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#+TITLE: AI Integration Languages: A Case Study in Constraint Machine Learning
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This repository contains the results of the [[https:www.humane-ai.eu][Humane AI Net]] Micro Project ~AI
Integration Languages: A Case Study in Constraint Machine Learning~.

The easiest way to try the results is via [[https:docs.docker.com/engine/install/][Docker]]. Docker essentially creates a
virtual machine starting from a base image and then performs the minimal setup
required to run some software (in this case our moving target notebooks).  All
information needed for this can be found in the ~Dockerfile~ at the root of this
repository. Alternatively, you can follow the instructions in the ~Dockerfile~ to
create an environment that can run our Jupyter notebooks (Python 3.7 is required
and we recommend setting up a virtual environment, e.g., via [[https:docs.conda.io/en/latest/miniconda.html][Miniconda]]).

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To build the container open a terminal in this folder and run:
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#+begin_src
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docker build -t moving-target-aiddl .
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#+end_src

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Then, to run the container:
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#+begin_src
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docker run --rm --publish=8888:8888 moving-target-aiddl:latest
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#+end_src
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A local link to the jupyter notebook will appear in the console. Open the link
in a browser and select a notebook to try it. For now there is a single notebook
running linear regression through AIDDL.
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~Note:~ currently [[https:www.ibm.com/analytics/cplex-optimizer][IBM's CPLEX Optimizter]] is required and the corresponding binary
file ~cplex_studio1210.linux-x86-64.bin~ must be placed in the ~deps~ folder.  For
obvious reasons we do not provide the binary ourselves but a free academic
edition of CPLEX is available following the link above. We will update the
notebooks to make CPLEX optional as soon as possible.