Self Organising Map (SOM) for clustering of atomistic samples through unsupervised learning.

Overview

Self Organising Map for Clustering of Atomistic Samples - V2

Description

Self Organising Map (also known as Kohonen Network) implemented in Python for clustering of atomistic samples through unsupervised learning. The program allows the user to select wich per-atom quantities to use for training and application of the network, this quantities must be specified in the LAMMPS input file that is being analysed. The algorithm also requires the user to introduce some of the networks parameters:

  • f: Fraction of the input data to be used when training the network, must be between 0 and 1.
  • SIGMA: Maximum value of the sigma function, present in the neighbourhood function.
  • ETA: Maximum value of the eta funtion, which acts as the learning rate of the network.
  • N: Number of output neurons of the SOM, this is the number of groups the algorithm will use when classifying the atoms in the sample.
  • Whether to use batched or serial learning for the training process.
  • B: Batch size, in case the training is performed with batched learning.

The input file must be inside the same folder as the main.py file. Furthermore, the input file passed to the algorithm must have the LAMMPS dump format, or at least have a line with the following format:

ITEM: ATOMS id x y z feature_1 feature_2 ...

To run the software, simply execute the following command in a terminal (from the folder that contains the files and with a python environment activated):

python3 main.py

Check the software report in the general repository for more information: https://github.com/rambo1309/SOM_for_Atomistic_Samples_GeneralRepo

Dependencies:

This software is written in Python 3.8.8 and uses the following external libraries:

  • NumPy 1.20.1
  • Pandas 1.2.4

(Both packages come with the basic installation of Anaconda)

What's new in V2:

Its important to clarify that V2 of the software isn't designed to replace V1, but to be used when multiple files need to be analysed sequentially with a network that has been trained using a specific training file. It is recommended for the user to first use V1 to explore the results given by different parameters and features of the sample, and then to use V2 to get consistent results for a series of samples. Another reason why V1 will be continually updated is its command-line interactive interface, which allows the users to implement the algorithm without ever having to open and edit a python file.

The most fundamental change with respect to V.1 is the way of communicating with the program. While V.1 uses an interactive command-line interface, V.2 requests an input_params.py file that contains a dictionary specifying the parameters and sample files for the algorithm.

Check the report file in the repository for a complete description of the changes made in the software.

Updates:

Currently working on giving the user the option to change the learning rate funtion, eta, with a few alternatives such as a power-law and an exponential decrease. Another important issue still to be addressed is the training time of the SOM.

Owner
Franco Aquistapace
Undergraduate Physics student at FCEN, UNCuyo
Franco Aquistapace
PySpark ML Bank Churn Prediction

PySpark-Bank-Churn Surname: corresponds to the record (row) number and has no effect on the output. CreditScore: contains random values and has no eff

kemalgunay 2 Nov 11, 2021
Create large-scale ML-driven multiscale simulation ensembles to study the interactions

MuMMI RAS v0.1 Released: Nov 16, 2021 MuMMI RAS is the application component of the MuMMI framework developed to create large-scale ML-driven multisca

4 Feb 16, 2022
PySurvival is an open source python package for Survival Analysis modeling

PySurvival What is Pysurvival ? PySurvival is an open source python package for Survival Analysis modeling - the modeling concept used to analyze or p

Square 265 Dec 27, 2022
A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

Aayush Malik 80 Dec 12, 2022
XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

92 Dec 14, 2022
机器学习检测webshell

ai-webshell-detect 机器学习检测webshell,利用textcnn+简单二分类网络,基于keras,花了七天 检测原理: 从文件熵 文件长度 文件语句提取出特征,然后文件熵与长度送入二分类网络,文件语句送入textcnn 项目原理,介绍,怎么做出来的

Huoji's 56 Dec 14, 2022
The unified machine learning framework, enabling framework-agnostic functions, layers and libraries.

The unified machine learning framework, enabling framework-agnostic functions, layers and libraries. Contents Overview In a Nutshell Where Next? Overv

Ivy 8.2k Dec 31, 2022
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learn

Vowpal Wabbit 8.1k Dec 30, 2022
MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training

MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th

MosaicML 2.8k Jan 06, 2023
distfit - Probability density fitting

Python package for probability density function fitting of univariate distributions of non-censored data

Erdogan Taskesen 187 Dec 30, 2022
Scikit-Learn useful pre-defined Pipelines Hub

Scikit-Pipes Scikit-Learn useful pre-defined Pipelines Hub Usage: Install scikit-pipes It's advised to install sklearn-genetic using a virtual env, in

Rodrigo Arenas 1 Apr 26, 2022
CVXPY is a Python-embedded modeling language for convex optimization problems.

CVXPY The CVXPY documentation is at cvxpy.org. We are building a CVXPY community on Discord. Join the conversation! For issues and long-form discussio

4.3k Jan 08, 2023
scikit-multimodallearn is a Python package implementing algorithms multimodal data.

scikit-multimodallearn is a Python package implementing algorithms multimodal data. It is compatible with scikit-learn, a popul

12 Jun 29, 2022
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Ray provides a simple, universal API for building distributed applications. Ray is packaged with the following libraries for accelerating machine lear

23.3k Dec 31, 2022
A collection of Machine Learning Models To Web Api which are built on open source technologies/frameworks like Django, Flask.

Author Ibrahim Koné From-Machine-Learning-Models-To-WebAPI A collection of Machine Learning Models To Web Api which are built on open source technolog

Ibrahim Koné 2 May 24, 2022
A linear regression model for house price prediction

Linear_Regression_Model A linear regression model for house price prediction. This code is using these packages, so please make sure your have install

ShawnWang 1 Nov 29, 2021
Fourier-Bayesian estimation of stochastic volatility models

fourier-bayesian-sv-estimation Fourier-Bayesian estimation of stochastic volatility models Code used to run the numerical examples of "Bayesian Approa

15 Jun 20, 2022
Binary Classification Problem with Machine Learning

Binary Classification Problem with Machine Learning Solving Approach: 1) Ultimate Goal of the Assignment: This assignment is about solving a binary cl

Dinesh Mali 0 Jan 20, 2022
Pragmatic AI Labs 421 Dec 31, 2022
Machine Learning for RC Cars

Suiron Machine Learning for RC Cars Prediction visualization (green = actual, blue = prediction) Click the video below to see it in action! Dependenci

Kendrick Tan 706 Jan 02, 2023