Since I don't quite know the language of these types of algorithms (i.e. how to google this), I'll just demonstrate what I'm looking for:
I have a three arrays (source arrays are of not equal lengths):
$array1 = array('A', 'B', 'C', 'D');
$array2 = array('x', 'y', 'z');
$array3 = array('1', '2', '3');
I would like all possible combinations of these arrays where:
No more than one element from each source array is taken.
The order of array1, array2, array3 is never broken (ABC always comes before xyz always comes before 123).
So the result would be:
array(
array('A', 'x', '1'),
array('A', 'x', '2'),
array('A', 'x', '3'),
array('A', 'y', '1'),
// etc ...
// But I also need all the partial sets, as long as the rule about
// ordering isn't broken i.e.:
array('B'),
array('B', 'x'),
array('B', 'x', '1'),
array('x'),
array('x', '1'),
array('1'),
);
The order of the results doesn't matter to me.
Working in php, but similar language or pseudo code is fine of course. Or I'd just take a tip on what specific types of permutation/combination algorithms I should be looking at.
I'd say these are Cartesian products. Generating them is quite easy.
for fixed number of arrays (in Perl):
for my $a(#arrayA) {
for my $b(#arrayB) {
push #result, [$a, $b];
}
}
general procedure: Assume #partial is an array for Cartesian product of A1 x A2 x ... x An and we want A1 x ... x An x An+1
for my $a(#partial) {
for my $b(#An_plus_1) {
push #result, [#$a, $b];
}
}
This would obviously need to iterate over all the arrays.
Now, that you want also to omit some of the elements in the sets, you just twist it a little. In the first method, you can just add another element to each of the arrays (undef is obvious choice, but anything will do) and then filter out these elements in the result sets. In the second method, it is even easier: You just add #partial and map { [$_] } #An_plus_1 to the result (or, in English, all the sets resulting from the partial Cartesian product of A1 x ... x An plus the single element sets made form the elements of the new set).
With RBarryYoung's hint, this is the shortest way to produce them, bash (and sed, to remove D, w, and 4):
echo {A..D}{w..z}{1..4} | sed 's/[Dw4]//g'
A1 A2 A3 A Ax1 Ax2 Ax3 Ax Ay1 Ay2 Ay3 Ay Az1 Az2 Az3 Az
B1 B2 B3 B Bx1 Bx2 Bx3 Bx By1 By2 By3 By Bz1 Bz2 Bz3 Bz
C1 C2 C3 C Cx1 Cx2 Cx3 Cx Cy1 Cy2 Cy3 Cy Cz1 Cz2 Cz3 Cz
1 2 3 x1 x2 x3 x y1 y2 y3 y z1 z2 z3 z
Another, easy way, is SQL, which does it by default:
SELECT upper, lower, num
FROM uppers, lowers, numbers
WHERE upper in ('A', 'B', 'C', ' ')
AND lower in (' ', 'x', 'y', 'z')
AND (number in (1, 2, 3) OR number IS NULL);
If your tables only contain 'A,B,C, ,' and 'x,y,z, ,' and '1,2,3, ' it is much shorter:
SELECT upper, lower, num
FROM uppers, lowers, numbers;
Another word, beside cartesian product, for this combinations is cross product.
For an unknown number of unknown size of Lists/Sequences/other collections, I would recommend an Iterator - if PHP has such things. Here is an implementation in Scala:
class CartesianIterator (val ll: Seq[Seq[_]]) extends Iterator [Seq[_]] {
var current = 0
def size = ll.map (_.size).product
lazy val last: Int = len
def get (n: Int, lili: Seq[Seq[_]]): List[_] = lili.length match {
case 0 => List ()
case _ => {
val inner = lili.head
inner (n % inner.size) :: get (n / inner.size, lili.tail)
}
}
override def hasNext () : Boolean = current != last
override def next (): Seq[_] = {
current += 1
get (current - 1, ll)
}
}
val ci = new CartesianIterator (List(List ('A', 'B', 'C', 'D', ' '), List ('x', 'y', 'z', ' '), List (1, 2, 3, 0)))
for (c <- ci) println (c)
List(A, x, 1)
List(B, x, 1)
List(C, x, 1)
List(D, x, 1)
List( , x, 1)
List(A, y, 1)
List(B, y, 1)
...
List( , z, 0)
List(A, , 0)
List(B, , 0)
List(C, , 0)
List(D, , 0)
List( , , 0)
A wrapper could be used to remove the '0' and ' ' from the output.
Related
I want to draw profiles similar to the ones at https://steeldoor.org like:
programmatically. (Probably in FreeSCAD, but possibly Python).
I want to define a global thickness and then specify the profiles with a vector of directions and another one of lengths (specifically the lengths of the outer edges). The vectors for the second drawing in the example might be ['N', 'E', 'S', 'W', 'S', 'W', 'N'] and [10,100,80,20,70,80,10].
Given a primitive for drawing a rectangle starting at (x,y) with length l and width (corresponding to line thickness) t, say, rect(x,y,l,t), which would draw a horizontal rectangle to the right with l and t positive, is there a generic algorithm that would take the two vectors as input and draw the profile using the primitive? I can solve for a specific profile, but not for the general case.
Perhaps you need something like this approach.
I assume that Y axis is up, and base line (current coordinate x,y) goes through middle of thick lines (E-F-K at the picture), rectangle is defined by left-down corner (points A and H), width and height.
dirs = ['N', 'E', 'S', 'W', 'S', 'W', 'N']
lens = [10,100,80,20,70,80,10]
half_thickness = 3
x = 0
y = 0
for i in range(dirs):
if dirs == 'N':
rect(x-half_thickness, y, 2*half_thickness, lens[i])
y += lens[i]
elif dirs == 'S':
rect(x-half_thickness, y - lens[i], 2*half_thickness, lens[i])
y -= lens[i]
elif dirs == 'E':
rect(x, y-half_thickness, lens[i], 2*half_thickness)
x += lens[i]
else:
rect(x - lens[i], y-half_thickness, lens[i], 2*half_thickness)
x -= lens[i]
I ended up using the answer above (fixing a mistake in the else clause). The Python code below generates the OpenSCAD file that in turn produces the desired profile.
def rect(x,y,w,h,hh):
print("translate([", x,",",y,"])")
print("\tlinear_extrude(height=",hh,")")
print("\t\tsquare([", w,",",h,"]);")
dirs = ['W', 'S', 'E', 'S', 'E', 'N', 'E', 'N', 'E', 'N', 'W']
lens = [14,68,14,6,90,13,35,7,13,68,14]
thickness = 4
height=20
ht = thickness/2;
x = 0
y = 0
li=0;
for dir in dirs:
len = lens[li]
li += 1
if dir == 'N':
rect(x-ht, y, 2*ht, len, height)
y += len
elif dir == 'S':
rect(x-ht, y - len, 2*ht, len, height)
y -= len
elif dir == 'E':
rect(x, y-ht, len, 2*ht, height)
x += len
else:
rect(x - len, y-ht, len, 2*ht, height)
x -= len
As I understand, it is related to the partition problem.
But I would like to ask a slightly different problem which I don't care about the sum but the average. In this case, it needs to optimize 2 constraints (sum and number of items) at the same time. It seems to be a harder problem and I cannot see any solutions online.
Are there any solutions for this variant? Or how does it relate to the partition problem?
Example:
input X = [1,1,1,1,1,6]
output based on sum: A = [1,1,1,1,1], B=[6]
output based on average: A = [1], B=[1,1,1,1,6]
On some inputs, a modification of the dynamic program for the usual partition problem will give a speedup. We have to classify each partial solution by its count and sum instead of just sum, which slows things down a bit. Python 3 below (note that the use of dictionaries implicitly collapses functionally identical partial solutions):
def children(ab, x):
a, b = ab
yield a + [x], b
yield a, b + [x]
def proper(ab):
a, b = ab
return a and b
def avg(lst):
return sum(lst) / len(lst)
def abs_diff_avg(ab):
a, b = ab
return abs(avg(a) - avg(b))
def min_abs_diff_avg(lst):
solutions = {(0, 0): ([], [])}
for x in lst:
solutions = {
(sum(a), len(a)): (a, b)
for ab in solutions.values()
for (a, b) in children(ab, x)
}
return min(filter(proper, solutions.values()), key=abs_diff_avg)
print(min_abs_diff_avg([1, 1, 1, 1, 1, 6]))
let S_i the sum of a subset of v of size i
let S be the total sum of v, n the length of v
the err to minimize is
err_i = |avg(S_i) - avg(S-S_i)|
err_i = |S_i/i - (S-S_i)/(n-i)|
err_i = |(nS_i - iS)/(i(n-i))|
algorithm below does:
for all tuple sizes (1,...,n/2) as i
- for all tuples of size i-1 as t_{i-1}
- generate all possible tuple of size i from t_{i-1} by adjoining one elem from v
- track best tuple in regard of err_i
The only cut I found being:
for two tuples of size i having the same sum, keep the one whose last element's index is the smallest
e.g given tuples A, B (where X is some taken element from v)
A: [X,....,X....]
B: [.,X,.....,X..]
keep A because its right-most element has the minimal index
(idea being that at size 3, A will offer the same candidates as B plus some more)
function generateTuples (v, tuples) {
const nextTuples = new Map()
for (const [, t] of tuples) {
for (let l = t.l + 1; l < v.length; ++l) {
const s = t.s + v[l]
if (!nextTuples.has(s) || nextTuples.get(s).l > l) {
const nextTuple = { v: t.v.concat(l), s, l }
nextTuples.set(s, nextTuple)
}
}
}
return nextTuples
}
function processV (v) {
const fErr = (() => {
const n = v.length
const S = v.reduce((s, x) => s + x, 0)
return ({ s: S_i, v }) => {
const i = v.length
return Math.abs((n * S_i - i * S) / (i * (n - i)))
}
})()
let tuples = new Map([[0, { v: [], s: 0, l: -1 }]])
let best = null
let err = 9e3
for (let i = 0; i < Math.ceil(v.length / 2); ++i) {
const nextTuples = generateTuples(v, tuples)
for (const [, t] of nextTuples) {
if (fErr(t) <= err) {
best = t
err = fErr(t)
}
}
tuples = nextTuples
}
const s1Indices = new Set(best.v)
return {
sol: v.reduce(([v1, v2], x, i) => {
(s1Indices.has(i) ? v1 : v2).push(x)
return [v1, v2]
}, [[], []]),
err
}
}
console.log('best: ', processV([1, 1, 1, 1, 1, 6]))
console.log('best: ', processV([1, 2, 3, 4, 5]))
console.log('best: ', processV([1, 3, 5, 7, 7, 8]))
Lets say I have an array declaration looking like this
array[1..5] of int: temp = [1,0,5,0,3];
Is there a way to initiate a new array looking the same as temp but without the 0's? The result would look like the following
[1,5,3]
or sort the array in such a way that the 0's would be either in the beginning or in the end of the array, which would be
[0,0,1,5,3]
or
[1,5,3,0,0]
Thanks
Even though Axel has answered this, I'll show another approach which - in my book is a little neater.
Case 1: the array ("temp") is a constant array.
Then one can simply write
array[int] of int: temp2 = [temp[i] | i in index_set(temp) where temp[i] != 0];
MiniZinc 2 (in contrast to version 1.*) don't need the size declaration if it can be calculated; it suffices to just use "array[int]". Also, "index_set" is used to be a little more general, e.g. to handle cases where the indices are from 0..4 (see the commented line).
Case 2: the array ("s") is an array of decision variables
If the array to handle is decision variables, we don't know (per definition) how many 0's there are and must rely on the alternative variant, namely to sort the array. One can then use the "sort" function, as shown in the model.
include "globals.mzn";
% constant
array[1..5] of int: temp = [1,0,5,0,3];
% array[0..4] of int: temp = array1d(0..4, [1,0,5,0,3]);
array[int] of int: temp2 = [temp[i] | i in index_set(temp) where temp[i] != 0];
% decision variables
array[1..5] of var int: s;
array[1..5] of var int: s2 = sort(s); % NOT CORRECT, see model below
solve satisfy;
constraint
s = [1,0,5,0,3]
;
% show our variables
output
[
"temp: \(temp)\n",
"temp2: \(temp2)\n",
"s: \(s)\n",
"s2: \(s2)\n",
];
Update
For the stable version of decision variables, this works what I can see. It calculating the position where to place this number depending on if "s[i]" is 0 or not. Not very pretty though.
int: n = 5;
array[1..n] of var 0..5: s;
array[1..n] of var lb_array(s)..ub_array(s): s2;
solve satisfy;
constraint
s = [1,0,5,0,3] /\
forall(i in 1..n) (
if s[i] != 0 then
s2[sum([s[j]!=0 | j in 1..i-1])+1] = s[i]
else
s2[sum([s[j]!=0 | j in 1..n]) + sum([s[j]=0 | j in 1..i-1])+1 ] = 0
endif
)
;
output
[
"s: \(s)\n",
"s2: \(s2)\n",
]
;
The output is
s: [1, 0, 5, 0, 3]
s2: [1, 5, 3, 0, 0]
Using MiniZinc 2, this can be done as follows:
array[1..5] of int: temp = [1,0,5,0,3];
% calculate upper bound of temp index
int: i_max = max(index_set(temp));
% use array comprehension to count non-zero elements
int: temp_non_zero = sum([1 | i in 1..i_max where temp[i] != 0]);
% copy non-zero elements to new array
array[1..temp_non_zero] of int: temp2 = [temp[i] | i in 1..i_max where temp[i] != 0];
% calculate upper bound for temp2 index
int: i2_max = max(index_set(temp2));
solve satisfy;
% show our variables
output
["\ni_max=" ++ show(i_max)]
++ ["\ni2_max=" ++ show(i2_max)]
++ ["\n" ++ show(temp2[i]) | i in 1..i2_max]
;
Another alternative:
A 1:1 mapping between the two arrays is established as an array of unique index values (= array positions). These indices are then sorted. The comparison weights elements higher if they point to a zero. Thus, the zero values are shifted to the back while leaving the order of the non-zero elements unchanged.
int: n = 5;
int: INF = 99999; % infinity
array[1..n] of var 0..5: s;
array[1..n] of var 1..n: map;
array[1..n] of var 0..5: s2;
solve satisfy;
% set s[]
constraint
s = [1,0,5,0,3]
;
% 1:1 mapping between s[] and s2[]
constraint
forall (i in 1..n) (
exists(j in 1..n) (
map[j] = i
)
)
;
constraint
forall(i in 1..n) (
s2[i] = s[map[i]]
)
;
% sort the map and move zero values to the back
constraint
forall(i in 1..n-1) (
(if s2[i] != 0 then map[i] else INF endif) <=
(if s2[i+1] != 0 then map[i+1] else INF endif)
)
;
output
[
"s: \(s)\n",
"map: \(map)\n",
"s2: \(s2)\n",
]
;
Output:
s: [1, 0, 5, 0, 3]
map: [1, 3, 5, 4, 2]
s2: [1, 5, 3, 0, 0]
I came across another codechef problem which I am attempting to solve in Scala. The problem statement is as follows:
Stepford Street was a dead end street. The houses on Stepford Street
were bought by wealthy millionaires. They had them extensively altered
so that as one progressed along the street, the height of the
buildings increased rapidly. However, not all millionaires were
created equal. Some refused to follow this trend and kept their houses
at their original heights. The resulting progression of heights was
thus disturbed. A contest to locate the most ordered street was
announced by the Beverly Hills Municipal Corporation. The criteria for
the most ordered street was set as follows: If there exists a house
with a lower height later in the street than the house under
consideration, then the pair (current house, later house) counts as 1
point towards the disorderliness index of the street. It is not
necessary that the later house be adjacent to the current house. Note:
No two houses on a street will be of the same height For example, for
the input: 1 2 4 5 3 6 The pairs (4,3), (5,3) form disordered pairs.
Thus the disorderliness index of this array is 2. As the criteria for
determining the disorderliness is complex, the BHMC has requested your
help to automate the process. You need to write an efficient program
that calculates the disorderliness index of a street.
A sample input output provided is as follows:
Input: 1 2 4 5 3 6
Output: 2
The output is 2 because of two pairs (4,3) and (5,3)
To solve this problem I thought I should use a variant of MergeSort,incrementing by 1 when the left element is greater than the right element.
My scala code is as follows:
def dysfunctionCalc(input:List[Int]):Int = {
val leftHalf = input.size/2
println("HalfSize:"+leftHalf)
val isOdd = input.size%2
println("Is odd:"+isOdd)
val leftList = input.take(leftHalf+isOdd)
println("LeftList:"+leftList)
val rightList = input.drop(leftHalf+isOdd)
println("RightList:"+rightList)
if ((leftList.size <= 1) && (rightList.size <= 1)){
println("Entering input where both lists are <= 1")
if(leftList.size == 0 || rightList.size == 0){
println("One of the lists is less than 0")
0
}
else if(leftList.head > rightList.head)1 else 0
}
else{
println("Both lists are greater than 1")
dysfunctionCalc(leftList) + dysfunctionCalc(rightList)
}
}
First off, my logic is wrong,it doesn't have a merge stage and I am not sure what would be the best way to percolate the result of the base-case up the stack and compare it with the other values. Also, using recursion to solve this problem may not be the most optimal way to go since for large lists, I maybe blowing up the stack. Also, there might be stylistic issues with my code as well.
I would be great if somebody could point out other flaws and the right way to solve this problem.
Thanks
Suppose you split your list into three pieces: the item you are considering, those on the left, and those on the right. Suppose further that those on the left are in a sorted set. Now you just need to walk through the list, moving items from "right" to "considered" and from "considered" to "left"; at each point, you look at the size of the subset of the sorted set that is greater than your item. In general, the size lookup can be done in O(log(N)) as can the add-element (with a Red-Black or AVL tree, for instance). So you have O(N log N) performance.
Now the question is how to implement this in Scala efficiently. It turns out that Scala has a Red-Black tree used for its TreeSet sorted set, and the implementation is actually quite simple (here in tail-recursive form):
import collection.immutable.TreeSet
final def calcDisorder(xs: List[Int], left: TreeSet[Int] = TreeSet.empty, n: Int = 0): Int = xs match {
case Nil => n
case x :: rest => calcDisorder(rest, left + x, n + left.from(x).size)
}
Unfortunately, left.from(x).size takes O(N) time (I believe), which yields a quadratic execution time. That's no good--what you need is an IndexedTreeSet which can do indexOf(x) in O(log(n)) (and then iterate with n + left.size - left.indexOf(x) - 1). You can build your own implementation or find one on the web. For instance, I found one here (API here) for Java that does exactly the right thing.
Incidentally, the problem with doing a mergesort is that you cannot easily work cumulatively. With merging a pair, you can keep track of how out-of-order it is. But when you merge in a third list, you must see how out of order it is with respect to both other lists, which spoils your divide-and-conquer strategy. (I am not sure whether there is some invariant one could find that would allow you to calculate directly if you kept track of it.)
Here is my try, I don't use MergeSort but it seems to solve the problem:
def calcDisorderness(myList:List[Int]):Int = myList match{
case Nil => 0
case t::q => q.count(_<t) + calcDisorderness(q)
}
scala> val input = List(1,2,4,5,3,6)
input: List[Int] = List(1, 2, 4, 5, 3, 6)
scala> calcDisorderness(input)
res1: Int = 2
The question is, is there a way to have a lower complexity?
Edit: tail recursive version of the same function and cool usage of default values in function arguments.
def calcDisorderness(myList:List[Int], disorder:Int=0):Int = myList match{
case Nil => disorder
case t::q => calcDisorderness(q, disorder + q.count(_<t))
}
A solution based on Merge Sort. Not super fast, potential slowdown could be in "xs.length".
def countSwaps(a: Array[Int]): Long = {
var disorder: Long = 0
def msort(xs: List[Int]): List[Int] = {
import Stream._
def merge(left: List[Int], right: List[Int], inc: Int): Stream[Int] = {
(left, right) match {
case (x :: xs, y :: ys) if x > y =>
cons(y, merge(left, ys, inc + 1))
case (x :: xs, _) => {
disorder += inc
cons(x, merge(xs, right, inc))
}
case _ => right.toStream
}
}
val n = xs.length / 2
if (n == 0)
xs
else {
val (ys, zs) = xs splitAt n
merge(msort(ys), msort(zs), 0).toList
}
}
msort(a.toList)
disorder
}
Another solution based on Merge Sort. Very fast: no FP or for-loop.
def countSwaps(a: Array[Int]): Count = {
var swaps: Count = 0
def mergeRun(begin: Int, run_len: Int, src: Array[Int], dst: Array[Int]) = {
var li = begin
val lend = math.min(begin + run_len, src.length)
var ri = begin + run_len
val rend = math.min(begin + run_len * 2, src.length)
var ti = begin
while (ti < rend) {
if (ri >= rend) {
dst(ti) = src(li); li += 1
swaps += ri - begin - run_len
} else if (li >= lend) {
dst(ti) = src(ri); ri += 1
} else if (a(li) <= a(ri)) {
dst(ti) = src(li); li += 1
swaps += ri - begin - run_len
} else {
dst(ti) = src(ri); ri += 1
}
ti += 1
}
}
val b = new Array[Int](a.length)
var run = 0
var run_len = 1
while (run_len < a.length) {
var begin = 0
while (begin < a.length) {
val (src, dst) = if (run % 2 == 0) (a, b) else (b, a)
mergeRun(begin, run_len, src, dst)
begin += run_len * 2
}
run += 1
run_len *= 2
}
swaps
}
Convert the above code to Functional style: no mutable variable, no loop.
All recursions are tail calls, thus the performance is good.
def countSwaps(a: Array[Int]): Count = {
def mergeRun(li: Int, lend: Int, rb: Int, ri: Int, rend: Int, di: Int, src: Array[Int], dst: Array[Int], swaps: Count): Count = {
if (ri >= rend && li >= lend) {
swaps
} else if (ri >= rend) {
dst(di) = src(li)
mergeRun(li + 1, lend, rb, ri, rend, di + 1, src, dst, ri - rb + swaps)
} else if (li >= lend) {
dst(di) = src(ri)
mergeRun(li, lend, rb, ri + 1, rend, di + 1, src, dst, swaps)
} else if (src(li) <= src(ri)) {
dst(di) = src(li)
mergeRun(li + 1, lend, rb, ri, rend, di + 1, src, dst, ri - rb + swaps)
} else {
dst(di) = src(ri)
mergeRun(li, lend, rb, ri + 1, rend, di + 1, src, dst, swaps)
}
}
val b = new Array[Int](a.length)
def merge(run: Int, run_len: Int, lb: Int, swaps: Count): Count = {
if (run_len >= a.length) {
swaps
} else if (lb >= a.length) {
merge(run + 1, run_len * 2, 0, swaps)
} else {
val lend = math.min(lb + run_len, a.length)
val rb = lb + run_len
val rend = math.min(rb + run_len, a.length)
val (src, dst) = if (run % 2 == 0) (a, b) else (b, a)
val inc_swaps = mergeRun(lb, lend, rb, rb, rend, lb, src, dst, 0)
merge(run, run_len, lb + run_len * 2, inc_swaps + swaps)
}
}
merge(0, 1, 0, 0)
}
It seems to me that the key is to break the list into a series of ascending sequences. For example, your example would be broken into (1 2 4 5)(3 6). None of the items in the first list can end a pair. Now you do a kind of merge of these two lists, working backwards:
6 > 5, so 6 can't be in any pairs
3 < 5, so its a pair
3 < 4, so its a pair
3 > 2, so we're done
I'm not clear from the definition on how to handle more than 2 such sequences.
I'm looking for an algorithm that will evenly distribute 1 to many items into three columns. No column can have more than one more item than any other column. I typed up an example of what I'm looking for below. Adding up Col1,Col2, and Col3 should equal ItemCount.
Edit: Also, the items are alpha-numeric and must be ordered within the column. The last item in the column has to be less than the first item in the next column.
Items Col1,Col2,Col3
A A
AB A,B
ABC A,B,C
ABCD AB,C,D
ABCDE AB,CD,E
ABCDEF AB,CD,EF
ABCDEFG ABC,DE,FG
ABCDEFGH ABC,DEF,GH
ABCDEFGHI ABC,DEF,GHI
ABCDEFHGIJ ABCD,EFG,HIJ
ABCDEFHGIJK ABCD,EFGH,IJK
Here you go, in Python:
NumCols = 3
DATA = "ABCDEFGHIJK"
for ItemCount in range(1, 12):
subdata = DATA[:ItemCount]
Col1Count = (ItemCount + NumCols - 1) / NumCols
Col2Count = (ItemCount + NumCols - 2) / NumCols
Col3Count = (ItemCount + NumCols - 3) / NumCols
Col1 = subdata[:Col1Count]
Col2 = subdata[Col1Count:Col1Count+Col2Count]
Col3 = subdata[Col1Count+Col2Count:]
print "%2d %5s %5s %5s" % (ItemCount, Col1, Col2, Col3)
# Prints:
# 1 A
# 2 A B
# 3 A B C
# 4 AB C D
# 5 AB CD E
# 6 AB CD EF
# 7 ABC DE FG
# 8 ABC DEF GH
# 9 ABC DEF GHI
# 10 ABCD EFG HIJ
# 11 ABCD EFGH IJK
This answer is now obsolete because the OP decided to simply change the question after I answered it. I’m just too lazy to delete it.
function getColumnItemCount(int items, int column) {
return (int) (items / 3) + (((items % 3) >= (column + 1)) ? 1 : 0);
}
This question was the closest thing to my own that I found, so I'll post the solution I came up with. In JavaScript:
var items = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
var columns = [[], [], []]
for (var i=0; i<items.length; i++) {
columns[Math.floor(i * columns.length / items.length)].push(items[i])
}
console.log(columns)
just to give you a hint (it's pretty easy, so figure out yourself)
divide ItemCount by 3, rounding down. This is what is at least in every column.
Now you do ItemCount % 3 (modulo), which is either 1 or 2 (because else it would be dividable by 3, right) and you distribute that.
I needed a C# version so here's what I came up with (the algorithm is from Richie's answer):
// Start with 11 values
var data = "ABCDEFGHIJK";
// Split in 3 columns
var columnCount = 3;
// Find out how many values to display in each column
var columnCounts = new int[columnCount];
for (int i = 0; i < columnCount; i++)
columnCounts[i] = (data.Count() + columnCount - (i + 1)) / columnCount;
// Allocate each value to the appropriate column
int iData = 0;
for (int i = 0; i < columnCount; i++)
for (int j = 0; j < columnCounts[i]; j++)
Console.WriteLine("{0} -> Column {1}", data[iData++], i + 1);
// PRINTS:
// A -> Column 1
// B -> Column 1
// C -> Column 1
// D -> Column 1
// E -> Column 2
// F -> Column 2
// G -> Column 2
// H -> Column 2
// I -> Column 3
// J -> Column 3
// K -> Column 3
It's quite simple
If you have N elements indexed from 0 to N-1 and column indexed from 0to 2, the i-th element will go in column i mod 3 (where mod is the modulo operator, % in C,C++ and some other languages)
Do you just want the count of items in each column? If you have n items, then
the counts will be:
round(n/3), round(n/3), n-2*round(n/3)
where "round" round to the nearest integer (e.g. round(x)=(int)(x+0.5))
If you want to actually put the items there, try something like this Python-style pseudocode:
def columnize(items):
i=0
answer=[ [], [], [] ]
for it in items:
answer[i%3] += it
i += 1
return answer
Here's a PHP version I hacked together for all the PHP hacks out there like me (yup, guilt by association!)
function column_item_count($items, $column, $maxcolumns) {
return round($items / $maxcolumns) + (($items % $maxcolumns) >= $column ? 1 : 0);
}
And you can call it like this...
$cnt = sizeof($an_array_of_data);
$col1_cnt = column_item_count($cnt,1,3);
$col2_cnt = column_item_count($cnt,2,3);
$col3_cnt = column_item_count($cnt,3,3);
Credit for this should go to #Bombe who provided it in Java (?) above.
NB: This function expects you to pass in an ordinal column number, i.e. first col = 1, second col = 2, etc...