101 lines
3.1 KiB
Markdown
101 lines
3.1 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 assumptions provided.**
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* The probability of making a medication/treatment 1 and use is OK of 42%;
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* One possibility of making a medication/treatment 1 and of the user staying KO is 22.5%;
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* The probability of making a medication/treatment 1 and is not conclusive is 35.6%;
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* The probability of making a medication/treatment 2 and staying OK is 62%;
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* The probability of making a medication/treatment 2 and getting KO is 38%.
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**2.There are ways to get the probabilities knowing certain parameters (they do not indicate the optimal point, but calculate a probability of success depending on the existing resources) - using the baysian networks.**
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What is the probability of giving the patient M2 and is OK, knowing that:
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* The probability of administering M1 and the patient is OK is 20%;
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* The probability of administering M2 knowing that I administered M1 and the patient being OK is 70%;
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* The probability of administering M2 knowing that I did not administer M1 and the patient is OK of 20%.
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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 install the erlang compiler, please search for the appropriate installation for your OS.
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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:
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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(engine).
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engine:doAll().
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```
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and the best solution is:
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** You can do a medication 1 and medication 2 of 97.6% of success to be OK.**
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2. For the second exercise (Baysian Networks):
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```
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c(engine3).
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engine3:getp({m2, ok}).
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```
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**There is a 16% chance that the patient will be OK administering the drug M2.**
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## Built 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 the versions available, see the [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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## License
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This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
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