Rust 2D sub-window iterator - matrix

I am using a Vec to store a 2D (row major) matrix of values. I would like to iterate over this matrix with a sliding 2D sub-window to apply a filter (which is unfortunately non-separable).
I have seen in the slice documentation that a windows function exists, which is what I want but in 2 dimensions.
I thought about implementing this as:
fn main() {
// 4 rows 3 columns
let dim: (usize, usize) = (4, 3);
// Place-holder matrix
#[rustfmt::skip]
let mat = vec![0, 1, 2,
3, 4, 5,
6, 7, 8,
9, 10, 11];
// 2D index to 1D index
let linearize = |r, c| r * dim.1 + c;
// The dimensions of my sub-window
let win_size: usize = 2;
// Calculate the bounds for which the top left corner of each window may exist
let bounds: (usize, usize) = (dim.0 - win_size + 1, dim.1 - win_size + 1);
// Convert window 1D index into a 2D index
let split = |i| (i / win_size, i % win_size);
// Iterate over all the top left corners
let window_2d = (0..bounds.0 * bounds.1).map(|i| {
// Get the 2D index of the top left corner
let (r, c) = (i / bounds.1, i % bounds.1);
// Borrow the matrix, so our closure may own the reference
let bmat = &mat;
// Return an iterator for this window
return (0..win_size * win_size).map(move |x| {
let (wr, wc) = split(x);
return bmat[linearize(wr + r, wc + c)];
});
});
// Print the windows out
window_2d.for_each(|it| {
print!("[ ");
it.for_each(|x| print!("{} ", x));
println!("]");
});
}
Essentially creating an iterator over a range of indices and then mapping to the square bracket operator of the matrix.
As far as I know, this is going to have the overhead of a bounds check for each deref of the iterator.
I'm wondering if there's an alternative which would elide the bounds checks? Maybe using a combination of chunks, windows and zip, to chunk the matrix into rows, each with a sliding window, then zip the row's windows and flatten the result?
Thanks!
Edit:
I'm not looking to simply iterate over a 2D array, I want to slide a 2D window over the array, similar to how the std::slice::windows function works.

The best I've got for now is wrapping the matrix access in an unsafe block to elide the bounds check.
With some other misc changes, this is the full example now:
fn split_factory(cols: usize) -> impl Fn(usize) -> (usize, usize) {
// Declaring that cols must be positive allows more aggressive optimisation of div and mod.
if cols < 1 {
unreachable!()
}
move |i| (i / cols, i % cols)
}
fn main() {
// 4 rows 3 columns
let dim: (usize, usize) = (4, 3);
// Place-holder matrix
#[rustfmt::skip]
let mat = vec![0, 1, 2,
3, 4, 5,
6, 7, 8,
9, 10, 11];
// The dimensions of my sub-window
let win_dim = (3usize, 2usize);
// Calculate the bounds for which the top left corner of each window may exist
let bounds = (dim.0 - win_dim.0 + 1, dim.1 - win_dim.1 + 1);
// Iterate over all the top left corners
let convolution_iter = (0..bounds.0 * bounds.1)
.map(split_factory(bounds.1))
.map(|(r, c)| {
// Borrow the matrix, so our closure may own the reference
let bmat = &mat;
// Return an iterator for this window
return (0..win_dim.0 * win_dim.1)
.map(split_factory(win_dim.1))
.map(move |(wr, wc)| {
let px = (wr + r) * dim.1 + (wc + c);
(px, unsafe { *bmat.get_unchecked(px) })
});
});
// Print the windows out (badly...)
convolution_iter.for_each(|it| println!("{:?}", it.collect::<Vec<(usize, i32)>>()));
}
Would still be nicer to avoid unsafe and the indirection of looking up the matrix with indices.

Related

How I connect dots using A* (Astart) pathfinding system? in GODOT

I'm trying to do something a little bit different then the usual.
I have a 3D gridmap node setup and I'm trying to autogenerate the dots and connections using A*
Instead of creating obstacles tiles, I'm creating walls in between the tiles, so the tiles are still walkable, you just cannot pass through a wall . I figure it that out all already
but I have no idea how to code how to connect the points in a easy way and not connect points that has walls in between...
I'm using a RaycastCast Node to detect the wall, and his position as it walk through every gridtile
but I can't figure it out a nested loop to find the neighbors points to connect
this is what I tried to do (obviously get_closest_point() is not working the way I wanted).
If I could get a point using only Vector3 Coordinates, I think I could make it work.
EXTRA: if you guys can show me a way to clean the code, especially on the "FORs" syntaxes, because I kind don't know what I'm doing
Any other clean code recommendations would be amazing and very much welcomed
At the end has a visual draw(image) of the logic of the idea.
onready var rc = $RayCast
onready var aS = AStar.new()
var floor_size = Vector3(12,0,12)
var origin = Vector3(-5.5, 0.5, -2.5)
var FRONT = Vector3(1,0,0)
var RIGHT = Vector3(0,0,1)
var BACK = Vector3(-1,0,0)
var LEFT = Vector3(-1,0,0)
func set_walkable():
var value = 0
var value2 = 0
var i = 0
for _length in range (origin.x, origin.x + floor_size.x + 1):
value += 1
value2 = 0
for _width in range(origin.z, origin.z + floor_size.z):
i += 1
value2 += 1
aS.add_point(i, Vector3(origin.x + value -1 , 0.5, origin.z + value2 -1) , 1.0)
value = 0
for _u in range(origin.x, origin.x + floor_size.x + 1):
value += 1
value2 = 0
for _v in range(origin.z, origin.z + floor_size.z):
value2 += 1
var from = aS.get_closest_point(Vector3(origin.x + value ,0.5, origin.z + value2) ) # Current
rc.translation = Vector3(origin.x + value -1 ,0.5, origin.z + value2 -1)
draw_points()
print(rc.translation)
rc.cast_to = FRONT
var to = aS.get_closest_point(rc.translation) # Front
if from != -1 and !rc.is_colliding():
aS.connect_points(from, to)
draw_connections(Vector3(rc.translation.x + 0.5,rc.translation.y,rc.translation.z))
rc.cast_to = BACK
to = aS.get_closest_point(rc.translation) # back
if from != -1 and !rc.is_colliding():
aS.connect_points(from, to)
draw_connections(Vector3(rc.translation.x + -0.5,rc.translation.y,rc.translation.z))
rc.cast_to = RIGHT
to = aS.get_closest_point(rc.translation) # right
if from != -1 and !rc.is_colliding():
aS.connect_points(from, to)
draw_connections(Vector3(rc.translation.x,rc.translation.y,rc.translation.z + 0.5))
rc.cast_to = LEFT
to = aS.get_closest_point(rc.translation) # left
if from != -1 and !rc.is_colliding():
aS.connect_points(from, to)
draw_connections(Vector3(rc.translation.x + 0.5,rc.translation.y,rc.translation.z + -0.5))
func draw_points(): # Make points visible
var cube = MeshInstance.new()
cube.mesh = CubeMesh.new()
cube.translation = rc.translation
cube.scale = Vector3(0.25,0.25,0.25)
add_child(cube)
print("Cubo adicionado")
func draw_connections(position): # Make connections visible
var line = MeshInstance.new()
line.mesh = PlaneMesh.new()
line.scale = Vector3(0.03,0.03,0.03)
line.translation = position
add_child(line)
print("Cubo adicionado")
Convert between Coordinates and Point ids
Let us establish a mapping between coordinates and point ids. Given that we have a floor_size, this is easy:
func vector_to_id(vector:Vector3, size:Vector3) -> int:
return int(int3(vector).dot(dimension_size(size)))
func id_to_vector(id:int, size:Vector3) -> Vector3:
var s:Vector3 = dimension_size(size)
var z:int = int(id / s.z)
var y:int = int((id % int(s.z)) / s.y)
var x:int = id % int(s.y)
return Vector3(x, y, z)
func int3(vector:Vector3) -> Vector3:
return Vector3(int(vector.x), int(vector.y), int(vector.z))
func dimension_size(size:Vector3) -> Vector3:
return Vector3(1, int(size.x + 1), int(size.x + 1) * int(size.y + 1))
Possible optimizations:
Store dimension_size(floor_size) and use that directly.
Skip calling int3 on the condition that the values you pass to vector_to_id are guaranteed to be integer.
We will need a function to get the total number of points:
func total_size(size:Vector3) -> int:
return int(size.x + 1) * int(size.y + 1) * int(size.z + 1)
Explanation
Let us start at 1D (one dimension). We will only have one coordinate. So we have an hypothetical Vector1 that has an x property. We are simply putting things in a line.
Then the mapping is trivial: to convert from the coordinates to the id, we take id = int(vector.x), and if we want the coordinate we simply do vector = Vector1(id).
Now, let us move to 2D. We have Vector2 with x and y. Thankfully we have a size (there are ways to do the mapping when the size is not known, but having a size is convenient).
Thus, we will be doing a 2D grid, with some width and height. The y coordinate tells us the row in which we are, and x tells us the position in the row.
Then if we have some id, we need to figure out how many rows we need to get there, and then in what position in that row we are. Figuring out the row is easy, we divide by the width of the grid. And the position in the row is the reminder. One caveat: We are measuring from 0 (so a width of 0 actually means 1 element per row).
We have:
func id_to_vector(id:int, size:Vector2) -> Vector2:
var y:int = int(id / (size.x + 1))
var x:int = id % int(size.x + 1)
return Vector2(x, y)
How about going the other way around? Well, we multiply y for the length of a row (the width), and add x:
func vector_to_id(vector:Vector2, size:Vector2) -> int:
return int(vector.x) + int(vector.y) * int(size.x + 1)
Notice:
We didn't need size.y.
We need size.x + 1 in both functions.
vector_to_id looks very similar to a dot product.
Thus, let us make a new function that returns the vector with which we would be making the dot product:
func dimension_size(size:Vector2) -> Vector2:
return Vector2(1, int(size.x + 1))
And use it:
func vector_to_id(vector:Vector2, size:Vector2) -> int:
return int(vector.dot(dimensional_size(size)))
func id_to_vector(id:int, size:Vector2) -> Vector2:
var s = dimensional_size(size)
var y:int = int(id / int(s.y))
var x:int = id % int(s.y)
return Vector2(x, y)
Note If there is no guarantee that vector only has integers in vector_to_id, the fractional part in the dot product make lead to a wrong result. Which is why I have a function to make it have only integer.
Time for 3D. We have Vector3 with x, y and z. We are making a 3D grid. Now the z will tell us the layer, and each layer is a 2D grid.
Let us review dimensional_size, We had:
func dimension_size(size:Vector2) -> Vector2:
return Vector2(1, int(size.x + 1))
That is the size of an element (1), the size of a row(size.x + 1), we need to add the size of a layer. That is, the size of the 2D grid, which is just width times height.
func dimension_size(size:Vector3) -> Vector3:
return Vector3(1, int(size.x + 1), int(size.x + 1) * int(size.y + 1))
And how do we get z from the id? We divide by the size of a grid (so we know on what grid we are). Then from the reminder of that division we can find y:
func vector_to_id(vector:Vector3, size:Vector3) -> int:
return int(vector.dot(dimensional_size(size)))
func id_to_vector(id:int, size:Vector3) -> Vector3:
var s = dimensional_size(size)
var z:int = int(id / int(s.z))
var y:int = int(int(id % int(s.z)) / int(s.y))
var x:int = id % int(s.y)
return Vector2(x, y, z)
In fact, technically, all these coordinates are computed on the same form:
func id_to_vector(id:int, size:Vector3) -> Vector3:
var s = dimensional_size(size)
var tot = total_size(size)
var z:int = int(int(id % int(tot)) / int(s.z))
var y:int = int(int(id % int(s.z)) / int(s.y))
var x:int = int(int(id % int(s.y)) / int(s.x))
return Vector2(x, y, z)
Except, there is no need to take the reminder with the total size because id should always be less than that. And there is no need to divide by s.x because the size of a single element is always 1. And I also removed some redundant int casts.
What is total_size? The next element of dimensional_size, of course:
func dimension_size(size:Vector3) -> Vector3:
return Vector3(1, int(size.x + 1), int(size.x + 1) * int(size.y + 1))
func total_size(size:Vector3) -> int:
return int(size.x + 1) * int(size.y + 1) * int(size.z + 1)
Checking connectivity
And a way to check connectivity:
func check_connected(start:Vector3, end:Vector3) -> bool:
rc.transform.origin = start
rc.cast_to = end
rc.force_update_transform()
rc.force_raycast_update()
return !raycast.is_colliding()
And you had the right idea with FRONT, RIGHT, BACK and LEFT but put them in an array:
var offsets = [Vector3(1,0,0), Vector3(0,0,1), Vector3(-1,0,0), Vector3(-1,0,0)]
Note I'm calling force_update_transform and force_raycast_update because are doing multiple raycast checks on the same frame.
Populating AStar
Alright, enough setup, we can now iterate:
for id in total_size(floor_size):
pass
On each iteration, we need to get the vector:
for id in total_size(floor_size):
var vector = id_to_vector(id, floor_size)
Posible optimization: Iterate over the vector coordinates directly to avoid calling id_to_vector.
We can add the vector to AStar:
for id in total_size(floor_size):
var vector = id_to_vector(id, floor_size)
aS.add_point(id, vector)
Next we need the adjacent vectors:
for id in total_size(floor_size):
var vector = id_to_vector(id, floor_size)
aS.add_point(id, vector)
for offset in offsets:
var adjacent = vector + offset
Let us add them to AStar too:
for id in total_size(floor_size):
var vector = id_to_vector(id, floor_size)
aS.add_point(id, vector)
for offset in offsets:
var adjacent = vector + offset
var adjacent_id = vector_to_id(adjacent, floor_size)
aS.add_point(adjacent_id, adjacent)
Possible optimizations:
Do not add if has_point returns true.
If the id of the adjacent vector is lower, do not process it.
Modify offsets so that you only check adjacent position that are yet to be added (and thus preventing the prior two cases).
Let us check connectivity:
for id in total_size(floor_size):
var vector = id_to_vector(id, floor_size)
aS.add_point(id, vector)
for offset in offsets:
var adjacent = vector + offset
var adjacent_id = vector_to_id(adjacent, floor_size)
aS.add_point(adjacent_id, adjacent)
if check_connected(vector, adjacent):
pass
And tell the AStar about the connectivity:
for id in total_size(floor_size):
var vector = id_to_vector(id, floor_size)
aS.add_point(id, vector)
for offset in offsets:
var adjacent = vector + offset
var adjacent_id = vector_to_id(adjacent, floor_size)
aS.add_point(adjacent_id, adjacent)
if check_connected(vector, adjacent):
connect_points(id, adjacent_id)

Why do I get different argument types when using filter on an iterator?

I'm using filter, but I don't understand why I should use **x > 1 for a slice but use *x > 1 for a range.
fn main() {
let a = [0, 1, 2, 3];
let a_iter = a.iter().filter(|x: &&i32| **x > 1); // x: &&i32
let x: Vec<&i32> = a_iter.collect();
println!("{:?}", x);
let b = 0..4;
let b_iter = b.filter(|x: &i32| *x > 1); // x: &i32
let y: Vec<i32> = b_iter.collect();
println!("{:?}", y);
}
The docs say it should be **x > 1.
slice::iter, such as a.iter() in your example, produces an iterator over references to values. Ranges are iterators that produce non-reference values.
The filter(<closure>) method takes a <closure> that takes iterator values by reference, so if your iterator already produces references you'll get a reference to a reference, and if your iterator produces non-reference values then you'll get references to those values.
The difference becomes easier to understand if we use a Vec for both examples:
fn main() {
let a = vec![0, 1, 2, 3];
let a_iter = a.iter(); // iter() returns iterator over references
let x: Vec<&i32> = a_iter.filter(|x: &&i32| **x > 1).collect();
println!("{:?}", x);
let b = vec![0, 1, 2, 3];
let b_iter = a.into_iter(); // into_iter() returns iterator over values
let y: Vec<i32> = b_iter.filter(|x: &i32| *x > 1).collect();
println!("{:?}", y);
}
playground
This is because in the array-example, you first call .iter() to create an iterator, which borrows the array and hence to values in it; the closure-argument to filter receives a borrowed version of the iterator's Item, so it's borrowed again, which makes it a &&i32.
In the Range-case, you call filter directly, as a Range is an iterator. The iterator, therefore, contains owned values and the closure in filter borrows that, which makes its type &i32.
You can see that if you try to let y = b; after the let b_iter = ...-line: You'll get a use of moved value-error because b was consumed by the iterator you used in b.filter

Split a random value into four that sum up to it

I have one value like 24, and I have four textboxes. How can I dynamically generate four values that add up to 24?
All the values must be integers and can't be negative, and the result cannot be 6, 6, 6, 6; they must be different like: 8, 2, 10, 4. (But 5, 6, 6, 7 would be okay.)
For your stated problem, it is possible to generate an array of all possible solutions and then pick one randomly. There are in fact 1,770 possible solutions.
var solutions = [[Int]]()
for i in 1...21 {
for j in 1...21 {
for k in 1...21 {
let l = 24 - (i + j + k)
if l > 0 && !(i == 6 && j == 6 && k == 6) {
solutions.append([i, j, k, l])
}
}
}
}
// Now generate 20 solutions
for _ in 1...20 {
let rval = Int(arc4random_uniform(UInt32(solutions.count)))
println(solutions[rval])
}
This avoids any bias at the cost of initial setup time and storage.
This could be improved by:
Reducing storage space by only storing the first 3 numbers. The 4th one is always 24 - (sum of first 3)
Reducing storage space by storing each solution as a single integer: (i * 10000 + j * 100 + k)
Speeding up the generation of solutions by realizing that each loop doesn't need to go to 21.
Here is the solution that stores each solution as a single integer and optimizes the loops:
var solutions = [Int]()
for i in 1...21 {
for j in 1...22-i {
for k in 1...23-i-j {
if !(i == 6 && j == 6 && k == 6) {
solutions.append(i * 10000 + j * 100 + k)
}
}
}
}
// Now generate 20 solutions
for _ in 1...20 {
let rval = Int(arc4random_uniform(UInt32(solutions.count)))
let solution = solutions[rval]
// unpack the values
let i = solution / 10000
let j = (solution % 10000) / 100
let k = solution % 100
let l = 24 - (i + j + k)
// print the solution
println("\([i, j, k, l])")
}
Here is a Swift implementation of the algorithm given in https://stackoverflow.com/a/8064754/1187415, with a slight
modification because all numbers are required to be positive.
The method to producing N positive random integers with sum M is
Build an array containing the number 0, followed by N-1 different
random numbers in the range 1 .. M-1, and finally the number M.
Compute the differences of subsequent array elements.
In the first step, we need a random subset of N-1 elements out of
the set { 1, ..., M-1 }. This can be achieved by iterating over this
set and choosing each element with probability n/m, where
m is the remaining number of elements we can choose from and
n is the remaining number of elements to choose.
Instead of storing the chosen random numbers in an array, the
difference to the previously chosen number is computed immediately
and stored.
This gives the following function:
func randomNumbers(#count : Int, withSum sum : Int) -> [Int] {
precondition(sum >= count, "`sum` must not be less than `count`")
var diffs : [Int] = []
var last = 0 // last number chosen
var m = UInt32(sum - 1) // remaining # of elements to choose from
var n = UInt32(count - 1) // remaining # of elements to choose
for i in 1 ..< sum {
// Choose this number `i` with probability n/m:
if arc4random_uniform(m) < n {
diffs.append(i - last)
last = i
n--
}
m--
}
diffs.append(sum - last)
return diffs
}
println(randomNumbers(count: 4, withSum: 24))
If a solution with all elements equal (e.g 6+6+6+6=24) is not
allowed, you can repeat the method until a valid solution is found:
func differentRandomNumbers(#count : Int, withSum sum : Int) -> [Int] {
precondition(count >= 2, "`count` must be at least 2")
var v : [Int]
do {
v = randomNumbers(count: count, withSum: sum)
} while (!contains(v, { $0 != v[0]} ))
return v
}
Here is a simple test. It computes 1,000,000 random representations
of 7 as the sum of 3 positive integers, and counts the distribution
of the results.
let set = NSCountedSet()
for i in 1 ... 1_000_000 {
let v = randomNumbers(count: 3, withSum: 7)
set.addObject(v)
}
for (_, v) in enumerate(set) {
let count = set.countForObject(v)
println("\(v as! [Int]) \(count)")
}
Result:
[1, 4, 2] 66786
[1, 5, 1] 67082
[3, 1, 3] 66273
[2, 2, 3] 66808
[2, 3, 2] 66966
[5, 1, 1] 66545
[2, 1, 4] 66381
[1, 3, 3] 67153
[3, 3, 1] 67034
[4, 1, 2] 66423
[3, 2, 2] 66674
[2, 4, 1] 66418
[4, 2, 1] 66292
[1, 1, 5] 66414
[1, 2, 4] 66751
Update for Swift 3:
func randomNumbers(count : Int, withSum sum : Int) -> [Int] {
precondition(sum >= count, "`sum` must not be less than `count`")
var diffs : [Int] = []
var last = 0 // last number chosen
var m = UInt32(sum - 1) // remaining # of elements to choose from
var n = UInt32(count - 1) // remaining # of elements to choose
for i in 1 ..< sum {
// Choose this number `i` with probability n/m:
if arc4random_uniform(m) < n {
diffs.append(i - last)
last = i
n -= 1
}
m -= 1
}
diffs.append(sum - last)
return diffs
}
print(randomNumbers(count: 4, withSum: 24))
Update for Swift 4.2 (and later), using the unified random API:
func randomNumbers(count : Int, withSum sum : Int) -> [Int] {
precondition(sum >= count, "`sum` must not be less than `count`")
var diffs : [Int] = []
var last = 0 // last number chosen
var m = sum - 1 // remaining # of elements to choose from
var n = count - 1 // remaining # of elements to choose
for i in 1 ..< sum {
// Choose this number `i` with probability n/m:
if Int.random(in: 0..<m) < n {
diffs.append(i - last)
last = i
n -= 1
}
m -= 1
}
diffs.append(sum - last)
return diffs
}
func getRandomValues(amountOfValues:Int, totalAmount:Int) -> [Int]?{
if amountOfValues < 1{
return nil
}
if totalAmount < 1{
return nil
}
if totalAmount < amountOfValues{
return nil
}
var values:[Int] = []
var valueLeft = totalAmount
for i in 0..<amountOfValues{
if i == amountOfValues - 1{
values.append(valueLeft)
break
}
var value = Int(arc4random_uniform(UInt32(valueLeft - (amountOfValues - i))) + 1)
valueLeft -= value
values.append(value)
}
var shuffledArray:[Int] = []
for i in 0..<values.count {
var rnd = Int(arc4random_uniform(UInt32(values.count)))
shuffledArray.append(values[rnd])
values.removeAtIndex(rnd)
}
return shuffledArray
}
getRandomValues(4, 24)
This is not a final answer, but it should be a (good) starting point.
How it works: It takes 2 parameters. The amount of random values (4 in your case) and the total amount (24 in your case).
It takes a random value between the total Amount and 0, stores this in an array and it subtracts this from a variable which stores the amount that is left and stores the new value.
Than it takes a new random value between the amount that is left and 0, stores this in an array and it again subtracts this from the amount that is left and stores the new value.
When it is the last number needed, it sees what amount is left and adds that to the array
EDIT:
Adding a +1 to the random value removes the problem of having 0 in your array.
EDIT 2:
Shuffling the array does remove the increased chance of having a high value as the first value.
One solution that is unfortunatly non-deterministic but completely random is as follows:
For a total of 24 in 4 numbers:
pick four random numbers between 1 and 21
repeat until the total of the numbers equals 24 and they are not all 6.
This will, on average, loop about 100 times before finding a solution.
Here's a solution which should have significantly* less bias than some of the other methods. It works by generating the requested number of random floating point numbers, multiplying or dividing all of them until they add up to the target total, and then rounding them into integers. The rounding process changes the total, so we need to correct for that by adding or subtracting from random terms until they add up to the right amount.
func getRandomDoubles(#count: Int, #total: Double) -> [Double] {
var nonNormalized = [Double]()
nonNormalized.reserveCapacity(count)
for i in 0..<count {
nonNormalized.append(Double(arc4random()) / 0xFFFFFFFF)
}
let nonNormalizedSum = reduce(nonNormalized, 0) { $0 + $1 }
let normalized = nonNormalized.map { $0 * total / nonNormalizedSum }
return normalized
}
func getRandomInts(#count: Int, #total: Int) -> [Int] {
let doubles = getRandomDoubles(count: count, total: Double(total))
var ints = [Int]()
ints.reserveCapacity(count)
for double in doubles {
if double < 1 || double % 1 >= 0.5 {
// round up
ints.append(Int(ceil(double)))
} else {
// round down
ints.append(Int(floor(double)))
}
}
let roundingErrors = total - (reduce(ints, 0) { $0 + $1 })
let directionToAdjust: Int = roundingErrors > 0 ? 1 : -1
var corrections = abs(roundingErrors)
while corrections > 0 {
let index = Int(arc4random_uniform(UInt32(count)))
if directionToAdjust == -1 && ints[index] <= 1 { continue }
ints[index] += directionToAdjust
corrections--
}
return ints
}
*EDIT: Martin R has correctly pointed out that this is not nearly as uniform as one might expect, and is in fact highly biased towards numbers in the middle of the 1-24 range. I would not recommend using this solution, but I'm leaving it up so that others can know not to make the same mistake.
As a recursive function the algorithm is very nice:
func getRandomValues(amount: Int, total: Int) -> [Int] {
if amount == 1 { return [total] }
if amount == total { return Array(count: amount, repeatedValue: 1) }
let number = Int(arc4random()) % (total - amount + 1) + 1
return [number] + getRandomValues(amount - 1, total - number)
}
And with safety check:
func getRandomValues(amount: Int, total: Int) -> [Int]? {
if !(1...total ~= amount) { return nil }
if amount == 1 { return [total] }
if amount == total { return Array(count: amount, repeatedValue: 1) }
let number = Int(arc4random()) % (total - amount + 1) + 1
return [number] + getRandomValues(amount - 1, total - number)!
}
As #MartinR pointed out the code above is extremely biased. So in order to have a uniform distribution of the output values you should use this piece of code:
func getRandomValues(amount: Int, total: Int) -> [Int] {
var numberSet = Set<Int>()
// add splitting points to numberSet
for _ in 1...amount - 1 {
var number = Int(arc4random()) % (total - 1) + 1
while numberSet.contains(number) {
number = Int(arc4random()) % (total - 1) + 1
}
numberSet.insert(number)
}
// sort numberSet and return the differences between the splitting points
let sortedArray = (Array(numberSet) + [0, total]).sort()
return sortedArray.enumerate().flatMap{
indexElement in
if indexElement.index == amount { return nil }
return sortedArray[indexElement.index + 1] - indexElement.element
}
}
A javascript implementation for those who may be looking for such case:
const numbersSumTo = (length, value) => {
const fourRandomNumbers = Array.from({ length: length }, () => Math.floor(Math.random() * 6) + 1);
const res = fourRandomNumbers.map(num => (num / fourRandomNumbers.reduce((a, b) => a + b, 0)) * value).map(num => Math.trunc(num));
res[0] += Math.abs(res.reduce((a, b) => a + b, 0) - value);
return res;
}
// Gets an array with 4 items which sum to 100
const res = numbersSumTo(4, 100);
const resSum = res.reduce((a, b) => a + b, 0);
console.log({
res,
resSum
});
Also plenty of different methods of approach can be found here on this question: https://math.stackexchange.com/questions/1276206/method-of-generating-random-numbers-that-sum-to-100-is-this-truly-random

constUnsafePointer unresolved identifier in Swift FFT

I have been looking at examples of FFTs in Swift, and they all seem to have ConstUnsafePointer when using vDSP_ctozD as in the example below:
import Foundation
import Accelerate
internal func spectrumForValues(signal: [Double]) -> [Double] {
// Find the largest power of two in our samples
let log2N = vDSP_Length(log2(Double(signal.count)))
let n = 1 << log2N
let fftLength = n / 2
// This is expensive; factor it out if you need to call this function a lot
let fftsetup = vDSP_create_fftsetupD(log2N, FFTRadix(kFFTRadix2))
var fft = [Double](count:Int(n), repeatedValue:0.0)
// Generate a split complex vector from the real data
var realp = [Double](count:Int(fftLength), repeatedValue:0.0)
var imagp = realp
withExtendedLifetimes(realp, imagp) {
var splitComplex = DSPDoubleSplitComplex(realp:&realp, imagp:&imagp)
// Take the fft
vDSP_fft_zripD(fftsetup, &splitComplex, 1, log2N, FFTDirection(kFFTDirection_Forward))
// Normalize
var normFactor = 1.0 / Double(2 * n)
vDSP_vsmulD(splitComplex.realp, 1, &normFactor, splitComplex.realp, 1, fftLength)
vDSP_vsmulD(splitComplex.imagp, 1, &normFactor, splitComplex.imagp, 1, fftLength)
// Zero out Nyquist
splitComplex.imagp[0] = 0.0
// Convert complex FFT to magnitude
vDSP_zvmagsD(&splitComplex, 1, &fft, 1, fftLength)
}
// Cleanup
vDSP_destroy_fftsetupD(fftsetup)
return fft
}
// To get rid of the `() -> () in` casting
func withExtendedLifetime<T>(x: T, f: () -> ()) {
return Swift.withExtendedLifetime(x, f)
}
// In the spirit of withUnsafePointers
func withExtendedLifetimes<A0, A1>(arg0: A0, arg1: A1, f: () -> ()) {
return withExtendedLifetime(arg0) { withExtendedLifetime(arg1, f) }
}
However when I try to use it in my project, this ConstUnsafePointer is seen as an unresolved identifier. Any clue how to fix this? Thanks in advance.
The name ConstUnsafePointer was used in early Swift betas last summer (at that time, UnsafePointer meant mutable). Now, constant pointers are just UnsafePointer and mutable pointers are UnsafeMutablePointer.

Algorithm for LCM of doubles in Swift

I need to turn an array of doubles to ints while keeping their ratios the same and being as simple as possible. For example [0.7, 0, -0.7] should become [1, 0, -1] and [24, 12, 0] should become [2, 1, 0]. I'm not certain if this would involve getting the least common multiple of the doubles or not, and how would this be done if so?
(The code has been updated for Swift 4 and later.)
First of all, there is no GCD or LCM for floating point numbers. You
have to convert the input to rational numbers first.
This is not as easy as it sounds, because decimal fractions like 0.7 cannot be represented exactly as a binary floating point number and
would be stored as something like 0.69999999999999996 in a Double.
So it is not completely obvious how to get from there to 7/10.
It is therefore necessary to specify a precision. Then you can
use
Continued Fractions
to efficiently create a (finite or infinite) sequence of fractions hn/kn that are arbitrary good approximations to a given real number x.
Here is a translation of this JavaScript implementation to Swift:
typealias Rational = (num : Int, den : Int)
func rationalApproximationOf(_ x0 : Double, withPrecision eps : Double = 1.0E-6) -> Rational {
var x = x0
var a = floor(x)
var (h1, k1, h, k) = (1, 0, Int(a), 1)
while x - a > eps * Double(k) * Double(k) {
x = 1.0/(x - a)
a = floor(x)
(h1, k1, h, k) = (h, k, h1 + Int(a) * h, k1 + Int(a) * k)
}
return (h, k)
}
Examples:
rationalApproximationOf(0.7) // (7, 10) i.e. 7/10
rationalApproximationOf(0.142857) // (1, 7) i.e. 1/7
I have set the default precision to 1.0E-6, but you can adjust that
to your needs.
Then you need functions for the GCD (greatest common divisor)
and LCM (lowest common multiple). Here are simple implementation:
// GCD of two numbers:
func gcd(_ a: Int, _ b: Int) -> Int {
var (a, b) = (a, b)
while b != 0 {
(a, b) = (b, a % b)
}
return abs(a)
}
// GCD of a vector of numbers:
func gcd(_ vector: [Int]) -> Int {
return vector.reduce(0, gcd)
}
// LCM of two numbers:
func lcm(a: Int, b: Int) -> Int {
return (a / gcd(a, b)) * b
}
// LCM of a vector of numbers:
func lcm(_ vector : [Int]) -> Int {
return vector.reduce(1, lcm)
}
With all these utilities, your function can now be implemented as
func simplifyRatios(_ numbers : [Double]) -> [Int] {
// Normalize the input vector to that the maximum is 1.0,
// and compute rational approximations of all components:
let maximum = numbers.map(abs).max()!
let rats = numbers.map { rationalApproximationOf($0/maximum) }
// Multiply all rational numbers by the LCM of the denominators:
let commonDenominator = lcm(rats.map { $0.den })
let numerators = rats.map { $0.num * commonDenominator / $0.den }
// Divide the numerators by the GCD of all numerators:
let commonNumerator = gcd(numerators)
return numerators.map { $0 / commonNumerator }
}
Examples:
simplifyRatios([0.7, 0, -0.7]) // [1, 0, -1]
simplifyRatios([24, 12, 0]) // [2, 1, 0]
simplifyRatios([1.3, 0.26, 0.9]) // [65, 13, 45]

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