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Viterbi algorithm is an efficient technique to decode convoluted codes. It is a dynamic programming algorithm which gives most likelihood decoding of convolution codes. It is the most widely used. Complete simulation source code examples of these algorithms follow the algorithm descriptions. I have also included some example results from the simulation code. Since the examples are written in the C programming language, your ability to read C code would be very helpful to achieving a clear understanding.
We went from the basics to a program that renders a chaotic composition. Today, we will add some more, important functions to the generation, and start to change the method of rendering. This is what the results will look like at the end of this part: The code and the generator can be found at:. So today, we will mature our generation process and turn renderings into paintings, or at least, lay the groundwork for it. More functions So far, we have three functions to combine previous neural layers, which all take in values in a 0-1 range and output values in a 0-1 range.
This number can be expanded as much as we feel is necessary, but I will just add a couple more for now. Case 3: //using value C to interpolate between A and B neuroni+10 = lerp(inputA,inputB,inputC); break; case 4: //taking the difference between A and B, offset by 0.5 neuroni+10 = (inputA-inputB)/2+0.5; break; case 5: //taking the distance between A and B, subtracted from 1 neuroni+10 = 1-abs(inputA-inputB); break; case 6: //find Perlin noise with coordinates offset by A and B neuroni+10 = noise((xcoord+inputA).5,(ycoord+inputB).5+10.i); break; case 7: //same as above, but multiplying the noise by input C neuroni+10 = inputC.noise((xcoord+inputA).5+10.i,(ycoord+inputB).5); break. The first new function works almost like the previous function, but instead of using a cutoff, it instead linearly interpolates between A and B. The next two functions are basically extensions of our previous, simple math functions, but here using subtraction instead of just addition and multiplication. The last two functions, however, deal with Perlin noise.
Here, the input values are used to offset the coordinates of the Perlin noise, meaning that the 'terrain' of the Perlin noise will see sudden shifts when the values of the inputs change. These are the single-dimension outputs of these functions when only looking at the 10th layer, that is, the very simplest. Well, they certainly are more interesting than before. But there are still two apparent problems. One is that the colours are often quite flat, only ever blending between a few different pigments, not experimenting with shades and tints and such. The second is that things are getting overly crowded and not always in an all too pleasant fashion.
Let us first tackle the easier problem to solve - the colours. Colour modification Last time, we looked at two ways to make colours - by generating them through digital channels or by mixing different pigments. Above, I have opted for the pigment approach. But the best system is to use the pigment for the general colour, and then use the digital system slightly alter the resultant colour. ColAB = lerpColor(pigmentA,pigmentB,neuron27) colCD = lerpColor(pigmentC,pigmentD,neuron28) colABCD = lerpColor(colAB,colCD,neuron29) colred = red(colorABCD)+32-64.neuron23 colgreen = green(colorABCD)+32-64.neuron23 colblue = blue(colorABCD)+32-64.neuron23 fill(color(colred,colgreen,colblue)).
As before, we first mix the pigments A, B, C and D into colABCD. But now, we split apart this colour into the three digital channels, red, green and blue. In each channel, we use a previously generated value to 'nudge' the colour. Here, I multiply by 64, but depending on how you like your pictures different factor.
If you want darker pictures, you might remove the +32, for instance. Finally, the three colour channels are combined again to create the colour that we use to paint the screen. Let us see how this changes our renderings.
Today, I want to discuss a strange experiment. While the results are not particularly impressive, I feel that there is something there in the method that could be salvaged and used for other purposes. At first, it might look like just another Perlin noise rendering, but the shapes it generates are strangely sinewy and odd. But the true monstrosity of this sort of terrain can only be appreciated across iterations: I might finally have cracked the nut to generating unique, alien terrains that are truly dissimilar, and not a single line of code has been added to create diversity. The diversity is inherent within the monstrous algorithm. What is the monstrous algorithm, then? Usually, you would generate a heightmap with this kind of formula: height(x,y) = noise(x,y) One of my favourite evolutions of this algorithm is, which would instead look like this: height(x,y) = noise(x+noise(x,y),y+noise(x,y)) The monstrous algorithm is then the continued evolution of this, comp.
Cozy Kingdoms is the marriage of a procedural map generation method, a really good tileset, and a name generation algorithm. It looks a bit like this: Let's talk about how to generate this beauty. The generation code is 430 lines long, with very nicely logical steps in the beginning and more and more conjointed code as it goes along into the realm of choosing specific subimages for tiling purposes. To give an overview of the whole process, here's a neat animation. Well, that was a bit short. Let's go over it once more, step by step, and I'll put down a couple of words about each.
At first, a number of regions are created at random positions. Well, semi-random. They cannot be too close to each-other, for one thing, or too close to the border of the screen. The regions are what will define the landmass, while the border of the screen will make up the surrounding sea.
This is an intermediate screenshot as the regions propagate quite at random. The best way to get a feel f. This post will outline an approach to making a dungeon generator mixing pre-made rooms and procedural rooms, which will allow arbitrary sizes and shapes of rooms. Better than telling is showing, so just look at the beautiful animation below! The basic part of the algorithm was kind of explained in, but let me go over it again in a bit more detail now that I have actually made it. Most of the rooms seen in the above are designed by hand. An algorithm is then able to look at the rooms and 'cut them out' - specifically, a grid data structure is made which holds a common index for each designed room.
When a new room is to be generated, it then chooses a random index and then copies each tile and doodad of the predesigned room into the new map. Linked to this are two complexities. The first is that the position of the tiles in the generated world should be different than in the designed world, so that a room can be created at any arbitrary position.