TheAlgorithms
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TheAlgorithms | NetworkX | |
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61 | 61 | |
179,165 | 14,153 | |
1.7% | 1.4% | |
9.7 | 9.6 | |
1 day ago | 2 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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TheAlgorithms
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Wikifunctions
Is it me or does it not seem very well thought out? Every example I've seen only has implementations in JavaScript and/or Python. I haven't seen any other languages nor a way to search by language. What a "string" means in one language can be completely different in another language. The primitive data types that the project assumes are not really supported across all programming languages.
Also if anyone hasn't already seen them, similar projects already exist and are more complete. E.g.
* https://programming-idioms.org/
Not to mention LeetCode, CodeWars, Project Euler, Exercism can kinda serve the same role.
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Introduction
Hey Everyone, My name is Rachit Chawla and Its my first blog on dev.to. I am currently a student of Computer Programming and Analysis at Seneca College. Also I'm currently on my co-op term working as an Automation Developer at Ontario Public Service. In this role, I am currently working with PowerShell scripting and Microsoft Azure for automating every manual tasks to reduce workload and increase efficiency. This blog is a part of OSD600 course at Seneca College. I am taking this course as I am big fan of open source and always wanted to contribute in open source projects but I am unaware of proper documentation and standards used for open source contributions. I am hoping to learn all the required stuff by the end of this course and I aim to be one of the 15k contributors to Linux's repo by Linus Torvald. Open Source interests me because it gives developers the power to customise the application they want to use, also a chance to help others and improve their skills. I found https://github.com/TheAlgorithms/Python interesting from the Monthly trending feed on Github as it has all the algorithms which help us improve time complexity and write better codes. I has about 1000 contributors which helped to code all the algorithms in Python which may help others for working or learning purposes. I myself was a student of Data Structures and Algorithms in Python Winter 2023 and hoping to even able to contribute to this repo itself, once I learn more about documentation & proper standards to be followed.
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I am studying my college Python so can I learn algorithms from it?
The Algorithms Contains many open source implementations of algorithms. Check it out.
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Where To Read About Python Algos?
If you want to see implementations of all possible traversal algorithms you can find it here.
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Book of pythonic code
The mother load of all algorithms in python is here. dfs/bfs in particular are in the graph section.
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Any tips to improve my coding abilites ?
There is no one way to learn all these but here are some resources: 1. Gooking algorithms [https://edu.anarcho-copy.org/Algorithm/grokking-algorithms-illustrated-programmers-curious.pdf\] 2. Algorithms in all languages [https://the-algorithms.com/] 3. Node js best practices. [https://github.com/goldbergyoni/nodebestpractices] 4. Refactoring [https://refactoring.guru/] 5. Learn about Clean Code and Clean Architecture from uncle bob. https://www.youtube.com/watch?v=NeXQEJNWO5w&ab_channel=StreamAConStreamingConferences
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Self taught developers: where are you in your journey?
DSA basics
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Algo and data structures
I would recommend The Algorithms, it comes with descriptions and examples in multiple programming languages.
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A site that hosts implementations of various programming algorithms in different languages
There's also The Algorithms. Many implementations are unfortunately low quality. The Lua ones (disclaimer: I wrote them) should be fine however.
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How worried are you about AI taking over music?
Python 940 contributors 152k stars
NetworkX
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Routes to LANL from 186 sites on the Internet
Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...
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The Hunt for the Missing Data Type
I think one of the elements that author is missing here is that graphs are sparse matrices, and thus can be expressed with Linear Algebra. They mention adjacency matrices, but not sparse adjacency matrices, or incidence matrices (which can express muti and hypergraphs).
Linear Algebra is how almost all academic graph theory is expressed, and large chunks of machine learning and AI research are expressed in this language as well. There was recent thread here about PageRank and how it's really an eigenvector problem over a matrix, and the reality is, all graphs are matrices, they're typically sparse ones.
One question you might ask is, why would I do this? Why not just write my graph algorithms as a function that traverses nodes and edges? And one of the big answers is, parallelism. How are you going to do it? Fork a thread at each edge? Use a thread pool? What if you want to do it on CUDA too? Now you have many problems. How do you know how to efficiently schedule work? By treating graph traversal as a matrix multiplication, you just say Ax = b, and let the library figure it out on the specific hardware you want to target.
Here for example is a recent question on the NetworkX repo for how to find the boundary of a triangular mesh, it's one single line of GraphBLAS if you consider the graph as a matrix:
https://github.com/networkx/networkx/discussions/7326
This brings a very powerful language to the table, Linear Algebra. A language spoken by every scientist, engineer, mathematician and researcher on the planet. By treating graphs like matrices graph algorithms become expressible as mathematical formulas. For example, neural networks are graphs of adjacent layers, and the operation used to traverse from layer to layer is matrix multiplication. This generalizes to all matrices.
There is a lot of very new and powerful research and development going on around sparse graphs with linear algebra in the GraphBLAS API standard, and it's best reference implementation, SuiteSparse:GraphBLAS:
https://github.com/DrTimothyAldenDavis/GraphBLAS
SuiteSparse provides a highly optimized, parallel and CPU/GPU supported sparse Matrix Multiplication. This is relevant because traversing graph edges IS matrix multiplication when you realize that graphs are matrices.
Recently NetworkX has grown the ability to have different "graph engine" backends, and one of the first to be developed uses the python-graphblas library that binds to SuiteSparse. I'm not a directly contributor to that particular work but as I understand it there has been great results.
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Build the dependency graph of your BigQuery pipelines at no cost: a Python implementation
In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.
- NetworkX – Network Analysis in Python
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Custom libraries and utility tools for challenges
If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time.
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Google open-sources their graph mining library
For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2].
Integrations include:
* NetworkX -- https://networkx.org/
* DeepGraphLibrary -- https://www.dgl.ai/
* cuGraph (Rapids.ai Graph) -- https://docs.rapids.ai/api/cugraph/stable/
* PyG (PyTorch Geometric) -- https://pytorch-geometric.readthedocs.io/en/latest/
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1: https://docs.arangodb.com/3.11/data-science/adapters/
2: https://github.com/arangodb/interactive_tutorials#machine-le...
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org-roam-pygraph: Build a graph of your org-roam collection for use in Python
org-roam-ui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx.
What are some alternatives?
new-world-fishing-bot - user friendly python script who is able to catch fish in the game New World
Numba - NumPy aware dynamic Python compiler using LLVM
python-ds - No non-sense and no BS repo for how data structure code should be in Python - simple and elegant.
Dask - Parallel computing with task scheduling
python-patterns - A collection of design patterns/idioms in Python
julia - The Julia Programming Language
algorithms
RDKit - The official sources for the RDKit library
more-itertools - More routines for operating on iterables, beyond itertools
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
ClointFusion - Cloint India Pvt. Ltd's (ClointFusion) Pythonic RPA (Automation) Platform
SymPy - A computer algebra system written in pure Python