Is there a way to weigh layout according to node attributes in igraph? In other words, how to get nodes that share the same characteristics (but do not have edges between them) cluster more closely together?
While many layout functions can take edge weight into account, the nodes that I want to be closer to each other do not have edges between them. An example of such situation is if the graph is bipartite. Using layouts such as fruchterman.reingold is not very informative as vertices of the two different types are interspersed. However, I do not want it be be as extreme as the layout.bipartite option either as it would be rather messy when there are lots of vertices. What I wish is to have a layout that is somewhere between these two, having vertices of the same type to be on one side, and also cluster according to certain attributes, with edges between the two types.
Any idea or suggestion will be greatly appreciated. Thanks!
igraph layouts are simply matrices with 2 columns and N rows, so you can easily re-use one layout with another graph as long as the two graphs share the same number of nodes. You can make use of this here: create a graph where you connect the nodes that you want to be placed close to each other, calculate the layout using this graph, and then plot your original graph with the layout you have calculated.
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I have an image(e.g. 60x60) with multiple items inside it. Items are in the shape of square boxes, with say 4x4 dimensions, and are randomly placed within the image. The boxes(items) themselves are created with random patterns, some random pixels switched on and others switched off. So, it could be the same box repeated twice(or more in case of more than 2 items) in the image or could be entirely different.
I'm looking to create a deep learning model that could take in the original image(60x60) and output all the patches in the image.
This is all I have for now, but I can definitely share more details as the discussion starts. I'd be interested to weigh in different options that can help me achieve this objective. Thanks.
I would solve this using object detection. First I would train a network to detect those box like objects by cutting out patches of those objects. Then I would run a Faster R-CNN or something like this on it.
You might want to take a look at the stanford lecture on detection (slides here: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf).
I'm currently building an as3 isometric game, but I'm having a lot of problem with depth sorting. I've searched for a solution, but didn't found anything that match my problem (rectangle objects).
Here is a screenshot of my game:
As you can see, depth sorting works well when it's between 1x1 tiles objects. I simply use their x and y coordinates (relative to the isometric map) to sort them.
The problem comes when I have bigger objects, like 2x2 or 1x4 or 4x1.
Any idea how should I handle depth sorting then?
I don't think it is possible to sort a scene based on a single x,y value for each object if some of them can be long enough that one end should be at a different depth than the other. For instance, consider how you'd handle the rendering if the brown chair in your picture was moved one square down-left (to the square between the blue chair and the long couch). It would be deeper in the scene than the red table behind the couch, but would need to be rendered on top of the couch, which would need to be on top of the table.
I think there are two simple solutions:
Design your
levels using only one sort of overlap for large objects. For
instance, you could specify that an object's depth is based on its
nearest corner, which would require you to avoid putting things in
front of its most distant bits (since it will render on top of them).
Or you could stick with your current code (which seems to use the
most distant corner for depth) and avoid putting anything behind the
nearer parts. You may still have trouble with characters and other
objects that move around though. You might be able to make the
troublesome tiles inaccessible if you're careful with your design,
but in some cases this may be too restrictive.
Break up your large objects into smaller ones
which would have their own depths. You will probably want to go right
down to 1x1 pieces, each of which will have an unambiguous depth. You
might choose keep the larger objects in the code as invisible
containers for the smaller pieces, or they could be eliminated
entirely, whichever makes it easier for you to load up and enable
interaction with the various bits.
Splitting larger objects in to 1x1 sized pieces can also be nice since you can make them modular. That is, you can build differently sized objects by putting together 1x1 pieces in different combinations. If you cut your 2x1 tables in your image in half vertically, for instance, and created a 1x1 middle tile that fit in between them, you could stretch the design out to 3x1 or 10x1, depending on how many times you repeat the middle tile. There's a lot of other ways to make tiled graphics look good with only a modest amount of art required.
Ultima Online emulators (specifically, POL, though there may be others) achieve this through the implementation and usage of the concept of a 'multi' -- a single object comprised of sections of cut-up larger graphics. These cut-up graphics are such that their sprites are vertically-split at the left- and right-corner points of iso grid boundaries.
Other considerations:
- render 'multi' pieces sorted screen-Y axis from top-to-bottom.
- the southern (i.e. screen bottom-left) component of a 'multi' becomes the anchoring tile position (in the case of your couch, its left-most piece).
- consider that each map location can also hold its own vertical stack of objects; offsetting each object's render by screen-Y simulates height/altitude, and these must be sorted bottom-to-top (e.g. lowest-altitude to highest altitude).
Good luck!
I am trying to randomly generate a directed graph for the purpose of making a puzzle game similar to the ice sliding puzzles from pokemon.
This is essentially what I want to be able to randomly generate: http://bulbanews.bulbagarden.net/wiki/Crunching_the_numbers:_Graph_theory
I need to be able to limit the size of the graph in an x and y dimension. In the example in the link, it would be restricted to an 8x4 grid.
The problem I am running in to is not randomly generating the graph, but randomly generating a graph which I can properly map out in a 2d space, since I need something (like a rock) on the opposite side of a node, to make it visually make sense when you stop sliding. The problem with this is sometimes the rock ends up in the path between two other nodes or possibly on another node itself, which causes the entire graph to become broken.
After discussing the problem with a few people I know, we came to a couple of conclusions that may lead to a solution. Including the obstacles in the grid as part of the graph when constructing it. Start out with a fully filled grid and just draw a random path and delete out blocks that will make that path work, though the problem then becomes figuring out which ones to delete so that you don't accidentally introduce an additional, shorter path. We were also thinking a dynamic programming algorithm may be beneficial, though none of us are too skilled with creating dynamic programming algorithms from nothing. Any ideas or references about what this problem is officially called (if it's an official graph problem) would be most helpful.
I wouldn't look at it as a graph problem, since as you say the representation is incomplete. To generate a puzzle I would work directly on a grid, and work backwards; first fix the destination spot, then place rocks in some way to reach it from one or more spots, and iteratively add stones to reach those other spots, with the constraint that you never add a stone which breaks all the paths to the destination.
You might want to generate a planar graph, which means that the edges of the graph will not overlap each other in a two dimensional space. Another definition of planar graphs ist that each planar graph does not have any subgraphs of the type K_3,3 (complete bi-partite with six nodes) or K_5 (complete graph with five nodes).
There's a paper on the fast generation of planar graphs.
Sometimes I would like to overlay a conceptual graph on top of a map to provide additional context of where elements belong. For example, if I wanted to show social relationships between people in different countries, I might want to have the people located in their appropriate country, but with the layout within those countries being automated.
I've drawn (poorly) a picture to help illustrate what I'm hoping to do.
I found this example, but this appears to be a fake geography with clustering. What I would like is a real map where entities are contained inside their correct region, but where the entities themselves are automagically arranged.
I don't think it's possible, either, at least not in a strict sense.
However, you might try to add an invisible node in the middle of each country (use "pos" for placing and "style=plain" with no label to make it invisible), and then link the people who live there with a short edge (use "len" to constrain the length and "weight" to make it more important in the ordering). You might add more than one such node to a country, if it has a funky shape.
I'm writing a game where a large number of objects will have "area effects" over a region of a tiled 2D map.
Required features:
Several of these area effects may overlap and affect the same tile
It must be possible to very efficiently access the list of effects for any given tile
The area effects can have arbitrary shapes but will usually be of the form "up to X tiles distance from the object causing the effect" where X is a small integer, typically 1-10
The area effects will change frequently, e.g. as objects are moved to different locations on the map
Maps could be potentially large (e.g. 1000*1000 tiles)
What data structure would work best for this?
Providing you really do have a lot of area effects happening simultaneously, and that they will have arbitrary shapes, I'd do it this way:
when a new effect is created, it is
stored in a global list of effects
(not necessarily a global variable,
just something that applies to the
whole game or the current game-map)
it calculates which tiles
it affects, and stores a list of those tiles against the effect
each of those tiles is
notified of the new effect, and
stores a reference back to it in a
per-tile list (in C++ I'd use a
std::vector for this, something with
contiguous storage, not a linked
list)
ending an effect is handled by iterating through
the interested tiles and removing references to it, before destroying it
moving it, or changing its shape, is handled by removing
the references as above, performing the change calculations,
then re-attaching references in the tiles now affected
you should also have a debug-only invariant check that iterates through
your entire map and verifies that the list of tiles in the effect
exactly matches the tiles in the map that reference it.
Usually it depends on density of your map.
If you know that every tile (or major part of tiles) contains at least one effect you should use regular grid – simple 2D array of tiles.
If your map is feebly filled and there are a lot of empty tiles it make sense to use some spatial indexes like quad-tree or R-tree or BSP-trees.
Usually BSP-Trees (or quadtrees or octrees).
Some brute force solutions that don't rely on fancy computer science:
1000 x 1000 isn't too large - just a meg. Computers have Gigs. You could have an 2d array. Each bit in the bytes could be a 'type of area'. The 'effected area' that's bigger could be another bit. If you have a reasonable amount of different types of areas you can still use a multi-byte bit mask. If that gets ridiculous you can make the array elements pointers to lists of overlapping area type objects. But then you lose efficiency.
You could also implement a sparse array - using a hashtable key'd off of the coords (e.g., key = 1000*x+y) - but this is many times slower.
If course if you don't mind coding the fancy computer science ways, they usually work much better!
If you have a known maximum range of each area effect, you could use a data structure of your choosing and store the actual sources, only, that's optimized for normal 2D Collision Testing.
Then, when checking for effects on a tile, simply check (collision detection style, optimized for your data structure) for all effect sources within the maximum range and then applying a defined test function (for example, if the area is a circle, check if the distance is less than a constant; if it's a square, check if the x and y distances are each within a constant).
If you have a small (<10) amount of effect "field" shapes, you can even do a unique collision detection for each effect field type, within their pre-computed maximum range.