and X.02 to line two ...
-41.01 #Line_1
-20.02 #Line_2
-6.03 #Line_3
-1.04 #Line_4
-14.05 #Line_5
-6.06 #Line_6
-1.07 #Line_7
...
int_list = [41, 20, 6, 1, 14, 6, 1]
metal_list = ['aluminium', 'iron', 'zinc', 'brass', 'stainless steel','copper', 'titanium']
max_quantity = [ 100, 30, 6, 2, 18, 7, 15]
max_q_in_half = [x // 2 for x in max_quantity]
new_list = [i for i, j in zip(int_list, max_q_in_half) if i < j]
x = min(new_list)
v = int_list.index(x)
w = metal_list.__getitem__(v)
print("Current List ", int_list)
print("what is below 50%",new_list)
print('lowest value ',x)
print("position in int_list ",v)
print("position Metal_list ",w)
You can divide the elements of the list by 2 when checking. It is better to number it when creating a list, but with the second member, so that the number does not interfere when checking for a minimum.
int_list = [41, 20, 6, 1, 14, 6, 1]
metal_list = ['aluminium', 'iron', 'zinc', 'brass', 'stainless steel', 'copper', 'titanium']
max_quantity = [100, 30, 6, 2, 18, 7, 15]
x = min([(i, n) for n, (i, j) in enumerate(zip(int_list, max_quantity)) if i < j//2])
print(x)
w = metal_list[x[1]]
print("position Metal_list ", w)
(1, 6)
position Metal_list titanium
d=[1,2,3,4,5,6,7]
g=[1,2,3,100,4,5,100]
m=max(g)
ll=[i for i, j in enumerate(g) if j == m]
print("The longest event time is",m,"for the event(s):",d[*ll])
I need to print after events the index of maximum values in d list
Such that , (The longest event time is 100 for the event(s):4 7
you can simply create a function to find the max and index of max in a list and return the corresponding value of the same index in a second list. here's the code :
# creating a function to get the index of Max in list A and return the corresponding value of the same index in list B
def get_id_max(first_list, second_list):
# Finding the max, and then it's index in the first list
global max_value
global max_index
max_value = max(first_list)
max_index = first_list.index(max_value)
# Return the corresponding value in the second list
return second_list[max_index]
# Defining A, B, and C
A = [2011,2012,2013,2014,2015]
B = [50, 60, 15, 76, 55]
C = [1.25, 2.2, 0.5, 1, 15]
print(f"The maximum value is {get_id_max(B,A)} and it's corresponding index is {max_index}")
# The maximum value is 2014 and it's corresponding index is 3
print(f"The maximum value is {get_id_max(C,A)} and it's corresponding index and value are {max_index,max_value}")
# The maximum value is 2015 and it's corresponding index and value are (4, 15)
events = [1, 2, 3, 4, 5, 6, 7]
durations = [1, 2, 3, 100, 4, 5, 100]
max_duration = max(durations)
longest_events = (str(event) for event, duration in zip(events, durations)
if duration == max_duration)
print(f"The longest event time is {max_duration}, for the event(s): {','.join(longest_events)}")
Your current approach requires several passes on the list. This is not the most optimal.
Here is an algorithm to compute the maxima in only one pass. It is similar to the classical manual computation of a maximum except it handles all maxima and the fact that the maximum is computed one one list, but the saved value is from another list (using zip)
m = float ('-inf')
out = [] # optional if -inf is not a possible value
for a,b in zip(d,g):
if b>m:
out = [a]
m=b
elif b == m:
out.append(a)
print(out)
Output: [4, 7]
Alternative without zip:
m = float ('-inf')
out = [] # optional if -inf is not a possible value
for i,b in enumerate(g):
if b>m:
out = [d[i]]
m=b
elif b == m:
out.append(d[i])
You can solve this with a little list comprehension:
vals = [dat[0] for dat in zip(d, g) if dat[1] == max(g)]
print(f"The longest event times are {max(g)}, for the event(s): {vals}")
I am trying to find elements from array(integer array) or list which are unique and those elements must not divisible by any other element from same array or list.
You can answer in any language like python, java, c, c++ etc.
I have tried this code in Python3 and it works perfectly but I am looking for better and optimum solution in terms of time complexity.
assuming array or list A is already sorted and having unique elements
A = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
while i<len(A)-1:
while j<len(A):
if A[j]%A[i]==0:
A.pop(j)
else:
j+=1
i+=1
j=i+1
For the given array A=[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] answer would be like ans=[2,3,5,7,11,13]
another example,A=[4,5,15,16,17,23,39] then ans would be like, ans=[4,5,17,23,39]
ans is having unique numbers
any element i from array only exists if (i%j)!=0, where i!=j
I think it's more natural to do it in reverse, by building a new list containing the answer instead of removing elements from the original list. If I'm thinking correctly, both approaches do the same number of mod operations, but you avoid the issue of removing an element from a list.
A = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
ans = []
for x in A:
for y in ans:
if x % y == 0:
break
else: ans.append(x)
Edit: Promoting the completion else.
This algorithm will perform much faster:
A = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
if (A[-1]-A[0])/A[0] > len(A)*2:
result = list()
for v in A:
for f in result:
d,m = divmod(v,f)
if m == 0: v=0;break
if d<f: break
if v: result.append(v)
else:
retain = set(A)
minMult = 1
maxVal = A[-1]
for v in A:
if v not in retain : continue
minMult = v*2
if minMult > maxVal: break
if v*len(A)<maxVal:
retain.difference_update([m for m in retain if m >= minMult and m%v==0])
else:
retain.difference_update(range(minMult,maxVal,v))
if maxVal%v == 0:
maxVal = max(retain)
result = list(retain)
print(result) # [2, 3, 5, 7, 11, 13]
In the spirit of the sieve of Eratostenes, each number that is retained, removes its multiples from the remaining eligible numbers. Depending on the magnitude of the highest value, it is sometimes more efficient to exclude multiples than check for divisibility. The divisibility check takes several times longer for an equivalent number of factors to check.
At some point, when the data is widely spread out, assembling the result instead of removing multiples becomes faster (this last addition was inspired by Imperishable Night's post).
TEST RESULTS
A = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] (100000 repetitions)
Original: 0.55 sec
New: 0.29 sec
A = list(range(2,5000))+[9697] (100 repetitions)
Original: 3.77 sec
New: 0.12 sec
A = list(range(1001,2000))+list(range(4000,6000))+[9697**2] (10 repetitions)
Original: 3.54 sec
New: 0.02 sec
I know that this is totally insane but i want to know what you think about this:
A = [4,5,15,16,17,23,39]
prova=[[x for x in A if x!=y and y%x==0] for y in A]
print([A[idx] for idx,x in enumerate(prova) if len(prova[idx])==0])
And i think it's still O(n^2)
If you care about speed more than algorithmic efficiency, numpy would be the package to use here in python:
import numpy as np
# Note: doesn't have to be sorted
a = [2, 2, 3, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 16, 29, 29]
a = np.unique(a)
result = a[np.all((a % a[:, None] + np.diag(a)), axis=0)]
# array([2, 3, 5, 7, 11, 13, 29])
This divides all elements by all other elements and stores the remainder in a matrix, checks which columns contain only non-0 values (other than the diagonal), and selects all elements corresponding to those columns.
This is O(n*M) where M is the max size of an integer in your list. The integers are all assumed to be none negative. This also assumes your input list is sorted (came to that assumption since all lists you provided are sorted).
a = [4, 7, 7, 8]
# a = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
# a = [4, 5, 15, 16, 17, 23, 39]
M = max(a)
used = set()
final_list = []
for e in a:
if e in used:
continue
else:
used.add(e)
for i in range(e, M + 1):
if not (i % e):
used.add(i)
final_list.append(e)
print(final_list)
Maybe this can be optimized even further...
If the list is not sorted then for the above method to work, one must sort it. The time complexity will then be O(nlogn + Mn) which equals to O(nlogn) when n >> M.
For instance, if I have a list
[1,4,2,3,5,4,5,6,7,8,1,3,4,5,9,10,11]
This algorithm should return [1,2,3,4,5,6,7,8,9,10,11].
To clarify, the longest list should run forwards. I was wondering what is an algorithmically efficient way to do this (preferably not O(n^2))?
Also, I'm open to a solution not in python since the algorithm is what matters.
Thank you.
Here is a simple one-pass O(n) solution:
s = [1,4,2,3,5,4,5,6,7,8,1,3,4,5,9,10,11,42]
maxrun = -1
rl = {}
for x in s:
run = rl[x] = rl.get(x-1, 0) + 1
print x-run+1, 'to', x
if run > maxrun:
maxend, maxrun = x, run
print range(maxend-maxrun+1, maxend+1)
The logic may be a little more self-evident if you think in terms of ranges instead of individual variables for the endpoint and run length:
rl = {}
best_range = xrange(0)
for x in s:
run = rl[x] = rl.get(x-1, 0) + 1
r = xrange(x-run+1, x+1)
if len(r) > len(best_range):
best_range = r
print list(best_range)
Not that clever, not O(n), could use a bit of optimization. But it works.
def longest(seq):
result = []
for v in seq:
for l in result:
if v == l[-1] + 1:
l.append(v)
else:
result.append([v])
return max(result, key=len)
You can use The Patience Sort implementation of the Largest Ascending Sub-sequence Algorithm
def LargAscSub(seq):
deck = []
for x in seq:
newDeck = [x]
i = bisect.bisect_left(deck, newDeck)
deck[i].insert(0, x) if i != len(deck) else deck.append(newDeck)
return [p[0] for p in deck]
And here is the Test results
>>> LargAscSub([1,4,2,3,5,4,5,6,7,8,1,3,4,5,9,10,11])
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
>>> LargAscSub([1, 2, 3, 11, 12, 13, 14])
[1, 2, 3, 11, 12, 13, 14]
>>> LargAscSub([11,12,13,14])
[11, 12, 13, 14]
The Order of Complexity is O(nlogn)
There was one note in the wiki link where they claimed that you can achieve O(n.loglogn) by relying on Van Emde Boas tree
How about using a modified Radix Sort? As JanneKarila pointed out the solution is not O(n). It uses Radix sort, which wikipedia says Radix sort's efficiency is O(k·n) for n keys which have k or fewer digits.
This will only work if you know the range of numbers that we're dealing with so that will be the first step.
Look at each element in starting list to find lowest, l and highest, h number. In this case l is 1 and h is 11. Note, if you already know the range for some reason, you can skip this step.
Create a result list the size of our range and set each element to null.
Look at each element in list and add them to the result list at the appropriate place if needed. ie, the element is a 4, add a 4 to the result list at position 4. result[element] = starting_list[element]. You can throw out duplicates if you want, they'll just be overwritten.
Go through the result list to find the longest sequence without any null values. Keep a element_counter to know what element in the result list we're looking at. Keep a curr_start_element set to the beginning element of the current sequence and keep a curr_len of how long the current sequence is. Also keep a longest_start_element and a `longest_len' which will start out as zero and be updated as we move through the list.
Return the result list starting at longest_start_element and taking longest_len
EDIT: Code added. Tested and working
#note this doesn't work with negative numbers
#it's certainly possible to write this to work with negatives
# but the code is a bit hairier
import sys
def findLongestSequence(lst):
#step 1
high = -sys.maxint - 1
for num in lst:
if num > high:
high = num
#step 2
result = [None]*(high+1)
#step 3
for num in lst:
result[num] = num
#step 4
curr_start_element = 0
curr_len = 0
longest_start_element = -1
longest_len = -1
for element_counter in range(len(result)):
if result[element_counter] == None:
if curr_len > longest_len:
longest_start_element = curr_start_element
longest_len = curr_len
curr_len = 0
curr_start_element = -1
elif curr_start_element == -1:
curr_start_element = element_counter
curr_len += 1
#just in case the last element makes the longest
if curr_len > longest_len:
longest_start_element = curr_start_element
longest_len = curr_len
#step 5
return result[longest_start_element:longest_start_element + longest_len-1]
If the result really does have to be a sub-sequence of consecutive ascending integers, rather than merely ascending integers, then there's no need to remember each entire consecutive sub-sequence until you determine which is the longest, you need only remember the starting and ending values of each sub-sequence. So you could do something like this:
def longestConsecutiveSequence(sequence):
# map starting values to largest ending value so far
map = collections.OrderedDict()
for i in sequence:
found = False
for k, v in map.iteritems():
if i == v:
map[k] += 1
found = True
if not found and i not in map:
map[i] = i + 1
return xrange(*max(map.iteritems(), key=lambda i: i[1] - i[0]))
If I run this on the original sample date (i.e. [1,4,2,3,5,4,5,6,7,8,1,3,4,5,9,10,11]) I get:
>>> print list(longestConsecutiveSequence([1,4,2,3,5,4,5,6,7,8,1,3,4,5,9,10,11]))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
If I run it on one of Abhijit's samples [1,2,3,11,12,13,14], I get:
>>> print list(longestConsecutiveSequence([1,2,3,11,12,13,14]))
[11, 12, 13, 14]
Regrettably, this algorithm is O(n*n) in the worst case.
Warning: This is the cheaty way to do it (aka I use python...)
import operator as op
import itertools as it
def longestSequence(data):
longest = []
for k, g in it.groupby(enumerate(set(data)), lambda(i, y):i-y):
thisGroup = map(op.itemgetter(1), g)
if len(thisGroup) > len(longest):
longest = thisGroup
return longest
longestSequence([1,4,2,3,5,4,5,6,7,8,1,3,4,5,9,10,11, 15,15,16,17,25])
You need the Maximum contiguous sum(Optimal Substructure):
def msum2(a):
bounds, s, t, j = (0,0), -float('infinity'), 0, 0
for i in range(len(a)):
t = t + a[i]
if t > s: bounds, s = (j, i+1), t
if t < 0: t, j = 0, i+1
return (s, bounds)
This is an example of dynamic programming and is O(N)
O(n) solution works even if the sequence does not start from the first element.
Warning does not work if len(A) = 0.
A = [1,4,2,3,5,4,5,6,7,8,1,3,4,5,9,10,11]
def pre_process(A):
Last = {}
Arrow = []
Length = []
ArgMax = 0
Max = 0
for i in xrange(len(A)):
Arrow.append(i)
Length.append(0)
if A[i] - 1 in Last:
Aux = Last[A[i] - 1]
Arrow[i] = Aux
Length[i] = Length[Aux] + 1
Last[A[i]] = i
if Length[i] > Max:
ArgMax = i
Max = Length[i]
return (Arrow,ArgMax)
(Arr,Start) = pre_process(A)
Old = Arr[Start]
ToRev = []
while 1:
ToRev.append(A[Start])
if Old == Start:
break
Start = Old
New = Arr[Start]
Old = New
ToRev.reverse()
print ToRev
Pythonizations are welcome!!
Ok, here's yet another attempt in python:
def popper(l):
listHolders = []
pos = 0
while l:
appended = False
item = l.pop()
for holder in listHolders:
if item == holder[-1][0]-1:
appended = True
holder.append((item, pos))
if not appended:
pos += 1
listHolders.append([(item, pos)])
longest = []
for holder in listHolders:
try:
if (holder[0][0] < longest[-1][0]) and (holder[0][1] > longest[-1][1]):
longest.extend(holder)
except:
pass
if len(holder) > len(longest):
longest = holder
longest.reverse()
return [x[0] for x in longest]
Sample inputs and outputs:
>>> demo = list(range(50))
>>> shuffle(demo)
>>> demo
[40, 19, 24, 5, 48, 36, 23, 43, 14, 35, 18, 21, 11, 7, 34, 16, 38, 25, 46, 27, 26, 29, 41, 8, 31, 1, 33, 2, 13, 6, 44, 22, 17,
12, 39, 9, 49, 3, 42, 37, 30, 10, 47, 20, 4, 0, 28, 32, 45, 15]
>>> popper(demo)
[1, 2, 3, 4]
>>> demo = [1,4,2,3,5,4,5,6,7,8,1,3,4,5,9,10,11]
>>> popper(demo)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
>>>
This should do the trick (and is O(n)):
target = 1
result = []
for x in list:
for y in result:
if y[0] == target:
y[0] += 1
result.append(x)
For any starting number, this works:
result = []
for x in mylist:
matched = False
for y in result:
if y[0] == x:
matched = True
y[0] += 1
y.append(x)
if not matched:
result.append([x+1, x])
return max(result, key=len)[1:]