Final write-up edits (turned in) and some build/source cleanup to ensure that turned in code can be run from command line.
This commit is contained in:
Woody Folsom
@@ -9,7 +9,7 @@ import java.util.List;
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import org.junit.Test;
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import dkohl.bayes.bayesnet.BayesNet;
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import dkohl.bayes.example.builders.FoodExampleBuilder;
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import dkohl.bayes.builders.FoodExampleBuilder;
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import dkohl.bayes.inference.EnumerateAll;
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import dkohl.bayes.probability.ProbabilityAssignment;
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import dkohl.bayes.probability.Variable;
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@@ -1,268 +0,0 @@
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package dkohl.bayes.example.builders;
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import java.util.HashSet;
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import java.util.LinkedList;
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import net.woodyfolsom.cs6601.p2.Ingredient.TYPE;
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import dkohl.bayes.bayesnet.BayesNet;
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import dkohl.bayes.estimation.MaximumLikelihoodEstimation;
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import dkohl.bayes.probability.Assignment;
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import dkohl.bayes.probability.Probability;
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import dkohl.bayes.probability.Variable;
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import dkohl.bayes.probability.distribution.ContinousDistribution;
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import dkohl.bayes.probability.distribution.ProbabilityDistribution;
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import dkohl.bayes.probability.distribution.ProbabilityTable;
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import dkohl.bayes.statistic.DataPoint;
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import dkohl.bayes.statistic.DataSet;
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import dkohl.onthology.Ontology;
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public class FoodExampleBuilder {
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public static final String TASTE = "Taste";
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public static final String SOMEONE_VEGETARIAN = "Vegetarian";
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public static final String CONTAINS_MEAT = "Meat";
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public static final String CONTAINS_VEGETABLE = "Vegetable";
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public static final String CONTAINS_BEEF = TYPE.BEEF.toString();
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public static final String CONTAINS_PORK = TYPE.PORK.toString();
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public static final String CONTAINS_TOMATOS = TYPE.TOMATO.toString();
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public static final String CONTAINS_POTATOS = TYPE.POTATO.toString();
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public static final String TRUE_VALUE = Boolean.TRUE.toString();
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public static final String FALSE_VALUE = Boolean.FALSE.toString();
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public static final String DOMAIN[] = { TRUE_VALUE, FALSE_VALUE };
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public static final String RATING_DOMAIN[] = { "1", "2", "3", "4", "5",
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"6", "7", "8", "9", "10" };
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private static final String[] VARIABLES = { SOMEONE_VEGETARIAN,
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CONTAINS_BEEF, CONTAINS_MEAT, CONTAINS_PORK, CONTAINS_POTATOS,
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CONTAINS_TOMATOS, CONTAINS_VEGETABLE, TASTE };
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private static final String[] OBSERVED = { CONTAINS_BEEF, CONTAINS_PORK,
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CONTAINS_POTATOS, CONTAINS_TOMATOS, };
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public static LinkedList<Assignment> completeQueryTasteBeef() {
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LinkedList<Assignment> assignment = new LinkedList<Assignment>();
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assignment.add(new Assignment(new Variable(CONTAINS_BEEF, DOMAIN),
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TRUE_VALUE));
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assignment.add(new Assignment(new Variable(CONTAINS_TOMATOS, DOMAIN),
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TRUE_VALUE));
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return new LinkedList<Assignment>(assignment);
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}
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public static LinkedList<Assignment> completeQueryTastePork() {
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LinkedList<Assignment> assignment = new LinkedList<Assignment>();
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assignment.add(new Assignment(new Variable(CONTAINS_PORK, DOMAIN),
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TRUE_VALUE));
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assignment.add(new Assignment(new Variable(CONTAINS_POTATOS, DOMAIN),
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TRUE_VALUE));
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return new LinkedList<Assignment>(assignment);
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}
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public static Ontology createOntology() {
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HashSet<String> classes = new HashSet<String>();
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classes.add(CONTAINS_MEAT);
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classes.add(CONTAINS_VEGETABLE);
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Ontology onthology = new Ontology(classes);
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onthology.define(CONTAINS_PORK, CONTAINS_MEAT);
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onthology.define(CONTAINS_BEEF, CONTAINS_MEAT);
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onthology.define(CONTAINS_TOMATOS, CONTAINS_VEGETABLE);
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onthology.define(CONTAINS_POTATOS, CONTAINS_VEGETABLE);
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return onthology;
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}
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private static Assignment build(String varible, String value) {
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return new Assignment(new Variable(varible, DOMAIN), value);
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}
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public static DataPoint beefPotatoDish(int weight) {
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DataPoint point = new DataPoint();
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point.add(build(CONTAINS_BEEF, TRUE_VALUE));
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point.add(build(CONTAINS_POTATOS, TRUE_VALUE));
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point.add(build(TASTE, "" + weight));
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return point;
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}
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public static DataPoint beefTomatoDish(int weight) {
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DataPoint point = new DataPoint();
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point.add(build(CONTAINS_BEEF, TRUE_VALUE));
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point.add(build(CONTAINS_TOMATOS, TRUE_VALUE));
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point.add(build(TASTE, "" + weight));
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return point;
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}
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public static DataPoint porkBeefDish(int weight) {
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DataPoint point = new DataPoint();
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point.add(build(CONTAINS_BEEF, TRUE_VALUE));
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point.add(build(CONTAINS_PORK, TRUE_VALUE));
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point.add(build(TASTE, "" + weight));
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return point;
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}
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public static DataPoint porkPotatoDish(int weight) {
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DataPoint point = new DataPoint();
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point.add(build(CONTAINS_PORK, TRUE_VALUE));
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point.add(build(CONTAINS_POTATOS, TRUE_VALUE));
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point.add(build(TASTE, "" + weight));
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return point;
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}
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public static DataPoint porkTomatoDish(int weight) {
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DataPoint point = new DataPoint();
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point.add(build(CONTAINS_PORK, TRUE_VALUE));
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point.add(build(CONTAINS_TOMATOS, TRUE_VALUE));
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point.add(build(TASTE, "" + weight));
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return point;
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}
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public static DataPoint potatoTomato(int weight) {
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DataPoint point = new DataPoint();
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point.add(build(CONTAINS_POTATOS, TRUE_VALUE));
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point.add(build(CONTAINS_TOMATOS, TRUE_VALUE));
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point.add(build(TASTE, "" + weight));
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return point;
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}
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public static DataPoint normalize(DataPoint point, Ontology onto) {
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// resolve onthology
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DataPoint normPoint = new DataPoint(point);
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for (String key : point.keySet()) {
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if (onto.getInheritance().containsKey(key)) {
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normPoint
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.add(build(onto.getInheritance().get(key), TRUE_VALUE));
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}
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}
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// implement closed world assumption
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// everything unknown is false
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for (String variable : VARIABLES) {
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if (!normPoint.containsKey(variable)) {
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normPoint.add(build(variable, FALSE_VALUE));
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}
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}
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return normPoint;
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}
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public static DataSet examples() {
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DataSet data = new DataSet();
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Ontology onto = createOntology();
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// user one ratings
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data.add(normalize(porkTomatoDish(10), onto));
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data.add(normalize(porkPotatoDish(9), onto));
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data.add(normalize(beefTomatoDish(3), onto));
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data.add(normalize(beefPotatoDish(0), onto));
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data.add(normalize(potatoTomato(0), onto));
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data.add(normalize(porkBeefDish(0), onto));
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// user two ratings
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data.add(normalize(porkTomatoDish(10), onto));
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data.add(normalize(porkTomatoDish(8), onto));
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data.add(normalize(porkPotatoDish(10), onto));
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data.add(normalize(beefTomatoDish(0), onto));
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data.add(normalize(beefPotatoDish(1), onto));
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data.add(normalize(potatoTomato(7), onto));
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data.add(normalize(porkBeefDish(10), onto));
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// user three ratings
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data.add(normalize(porkTomatoDish(10), onto));
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data.add(normalize(porkPotatoDish(10), onto));
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data.add(normalize(beefTomatoDish(3), onto));
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data.add(normalize(beefPotatoDish(3), onto));
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data.add(normalize(potatoTomato(3), onto));
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data.add(normalize(porkBeefDish(4), onto));
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return data;
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}
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public static ProbabilityDistribution vegi() {
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String names[] = { SOMEONE_VEGETARIAN };
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ProbabilityTable table = new ProbabilityTable(names);
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table.setProbabilityForAssignment("true;", new Probability(0));
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table.setProbabilityForAssignment("false;", new Probability(1));
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return table;
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}
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public static ProbabilityDistribution beef() {
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String names[] = { CONTAINS_MEAT, CONTAINS_BEEF };
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ProbabilityTable table = new ProbabilityTable(names);
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return table;
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}
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public static ProbabilityDistribution pork() {
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String names[] = { CONTAINS_MEAT, CONTAINS_PORK };
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ProbabilityTable table = new ProbabilityTable(names);
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return table;
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}
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public static ProbabilityDistribution meet() {
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String names[] = { SOMEONE_VEGETARIAN, CONTAINS_MEAT };
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ProbabilityTable table = new ProbabilityTable(names);
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return table;
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}
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public static ProbabilityDistribution tomatos() {
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String names[] = { CONTAINS_VEGETABLE, CONTAINS_TOMATOS };
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ProbabilityTable table = new ProbabilityTable(names);
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return table;
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}
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public static ProbabilityDistribution potatos() {
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String names[] = { CONTAINS_VEGETABLE, CONTAINS_POTATOS };
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ProbabilityTable table = new ProbabilityTable(names);
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return table;
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}
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public static ProbabilityDistribution vegetables() {
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String names[] = { CONTAINS_VEGETABLE, SOMEONE_VEGETARIAN };
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ProbabilityTable table = new ProbabilityTable(names);
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return table;
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}
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public static ProbabilityDistribution taste() {
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String names[] = { TASTE, CONTAINS_BEEF, CONTAINS_PORK,
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CONTAINS_POTATOS, CONTAINS_TOMATOS };
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ContinousDistribution distribution = new ContinousDistribution(names, 0);
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return distribution;
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}
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public static BayesNet dishNet() {
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BayesNet net = new BayesNet(VARIABLES);
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net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN), vegi());
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net.setDistribution(new Variable(CONTAINS_MEAT, DOMAIN), meet());
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net.setDistribution(new Variable(CONTAINS_VEGETABLE, DOMAIN),
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vegetables());
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net.setDistribution(new Variable(CONTAINS_BEEF, DOMAIN), beef());
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net.setDistribution(new Variable(CONTAINS_PORK, DOMAIN), pork());
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net.setDistribution(new Variable(CONTAINS_POTATOS, DOMAIN), potatos());
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net.setDistribution(new Variable(CONTAINS_TOMATOS, DOMAIN), tomatos());
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net.setDistribution(new Variable(TASTE, RATING_DOMAIN), taste());
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Ontology ontology = createOntology();
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for (String category : ontology.getClasses()) {
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net.connect(category, SOMEONE_VEGETARIAN);
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}
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for (String thing : OBSERVED) {
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net.connect(thing, ontology.getInheritance().get(thing));
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net.connect(TASTE, thing);
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}
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for (String category : ontology.getClasses()) {
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MaximumLikelihoodEstimation.estimate(examples(), net, category);
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for (String thing : ontology.getClasses2thing().get(category)) {
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MaximumLikelihoodEstimation.estimate(examples(), net, thing);
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}
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}
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MaximumLikelihoodEstimation.estimate(examples(), net, TASTE);
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ContinousDistribution distribturion = (ContinousDistribution) net
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.getNodes().get(TASTE);
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distribturion.estimate();
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return net;
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}
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}
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