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
CVPRW 2021: How to calibrate your event camera

E2Calib: How to Calibrate Your Event Camera This repository contains code that implements video reconstruction from event data for calibration as desc

Robotics and Perception Group 104 Nov 16, 2022
This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)"

Gait3D-Benchmark This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild

82 Jan 04, 2023
Supervised & unsupervised machine-learning techniques are applied to the database of weighted P4s which admit Calabi-Yau hypersurfaces.

Weighted Projective Spaces ML Description: The database of 5-vectors describing 4d weighted projective spaces which admit Calabi-Yau hypersurfaces are

Ed Hirst 3 Sep 08, 2022
The Unsupervised Reinforcement Learning Benchmark (URLB)

The Unsupervised Reinforcement Learning Benchmark (URLB) URLB provides a set of leading algorithms for unsupervised reinforcement learning where agent

259 Dec 26, 2022
[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"

FiGNN for CTR prediction The code and data for our paper in CIKM2019: Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Predicti

Big Data and Multi-modal Computing Group, CRIPAC 75 Dec 30, 2022
(ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning"

CLNet (ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning" [project page] [paper] Citing CLNet If yo

Chen Zhao 22 Aug 26, 2022
Project page for End-to-end Recovery of Human Shape and Pose

End-to-end Recovery of Human Shape and Pose Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018 Project Page Requirements Pyt

1.4k Dec 29, 2022
Multi-modal Vision Transformers Excel at Class-agnostic Object Detection

Multi-modal Vision Transformers Excel at Class-agnostic Object Detection

Muhammad Maaz 206 Jan 04, 2023
Pytorch code for our paper "Feedback Network for Image Super-Resolution" (CVPR2019)

Feedback Network for Image Super-Resolution [arXiv] [CVF] [Poster] Update: Our proposed Gated Multiple Feedback Network (GMFN) will appear in BMVC2019

Zhen Li 539 Jan 06, 2023
Perform zero-order Hankel Transform for an 1D array (float or real valued).

perform zero-order Hankel Transform for an 1D array (float or real valued). An discrete form of Parseval theorem is guaranteed. Suit for iterative problems.

1 Jan 17, 2022
Omniscient Video Super-Resolution

Omniscient Video Super-Resolution This is the official code of OVSR (Omniscient Video Super-Resolution, ICCV 2021). This work is based on PFNL. Datase

36 Oct 27, 2022
Code to reproduce the experiments from our NeurIPS 2021 paper " The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective"

Code To run: python runner.py new --save SAVE_NAME --data PATH_TO_DATA_DIR --dataset DATASET --model model_name [options] --n 1000 - train - t

Geoff Pleiss 5 Dec 12, 2022
Code for "OctField: Hierarchical Implicit Functions for 3D Modeling (NeurIPS 2021)"

OctField(Jittor): Hierarchical Implicit Functions for 3D Modeling Introduction This repository is code release for OctField: Hierarchical Implicit Fun

55 Dec 08, 2022
Auto HMM: Automatic Discrete and Continous HMM including Model selection

Auto HMM: Automatic Discrete and Continous HMM including Model selection

Chess_champion 29 Dec 07, 2022
Neural networks applied in recognizing guitar chords using python, AutoML.NET with C# and .NET Core

Chord Recognition Demo application The demo application is written in C# with .NETCore. As of July 9, 2020, the only version available is for windows

Andres Mauricio Rondon Patiño 24 Oct 22, 2022
On the adaptation of recurrent neural networks for system identification

On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape

Marco Forgione 3 Jan 13, 2022
Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"

This is the codebase for the paper: Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs Directory Structur

Peter Hase 19 Aug 21, 2022
ROS-UGV-Control-Interface - Control interface which can be used in any UGV

ROS-UGV-Control-Interface Cam Closed: Cam Opened:

Ahmet Fatih Akcan 1 Nov 04, 2022
Yolox-bytetrack-sample - Python sample of MOT (Multiple Object Tracking) using YOLOX and ByteTrack

yolox-bytetrack-sample YOLOXとByteTrackを用いたMOT(Multiple Object Tracking)のPythonサン

KazuhitoTakahashi 12 Nov 09, 2022
Differential Privacy for Heterogeneous Federated Learning : Utility & Privacy tradeoffs

Differential Privacy for Heterogeneous Federated Learning : Utility & Privacy tradeoffs In this work, we propose an algorithm DP-SCAFFOLD(-warm), whic

19 Nov 10, 2022