Non-stationary GP package written from scratch in PyTorch

Related tags

Deep Learningnsgptorch
Overview

NSGP-Torch

Examples

gpytorch model with skgpytorch

# Import packages
import torch
from regdata import NonStat2D
from gpytorch.kernels import RBFKernel, ScaleKernel
from skgpytorch.models import ExactGPRegressor
from skgpytorch.metrics import mean_squared_error

# Hyperparameters
n_iters = 100

# Load data
datafunc = NonStat2D(backend="torch")
X_train, y_train, X_test = map(lambda x: x.to(torch.float32), datafunc.get_data())
y_test = datafunc.f(X_test[:, 0], X_test[:, 1]).to(torch.float32)

# Define a kernel
kernel = ScaleKernel(RBFKernel(ard_num_dims=X_train.shape[1]))

# Define a model 
model = ExactGPRegressor(X_train, y_train, kernel, device='cpu')

# Train the model
model.fit(n_iters=n_iters, random_state=seed)

# Predict the distribution
pred_dist = model.predict(X_train, y_train, X_test)

# Compute RMSE and/or NLPD
mse = mean_squared_error(pred_dist, y_test, squared=False)
nlpd = neg_log_posterior_density(pred_dist, y_test)

nsgptorch model with skgpytorch

# Import packages
import torch
from regdata import NonStat2D

from nsgptorch.kernels import rbf

from skgpytorch.models import ExactNSGPRegressor
from skgpytorch.metrics import mean_squared_error

# Hyperparameters
n_iters = 100

# Load data
datafunc = NonStat2D(backend="torch")
X_train, y_train, X_test = map(lambda x: x.to(torch.float32), datafunc.get_data())
y_test = datafunc.f(X_test[:, 0], X_test[:, 1]).to(torch.float32)

# Define a kernel list for each dimension
kernel_list = [rbf, rbf]

# Define inducing points for each dimension (must be none if not applicable)
inducing_points = [None, None]

# Define a model 
model = ExactNSGPRegressor(kernel_list, input_dim=2, inducing_points, device='cpu')

# Train the model
model.fit(X_train, y_train, n_iters=n_iters, random_state=seed)

# Predict the distribution
pred_dist = model.predict(X_train, y_train, X_test)

# Compute RMSE and/or NLPD
mse = mean_squared_error(pred_dist, y_test, squared=False)
nlpd = neg_log_posterior_density(pred_dist, y_test)

Plan

  • Each kernel is 1D
  • Multiply kernels to each other

Ideas

  • Compute distance once and save it
  • Update skgpytorch to use 1 std instead of 0.1
  • Do something about mean learning of gpytorch for comparison
You might also like...
Keras like implementation of Deep Learning architectures from scratch using numpy.

Mini-Keras Keras like implementation of Deep Learning architectures from scratch using numpy. How to contribute? The project contains implementations

MANU S PILLAI 5 Oct 10, 2021
This repo is to be freely used by ML devs to check the GAN performances without coding from scratch.
This repo is to be freely used by ML devs to check the GAN performances without coding from scratch.

GANs for Fun Created because I can! GOAL The goal of this repo is to be freely used by ML devs to check the GAN performances without coding from scrat

Sagnik Roy 13 Jan 26, 2022
Train a state-of-the-art yolov3 object detector from scratch!
Train a state-of-the-art yolov3 object detector from scratch!

TrainYourOwnYOLO: Building a Custom Object Detector from Scratch This repo let's you train a custom image detector using the state-of-the-art YOLOv3 c

AntonMu 616 Jan 8, 2023
ML From Scratch

ML from Scratch MACHINE LEARNING TOPICS COVERED - FROM SCRATCH Linear Regression Logistic Regression K Means Clustering K Nearest Neighbours Decision

Tanishq Gautam 66 Nov 2, 2022
In this project, we create and implement a deep learning library from scratch.
In this project, we create and implement a deep learning library from scratch.

ARA In this project, we create and implement a deep learning library from scratch. Table of Contents Deep Leaning Library Table of Contents About The

null 22 Aug 23, 2022
Create and implement a deep learning library from scratch.
Create and implement a deep learning library from scratch.

In this project, we create and implement a deep learning library from scratch. Table of Contents Deep Leaning Library Table of Contents About The Proj

Rishabh Bali 22 Aug 23, 2022
AbelNN: Deep Learning Python module from scratch

AbelNN: Deep Learning Python module from scratch I have implemented several neural networks from scratch using only Numpy. I have designed the module

Abel 2 Apr 12, 2022
Weighted K Nearest Neighbors (kNN) algorithm implemented on python from scratch.
Weighted K Nearest Neighbors (kNN) algorithm implemented on python from scratch.

kNN_From_Scratch I implemented the k nearest neighbors (kNN) classification algorithm on python. This algorithm is used to predict the classes of new

null 1 Dec 14, 2021
Controlling the MicriSpotAI robot from scratch
Controlling the MicriSpotAI robot from scratch

Project-MicroSpot-AI Controlling the MicriSpotAI robot from scratch Colaborators Alexander Dennis Components from MicroSpot The MicriSpotAI has the fo

Dennis Núñez-Fernández 5 Oct 20, 2022
Releases(v0.1.2)
Owner
Zeel B Patel
Ph.D. student at sustainability lab
Zeel B Patel
GitHub Repository
Repository for RNNs using TensorFlow and Keras - LSTM and GRU Implementation from Scratch - Simple Classification and Regression Problem using RNNs

RNN 01- RNN_Classification Simple RNN training for classification task of 3 signal: Sine, Square, Triangle. 02- RNN_Regression Simple RNN training for

Nahid Ebrahimian 13 Dec 13, 2022
[ICLR'21] Counterfactual Generative Networks

This repository contains the code for the ICLR 2021 paper "Counterfactual Generative Networks" by Axel Sauer and Andreas Geiger. If you want to take the CGN for a spin and generate counterfactual ima

88 Jan 02, 2023
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations This is the repository for the paper Consumer Fairness in Recomm

7 Nov 30, 2022
CNN visualization tool in TensorFlow

tf_cnnvis A blog post describing the library: https://medium.com/@falaktheoptimist/want-to-look-inside-your-cnn-we-have-just-the-right-tool-for-you-ad

InFoCusp 778 Jan 02, 2023
Benchmark for evaluating open-ended generation

OpenMEVA Contributed by Jian Guan, Zhexin Zhang. Thank Jiaxin Wen for DeBugging. OpenMEVA is a benchmark for evaluating open-ended story generation me

25 Nov 15, 2022
U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI

U-Net for brain segmentation U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI based on a deep learning segmentation alg

562 Jan 02, 2023
[CVPR 2021] Region-aware Adaptive Instance Normalization for Image Harmonization

RainNet — Official Pytorch Implementation Region-aware Adaptive Instance Normalization for Image Harmonization Jun Ling, Han Xue, Li Song*, Rong Xie,

130 Dec 11, 2022
Code for Towards Streaming Perception (ECCV 2020) :car:

sAP — Code for Towards Streaming Perception ECCV Best Paper Honorable Mention Award Feb 2021: Announcing the Streaming Perception Challenge (CVPR 2021

Martin Li 85 Dec 22, 2022
Framework for training options with different attention mechanism and using them to solve downstream tasks.

Using Attention in HRL Framework for training options with different attention mechanism and using them to solve downstream tasks. Requirements GPU re

5 Nov 03, 2022
Code for Transformer Hawkes Process, ICML 2020.

Transformer Hawkes Process Source code for Transformer Hawkes Process (ICML 2020). Run the code Dependencies Python 3.7. Anaconda contains all the req

Simiao Zuo 111 Dec 26, 2022
My implementation of Image Inpainting - A deep learning Inpainting model

Image Inpainting What is Image Inpainting Image inpainting is a restorative process that allows for the fixing or removal of unwanted parts within ima

Joshua V Evans 1 Dec 12, 2021
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective

FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective Official implementation of "FL-WBC: Enhan

Jingwei Sun 26 Nov 28, 2022
Code for a real-time distributed cooperative slam(RDC-SLAM) system for ROS compatible platforms.

RDC-SLAM This repository contains code for a real-time distributed cooperative slam(RDC-SLAM) system for ROS compatible platforms. The system takes in

40 Nov 19, 2022
Tensorflow implementation of ID-Unet: Iterative Soft and Hard Deformation for View Synthesis.

ID-Unet: Iterative-view-synthesis(CVPR2021 Oral) Tensorflow implementation of ID-Unet: Iterative Soft and Hard Deformation for View Synthesis. Overvie

17 Aug 23, 2022
Simply enable or disable your Nvidia dGPU

EnvyControl (WIP) Simply enable or disable your Nvidia dGPU Usage First clone this repo and install envycontrol with sudo pip install . CLI Turn off y

Victor Bayas 292 Jan 03, 2023
PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, wav2lip, picture repair, image editing, photo2cartoon, image style transfer, and so on.

English | 简体中文 PaddleGAN PaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks, and s

6.4k Jan 09, 2023
RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving (AAAI2021). RTS3D is efficiency and accuracy s

71 Nov 29, 2022
Si Adek Keras is software VR dangerous object detection.

Si Adek Python Keras Sistem Informasi Deteksi Benda Berbahaya Keras Python. Version 1.0 Developed by Ananda Rauf Maududi. Developed date: 24 November

Ananda Rauf 1 Dec 21, 2021
OpenVINO黑客松比赛项目

Window_Guard OpenVINO黑客松比赛项目 英文名称：Window_Guard 中文名称：窗口卫士 硬件 树莓派4B 8G版本 一个磁石开关 USB摄像头(MP4视频文件也可以) 软件(库) OpenVINO RPi 使用方法 本项目使用的OPenVINO是是2021.3版本，并使用了

Tango 6 Jul 04, 2021
Rax is a Learning-to-Rank library written in JAX

🦖 Rax: Composable Learning to Rank using JAX Rax is a Learning-to-Rank library written in JAX. Rax provides off-the-shelf implementations of ranking

Google 247 Dec 27, 2022