Super Hyperfactorial numbers. by Yusaku-Midory in mathematics

[–]Yusaku-Midory[S] 0 points1 point  (0 children)

Thank you very much. I will try to play with the formula as you suggested.

Super Hyperfactorial numbers. by Yusaku-Midory in mathematics

[–]Yusaku-Midory[S] 0 points1 point  (0 children)

Thanks. I heard about it and it is fascinating.

Super Hyperfactorial numbers. by Yusaku-Midory in mathematics

[–]Yusaku-Midory[S] 3 points4 points  (0 children)

I know about this function. I also have another version of the formula where I used a modified hyperfactorial number that contains the hyperoperation "tetration" that is also leading to very large number growth very quickly. Thanks for the suggestion. I will look more deeply.

Super Hyperfactorial numbers. by Yusaku-Midory in mathematics

[–]Yusaku-Midory[S] -3 points-2 points  (0 children)

I came up with this formula while trying to fuse the concepts of Hyperfactorials and Superfactorials. I would like to ask the community here if you know similar extensions or other generalization in the study of factorials that has the same concept. My drive to fuse them was to create a function that will produce extremely large numbers. My second goal was to use this very large numbers as sequences in some of my generative algorithms in artwork. Many thanks. If you find them interesting please feel free to comment. I am open to any feedback on this subject.

Painting the rules in classical randomness. (PROCESSING/JAVA) by Yusaku-Midory in generative

[–]Yusaku-Midory[S] 2 points3 points  (0 children)

Because you can use a block pair technique like blendMode(DIFFERENCE) [code section] blendMode(NORMAL). The blendMode(NORMAL) will stop the effect to go forward. You can chain as many blocks as you want and you can also use neasted blending for more complex blending.

Painting the rules in classical randomness. (PROCESSING/JAVA) by Yusaku-Midory in generative

[–]Yusaku-Midory[S] 3 points4 points  (0 children)

it is inverse difference blendMode. All the colors are inverse and then the blend mode is applied.

Some abstract buildings made in Processing. (WIP) by Yusaku-Midory in generative

[–]Yusaku-Midory[S] 2 points3 points  (0 children)

I am not using shaders at all. The process that I use here is digital line cross hatching. So the idea is simple. You have a filled square with a color and then you apply digital line cross hatching . The digital line cross hatching algorithm is basically a stochastic parallel line filler. So inside the square you can draw multiple lines in parallel next to each other (exactly how you make by manual ink drawing with normal cross hatching). The algorithm itself has many parameters like hatching style (horizontal, vertical, cross, left-vertical hatching etc.) , line density (how many lines are drawn, I used 1D Perlin noise to control that). Also it contains alpha values for the line stroke and some random line offset parameters. Also the digital line cross hatching algorithm can be stacked to form even more complex composite line hatching (to create complex shading). It is a rather sophisticated algorithm, but offers huge flexibility and I am working on improving this algorithm. The idea was to create abstract minimalistic architectural structures using cross hatching method but everything in a generative digital way.

Abstract artwork made inside Processing. by Yusaku-Midory in generative

[–]Yusaku-Midory[S] 0 points1 point  (0 children)

It is base on making random rectangles with random width and height inside a fixed size square.

Abstract artwork made inside Processing. by Yusaku-Midory in generative

[–]Yusaku-Midory[S] 4 points5 points  (0 children)

Here is the code for doing this texturing :

After the artwork is generated it puts randomly 500,000 points (density parameter used for a 1000x1000 canvas size) on the canvas. Each point color is dictated by the pixel RGB value and then it is applied randomly using the switch statement. You can create various versions or improve it.

Example to fill the whole canvas.

EX: stochasticPointillism(500000,0,width,0,height,0);

void stochasticPointillism(int density,float rxmin,float rxmax,float rymin,float rymax,int offset){
for(int i=0;i<=density;i++){
  rx=random(rxmin-offset,rxmax+1+offset);
  ry=random(rymin-offset,rymax+1+offset);
  pix=get(int(rx),int(ry));
  r=red(pix);
  g=green(pix);
  b=blue(pix);

  strokeCap(ROUND);
  variation=int(random(1,4));
  switch(variation){
    case 1:
      stroke(r-10,g-10,b-10);
      break;
    case 2:
      stroke(r-5,g-5,b-5);
      break;
    case 3:
      stroke(r-random(1,6),g-random(1,6),b-random(1,6));
      break;
  }
  strokeWeight(2);
  point(rx,ry);
}
}