• The BRUTE uses python version 3.7.3
• Use the forum to discuss any Python issues.
• If you want to use an IDE, there is a professional PyCharm licence available at https://download.cvut.cz (but feel free to use butterflies instead).
• If you have experience with MATLAB programming, you can start by Numpy for MATLAB users.

• Not working with correct shapes of input/output data: Shapes of all input/output data are defined in the docstring of individual methods. Please strictly stick to the prescribed shapes.
  • If you want to use i.e. np array <1xn> as (n,), you can use np.squeeze() in the beginning of the method.
  • For returning data in certain shape, you can use np.expand_dims() or np.at_leastXd() (where X is the number of dimmensions).
• The template does not specify something correctly or differs from the assignment text
  • You are probably using an old version of the template. As noted in the git repository readme: Keep in mind that the assignments and the assignment templates will be updated during the semester. Always pull the current template version before starting to work on an assignment!

• All assignment templates are stored in a git repository
  • If you don't know how to use git: Pro Git book, https://try.github.io/, duckduck, google, …
• Follow the instruction in the repository README
• Make sure to get the current template version before starting to work on an assignment
• Make sure not to push the repository with your solutions to any public remote (=plagiarism), you can use FEE GitLab for private remote repo.
• Each assignment template contains a Jupyter notebook (.ipynb) that you can use to testrun the assignment functions you implement. This notebook will not be checked by the BRUTE.

• The evaluation server has the following packages installed:
  • numpy (1.16.2)
  • matplotlib (3.0.2)
  • scipy (1.1.0)
  • PIL (5.4.1)
  • pandas (0.23.3)
  • torch (1.1.0)
  • torchvision (0.3.0)

• If there is something important missing, please contact us on forum. We will do our best to install it on the evaluation server.

This setup is recommended by Radim Shpetleek™® .
Works both on Windows and Linux. ^{The macOS is not for serious work anymore.}

1. Install Miniconda, Python version 3.X.
   
2. Run conda CLI.
   
3. Create conda environment and install basic apps with

   conda create --name rpz python=3 numpy matplotlib scipy pillow pandas

4. Do one of the following:

Work in the interactive python interpreter after activating the environment with

conda activate rpz