From 93be638d0703da545c994c97434c62ea5f5c9702 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9=20Almeida?= Date: Thu, 9 May 2019 07:43:20 -0700 Subject: [PATCH] Update README.md --- README.md | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 67fe038..0061f5d 100644 --- a/README.md +++ b/README.md @@ -4,12 +4,11 @@ This repository have two different exercises in erlang: 1. Decision tree that maximizes the optimal point and consequently provides decision support based on the assumptions provided. -* The probability of making a medication/treatment 1 and use is OK of 42%; -* One possibility of making a medication/treatment 1 and of the user staying KO is 22.5%; -* The probability of making a medication/treatment 1 and is not conclusive is 35.6%; - -* The probability of making a medication/treatment 2 and staying OK is 62%; -* The probability of making a medication/treatment 2 and getting KO is 38%. +* The probability of making a medication/treatment 1 and use is OK of 42%; +* One possibility of making a medication/treatment 1 and of the user staying KO is 22.5%; +* The probability of making a medication/treatment 1 and is not conclusive is 35.6%; +* The probability of making a medication/treatment 2 and staying OK is 62%; +* The probability of making a medication/treatment 2 and getting KO is 38%. ![alt text](https://i.postimg.cc/5207cpvs/Captura-de-ecr-2019-04-09-s-13-06-01.png) @@ -17,6 +16,7 @@ This repository have two different exercises in erlang: 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. What is the probability of giving the patient M2 and is OK, knowing that: + * The probability of administering M1 and the patient is OK is 20%; * The probability of administering M2 knowing that I administered M1 and the patient being OK is 70%; * The probability of administering M2 knowing that I did not administer M1 and the patient is OK of 20%. @@ -31,17 +31,20 @@ Basic knowledge of erlang, algorithm and statistics. ### Install or use Docker If you prefer install the erlang compiler, please search for the appropriate installation for your OS. -If you prefer you can use Docker to test this solution. +If you prefer you can use Docker to test this solution. + Get a docker container like https://hub.docker.com/r/bitwalker/alpine-erlang and start the container: -* docker run --rm -it --user=root bitwalker/alpine-erlang +`docker run --rm -it --user=root bitwalker/alpine-erlang` ### Run the solution 1. For the first exercise (Decision Tree): +``` c(engine). engine:doAll(). +``` and the best solution is: @@ -50,12 +53,15 @@ and the best solution is: 2. For the second exercise (Baysian Networks): +``` c(engine2). engine2:getp({m2, ok}). +``` + ![alt text](httpshttps://i.postimg.cc/QxBm0Vrz/Captura-de-ecr-2019-04-09-s-15-57-38.png) -There is a 16% chance that the patient will be OK administering the drug M2. +**There is a 16% chance that the patient will be OK administering the drug M2.** ## Built With