# 1. Installing Maven & Pandas First, please install Java (JDK11) and Python 3 if they are not already. Next, make sure that Maven (for importing JGraphT) and Pandas(for data analysis) are installed. To install Maven on Ubuntu, type the following commands on terminal: sudo apt-get update sudo apt-get install maven For Pandas, type the following: pip3 install pandas ( sudo apt-get install python3-pip if pip is not installed already) # 2. Compilation Type the following to compile this project: mvn compile # 3. Running the code Below is the command for running tests for SNAP(DIMACS) and grid data. java -Xms24G -Xmx48G -Xmn36G -Xss1G -cp $CLASSPATHS shell.TestSNAP (the filename of data; just the name and not the path) (# of tests) (randomization seed) java -Xms32G -Xmx64G -Xmn48G -Xss1G -cp $CLASSPATHS shell.TestGrid (Maximum dimension) (dimension increment) [List of the values for k, space-separated] You may change the randomization seed (vertex selection) to assess reproducibility. (In our experiment, the seed was set to 2021.) For the data, check "src/SNAP(or DIMACS)". Output "test_result.csv" will be saved on "target" directory. Check if 'CLASSPATHS' is set properly. Please refer to " sample.sh " for examples & further details. #4. Obtaining average processing time and diversity First, move to the target directory. Then run get_averages.py python3 get_averages (.csv file name) [list of the values for k, space-separated. Optional parameter.]
Diverse graph algorithms implemented using JGraphT library.
Overview
You might also like...
Codes for paper "Towards Diverse Paragraph Captioning for Untrimmed Videos". CVPR 2021
Towards Diverse Paragraph Captioning for Untrimmed Videos This repository contains PyTorch implementation of our paper Towards Diverse Paragraph Capti
Supplementary code for SIGGRAPH 2021 paper: Discovering Diverse Athletic Jumping Strategies
SIGGRAPH 2021: Discovering Diverse Athletic Jumping Strategies project page paper demo video Prerequisites Important Notes We suspect there are bugs i
A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.
Note: This is an alpha (preview) version which is still under refining. nn-Meter is a novel and efficient system to accurately predict the inference l
[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers Created by Xumin Yu*, Yongming Rao*, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie Zhou
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion Yinghao Aaron Li, Ali Zare, Nima Mesgarani We pres
Code and data for "Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning" (EMNLP 2021).
GD-VCR Code for Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning (EMNLP 2021). Research Questions and Aims: How well can a model perform o
MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning
MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL [ Documentation | Demo Video ] MetaDrive is a driving simulator with the following
Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels.
The Face Synthetics dataset Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels. It was introduced in ou
[ACM MM 2021] Diverse Image Inpainting with Bidirectional and Autoregressive Transformers
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers Installation pip install -r requirements.txt Dataset Preparation Given the
Reinforcement learning framework and algorithms implemented in PyTorch.
Reinforcement learning framework and algorithms implemented in PyTorch.
Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.
Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t
This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning"
CSP_Deep_EEG This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning" {https://www
Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange
MyTT Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! to Stock Market Financial Technical Analysis Python
CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"
Diverse Structure Inpainting ArXiv | Papar | Supplementary Material | BibTex This repository is for the CVPR 2021 paper, "Generating Diverse Structure
Diverse Image Captioning with Context-Object Split Latent Spaces (NeurIPS 2020)
Diverse Image Captioning with Context-Object Split Latent Spaces This repository is the PyTorch implementation of the paper: Diverse Image Captioning
Diverse Branch Block: Building a Convolution as an Inception-like Unit
Diverse Branch Block: Building a Convolution as an Inception-like Unit (PyTorch) (CVPR-2021) DBB is a powerful ConvNet building block to replace regul
This is the PyTorch implementation of GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation
Official PyTorch repo for GAN's N' Roses. Diverse im2im and vid2vid selfie to anime translation.
Code for our ACL 2021 paper "One2Set: Generating Diverse Keyphrases as a Set"
One2Set This repository contains the code for our ACL 2021 paper “One2Set: Generating Diverse Keyphrases as a Set”. Our implementation is built on the
Implementation of Diverse Semantic Image Synthesis via Probability Distribution Modeling
Diverse Semantic Image Synthesis via Probability Distribution Modeling (CVPR 2021) Paper Zhentao Tan, Menglei Chai, Dongdong Chen, Jing Liao, Qi Chu,