====== SW tools ======

This page summarizes installation and usage of several tools for machine learning and data mining.

===== Python =====

For tutorials and homeworks we will be using (among others) python and jupyter notebooks. For convenience, we use conda distribution of Python. Download miniconda from [[https://conda.io/miniconda.html|https://conda.io/miniconda.html]], run the installer and add miniconda to your path. Bellow see how to do this in Linux.


<code|bash>
$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh # if you use linux, otherwise follow instructions for your OS
$ bash Miniconda3-latest-Linux-x86_64.sh
...
Do you wish the installer to prepend the Miniconda3 install location
to PATH in your /home/petr/.bashrc ? [yes|no]
[no] >>> yes

$ bash # reset your bash terminal
$ conda create -n SMU python=3
$ source activate SMU # on windows use activate SMU
$ conda install jupyter
$ conda install numpy
</code>

When installation is finished, you can try how to use the tools yourselves. A quickstart tutorial can be found on [[https://docs.scipy.org/doc/numpy-dev/user/quickstart.html|https://docs.scipy.org/doc/numpy-dev/user/quickstart.html]]. To see differences between numpy and MATLAB, visit [[https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html|https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html]].

In jupyter, you can use markdown language, see [[https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet|https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet]]. To run Jupyter, type
<code|bash>
jupyter notebook
</code>
in your terminal and follow the instructions.

Use command
<code|bash>source activate SMU</code>
each time when you want to use the environment and/or run Jupyter.