moa

Optimizing inference proxy for LLMs

A machine learning package for streaming data in Python. The other ancestor of River.

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

My Java codes for the MOA framework. It includes the implementations of FHDDM, FHDDMS, and MDDMs.

🧭🧭 An intelligent load balancer with smart scheduling that unifies diverse LLMs.

Elucidate and visualise a compound's mechanism of action by combining structure-based target prediction with gene expression-based causal reasoning, plus pathway enrichment to put results into biological context. GUI-based (minimal coding experience required).

UMM-Discovery is a fully unsupervised deep learning method to cluster cellular images with similar phenotypes together, solely based on the intensity values.

iGNGSVM, OI-SVM, GNG and ENN developed in MOA/WEKA.

Official repo for DK904 - IOT Stream Data Mining

A simplified agentic workflow process based on the Mixture of Agents (MoA) system for Large Language Models (LLMs)

Adaptive Decision Forest(ADF) is an incremental machine learning framework called to produce a decision forest to classify new records. ADF is capable to classify new records even if they are associated with previously unseen classes. ADF also is capable of identifying and handling concept drift; it, however, does not forget previously gained knowledge. Moreover, ADF is capable of handling big data if the data can be divided into batches.

MODIS Mosaic of Antarctica

Repository for the StreamingRandomPatches algorithm implemented in MOA 2019.04

Incremental Gaussian Mixture Network for Non-Stationary Environments

Classifying drugs based on their biological activity.

Simple workflow API for MOA.

Install and configure CP4I

It's a middleware for swagger-mock!