Do you remember when you first learned about recursion? The thought triggered a memory from an old CS101 Java course. The textbook had some fractal triangle thing made with only 20 lines of code. At the time, it was a confusing 20 lines of code.
A quick search on Google for “fractal triangle recursion” led straight to the Sierpinski triangle^{1}. A Sierpinski triangle generator with an ncurses visualization is a fun afternoon project.
The Recursive Approach
Here’s the description of the Sierpinski triangle algorithm straight from Wikipedia:
 Start with an equilateral triangle.
 Subdivide it into four smaller congruent equilateral triangles and remove the central triangle.
 Repeat step 2 with each of the remaining smaller triangles infinitely.
Below is one possible implementation of the algorithm:
struct Point2D {
int x;
int y;
};
struct Triangle {
Point2D vertices[3];
};
[[nodiscard]] int MidPoint(const Point2D& a,
const Point2D& b) noexcept {
return {.x = (a.x + b.x) / 2, .y = (a.y + b.y) / 2};
}
void Sierpinski(const Triangle& tri, int degree) noexcept {
PrintTriangle(triangle);
if (degree > 0) {
Triangle t1;
t1.vertices[0] = {.x = tri.vertices[0].x, tri.vertices[0].y};
t1.vertices[1] = MidPoint(tri.vertices[0], tri.vertices[1]);
t1.vertices[2] = MidPoint(tri.vertices[0], tri.vertices[2]);
Triangle t2;
t2.vertices[0] = {.x = tri.vertices[1].x, tri.vertices[1].y};
t2.vertices[1] = MidPoint(tri.vertices[0], tri.vertices[1]);
t2.vertices[2] = MidPoint(tri.vertices[1], tri.vertices[2]);
Triangle t3;
t3.vertices[0] = {.x = tri.vertices[2].x, tri.vertices[2].y};
t3.vertices[1] = MidPoint(tri.vertices[2], tri.vertices[1]);
t3.vertices[2] = MidPoint(tri.vertices[0], tri.vertices[2]);
Sierpinski(t1, degree  1);
Sierpinski(t2, degree  1);
Sierpinski(t3, degree  1);
}
}
The code implements a Triangle
type where a Triangle
is an array of three
vertices in 2D space. The Midpoint()
function calculates the midpoint of two
2D points. Sierpinski()
is the recursive function where the magic happens. The
degree
parameter controls the number of algorithm iterations. At each
iteration, you subdivide the previous iteration’s triangles into three smaller
triangles using the midpoint of each side of the “parent” triangle. Each
subtriangle then calls Sierpinski()
with a reduced degree
. degree = 0
is
the base case. In the base case, you print the input triangle before returning.
If you have some experience with recursive algorithms, the implementation isn’t
too hard to grok. If you are a newbie, do a run on paper with a small degree
.
You’ll get a feel for how the execution plays out.
If you were paying attention in your algorithms course, you’d know the time
complexity of this implementation isn’t so great. Below is the call tree for a
Sierpinski(2)
run.
++
 Sierpinski(2) 
+++

+++
  
+v+ +v+ 
 Sierpinski(1)   Sierpinski(1)  +v+
+++ +++  Sierpinski(1) 
  +++
 +++ 
+++    +++
        
+v+ +v+ +v+ +v+ +v+ +v+ +v+ +v+ +v+
 Sierpinski(0)   Sierpinski(0)   Sierpinski(0)   Sierpinski(0)   Sierpinski(0)   Sierpinski(0)   Sierpinski(0)   Sierpinski(0)   Sierpinski(0) 
++ ++ ++ ++ ++ ++ ++ ++ ++
At each node in the tree you make 3 calls to Sierpinski()
. The depth of this
tree is equal to the degree of the toplevel Sierpinski()
call. You can
imagine for higher degree values, the tree just blows up. In fact, you can
deduce the Sierpinski()
implementation has an exponential time complexity of
\(\mathcal{O}(3^{degree})\). Ouch.
The space complexity is \(\mathcal{O}(degree)\) due to the depth of the call stack scaling linearly with the degree.
Randomization to the Rescue
An exponential algorithm just isn’t going to work. At \(N = 10\), the algorithm takes well over 5 seconds to finish on a PC with an Intel i5 processor. So what can you do? Well, scroll a little further down that Wikipedia page and you’ll find a section labeled “Chaos Game”^{2}. You can read the wiki to get a technical description of the algorithm. Here’s the for dummies version:
 Take three points in a plane to form a triangle.
 Randomly select any point inside the triangle and consider that your current position.
 Randomly select any one of the three vertex points.
 Move half the distance from your current position to the selected vertex.
 Plot the current position.
 Repeat from step 3.
Here’s an implementation of the “chaos” approach:
static void DrawSierpinskiTriangles(
const sierpinski::graphics::ScreenDimension& screen_dim,
unsigned int max_iterations, unsigned int refresh_rate_usec) noexcept {
sierpinski::common::Triangle base;
base.vertices[0] = {.x = 0, .y = 0};
base.vertices[1] = {.x = screen_dim.width / 2, .y = screen_dim.height};
base.vertices[2] = {.x = screen_dim.width, .y = 0};
int xi = GetRandomInt(0, screen_dim.height);
int yi = GetRandomInt(0, screen_dim.width);
sierpinski::graphics::DrawChar({.x = xi, .y = yi}, '*', GetRandColor());
int index = 0;
for (unsigned int i = 0; i < max_iterations; ++i) {
index = GetRandomInt(0, std::numeric_limits<int>::max()) %
sierpinski::common::kTriangleVertices;
xi = (xi + base.vertices[index].x) / 2;
yi = (yi + base.vertices[index].y) / 2;
sierpinski::graphics::DrawChar({.x = xi, .y = yi}, '*', GetRandColor());
/* A delay inserted to speed or slow down the spawn rate of the points. */
std::this_thread::sleep_for(std::chrono::microseconds(refresh_rate_usec));
}
}
A couple of notes on the code. The initial base triangle has its vertices set to
the edges of the terminal screen in an upside down orientation. The
implementation follows the steps outlined previously with the addition of
max_iterations
and refresh_rate_usec
parameters. You control the total
number of points via max_iterations
. You control the draw speed via
refresh_rate_usec
.
The chaos game approach is fast. Assuming you can generate random numbers in
\(\mathcal{O}(1)\) time, the time complexity of DrawSierpinskiTriangles()
is \(\mathcal{O}(max\_iterations)\). A linear algorithm that scales with a
tunable iteration count is much nicer than the exponential previously
encountered. The space complexity is also optimal here coming in at
\(\mathcal{O}(1)\).
Visualization Using ncurses
The setup is mostly straightforward. Assume the screen is a quadrant of the 2D coordinate plane. Every time you generate a new point, draw it on the screen using some marker symbol such as an asterisk. To make it look nice, bold the character and randomly assign it a color.
Below is the relevant ncurses draw snippet:
void DrawChar(const sierpinski::common::Point2D& pos, char symbol,
Color color) noexcept {
::attron(COLOR_PAIR(color)  A_BOLD);
mvaddch(pos.y, pos.x, symbol);
::attroff(COLOR_PAIR(color)  A_BOLD);
::refresh();
}
If you’re interested in more of the gritty details of using ncurses, checkout this other post^{3} that dives into the details.
Conclusion
The end result looks pretty neat:
Generating the Sierpinski triangle was a problem with surprising complexity (pun
intended). The naive solution is easy to implement but has impractical
time/space complexity. Randomization saved the day, reducing the complexity
significantly making it possible to generate higher degree triangles in a
reasonable amount of time. It’s also nice to flex on the original textbook’s
System.out.println()
triangle by making a ncurses based visualization.
The complete project source with build instructions, usage, etc. is available on GitHub under sierpinski.
As always, Wikipedia has all the details: “Sierpinski triangle” ↩︎
This algorithm is worth implementing just based off the name along: “Chaos Game”. ↩︎
Checkout this Snake implementation using ncurses. That article covers a few of the nuisances of using ncurses. ↩︎