hottbox / hottbox-tutorials Goto Github PK

This repository contains a series of tutorials on how to use hottbox.

In order to get started you need to clone this repository and install packages specified in requirements.txt:

git clone https://github.com/hottbox/hottbox-tutorials

cd hottbox-tutorials

pip install -r requirements.txt

If you are on Unix and have anaconda installed, you can execute bootstrap_venv.sh. This script will prepare a new virtual environment for these tutorials.:

git clone https://github.com/hottbox/hottbox-tutorials

source bootstrap_venv.sh

Focus of the tutorial	Static notebook on github.com	Interactive notebook on mybinder.org
N-dimensional arrays and its functionality: Tensor	Tutorial1
Efficient representation of N-dimensional arrays: TensorCPD, TensorTKD, TensorTT	Tutorial2
Fundamental tensor decompositions: CPD, HOSVD, HOOI, TTSVD	Tutorial3
Ecosystem of Tensor class and transformations	Tutorial4
Tensor meta information and pandas integration	Tutorial5

All data for these tutorials can be found under data/ directory.

• ETH80 dataset

  This dataset consists of 3,280 images of natural objects from 8 categories (apple, car, cow, cup, dog, horse, pera, tomato), each containing 10 objects with 41 views per object. More info about this dataset can be found on here.

1. data/ETH80/basic_066-063.npy

   Contains only one RGB image of one object from each category, which makes it a total of 8 samples. The view point identifier for all of them is 066-063. These images are 128 by 128 pixes and are stored in the unfolded form. Thus, when this file is read by numpy it outputs array with 8 rows and 128*128*3 = 49152 columns.