PICO is an algorithm for exploiting Reinforcement Learning (RL) on Multi-agent Path Finding tasks.It is developed by the Multi-Agent Artificial Intelligence Lab (MAIL) in East China Normal University and the AI Research Institute in Geekplus Technology Co., Ltd.PICO is constructed based on the framework of PRIMAL:Pathfinding via Reinforcement and Imitation Multi-Agent Learning and focuses more on the collision avoidance rather than manual post-processing when collision occurs.Exploiting the design of decentralized communication and implicit priority in these secenarios benifits better path […]

“# bpe_algorithm_can_finetune_tokenizer” this is an implyment for https://github.com/huggingface/transformers/issues/15153 I just add tens of lines of code into the py_bpe algorithm.function finetune_tokenizer is main function added. Details can be see in example.py , actuctally it is very simple.the official python library tokenizer is written is rust. I am learning hoping to give a rust version of this code. ps:the_factor_of_new_added_token_divided_unk_number is the only param you should set.hoping can find a auto algorithm to set it. GitHub View Github    

© Zulkarnine, 2022. Algorithm to solve Wordle 100% of the time within 6 attempts. You can go ahead and run main.py to run it for all 2315 Wordle words and it solves 100% of them correctly within 6 attempts.Example output: Ran: 2315 games. Solved: 2315/2315 = 100.00% You can also run solver.py to get a sense of how it’s guessing and what is the Wordle game simulation returning. (I.e. the colored blocks) Example output:

This is the implementation of our paper “Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks“. Dataset To successfully test performance, we created TPIC Dataset, a temporal popularity image collection dataset. Overview Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently […]

All algorithms have a complexity known as Big O Notation, the more complex an algorithm, the less efficient it gets as more data is introduced. Listed below are the Big-O complexities listed from best to worst:

This is a demo for AAD algorithm. The subroutine of the anisotropic diffusion algorithm is modified from Mr. Michael Aye’s repository in https://github.com/michaelaye/pymars/blob/ca62a17c682f999c490cc0dbceb01433c385ced0/pymars/anisodiff2D.py.We have provided the code of Asynchronous-Anisotropic-Diffusion-Algorithm, and the Optics Express paper here, please access the data we used refer to https://github.com/BJTUSensor/AD-algorithm/blob/main/data.mat. Operating environment: Python 3.7 GitHub View Github    

GNAT GNAT is NOT Algorithmic Trading! GNAT is a financial tool with two goals in mind: To provide a dashboard with the detailed stock/crypto information of your choice. To provide confidence in the Harvest framework which GNAT heavily relies on. How to use pip install -r requirements.txt python gnat.py -OR- python gnat.py my_config.yaml Input the requested information and then go to localhost:8050 to see the dashboard. How it works On start, GNAT asks the user what assets they want to […]

An algorithm (implemented here in Python) mainly inspired by the Bucket Sort (and the Pigeonhole Sort) and works by placing the values of a given unsorted array into buckets where the number of buckets is equal to the length of the unsorted array. In addition, it recursively runs the sorting algorithm on any buckets that contain more than one element and is unsorted. For more, including why the algorithm has been implemented in this way and how it compares against […]