134 lines
4.1 KiB
Markdown
134 lines
4.1 KiB
Markdown
# Erlang Decision Tree and Baysian Networks
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This repository have two different exercises in erlang:
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**1.Decision tree that maximizes the optimal point and consequently provides decision support based on the given assumptions.**
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* The probability of taking medication/treatment 1 and the user being OK is 42%;
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* The probability of taking medication/treatment 1 and the user becoming KO is 22.5%;
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* The probability of taking medication/treatment 1 and it not being conclusive is 35.6%;
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* The probability of taking medication/treatment 2 and being OK is 62%;
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* The probability of taking a medication/treatment 2 and getting KO is 38%.
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```
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-module(engine).
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-compile([export_all]).
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% Decision Tree
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%
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% +----+
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% +---> OK |
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% | +----+
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% |
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% +----------| +----+
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% | DO MED 1 +---> KO |
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% +----------+ +----+ +----+
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% | +---> OK |
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% | +---------------| +----+
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% +---> DO MED 2 +
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% +---------------| +----+
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% +---> KO |
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% +----+
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% Each edge can be calculated from the table
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doAll() ->
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T = dec_tree:start(),
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T ! {create_node, 'DO MED1', 0, none},
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T ! {create_node, 'MED1 OK', 0.980, 'DO MED1'},
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T ! {create_node, 'MED1 KO', 0.225, 'DO MED1'},
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T ! {create_node, 'DO MED2', 0.356, 'DO MED1'},
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T ! {create_node, 'MED 2 OK', 0.62, 'DO MED2'},
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T ! {create_node, 'MED 2 KO', 0.38, 'DO MED2'},
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T ! {solve}.
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```
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**2. There are ways of obtaining probabilities knowing certain parameters (they don't indicate the optimum point, but calculate a probability of success depending on the existing resources) - using Baysian networks.**
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What is the probability of giving the patient M2 and the patient being well, knowing that:
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* The probability of administering M1 and the patient being well is 20 per cent;
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* The probability of administering M2 knowing that I have administered M1 and the patient is well is 70%;
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* The probability of administering M2 knowing that I have not administered M1 and the patient is OK is 20 per cent.
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### Prerequisites
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Basic knowledge of erlang, algorithm and statistics.
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* erlang - https://www.erlang.org;
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### Install or use Docker
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If you prefer to install the erlang compiler, please find the appropriate installation for your operating system.
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If you prefer, you can use Docker to test this solution.
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Get a [docker container](https://hub.docker.com/search?q=erlang&type=image) and start the container.
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I suggest [this container](https://hub.docker.com/r/bitwalker/alpine-erlang) and run it:
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```
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docker pull bitwalker/alpine-erlang
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docker run --rm -it --user=root bitwalker/alpine-erlang
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```
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### Run the solution
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1. For the first exercise (Decision Tree):
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```
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c(motor).
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motor:doAll().
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```
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and the best solution is:
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** You can take medication 1 and medication 2 with a 97.6 per cent success rate to get well.
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2. For the second exercise (Baysian Networks):
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```
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c(motor3).
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motor3:getp({m2, ok}).
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```
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**There is a 16 per cent chance that the patient will be OK when administering drug M2.
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## Created with
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* erlang - https://www.erlang.org;
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## Contributing
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Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on my code of conduct, and the process for submitting pull requests to me.
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## Versioning
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I use [SemVer](http://semver.org/) for versioning. For available versions, see [tags on this repository](https://gitlab.andrealmeida.net/public_projects/erlang-decision-tree/tags).
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## Authors
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[](https://andrealmeida.net)
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## Licence
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This project is licensed under the MIT licence - see the file [LICENSE.md](LICENSE.md) for more details.
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