natural-selection
CoinBLAS
natural-selection | CoinBLAS | |
---|---|---|
8 | 3 | |
164 | 21 | |
- | - | |
1.4 | 1.8 | |
about 1 year ago | almost 3 years ago | |
CSS | Jupyter Notebook | |
MIT License | - |
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natural-selection
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Explaining CSS Organisational Layout
Maybe https://github.com/frontaid/natural-selection can give you some pointers. It is a blueprint of a possible CSS file structure that applies globally. This covers at least 1. and 2. of your example.
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How do you write custom CSS?
[1] https://github.com/frontaid/natural-selection
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"Global" vs "Local" styles
PS: If you want to get a jump start for the global styling of your projects, you might want to have a look at https://github.com/frontaid/natural-selection
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Ask HN: What is your CSS framework of choice?
I rarely use any of the popular CSS frameworks. Unless you are making a small(ish) website where the styling is not important, it is actually more work to change an existing frameworks than to just use CSS yourself.
But what I do use sometimes is a CSS reset like https://github.com/necolas/normalize.css
And what I also use is this CSS boilerplate: https://github.com/frontaid/natural-selection (Disclaimer: I created it because I wanted to avoid the repetitive work of writing out all the selectors).
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Ask HN: What Are You Working On?
I'm working on a CSS framework without any styling (!). It is a collection of selectors and meant to be used as a clean CSS boilerplate. Thus it can jump start your your next project or design system.
https://github.com/frontaid/natural-selection
- Show HN: Natural Selection – CSS framework without any styling
- Natural Selection - CSS framework without any styling.
- Natural Selection - CSS Framework without any styling.
CoinBLAS
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The "missing" graph datatype already exists. It was invented in the '70s
When you consider that a graph and a matrix are isomorphic, doing vector matrix multiplication takes a vector with a set value, say row 4, and multiplies it by a matrix where row 4 has values present that represent edges to the nodes that are adjacent to it (ie "adjacency" matrix). The result is a vector with the next "step" in a BFS across the graph, do that in a loop and you step across the whole graph.
A cool result of this is, for example, taking an adjacency matrix and squaring it is the "Friend of a Friend" graph. It takes every node/row and multiplies it by itself, returning a matrix that are adjacent to the adjacencies of each node, ie, the friends (adjacencies of the adjacencies) of friends (adjacencies) of the nodes.
Deeper traversal are just higher nodes, a matrix cubed are the friends of the friends of the friends.
A picture is worth a thousand words, see figure 7 of this paper:
https://arxiv.org/pdf/1606.05790.pdf
Also check out figure 8, this shows how incidence matrices can work to represent hyper and multi graphs. An pair of incidence matrices reprsent two graphs, one from nodes to edges and the other from edges to nodes, these are n by m and m by n. When you multiply them, you get a square adjacency matrix that "projects" the incidence into an adjacency. This can be used to collapse hypergraphs into simple graphs that use different semirings to combine the multiple edges.
For some pretty pictures of this kind of stuff, check out CoinBLAS (note I am not a crypto-bro, it was just a very handy extremely large multi-graph that I could easily download in chunks to play with):
https://github.com/Graphegon/CoinBLAS/
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Ask HN: What Are You Working On?
Python wrapper around The GraphBLAS API:
https://github.com/michelp/pygraphblas
For an upcoming paper we've open sourced using pygraphblas to analyse the bitcoin graph using the GAP benchmarks on a server with 1TB of RAM:
https://github.com/Graphegon/CoinBLAS
- Show HN: CoinBLAS – Bitcoin Analysis with the GraphBLAS
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