I need to make a random list of permutations. The elements can be anything but assume that they are the integers 0 through x-1. I want to make y lists, each containing z elements. The rules are that no list may contain the same element twice and that over all the lists, the number of times each elements is used is the same (or as close as possible). For instance, if my elements are 0,1,2,3, y is 6, and z is 2, then one possible solution is:
0,3
1,2
3,0
2,1
0,1
2,3
Each row has only unique elements and no element has been used more than 3 times. If y were 7, then 2 elements would be used 4 times, the rest 3.
This could be improved, but it seems to do the job (Python):
import math, random
def get_pool(items, y, z):
slots = y*z
use_each_times = slots/len(items)
exceptions = slots - use_each_times*len(items)
if (use_each_times > y or
exceptions > 0 and use_each_times+1 > y):
raise Exception("Impossible.")
pool = {}
for n in items:
pool[n] = use_each_times
for n in random.sample(items, exceptions):
pool[n] += 1
return pool
def rebalance(ret, pool, z):
max_item = None
max_times = None
for item, times in pool.items():
if times > max_times:
max_item = item
max_times = times
next, times = max_item, max_times
candidates = []
for i in range(len(ret)):
item = ret[i]
if next not in item:
candidates.append( (item, i) )
swap, swap_index = random.choice(candidates)
swapi = []
for i in range(len(swap)):
if swap[i] not in pool:
swapi.append( (swap[i], i) )
which, i = random.choice(swapi)
pool[next] -= 1
pool[swap[i]] = 1
swap[i] = next
ret[swap_index] = swap
def plist(items, y, z):
pool = get_pool(items, y, z)
ret = []
while len(pool.keys()) > 0:
while len(pool.keys()) < z:
rebalance(ret, pool, z)
selections = random.sample(pool.keys(), z)
for i in selections:
pool[i] -= 1
if pool[i] == 0:
del pool[i]
ret.append( selections )
return ret
print plist([0,1,2,3], 6, 2)
Ok, one way to approximate that:
1 - shuffle your list
2 - take the y first elements to form the next row
4 - repeat (2) as long as you have numbers in the list
5 - if you don't have enough numbers to finish the list, reshuffle the original list and take the missing elements, making sure you don't retake numbers.
6 - Start over at step (2) as long as you need rows
I think this should be as random as you can make it and will for sure follow your criteria. Plus, you have very little tests for duplicate elements.
First, you can always randomly sort the list in the end, so let's not worry about making "random permutations" (hard); and just worry about 1) making permutations (easy) and 2) randomizing them (easy).
If you want "truly" random groups, you have to accept that randomization by nature doesn't really allow for the constraint of "even distribution" of results -- you may get that or you may get a run of similar-looking ones. If you really want even distribution, first make the sets evenly distributed, and then randomize them as a group.
Do you have to use each element in the set x evenly? It's not clear from the rules that I couldn't just make the following interpretation:
Note the following: "over all the lists, the number of times each elements is used is the same (or as close as possible)"
Based on this criteria, and the rule that z < x*, I postulate that you can simply enumerate all the items over all the lists. So you automatically make y list of the items enumerated to position z. Your example doesn't fulfill the rule above as closely as my version will. Using your example of x={0,1,2,3} y=6 and z=2, I get:
0,1 0,1 0,1 0,1 0,1 0,1
Now I didn't use 2 or 3, but you didn't say I had to use them all. If I had to use them all and I don't care to be able to prove that I am "as close as possible" to even usage, I would just enumerate across all the items through the lists, like this:
0,1 2,3 0,1 2,3 0,1 2,3
Finally, suppose I really do have to use all the elements. To calculate how many times each element can repeat, I just take (y*z)/(count of x). That way, I don't have to sit and worry about how to divide up the items in the list. If there is a remainder, or the result is less than 1, then I know that I will not get an exact number of repeats, so in those cases, it doesn't much matter to try to waste computational energy to make it perfect. I contend that the fastest result is still to just enumerate as above, and use the calculation here to show why either a perfect result was or wasn't achieved. A fancy algorithm to extract from this calculation how many positions will be duplicates could be achieved, but "it's too long to fit here in the margin".
*Each list has the same z number of elements, so it will be impossible to make lists where z is greater than x and still fulfill the rule that no list may contain the same element twice. Therefore, this rule demands that z cannot be greater than x.
Based on new details in the comments, the solution may simply be an implementation of a standard random permutation generation algorithm. There is a lengthy discussion of random permutation generation algorithms here:
http://www.techuser.net/randpermgen.html
(From Google search: random permutation generation)
This works in Ruby:
# list is the elements to be permuted
# y is the number of results desired
# z is the number of elements per result
# equalizer keeps track of who got used how many times
def constrained_permutations list, y, z
list.uniq! # Never trust the user. We want no repetitions.
equalizer = {}
list.each { |element| equalizer[element] = 0 }
results = []
# Do this until we get as many results as desired
while results.size < y
pool = []
puts pool
least_used = equalizer.each_value.min
# Find how used the least used element was
while pool.size < z
# Do this until we have enough elements in this resultset
element = nil
while element.nil?
# If we run out of "least used elements", then we need to increment
# our definition of "least used" by 1 and keep going.
element = list.shuffle.find do |x|
!pool.include?(x) && equalizer[x] == least_used
end
least_used += 1 if element.nil?
end
equalizer[element] += 1
# This element has now been used one more time.
pool << element
end
results << pool
end
return results
end
Sample usage:
constrained_permutations [0,1,2,3,4,5,6], 6, 2
=> [[4, 0], [1, 3], [2, 5], [6, 0], [2, 5], [3, 6]]
constrained_permutations [0,1,2,3,4,5,6], 6, 2
=> [[4, 5], [6, 3], [0, 2], [1, 6], [5, 4], [3, 0]]
enter code here
http://en.wikipedia.org/wiki/Fisher-Yates_shuffle
Related
I am trying to write a method that returns the highest product from adjacent values within an array. Below is my attempt to do so however it fails to return the highest products in some instances, patterns of which I am unclear on and I cannot see why the problems with this code:
def adjacentElementsProduct(inputArray)
inputArray.each_with_index do |value, index|
if inputArray[index+1]
products = [] << value * inputArray[index + 1]
return products.max
end
end
end
a) Can anyone help me understand what is wrong with the above implementation
b) Can anyone suggest a less verbose and simpler method of achieving the desired.
Here are the fails and passes (this is from codefights.com, 1st question, 2nd chapter 'Edge of the Ocean'):
First of all, you're returning from the method after calculating only the first product. So all your answers are simply the product of the first two numbers. To fix it, initialize your products variable before the loop and put your return after the loop.
As for a cleaner implementation, take a look at Enumerable's each_cons, which returns consecutive members of an array (for example, each_cons(2) returns consecutive pairs). Then you can multiply each pair in one fell swoop via map and return the maximum.
def adjacentElementsProduct(inputArray)
inputArray.each_cons(2).map{|a,b| a*b}.max
end
I presume that, for an array arr, you want the largest product
arr[i] * arr[i+1] * arr[i+2] *...* arr[j-1] * arr[j]
where 0 <= i <= j <= arr.size-1. We can do that in a Ruby-like way using Enumerable#each_cons, as #Mark suggested.
def max_adjacent_product(arr)
n = (1..arr.size).each_with_object({prod: -Float::INFINITY, adj: []}) do |len, best|
arr.each_cons(len) do |a|
pr = a.reduce(:*)
best.replace({prod: pr, adj: a}) if pr > best[:prod]
end
end
end
max_adjacent_product [2, -4, 3, -5, -10]
#=> {:prod=>150, :adj=>[3, -5, -10]}
I loop through the number of adjacent elements to consider. In the example, that would be from 1 to 5. For each number of adjacent elements, len, I then loop through each subarray a of adjacent elements of arr for which a.size equals len. In the example, for len = 3, that would be [2, -4, 3], [-4, 3, -5] and [3, -5, -10]. For each of those subarrays of adjacent elements I then compute the product of its elements (-24, 60 and 150 for the example just given). If a product is greater than the best known product so far I make it the best subarray so far. The final value of best is returned by the method.
I would like to know, what is the best approach to solve this problem:
Given x, y and y integers: a1, a2, a3 .. ay find all combinations of
a1 ± a2 ± ... ± ay = x, y < 20.
My recent approach is to find all permutations of 1 and 0 stored in table T and then, depending on whether number T[i] is 1 and 0, add or subtract ai from sum. The problem is that there are n! permutations of n-element array. Hence, for 20-element array, I have to check 20! possibilities where most of them are repeated. Could you please suggest me any potential approach to solving my problem?
There are only 2^20 (just over a million) binary vectors of length 20 rather than the infeasible 20!. Use should be able to brute-force that few in less than a second, especially if you use a Gray Code which would allow you to pass from one candidate sum to another in a single step (e.g. to go from a + b - c -d to a + b - c + d just add 2*d.
The excellent branch and bound idea of #MikeWise would be good if y gets much larger. Generate a tree starting with a root node of 0. Give it children of -a1 and +a1. Then 4 grand children by adding and subtracting a2, etc. If you ever get farther than the sum of the remaining ai from the target x -- you can prune that branch. In the worst case, this might be slightly worse than the Gray-code based brute force (because you need to do so much more processing at each node), but in the best case you might be able to prune away most possibilities.
On Edit: Here is some Python code. First I define a generator which, given an integer n, successively returns which bit position needs to flip to step through a Gray code:
def grayBit(n):
code = [0]*n
odd = True
done = False
while not done:
if odd:
code[0] = 1 - code[0] #flip bit
odd = False
yield 0
else:
i = code.index(1)
if i == n-1:
done = True
else:
code[i+1] = 1 - code[i+1]
odd = True
yield i+1
(This uses an algorithm which I learned years ago in the excellent book "Constructive Combinatorics" by Stanton and White).
Then -- I use this to return all solutions (as lists consisting of the input list of numbers with negative signs inserted as needed). The key point is that I can take the current bit-to-flip and either add or subtract twice the corresponding number:
def signedSums(nums, target):
n = len(nums)
patterns = []
total = sum(nums)
pattern = [1]*n
if target == total: patterns.append([x*y for x,y in zip(nums,pattern)])
deltas = [2*i for i in nums]
for i in grayBit(n):
if pattern[i] == 1:
total -= deltas[i]
else:
total += deltas[i]
pattern[i] = -1 * pattern[i]
if target == total: patterns.append([x*y for x,y in zip(nums,pattern)])
return patterns
Typical output:
>>> signedSums([1,2,3,4,5,9],6)
[[1, -2, -3, -4, 5, 9], [1, 2, 3, -4, -5, 9], [-1, 2, -3, 4, -5, 9], [1, 2, 3, 4, 5, -9]]
It only takes about a second to evaluate:
>>> len(signedSums([i for i in range(1,21)],100))
2865
Hence there are 2865 ways to add or subtract the integers in the range 1,2,..,20 to get a net sum of 100.
I assumed that a1 can be either added or subtracted (instead of just added, which is what your question implies if taken literally). Note that if you really want to insist that a1 occurs positively, then you could just subtract it from x and apply the above algorithm to the rest of the list and the adjusted target.
Finally, it is not too hard to see that if you solve the subset sub problem with the set of weights {2*a1, 2*a2, 2*a3, .... 2*ay} and with a target sum of x + a1 + a2 + ... + ay then the subsets selected will correspond exactly to the subsets where the positive signs occur in the solution to the original problem. Thus your problem is easily reducible to the subset-sum problem and it is thus NP-complete to determine if it has any solutions (and NP-hard to list them all).
We have conditions:
a1 ± a2 ± ... ± ay = x, y<20 [1]
First of all, I would generalize the condition [1], allowing all 'a' including 'a1' to be ±:
±a1 ± a2 ± ... ± ay = x [2]
If we have solution for [2], we can easily get solution for [1]
To solve [2] we can use the following approach:
combinations list x
| x == 0 && null list = [[]]
| null list = []
| otherwise = plusCombinations ++ minusCombinations where
a = head list
rest = tail list
plusCombinations = map (\c -> a:c) $ combinations rest (x-a)
minusCombinations = map (\c -> -a:c) $ combinations rest (x+a)
Explanation:
First condition checks if x reached zero and used all numbers from list. This means that solution found and we return single solution: [[]]
Second condition checks that list is empty and as far as x is not 0 this means that no solution can be found, returning empty solution: []
Third branch means that we can two alternatives: to use ai with '+' or with '-' so we concatenate plus and minus combinations
Example output:
*Main> combinations [1,2,3,4] 2
[[1,2,3,-4],[-1,2,-3,4]]
*Main> combinations [1,2,3,4] 3
[]
*Main> combinations [1,2,3,4] 4
[[1,2,-3,4],[-1,-2,3,4]]
I have an array of non-negative values. I want to build an array of values who's sum is 20 so that they are proportional to the first array.
This would be an easy problem, except that I want the proportional array to sum to exactly
20, compensating for any rounding error.
For example, the array
input = [400, 400, 0, 0, 100, 50, 50]
would yield
output = [8, 8, 0, 0, 2, 1, 1]
sum(output) = 20
However, most cases are going to have a lot of rounding errors, like
input = [3, 3, 3, 3, 3, 3, 18]
naively yields
output = [1, 1, 1, 1, 1, 1, 10]
sum(output) = 16 (ouch)
Is there a good way to apportion the output array so that it adds up to 20 every time?
There's a very simple answer to this question: I've done it many times. After each assignment into the new array, you reduce the values you're working with as follows:
Call the first array A, and the new, proportional array B (which starts out empty).
Call the sum of A elements T
Call the desired sum S.
For each element of the array (i) do the following:
a. B[i] = round(A[i] / T * S). (rounding to nearest integer, penny or whatever is required)
b. T = T - A[i]
c. S = S - B[i]
That's it! Easy to implement in any programming language or in a spreadsheet.
The solution is optimal in that the resulting array's elements will never be more than 1 away from their ideal, non-rounded values. Let's demonstrate with your example:
T = 36, S = 20. B[1] = round(A[1] / T * S) = 2. (ideally, 1.666....)
T = 33, S = 18. B[2] = round(A[2] / T * S) = 2. (ideally, 1.666....)
T = 30, S = 16. B[3] = round(A[3] / T * S) = 2. (ideally, 1.666....)
T = 27, S = 14. B[4] = round(A[4] / T * S) = 2. (ideally, 1.666....)
T = 24, S = 12. B[5] = round(A[5] / T * S) = 2. (ideally, 1.666....)
T = 21, S = 10. B[6] = round(A[6] / T * S) = 1. (ideally, 1.666....)
T = 18, S = 9. B[7] = round(A[7] / T * S) = 9. (ideally, 10)
Notice that comparing every value in B with it's ideal value in parentheses, the difference is never more than 1.
It's also interesting to note that rearranging the elements in the array can result in different corresponding values in the resulting array. I've found that arranging the elements in ascending order is best, because it results in the smallest average percentage difference between actual and ideal.
Your problem is similar to a proportional representation where you want to share N seats (in your case 20) among parties proportionnaly to the votes they obtain, in your case [3, 3, 3, 3, 3, 3, 18]
There are several methods used in different countries to handle the rounding problem. My code below uses the Hagenbach-Bischoff quota method used in Switzerland, which basically allocates the seats remaining after an integer division by (N+1) to parties which have the highest remainder:
def proportional(nseats,votes):
"""assign n seats proportionaly to votes using Hagenbach-Bischoff quota
:param nseats: int number of seats to assign
:param votes: iterable of int or float weighting each party
:result: list of ints seats allocated to each party
"""
quota=sum(votes)/(1.+nseats) #force float
frac=[vote/quota for vote in votes]
res=[int(f) for f in frac]
n=nseats-sum(res) #number of seats remaining to allocate
if n==0: return res #done
if n<0: return [min(x,nseats) for x in res] # see siamii's comment
#give the remaining seats to the n parties with the largest remainder
remainders=[ai-bi for ai,bi in zip(frac,res)]
limit=sorted(remainders,reverse=True)[n-1]
#n parties with remainter larger than limit get an extra seat
for i,r in enumerate(remainders):
if r>=limit:
res[i]+=1
n-=1 # attempt to handle perfect equality
if n==0: return res #done
raise #should never happen
However this method doesn't always give the same number of seats to parties with perfect equality as in your case:
proportional(20,[3, 3, 3, 3, 3, 3, 18])
[2,2,2,2,1,1,10]
You have set 3 incompatible requirements. An integer-valued array proportional to [1,1,1] cannot be made to sum to exactly 20. You must choose to break one of the "sum to exactly 20", "proportional to input", and "integer values" requirements.
If you choose to break the requirement for integer values, then use floating point or rational numbers. If you choose to break the exact sum requirement, then you've already solved the problem. Choosing to break proportionality is a little trickier. One approach you might take is to figure out how far off your sum is, and then distribute corrections randomly through the output array. For example, if your input is:
[1, 1, 1]
then you could first make it sum as well as possible while still being proportional:
[7, 7, 7]
and since 20 - (7+7+7) = -1, choose one element to decrement at random:
[7, 6, 7]
If the error was 4, you would choose four elements to increment.
A naïve solution that doesn't perform well, but will provide the right result...
Write an iterator that given an array with eight integers (candidate) and the input array, output the index of the element that is farthest away from being proportional to the others (pseudocode):
function next_index(candidate, input)
// Calculate weights
for i in 1 .. 8
w[i] = candidate[i] / input[i]
end for
// find the smallest weight
min = 0
min_index = 0
for i in 1 .. 8
if w[i] < min then
min = w[i]
min_index = i
end if
end for
return min_index
end function
Then just do this
result = [0, 0, 0, 0, 0, 0, 0, 0]
result[next_index(result, input)]++ for 1 .. 20
If there is no optimal solution, it'll skew towards the beginning of the array.
Using the approach above, you can reduce the number of iterations by rounding down (as you did in your example) and then just use the approach above to add what has been left out due to rounding errors:
result = <<approach using rounding down>>
while sum(result) < 20
result[next_index(result, input)]++
So the answers and comments above were helpful... particularly the decreasing sum comment from #Frederik.
The solution I came up with takes advantage of the fact that for an input array v, sum(v_i * 20) is divisible by sum(v). So for each value in v, I mulitply by 20 and divide by the sum. I keep the quotient, and accumulate the remainder. Whenever the accumulator is greater than sum(v), I add one to the value. That way I'm guaranteed that all the remainders get rolled into the results.
Is that legible? Here's the implementation in Python:
def proportion(values, total):
# set up by getting the sum of the values and starting
# with an empty result list and accumulator
sum_values = sum(values)
new_values = []
acc = 0
for v in values:
# for each value, find quotient and remainder
q, r = divmod(v * total, sum_values)
if acc + r < sum_values:
# if the accumlator plus remainder is too small, just add and move on
acc += r
else:
# we've accumulated enough to go over sum(values), so add 1 to result
if acc > r:
# add to previous
new_values[-1] += 1
else:
# add to current
q += 1
acc -= sum_values - r
# save the new value
new_values.append(q)
# accumulator is guaranteed to be zero at the end
print new_values, sum_values, acc
return new_values
(I added an enhancement that if the accumulator > remainder, I increment the previous value instead of the current value)
This is what I have so far:
myArray.map!{ rand(max) }
Obviously, however, sometimes the numbers in the list are not unique. How can I make sure my list only contains unique numbers without having to create a bigger list from which I then just pick the n unique numbers?
Edit:
I'd really like to see this done w/o loop - if at all possible.
(0..50).to_a.sort{ rand() - 0.5 }[0..x]
(0..50).to_a can be replaced with any array.
0 is "minvalue", 50 is "max value"
x is "how many values i want out"
of course, its impossible for x to be permitted to be greater than max-min :)
In expansion of how this works
(0..5).to_a ==> [0,1,2,3,4,5]
[0,1,2,3,4,5].sort{ -1 } ==> [0, 1, 2, 4, 3, 5] # constant
[0,1,2,3,4,5].sort{ 1 } ==> [5, 3, 0, 4, 2, 1] # constant
[0,1,2,3,4,5].sort{ rand() - 0.5 } ==> [1, 5, 0, 3, 4, 2 ] # random
[1, 5, 0, 3, 4, 2 ][ 0..2 ] ==> [1, 5, 0 ]
Footnotes:
It is worth mentioning that at the time this question was originally answered, September 2008, that Array#shuffle was either not available or not already known to me, hence the approximation in Array#sort
And there's a barrage of suggested edits to this as a result.
So:
.sort{ rand() - 0.5 }
Can be better, and shorter expressed on modern ruby implementations using
.shuffle
Additionally,
[0..x]
Can be more obviously written with Array#take as:
.take(x)
Thus, the easiest way to produce a sequence of random numbers on a modern ruby is:
(0..50).to_a.shuffle.take(x)
This uses Set:
require 'set'
def rand_n(n, max)
randoms = Set.new
loop do
randoms << rand(max)
return randoms.to_a if randoms.size >= n
end
end
Ruby 1.9 offers the Array#sample method which returns an element, or elements randomly selected from an Array. The results of #sample won't include the same Array element twice.
(1..999).to_a.sample 5 # => [389, 30, 326, 946, 746]
When compared to the to_a.sort_by approach, the sample method appears to be significantly faster. In a simple scenario I compared sort_by to sample, and got the following results.
require 'benchmark'
range = 0...1000000
how_many = 5
Benchmark.realtime do
range.to_a.sample(how_many)
end
=> 0.081083
Benchmark.realtime do
(range).sort_by{rand}[0...how_many]
end
=> 2.907445
Just to give you an idea about speed, I ran four versions of this:
Using Sets, like Ryan's suggestion.
Using an Array slightly larger than necessary, then doing uniq! at the end.
Using a Hash, like Kyle suggested.
Creating an Array of the required size, then sorting it randomly, like Kent's suggestion (but without the extraneous "- 0.5", which does nothing).
They're all fast at small scales, so I had them each create a list of 1,000,000 numbers. Here are the times, in seconds:
Sets: 628
Array + uniq: 629
Hash: 645
fixed Array + sort: 8
And no, that last one is not a typo. So if you care about speed, and it's OK for the numbers to be integers from 0 to whatever, then my exact code was:
a = (0...1000000).sort_by{rand}
Yes, it's possible to do this without a loop and without keeping track of which numbers have been chosen. It's called a Linear Feedback Shift Register: Create Random Number Sequence with No Repeats
[*1..99].sample(4) #=> [64, 99, 29, 49]
According to Array#sample docs,
The elements are chosen by using random and unique indices
If you need SecureRandom (which uses computer noise instead of pseudorandom numbers):
require 'securerandom'
[*1..99].sample(4, random: SecureRandom) #=> [2, 75, 95, 37]
How about a play on this? Unique random numbers without needing to use Set or Hash.
x = 0
(1..100).map{|iter| x += rand(100)}.shuffle
You could use a hash to track the random numbers you've used so far:
seen = {}
max = 100
(1..10).map { |n|
x = rand(max)
while (seen[x])
x = rand(max)
end
x
}
Rather than add the items to a list/array, add them to a Set.
If you have a finite list of possible random numbers (i.e. 1 to 100), then Kent's solution is good.
Otherwise there is no other good way to do it without looping. The problem is you MUST do a loop if you get a duplicate. My solution should be efficient and the looping should not be too much more than the size of your array (i.e. if you want 20 unique random numbers, it might take 25 iterations on average.) Though the number of iterations gets worse the more numbers you need and the smaller max is. Here is my above code modified to show how many iterations are needed for the given input:
require 'set'
def rand_n(n, max)
randoms = Set.new
i = 0
loop do
randoms << rand(max)
break if randoms.size > n
i += 1
end
puts "Took #{i} iterations for #{n} random numbers to a max of #{max}"
return randoms.to_a
end
I could write this code to LOOK more like Array.map if you want :)
Based on Kent Fredric's solution above, this is what I ended up using:
def n_unique_rand(number_to_generate, rand_upper_limit)
return (0..rand_upper_limit - 1).sort_by{rand}[0..number_to_generate - 1]
end
Thanks Kent.
No loops with this method
Array.new(size) { rand(max) }
require 'benchmark'
max = 1000000
size = 5
Benchmark.realtime do
Array.new(size) { rand(max) }
end
=> 1.9114e-05
Here is one solution:
Suppose you want these random numbers to be between r_min and r_max. For each element in your list, generate a random number r, and make list[i]=list[i-1]+r. This would give you random numbers which are monotonically increasing, guaranteeing uniqueness provided that
r+list[i-1] does not over flow
r > 0
For the first element, you would use r_min instead of list[i-1]. Once you are done, you can shuffle the list so the elements are not so obviously in order.
The only problem with this method is when you go over r_max and still have more elements to generate. In this case, you can reset r_min and r_max to 2 adjacent element you have already computed, and simply repeat the process. This effectively runs the same algorithm over an interval where there are no numbers already used. You can keep doing this until you have the list populated.
As far as it is nice to know in advance the maxium value, you can do this way:
class NoLoopRand
def initialize(max)
#deck = (0..max).to_a
end
def getrnd
return #deck.delete_at(rand(#deck.length - 1))
end
end
and you can obtain random data in this way:
aRndNum = NoLoopRand.new(10)
puts aRndNum.getrnd
you'll obtain nil when all the values will be exausted from the deck.
Method 1
Using Kent's approach, it is possible to generate an array of arbitrary length keeping all values in a limited range:
# Generates a random array of length n.
#
# #param n length of the desired array
# #param lower minimum number in the array
# #param upper maximum number in the array
def ary_rand(n, lower, upper)
values_set = (lower..upper).to_a
repetition = n/(upper-lower+1) + 1
(values_set*repetition).sample n
end
Method 2
Another, possibly more efficient, method modified from same Kent's another answer:
def ary_rand2(n, lower, upper)
v = (lower..upper).to_a
(0...n).map{ v[rand(v.length)] }
end
Output
puts (ary_rand 5, 0, 9).to_s # [0, 8, 2, 5, 6] expected
puts (ary_rand 5, 0, 9).to_s # [7, 8, 2, 4, 3] different result for same params
puts (ary_rand 5, 0, 1).to_s # [0, 0, 1, 0, 1] repeated values from limited range
puts (ary_rand 5, 9, 0).to_s # [] no such range :)
Given a non-negative integer n and an arbitrary set of inequalities that are user-defined (in say an external text file), I want to determine whether n satisfies any inequality, and if so, which one(s).
Here is a points list.
n = 0: 1
n < 5: 5
n = 5: 10
If you draw a number n that's equal to 5, you get 10 points.
If n less than 5, you get 5 points.
If n is 0, you get 1 point.
The stuff left of the colon is the "condition", while the stuff on the right is the "value".
All entries will be of the form:
n1 op n2: val
In this system, equality takes precedence over inequality, so the order that they appear in will not matter in the end. The inputs are non-negative integers, though intermediary and results may not be non-negative. The results may not even be numbers (eg: could be strings). I have designed it so that will only accept the most basic inequalities, to make it easier for writing a parser (and to see whether this idea is feasible)
My program has two components:
a parser that will read structured input and build a data structure to store the conditions and their associated results.
a function that will take an argument (a non-negative integer) and return the result (or, as in the example, the number of points I receive)
If the list was hardcoded, that is an easy task: just use a case-when or if-else block and I'm done. But the problem isn't as easy as that.
Recall the list at the top. It can contain an arbitrary number of (in)equalities. Perhaps there's only 3 like above. Maybe there are none, or maybe there are 10, 20, 50, or even 1000000. Essentially, you can have m inequalities, for m >= 0
Given a number n and a data structure containing an arbitrary number of conditions and results, I want to be able to determine whether it satisfies any of the conditions and return the associated value. So as with the example above, if I pass in 5, the function will return 10.
They condition/value pairs are not unique in their raw form. You may have multiple instances of the same (in)equality but with different values. eg:
n = 0: 10
n = 0: 1000
n > 0: n
Notice the last entry: if n is greater than 0, then it is just whatever you got.
If multiple inequalities are satisfied (eg: n > 5, n > 6, n > 7), all of them should be returned. If that is not possible to do efficiently, I can return just the first one that satisfied it and ignore the rest. But I would like to be able to retrieve the entire list.
I've been thinking about this for a while and I'm thinking I should use two hash tables: the first one will store the equalities, while the second will store the inequalities.
Equality is easy enough to handle: Just grab the condition as a key and have a list of values. Then I can quickly check whether n is in the hash and grab the appropriate value.
However, for inequality, I am not sure how it will work. Does anyone have any ideas how I can solve this problem in as little computational steps as possible? It's clear that I can easily accomplish this in O(n) time: just run it through each (in)equality one by one. But what happens if this checking is done in real-time? (eg: updated constantly)
For example, it is pretty clear that if I have 100 inequalities and 99 of them check for values > 100 while the other one checks for value <= 100, I shouldn't have to bother checking those 99 inequalities when I pass in 47.
You may use any data structure to store the data. The parser itself is not included in the calculation because that will be pre-processed and only needs to be done once, but if it may be problematic if it takes too long to parse the data.
Since I am using Ruby, I likely have more flexible options when it comes to "messing around" with the data and how it will be interpreted.
class RuleSet
Rule = Struct.new(:op1,:op,:op2,:result) do
def <=>(r2)
# Op of "=" sorts before others
[op=="=" ? 0 : 1, op2.to_i] <=> [r2.op=="=" ? 0 : 1, r2.op2.to_i]
end
def matches(n)
#op2i ||= op2.to_i
case op
when "=" then n == #op2i
when "<" then n < #op2i
when ">" then n > #op2i
end
end
end
def initialize(text)
#rules = text.each_line.map do |line|
Rule.new *line.split(/[\s:]+/)
end.sort
end
def value_for( n )
if rule = #rules.find{ |r| r.matches(n) }
rule.result=="n" ? n : rule.result.to_i
end
end
end
set = RuleSet.new( DATA.read )
-1.upto(8) do |n|
puts "%2i => %s" % [ n, set.value_for(n).inspect ]
end
#=> -1 => 5
#=> 0 => 1
#=> 1 => 5
#=> 2 => 5
#=> 3 => 5
#=> 4 => 5
#=> 5 => 10
#=> 6 => nil
#=> 7 => 7
#=> 8 => nil
__END__
n = 0: 1
n < 5: 5
n = 5: 10
n = 7: n
I would parse the input lines and separate them into predicate/result pairs and build a hash of callable procedures (using eval - oh noes!). The "check" function can iterate through each predicate and return the associated result when one is true:
class PointChecker
def initialize(input)
#predicates = Hash[input.split(/\r?\n/).map do |line|
parts = line.split(/\s*:\s*/)
[Proc.new {|n| eval(parts[0].sub(/=/,'=='))}, parts[1].to_i]
end]
end
def check(n)
#predicates.map { |p,r| [p.call(n) ? r : nil] }.compact
end
end
Here is sample usage:
p = PointChecker.new <<__HERE__
n = 0: 1
n = 1: 2
n < 5: 5
n = 5: 10
__HERE__
p.check(0) # => [1, 5]
p.check(1) # => [2, 5]
p.check(2) # => [5]
p.check(5) # => [10]
p.check(6) # => []
Of course, there are many issues with this implementation. I'm just offering a proof-of-concept. Depending on the scope of your application you might want to build a proper parser and runtime (instead of using eval), handle input more generally/gracefully, etc.
I'm not spending a lot of time on your problem, but here's my quick thought:
Since the points list is always in the format n1 op n2: val, I'd just model the points as an array of hashes.
So first step is to parse the input point list into the data structure, an array of hashes.
Each hash would have values n1, op, n2, value
Then, for each data input you run through all of the hashes (all of the points) and handle each (determining if it matches to the input data or not).
Some tricks of the trade
Spend time in your parser handling bad input. Eg
n < = 1000 # no colon
n < : 1000 # missing n2
x < 2 : 10 # n1, n2 and val are either number or "n"
n # too short, missing :, n2, val
n < 1 : 10x # val is not a number and is not "n"
etc
Also politely handle non-numeric input data
Added
Re: n1 doesn't matter. Be careful, this could be a trick. Why wouldn't
5 < n : 30
be a valid points list item?
Re: multiple arrays of hashes, one array per operator, one hash per point list item -- sure that's fine. Since each op is handled in a specific way, handling the operators one by one is fine. But....ordering then becomes an issue:
Since you want multiple results returned from multiple matching point list items, you need to maintain the overall order of them. Thus I think one array of all the point lists would be the easiest way to do this.