HOTTBOX tutorials

|Binder|_

.. |Binder| image:: https://mybinder.org/badge.svg .. _Binder: https://mybinder.org/v2/gh/hottbox/hottbox-tutorials/master

This repository contains a series of tutorials on how to use hottbox <https://github.com/hottbox/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:

.. |ti1| image:: https://mybinder.org/badge.svg .. _ti1: https://mybinder.org/v2/gh/hottbox/hottbox-tutorials/master?filepath=1_N-dimensional_arrays_and_Tensor_class.ipynb .. _Tutorial1: https://github.com/hottbox/hottbox-tutorials/blob/master/1_N-dimensional_arrays_and_Tensor_class.ipynb

.. |ti2| image:: https://mybinder.org/badge.svg .. _ti2: https://mybinder.org/v2/gh/hottbox/hottbox-tutorials/master?filepath=2_Efficient_representations_of_tensors.ipynb .. _Tutorial2: https://github.com/hottbox/hottbox-tutorials/blob/master/2_Efficient_representations_of_tensors.ipynb

.. |ti3| image:: https://mybinder.org/badge.svg .. _ti3: https://mybinder.org/v2/gh/hottbox/hottbox-tutorials/master?filepath=3_Fundamental_tensor_decompositions.ipynb .. _Tutorial3: https://github.com/hottbox/hottbox-tutorials/blob/master/3_Fundamental_tensor_decompositions.ipynb

.. |ti4| image:: https://mybinder.org/badge.svg .. _ti4: https://mybinder.org/v2/gh/hottbox/hottbox-tutorials/master?filepath=4_Ecosystem_of_Tensor_class.ipynb .. _Tutorial4: https://github.com/hottbox/hottbox-tutorials/blob/master/4_Ecosystem_of_Tensor_class.ipynb

.. |ti5| image:: https://mybinder.org/badge.svg .. _ti5: https://mybinder.org/v2/gh/hottbox/hottbox-tutorials/master?filepath=5_Tensor_meta_information_and_pandas_integration.ipynb .. _Tutorial5: https://github.com/hottbox/hottbox-tutorials/blob/master/5_Tensor_meta_information_and_pandas_integration.ipynb

+--------------------------------------------------------------------------------------+-------------------------------+--------------------------------------+ | Focus of the tutorial | Static notebook on github.com | Interactive notebook on mybinder.org | +======================================================================================+===============================+======================================+ | 1. N-dimensional arrays and its functionality: Tensor | Tutorial1_ | |ti1|_ | +--------------------------------------------------------------------------------------+-------------------------------+--------------------------------------+ | 2. Efficient representation of N-dimensional arrays: TensorCPD, TensorTKD, TensorTT | Tutorial2_ | |ti2|_ | +--------------------------------------------------------------------------------------+-------------------------------+--------------------------------------+ | 3. Fundamental tensor decompositions: CPD, HOSVD, HOOI, TTSVD | Tutorial3_ | |ti3|_ | +--------------------------------------------------------------------------------------+-------------------------------+--------------------------------------+ | 4. Ecosystem of Tensor class and transformations | Tutorial4_ | |ti4|_ | +--------------------------------------------------------------------------------------+-------------------------------+--------------------------------------+ | 5. Tensor meta information and pandas integration | Tutorial5_ | |ti5|_ | +--------------------------------------------------------------------------------------+-------------------------------+--------------------------------------+

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 <https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/analyzing-appearance-and-contour-based-methods-for-object-categorization/>`_.



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.