HOTTBOX tutorials

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

Local Installation

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

Table of contents:

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	_

Data used in these tutorials

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

Short description of datasets

• 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.

Short description of files with data

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.