True per-item rarity for Loot

Overview

True-Rarity

True per-item rarity for Loot (For Adventurers) and More Loot A.K.A mLoot

  • each out/true_rarity_{item_type}.json file contains probabilities for each item of that type, even if the item is impossible.
  • out/true_rarity_condensed.json contains probabilities for every possible item.

Item probabilities by tier:

Item counts by tier:

Background & personal notes

Rarity metrics

The community currently (2021-09-06) tends to judge bags based on a few different metrics, including:

  1. Rarity of individual items within the bag, where rarity is based on how many exist today
  2. Combined rarity of individual items within the bag, where rarity is based on how many exist today
  3. Combined greatness value of the items within the bag

1 and 2 are closely related, and the "true rarity" data in this repo is meant to complement those. Since mLoot is perpetually releasing new bags, an item that's rare today may not be that rare soon. For example: "Blight Peak Shoes of Perfection +1" are currently 1/1, but really are expected to come up every ~116k bags. It just so happened there was only 1 in the first 1.3m. Next will be in 1400184.
"True rarity" doesn't care about which mLoot bags are released today, and instead shows the actual rarity of each item, assuming infinite bags.

This repo does not focus on any greatness calculations (3), because AFAIA, greatness is not directly used by any derivatives.

Impossible items

Also, as demonstrated in the true rarity data, a huge number of items are simply impossible. As an example, it's impossible to have Divine Robe with +1 or even a prefix. This is due to the modular arithmetic in the original implementation.

Of 4,015,861 total items, only 72,229 (1.8%) are possible.

Owner
Dan R.
Enthusiastic about tech, music, and their intersection! Side projects at sadlok.com. Member of youtube.com/plentakill
Dan R.
BanditPAM: Almost Linear-Time k-Medoids Clustering

BanditPAM: Almost Linear-Time k-Medoids Clustering This repo contains a high-performance implementation of BanditPAM from BanditPAM: Almost Linear-Tim

254 Dec 12, 2022
A new play-and-plug method of controlling an existing generative model with conditioning attributes and their compositions.

Viz-It Data Visualizer Web-Application If I ask you where most of the data wrangler looses their time ? It is Data Overview and EDA. Presenting "Viz-I

NVIDIA Research Projects 66 Jan 01, 2023
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning The predictive learning of spatiotemporal sequences aims to generate future

THUML: Machine Learning Group @ THSS 243 Dec 26, 2022
Multiview Dataset Toolkit

Multiview Dataset Toolkit Using multi-view cameras is a natural way to obtain a complete point cloud. However, there is to date only one multi-view 3D

11 Dec 22, 2022
Generative Autoregressive, Normalized Flows, VAEs, Score-based models (GANVAS)

GANVAS-models This is an implementation of various generative models. It contains implementations of the following: Autoregressive Models: PixelCNN, G

MRSAIL (Mini Robotics, Software & AI Lab) 6 Nov 26, 2022
Jittor 64*64 implementation of StyleGAN

StyleGanJittor (Tsinghua university computer graphics course) Overview Jittor 64

Song Shengyu 3 Jan 20, 2022
Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

Weakly_detector Tensorflow implementation of "Learning Deep Features for Discriminative Localization" B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and

Taeksoo Kim 363 Jun 29, 2022
EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections Ruiqi Zhong, Kristy Lee*, Zheng Zhang*, Dan Klein EMN

Ruiqi Zhong 42 Nov 03, 2022
Hashformers is a framework for hashtag segmentation with transformers.

Hashtag segmentation is the task of automatically inserting the missing spaces between the words in a hashtag. Hashformers applies Transformer models

Ruan Chaves 41 Nov 09, 2022
A simple approach to emable dense segmentation with ViT.

Vision Transformer Segmentation Network This implementation of ViT in pytorch uses a super simple and straight-forward way of generating an output of

HReynaud 5 Jan 03, 2023
Airborne magnetic data of the Osborne Mine and Lightning Creek sill complex, Australia

Osborne Mine, Australia - Airborne total-field magnetic anomaly This is a section of a survey acquired in 1990 by the Queensland Government, Australia

Fatiando a Terra Datasets 1 Jan 21, 2022
A Simple Key-Value Data-store written in Python

mercury-db This is a File Based Key-Value Datastore that supports basic CRUD (Create, Read, Update, Delete) operations developed using Python. The dat

Vaidhyanathan S M 1 Jan 09, 2022
Pipeline for employing a Lightweight deep learning models for LOW-power systems

PL-LOW A high-performance deep learning model lightweight pipeline that gradually lightens deep neural networks in order to utilize high-performance d

POSTECH Data Intelligence Lab 9 Aug 13, 2022
Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow.

Denoised-Smoothing-TF Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow. Denoised Smoothing is

Sayak Paul 19 Dec 11, 2022
Unofficial implementation of PatchCore anomaly detection

PatchCore anomaly detection Unofficial implementation of PatchCore(new SOTA) anomaly detection model Original Paper : Towards Total Recall in Industri

Changwoo Ha 268 Dec 22, 2022
Polynomial-time Meta-Interpretive Learning

Louise - polynomial-time Program Learning Getting help with Louise Louise's author can be reached by email at Stassa Patsantzis 64 Dec 26, 2022

Predicting Auction Sale Price using the kaggle bulldozer auction sales data: Modeling with Ensembles vs Neural Network

Predicting Auction Sale Price using the kaggle bulldozer auction sales data: Modeling with Ensembles vs Neural Network The performances of tree ensemb

Mustapha Unubi Momoh 2 Sep 13, 2022
Harmonic Memory Networks for Graph Completion

HMemNetworks Code and documentation for Harmonic Memory Networks, a series of models for compositionally assembling representations of graph elements

mlalisse 0 Oct 27, 2021
A collection of resources, problems, explanations and concepts that are/were important during my Data Science journey

Data Science Gurukul List of resources, interview questions, concepts I use for my Data Science work. Topics: Basics of Programming with Python + Unde

Smaranjit Ghose 10 Oct 25, 2022