initial readme
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# mlpractical
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Machine Learning Practical (INFR11119)
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## Machine Learning Practical (INFR11119)
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To run the notebooks (and later the code you are going to write within this course)
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you are expected to have installed the following packages:
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python 2.7+
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numpy (anything above 1.6, 1.9+ recommended, optimally
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compiled with some BLAS library [MKL, OpenBLAS, ATLAS, etc.)
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scipy (optional, but may be useful to do some tests)
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matplotlib (for plotting)
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ipython (v3.0+, 4.0 recommended)
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notebook (notebooks are in version 4.0)
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You can install them straight away on your personal computer,
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there is also a notebook tutorial (00_Introduction) on how to
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do this on DICE, and what configuration you are expected to have.
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For now, it suffices if you get the software working on your
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personal computers so you can start ipython notebook server
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and open the inital introductory tutorial (which will be make
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publicitly available next Monday).
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I) Installing the software on personal computers
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a) On Windows: download and install the Anaconda package
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(https://store.continuum.io/cshop/anaconda/)
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b) On Mac (use macports):
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Install macports following instructions at https://www.macports.org/install.php
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Install the relevant python packages in macports
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sudo port install py27-scipy +openblas
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sudo port install py27-ipython +notebook
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sudo port install py27-notebook
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sudo port install py27-matplotlib
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sudo port select --set python python27
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sudo port select --set ipython2 py27-ipython
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sudo port select --set ipython py27-ipython
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Also, make sure that your $PATH has /opt/local/bin before /usr/bin
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so you pick up the version of python you just installed
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c) On DICE (we will do this during the first lab)
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II) Setting up the repository
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Assuming ~/mlpractical is a target workspace you want to use during
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this course (where ~ denotes your home path, i.e. /home/user1).
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To start, open the terminal and clone the github mlpractical
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repository to your local disk:
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git clone https://github.com/CSTR-Edinburgh/mlpractical.git
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(Note: you can do it from your git account if you have one as the
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above just clone the repo as anonymous user, though it does not
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matter at this point, as you will not submit pull requests)
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Naviagate to the checked out directory by typing cd ~/mlpractical and type:
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ipython notebook
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This should start notebook server and open the browser with the page
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listing files/subdirs in the current directory.
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To update the repository (for example, on Monday), enter ~/mlpractical
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and type git pull.
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