I am wondering how to get an array in Ruby with all combinations of positive and negative values based on the values in an input array. Order does not matter, but must be accommodating to input arrays of all sizes. It must be flexible so negative numbers could be in the input, even though I didn't include them in the example below.
For example:
input:
a = [1,2,3,4]
output:
b = [[1,2,3,4],[1,2,3,-4],[1,2,-3,-4],[-1,2,-3,-4]...[1,-2,3,-4],[1,-2,-3,-4],[-1,-2,-3,-4],[-1,-2,-3,4],[-1,-2,3,4],[-1,2,3,4]]
Your help is much appreciated!
You'll notice, as you iterate through the positives and negatives, that the pattern with which you apply the negativity is the same as the pattern you would use to increment bits as you count in binary. This is because each index in the array can have one of two values (positive or negative), just like each bit in a binary number can have one of two values (0 or 1). So, the easy solution is to map 0 and 1 to positive and negative. Then we can just do normal Ruby iterating, and check the bit at the element's corresponding index.
a = [1,2,3,4] # => [1, 2, 3, 4]
signed = [a, a.map(&:-#)] # => [[1, 2, 3, 4], [-1, -2, -3, -4]]
(0...2**a.size).each do |n| # => 0...16
p Array.new(a.size) { |i| signed[n[i]][i] } # => [1, 2, 3, 4], [-1, 2, 3, 4], [1, -2, 3, 4], [-1, -2, 3, 4], [1, 2, -3, 4], [-1, 2, -3, 4], [1, -2, -3, 4], [-1, -2, -3, 4], [1, 2, 3, -4], [-1, 2, 3, -4], [1, -2, 3, -4], [-1, -2, 3, -4], [1, 2, -3, -4], [-1, 2, -3, -4], [1, -2, -3, -4], [-1, -2, -3, -4]
end # => 0...16
# >> [1, 2, 3, 4]
# >> [-1, 2, 3, 4]
# >> [1, -2, 3, 4]
# >> [-1, -2, 3, 4]
# >> [1, 2, -3, 4]
# >> [-1, 2, -3, 4]
# >> [1, -2, -3, 4]
# >> [-1, -2, -3, 4]
# >> [1, 2, 3, -4]
# >> [-1, 2, 3, -4]
# >> [1, -2, 3, -4]
# >> [-1, -2, 3, -4]
# >> [1, 2, -3, -4]
# >> [-1, 2, -3, -4]
# >> [1, -2, -3, -4]
# >> [-1, -2, -3, -4]
Another way (a variant of #JoshuaCheek's answer):
a = [1,2,3,4]
n = a.size
(2**n).times.map { |i|
("%0#{n}b" % i).split('').zip(a).map { |b,e| (b=='1') ? e : -e } }
#=> [[-1, -2, -3, -4], [-1, -2, -3, 4], [-1, -2, 3, -4], [-1, -2, 3, 4],
# [-1, 2, -3, -4], [-1, 2, -3, 4], [-1, 2, 3, -4], [-1, 2, 3, 4],
# [ 1, -2, -3, -4], [ 1, -2, -3, 4], [ 1, -2, 3, -4], [ 1, -2, 3, 4],
# [ 1, 2, -3, -4], [ 1, 2, -3, 4], [ 1, 2, 3, -4], [ 1, 2, 3, 4]]
While other answers already dabbled in the Array methods, Array#repeated_permutation is what is really needed here:
[ 1, -1 ].repeated_permutation( 4 ).map { |p| [ 1, 2, 3, 4 ].zip( p ).map { |u, v| u * v } }
The below approach works - basically, we use a bit mask between binary 0000 to 1111 (or decimal 0 to 15) to decide which numbers should be negative (0 is positive, 1 is negative) - more details in the comments in the code below :
require 'pp'
result = []
# consider binary number mask from binary 0000 to 1111,
# where each digit if 0 uses the positive number, and if 1 uses the negative number
(0..15).each do |mask|
combin = [] # each combination
# Next, loop through the four place values (1,2,4,8)
(0..3).each do |pwr|
pv = 2 ** pwr # each place value
if ((mask & pv) == pv) # if the mask has the bit set at this place value,
combin << -(pwr + 1) # use the negative of the number (pwr + 1 gives 1, 2, 3, 4 nicely)
else # if mask doesn't have the bit set at this place value
combin << (pwr + 1) # use the positive value of the number
end
end
result << combin
end
pp result
# Output:
# [[1, 2, 3, 4],
# [-1, 2, 3, 4],
# [1, -2, 3, 4],
# [-1, -2, 3, 4],
# [1, 2, -3, 4],
# [-1, 2, -3, 4],
# [1, -2, -3, 4],
# [-1, -2, -3, 4],
# [1, 2, 3, -4],
# [-1, 2, 3, -4],
# [1, -2, 3, -4],
# [-1, -2, 3, -4],
# [1, 2, -3, -4],
# [-1, 2, -3, -4],
# [1, -2, -3, -4],
# [-1, -2, -3, -4]]
array=[]
[1,-1].each do |a|
[2,-2].each do |b|
[3,-3].each do |c|
[4,-4].each do |d|
array<<[a,b,c,d]
end
end
end
end
I'll elaborate on a more complete amswer as soon as I get my hands on a keyboard
Update:
A lot of good answer, here is the recursive way:
#input = [1,2,3,4] # or whatever
#output = []
def pos_neg(ind,in_array)
a=#input[ind]
[a,-a].each do |b|
arr=in_array.dup
arr[ind]=b
if #input.size > ind+1
pos_neg(ind+1,arr)
else
#output << arr
end
end
end
Then, you run:
pos_neg(0,[])
#output
[[1, 2, 3, 4],
[1, 2, 3, -4],
[1, 2, -3, 4],
[1, 2, -3, -4],
[1, -2, 3, 4],
[1, -2, 3, -4],
[1, -2, -3, 4],
[1, -2, -3, -4],
[-1, 2, 3, 4],
[-1, 2, 3, -4],
[-1, 2, -3, 4],
[-1, 2, -3, -4],
[-1, -2, 3, 4],
[-1, -2, 3, -4],
[-1, -2, -3, 4],
[-1, -2, -3, -4]]
Well, Ruby conveniently includes a combination method on the Array object, but first you need to create the opposite values for the numbers in the original array:
a = [1,2,3,4]
b = a.map(&:-#)
Then you'd want to concatenate the two arrays into a single array:
c = a + b
And finally you can call the combination method of the array that contains all the positive and negative values:
c.combination(4).to_a # => [[1,2,3,4], [1,2,3,-1], ...]
Here is the documentation for the combination method.
Update: I like what Boris Stitnicky came up with. Here's a variation on that:
a = [1,2,3,4]
def sign_permutations(arr)
[1, -1].repeated_permutation(arr.length).map do |signs|
signs.map.with_index do |sign, index|
arr[index] * sign
end
end
end
puts sign_permutations(a).inspect
Related
I have written the following program to find the permutation of all the elements in an array. The values are created properly but the problem occurs when I try to assign the generated sequence into a new array. The old values will get cleared and the new values are copied as per the array size
def find_perm(nums, answer, set)
if nums.empty?
p set
answer.push(set)
p answer.object_id
p answer
return true
end
for i in (0..nums.length - 1) do
new_nums = nums.clone
new_nums.delete_at(i)
set.push(nums[i])
find_perm(new_nums, answer, set)
set.pop
end
end
def permute(nums)
answer = []
set = []
element = find_perm(nums, answer, set)
return element
end
permute([1,2,3])
This are the observations that I have found out while debugging:
[1, 2, 3]
47167191669680
[[1, 2, 3]]
[1, 3, 2]
47167191669680
[[1, 3, 2], [1, 3, 2]]
[2, 1, 3]
47167191669680
[[2, 1, 3], [2, 1, 3], [2, 1, 3]]
[2, 3, 1]
47167191669680
[[2, 3, 1], [2, 3, 1], [2, 3, 1], [2, 3, 1]]
[3, 1, 2]
47167191669680
[[3, 1, 2], [3, 1, 2], [3, 1, 2], [3, 1, 2], [3, 1, 2]]
[3, 2, 1]
47167191669680
[[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1]]
The problem was each time I was pushing the same set array into the answer array so each element in the answer array will have same reference (same object_id).
Solution: Is to clone the set array during the each push to the answer array so that each element have different reference.
The solution:
def fact(n)
return 1 if n == 1
n*fact(n-1)
end
def find_perm(nums, answer, set)
if nums.empty?
answer.push(set.clone)
end
for i in (0..nums.length - 1) do
new_nums = nums.clone
new_nums.delete_at(i)
set.push(nums[i])
find_perm(new_nums, answer, set)
set.pop
return answer if fact(nums.count) == answer.count
end
end
def permute(nums)
answer = []
set = []
element = find_perm(nums, answer, set)
return element
end
p permute([1,2,3])
I believe the approach you are taking is similar to the following.
Suppose we wish to obtain the permutations of the elements of the array
arr = [1, 2, 3, 4]
Begin with the array
[4]
This array has only a single perumutation:
perms3 = [[4]]
(3 in perms3 denotes the index of 4 in arr.) Now obtain the permuations of
[3, 4]
We see that is
perms2 = [[3, 4], [4, 3]]
We simply take each element of perms3 ([4] is the only one) and create two permuations by inserting 3 before 4 and then 3 after 4;
Now suppose the array were
[2, 3, 4]
Then
perms1 = [[2, 3, 4], [3, 2, 4], [3, 4, 2], [2, 4, 3], [4, 2, 3], [4, 3, 2]]
We create three 3-element arrays from [3, 4], one by inserting 2 before 3, one by inserting 2 between 3 and 4 and one by inserting 2 after 4. Simlarly, three 3-element arrays are generated from [4, 3] in a simlar way. This generates six arrays. (Indeed, 3! #=> 6).
Lastly we generating the 4! #=> 24 permuations of [1, 2, 3, 4] by inserting 1 in four locations of each element of perm1, the first four derived from perms1[0] #=> [2, 3, 4]:
[[1, 2, 3, 4], [2, 1, 3, 4], [2, 3, 1, 4], [2, 3, 4, 1]]
We can do this in code as follows.
def my_permutations(arr)
perms = [[arr.last]]
(arr.size-2).downto(0) do |i|
x = arr[i]
perms = perms.flat_map do |perm|
(0..(perm.size)).map { |i| perm.dup.insert(i, x) }
end
end
perms
end
my_permutations(arr)
#=> [[1, 2, 3, 4], [2, 1, 3, 4], [2, 3, 1, 4], [2, 3, 4, 1], [1, 3, 2, 4],
# [3, 1, 2, 4], [3, 2, 1, 4], [3, 2, 4, 1], [1, 3, 4, 2], [3, 1, 4, 2],
# [3, 4, 1, 2], [3, 4, 2, 1], [1, 2, 4, 3], [2, 1, 4, 3], [2, 4, 1, 3],
# [2, 4, 3, 1], [1, 4, 2, 3], [4, 1, 2, 3], [4, 2, 1, 3], [4, 2, 3, 1],
# [1, 4, 3, 2], [4, 1, 3, 2], [4, 3, 1, 2], [4, 3, 2, 1]]
See Enumerable#flat_map and Array#insert. Note that we need to make a copy of the array perm before invoking insert.
We could of course have gone "forward" in arr (starting with [[1]]), rather than "backward", though the elements of the array of permutations would be ordered differently.
Say I have the following input:
inp = [2, 9, 3]
I need output as all tuples in mixed counting, like this:
outp = [[0, 0, 0], [0, 0, 1], [0, 0, 2], [0, 1, 0], [0, 1, 1], ..., [1, 8, 2]]
I know algorithm from Knuth vol 4a as direct loop solution, but I've heard ruby has some magic inside.
I am mostly C++ developer. My direct solution now looks like:
inparr = [2, 9, 3]
bmix = Array.new(inparr.size) { |i| 0 }
outp = Array.new
loop do
# some debug output
puts bmix.to_s
#visit next tuple
outp << bmix.clone
digit = inparr.size
while digit > 0 do
digit -= 1
if bmix[digit] + 1 < inparr[digit]
bmix[digit] += 1
break
end
bmix[digit] = 0
end
break if (bmix.select{|x| x != 0}.empty?)
end
How to rewrite it in several simple lines?
inp.
map { |i| (0...i).to_a }.
reduce(&:product).
map(&:flatten)
Used operations: Range, Enumerable#map, Enumerable#reduce, Array#product, Array#flatten.
You could use recursion.
def recurse(inp)
first, *rest = inp
rest.empty? ? [*0..first-1] : (0..first-1).flat_map do |e|
recurse(rest).map { |arr| [e, *arr] }
end
end
recurse [2, 4, 3]
#=> [[0, 0, 0], [0, 0, 1], [0, 0, 2],
# [0, 1, 0], [0, 1, 1], [0, 1, 2],
# [0, 2, 0], [0, 2, 1], [0, 2, 2],
# [0, 3, 0], [0, 3, 1], [0, 3, 2],
# [1, 0, 0], [1, 0, 1], [1, 0, 2],
# [1, 1, 0], [1, 1, 1], [1, 1, 2],
# [1, 2, 0], [1, 2, 1], [1, 2, 2],
# [1, 3, 0], [1, 3, 1], [1, 3, 2]]
If first, *rest = [2,4,3], then first #=> 2 and rest #=> [4,3].
See Enumerable#flat_map and Array#map. a ? b : c is called a ternery expression.
If e #=> 1 and arr #=> [2,1] then [e, *arr] #=> [1,2,1].
I will go to great lengths to avoid the use of Array#flatten. It's irrational, but to me it's an ugly method. That's usually possible using flat_map and/or the splat operator *.
Here's a mix of the 2 existing answers. It might be a bit more concise and readable:
head, *rest = inp.map{ |n| n.times.to_a }
head.product(*rest)
As an example:
inp = [2, 4, 3]
# => [2, 4, 3]
head, *rest = inp.map{ |n| n.times.to_a }
# => [[0, 1], [0, 1, 2, 3], [0, 1, 2]]
head.product(*rest)
# => [[0, 0, 0], [0, 0, 1], [0, 0, 2], [0, 1, 0], [0, 1, 1], [0, 1, 2], [0, 2, 0], [0, 2, 1], [0, 2, 2], [0, 3, 0], [0, 3, 1], [0, 3, 2], [1, 0, 0], [1, 0, 1], [1, 0, 2], [1, 1, 0], [1, 1, 1], [1, 1, 2], [1, 2, 0], [1, 2, 1], [1, 2, 2], [1, 3, 0], [1, 3, 1], [1, 3, 2]]
So what Im trying to accomplish is write a (shorter) condition that makes sure each element is different from the other array. This is confusing but I hope this example clears it up.
array = [1, 2, 3]
new_array = array.shuffle
until array[0] != new_array[0] &&
array[1] != new_array[1] &&
array[2] != new_array[2]
new_array = array.shuffle
end
So what Im doing is making sure that every single element/index pair does not match in the other array.
# [1, 2, 3] => [3, 1, 2] yayyyy
# [1, 2, 3] => [3, 2, 1] not what I want because the 2 didnt move
Is there a better way to do what I want to do? Ive looked up the .any? and .none? but I cant seem to figure out how to implement them. Thanks!
I would do this:
array.zip(new_array).all? { |left, right| left != right }
Here are two approaches that do not involve repeated sampling until a valid sample is obtained:
Sample from the population of valid permutations
Construct the population from which you are sampling:
array = [1, 2, 3, 4]
population = array.permutation(array.size).reject do |a|
a.zip(array).any? { |e,f| e==f }
end
#=> [[2, 1, 4, 3], [2, 3, 4, 1], [2, 4, 1, 3], [3, 1, 4, 2], [3, 4, 1, 2],
# [3, 4, 2, 1], [4, 1, 2, 3], [4, 3, 1, 2], [4, 3, 2, 1]]
Then just choose one at random:
10.times { p population.sample }
# [4, 3, 1, 2]
# [3, 4, 1, 2]
# [3, 4, 1, 2]
# [4, 3, 1, 2]
# [2, 1, 4, 3]
# [2, 1, 4, 3]
# [4, 1, 2, 3]
# [2, 1, 4, 3]
# [4, 3, 1, 2]
# [3, 4, 1, 2]
Sequentially sample for each position in the array
def sample_no_match(array)
a = array.each_index.to_a.shuffle
last_ndx = a[-1]
a.dup.map do |i|
if a.size == 2 && a[-1] == last_ndx
select = a[-1]
else
select = (a-[i]).sample
end
a.delete(select)
array[select]
end
end
10.times.each { p sample_no_match(array) }
# [2, 4, 3, 1]
# [4, 3, 1, 2]
# [2, 1, 3, 4]
# [1, 3, 4, 2]
# [1, 3, 2, 4]
# [1, 3, 2, 4]
# [1, 4, 3, 2]
# [3, 4, 2, 1]
# [1, 3, 4, 2]
# [1, 3, 4, 2]
I have been unable to prove or disprove that the second method produces a random sample. We can, however, determine relative frequencies of outcomes:
n = 500_000
h = n.times.with_object(Hash.new(0)) { |_,h| h[sample_no_match(array)] += 1 }
h.keys.each { |k| h[k] = (h[k]/(n.to_f)).round(4) }
h #=> {[1, 2, 3, 4]=>0.0418, [2, 1, 3, 4]=>0.0414, [1, 4, 2, 3]=>0.0418,
# [3, 4, 2, 1]=>0.0417, [4, 3, 2, 1]=>0.0415, [3, 1, 4, 2]=>0.0419,
# [2, 3, 1, 4]=>0.0420, [4, 2, 3, 1]=>0.0417, [3, 2, 1, 4]=>0.0413,
# [4, 2, 1, 3]=>0.0417, [2, 1, 4, 3]=>0.0419, [1, 3, 2, 4]=>0.0415,
# [1, 2, 4, 3]=>0.0418, [1, 3, 4, 2]=>0.0417, [2, 4, 1, 3]=>0.0414,
# [3, 4, 1, 2]=>0.0412, [1, 4, 3, 2]=>0.0423, [4, 1, 3, 2]=>0.0411,
# [3, 2, 4, 1]=>0.0411, [2, 4, 3, 1]=>0.0418, [3, 1, 2, 4]=>0.0419,
# [4, 3, 1, 2]=>0.0412, [4, 1, 2, 3]=>0.0421, [2, 3, 4, 1]=>0.0421}
avg = (h.values.reduce(:+)/h.size.to_f).round(4)
#=> 0.0417
mn, mx = h.values.minmax
#=> [0.0411, 0.0423]
([avg-mn,mx-avg].max/avg).round(6)
#=> 0.014388
which means that the maximum deviation from the average was only 1.4% percent of the average.
This suggests that the second method is a reasonable way of producing pseudo-random samples.
Initially, the first line of this method was:
a = array.each_index.to_a
By looking at the frequency distribution for outcomes, however, it was clear that that method did not produce a pseudo-random sample; hence, the need to shuffle a.
Here's one possibility:
until array.zip(new_array).reject{ |x, y| x == y }.size == array.size
new_array = array.shuffle
end
Note, though, that it will break for arrays like [1] or [1, 1, 1, 2, 3], where the number of instances of 1 exceeds half the size of the array. Recommend Array#uniq or similar, along with checking for arrays of sizes 0 or 1, depending on how trustworthy your input is!
Normal uniq:
[1, 2, 3, 1, 1, 4].uniq => [1, 2, 3, 4]
I want to replace the duplicate with a replacement at where it was.
Is there a method or way to achieve something like this?
[1, 2, 3, 1, 1, 4].uniq_with_replacement(-1) => [1, 2, 3, -1, -1, 4]
Thanks in advance!
Here's a one-liner:
a.fill{ |i| a.index(a[i]) == i ? a[i] : -1 }
Something like this?:
class Array
def uniq_with_replacement(v)
map.with_object([]){|value, obj| obj << (obj.include?(value) ? v : value) }
end
end
Now:
[1, 2, 3, 1, 1, 4].uniq_with_replacement(-1)
# => [1, 2, 3, -1, -1, 4]
[1, 2, 3, 1, 1, 2, 4].uniq_with_replacement(-1)
# => [1, 2, 3, -1, -1, -1, 4]
1 more:
arr = [1, 2, 3, 1, 1, 4]
value = -1
a = arr.each_with_index.to_a
#=> [[1, 0], [2, 1], [3, 2], [1, 3], [1, 4], [4, 5]]
b = (a - a.uniq(&:first)).map(&:last)
#=> [3, 4]
arr.map.with_index { |e,i| b.include?(i) ? value : e }
#=> [1, 2, 3, -1, -1, 4]
Given a set C with n elements (duplicates allowed) and a partition P of n
P = {i1, i2, ... / i1+i2+... = n}
how many different decompositions of C in subsets of size i1, i2, ... are there ?
Example :
C = {2 2 2 3}
P = {2 2}
C = {2 2} U {2 3}
P = {1 1 2}
C = {2} U {2} U {2 3}
C = {2} U {3} U {2 2}
P = {1 3}
C = {2} U {2 2 3}
C = {3} U {2 2 2}
I have a solution, but it is inefficient when C has more than a dozen of elements.
Thanks in advance
Philippe
The fact that the order of decomposition does not matter to you makes it much harder. That is, you are viewing {2 2} U {2 3} as the same as {2 3} U {2 2}. Still I have an algorithm that is better than what you have, but is not great.
Let me start it with a realistically complicated example. Our set will be A B C D E F F F F G G G G. The partition will be 1 1 1 1 2 2 5.
My first simplification will be to represent the information we care about in the set with the data structure [[2, 4], [5, 1]], meaning 2 elements are repeated 4 times, and 5 are repeated once.
My second apparent complication will be to represent the partition with [[5, 1, 1], [2, 2, 1], [4, 1, 1]. The pattern may not be obvious. Each entry is of the form [size, count, frequency]. So the one distinct instance of 2 partitions of size 2 turn into [2, 2, 1]. We're not using frequency yet, but it is counting distinguishable piles of the same size and commonness.
Now we're going to recurse as follows. We'll take the most common element, and find all of the ways to use it up. So in our case we take one of the piles of size 4, and find that we can divide it as follows, rearranging each remaining partition strategy in lexicographic order:
[4] leaving [[1, 1, 1], [2, 2, 1], [1, 4, 1]] = [[2, 2, 1], [1, 4, 1], [1, 1, 1]].
[3, [1, 0], 0] leaving [[2, 1, 1], [1, 1, 1], [2, 1, 1], [1, 4, 1]] = [[2, 1, 2], [1, 4, 1], [1, 1, 1]. (Note that we're now using frequency.)
[3, 0, 1] leaving [[2, 1, 1], [2, 2, 1], [0, 1, 1], [1, 3, 1]] = [[2, 2, 1], [2, 1, 1], [1, 3, 1]]
[2, [2, 0], 0] leaving [[3, 1, 1], [0, 1, 1], [2, 1, 1], [1, 4, 1]] = [[3, 1, 1], [2, 1, 1], [1, 4, 1]]
[2, [1, 1], 0] leaving [[3, 1, 1], [1, 2, 1], [1, 4, 1]] = [[3, 1, 1], [1, 4, 1], [1, 2, 1]]
[2, [1, 0], [1]] leaving [[3, 1, 1], [1, 1, 1], [2, 1, 1], [0, 1, 1], [1, 3, 1]] = [[3, 1, 1], [2, 1, 1], [1, 4, 1], [1, 1, 1]]
[2, 0, [1, 1]] leaving `[[3, 1, 1], [2, 2, 1], [0, 2, 1], [1, 2, 1]] = [[3, 1, 1], [2, 2, 1], [1, 2, 1]]1
[1, [2, 1]] leaving [[4, 1, 1], [0, 1, 1], [1, 1, 1], [1, 4, 1]] = [[4, 1, 1], [1, 4, 1], [1, 1, 1]]
[1, [2, 0], [1]] leaving [[4, 1, 1], [0, 1, 1], [2, 1, 1], [0, 1, 1], [1, 3, 1]] = [[4, 1, 1], [2, 1, 1], [1, 3, 1]]
[1, [1, 0], [1, 1]] leaving [[4, 1, 1], [1, 1, 1], [2, 1, 1], [0, 2, 1], [1, 2, 1]] = [[4, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 1]]
[1, 0, [1, 1, 1]] leaving [[4, 1, 1], [2, 2, 1], [0, 3, 1], [1, 1, 1]] = [[4, 1, 1], [2, 2, 1], [1, 1, 1]]
[0, [2, 2]] leaving [[5, 1, 1], [0, 2, 1], [1, 4, 1]] = [[5, 1, 1], [1, 4, 1]]
[0, [2, 1], [1]] leaving [[5, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [1, 3, 1]] = [[5, 1, 1], [1, 3, 1], [1, 1, 1]]
[0, [2, 0], [1, 1]] leaving [[5, 1, 1], [0, 2, 1], [2, 1, 1], [0, 2, 1], [1, 2, 1]] = [[5, 1, 1], [2, 1, 1], [1, 2, 1]]
[0, [1, 1], [1, 1]] leaving [[5, 1, 1], [1, 2, 1], [0, 2, 1], [1, 2, 1]] = [[5, 1, 1,], [1, 2, 2]]
[0, [1, 0], [1, 1, 1]] leaving [[5, 1, 1], [1, 1, 1], [2, 1, 1], [0, 3, 1], [1, 1, 1]] = [[5, 1, 1], [2, 1, 1], [1, 1, 2]]
[0, 0, [1, 1, 1, 1]] leaving [[5, 1, 1], [2, 2, 1], [0, 4, 1]] = [[5, 1, 1], [2, 2, 1]]
Now each of those subproblems can be solved recursively. This may feel like we're on the way to constructing them all, but we aren't, because we memoize the recursive steps. It turns out that there are a lot of ways that the first two groups of 8 can wind up with the same 5 left overs. With memoization we don't need to repeatedly recalculate those solutions.
That said, we'll do better. Groups of 12 elements should not pose a problem. But we're not doing that much better. I wouldn't be surprised if it starts breaking down somewhere around groups of 30 or so elements with interesting sets of partitions. (I haven't coded it. It may be fine at 30 and break down at 50. I don't know where it will break down. But given that you're iterating over sets of partitions, at some fairly small point it will break down.)
All partitions can be found in 2 stages.
First: from P make new ordered partition of n, P_S={P_i1, P_i2, ..., P_ip}, summing identical i's.
P = {1, 1, 1, 1, 2, 2, 5}
P_S = (4, 4, 5)
Make partitions {C_i1, C_i2, ..., C_ip} of C with respect to P_S. Note, C_ix is multi-set like C. It is partitioning C into multi-sets by sizes of final partitions.
Second: for each {C_i1, C_i2, ..., C_ip} and for each ix, x={1,2,...,p} find number of partitions of C_ix into t (number of ix's in P) sets with ix elements. Call this number N(C_ix,ix,t).
Total number of partitions is:
sum by all {C_i1, C_i2, ..., C_ip} ( product N(C_ix,ix,t) ix={1,2,...,p} )
First part can be done recursively quite simple. Second is more complicated. Partition of multi-set M into n parts with k elements is same as finding all partially sorted list with elements from M. Partially order list is of type:
a_1_1, a_1_2, ..., a_1_k, a_2_1, a_2_2, ..., a_2_k, ....
Where a_i_x <= a_i_y if x < y and (a_x_1, a_x_2, ..., a_x_k) lexicographic <= (a_y_1, a_y_2, ..., a_y_k) if x < y. With these 2 conditions it is possible to create all partition from N(C_ix,ix,t) recursively.
For some cases N(C_ix,ix,t) is easy to calculate. Define |C_ix| as number of different elements in multi-set C_ix.
if t = 1 than 1
if |C_ix| = 1 than 1
if |C_ix| = 2 than (let m=minimal number of occurrences of elements in C_ix) floor(m/2) + 1
in general if |C_ix| = 2 than partition of m in numbers <= t.