Merge branch 'master' of woodyfolsom.net:/opt/git/cs6601p2

src/dkohl/bayes/builders/FoodNetBuilder.java

@@ -25,7 +25,6 @@ public class FoodNetBuilder {
     public static final String TASTE = "Taste";
 
     public static final String SOMEONE_VEGETARIAN = "vegetarian";
-    // public static final String SOMEONE_ALLERGIC_NUTS = "allergic-nuts";
 
     public static final String CONTAINS_MEAT = "Meat";
     public static final String CONTAINS_VEGETABLE = "Vegetable";
@@ -33,9 +32,6 @@ public class FoodNetBuilder {
     public static final String CONTAINS_PORK = TYPE.PORK.toString();
     public static final String CONTAINS_TOMATOS = TYPE.TOMATO.toString();
     public static final String CONTAINS_POTATOS = TYPE.POTATO.toString();
-    // public static final String CONTAINS_NUTS = "Nuts";
-    // public static final String CONTAINS_GENERIC_NUTS =
-    // TYPE.GENERIC_NUTS.toString();
 
     public static final String TRUE_VALUE = "true";
     public static final String FALSE_VALUE = "false";
@@ -45,24 +41,24 @@ public class FoodNetBuilder {
     public static final String RATING_DOMAIN[] = { "1", "2", "3", "4", "5",
 	    "6", "7", "8", "9", "10" };
 
-    private static final String[] VARIABLES = { SOMEONE_VEGETARIAN, /*
+    private static final String[] VARIABLES = { SOMEONE_VEGETARIAN, 
-								     * SOMEONE_ALLERGIC_NUTS
-								     * ,
-								     */
     CONTAINS_BEEF, CONTAINS_MEAT, CONTAINS_PORK, CONTAINS_POTATOS,
-	    CONTAINS_TOMATOS, CONTAINS_VEGETABLE, /*
+	    CONTAINS_TOMATOS, CONTAINS_VEGETABLE, TASTE };
-						   * CONTAINS_NUTS,
-						   * CONTAINS_GENERIC_NUTS,
-						   */TASTE };
 
     private static final String[] OBSERVED = { CONTAINS_BEEF, CONTAINS_PORK,
-	    CONTAINS_POTATOS, CONTAINS_TOMATOS /* , CONTAINS_GENERIC_NUTS */};
+	    CONTAINS_POTATOS, CONTAINS_TOMATOS };
 
+    /**
+     * Build the onthology:
+     * 
+     * 	Ingredient -> category
+     * 
+     * @return
+     */
     public static Ontology createOntology() {
 	HashSet<String> classes = new HashSet<String>();
 
 	classes.add(CONTAINS_MEAT);
-	// classes.add(CONTAINS_NUTS);
 	classes.add(CONTAINS_VEGETABLE);
 
 	Ontology ontology = new Ontology(classes);
@@ -70,17 +66,30 @@ public class FoodNetBuilder {
 	ontology.define(CONTAINS_PORK, CONTAINS_MEAT);
 	ontology.define(CONTAINS_BEEF, CONTAINS_MEAT);
 
-	// ontology.define(CONTAINS_GENERIC_NUTS, CONTAINS_NUTS);
-
 	ontology.define(CONTAINS_TOMATOS, CONTAINS_VEGETABLE);
 	ontology.define(CONTAINS_POTATOS, CONTAINS_VEGETABLE);
 	return ontology;
     }
 
+    /**
+     * Build an assignment given a variable and a value
+     * @param varible 
+     * @param value
+     * @return
+     */
     private static Assignment build(String varible, String value) {
 	return new Assignment(new Variable(varible, DOMAIN), value);
     }
     
+    /**
+     * Resolve onthology by adding category for
+     * each ingredient and add all categories 
+     * that are not in a dish as false.
+     * 
+     * @param point
+     * @param onto
+     * @return
+     */
     public static DataPoint normalize(DataPoint point, Ontology onto) {
 	// resolve onthology
 	DataPoint normPoint = new DataPoint(point);
@@ -104,6 +113,15 @@ public class FoodNetBuilder {
 	return normPoint;
     }
     
+    /**
+     * Build training data from survey.
+     * 
+     * @param survey
+     * @param recipeBook
+     * @param startIndex
+     * @param endIndex
+     * @return
+     */
     public static DataSet getSurveyDataSet(Survey survey,
 	    RecipeBook recipeBook, int startIndex, int endIndex) {
 	DataSet data = FoodExampleBuilder.examples();
@@ -120,6 +138,16 @@ public class FoodNetBuilder {
 	return data;
     }
 
+    /**
+     * Build a data point with as rating.
+     * If something rated above a threshold
+     * it is true, else it is false
+     * 
+     * @param recipeBook
+     * @param recipeName
+     * @param weight
+     * @return
+     */
     public static DataPoint createDataPoint(RecipeBook recipeBook,
 	    String recipeName, int weight) {
 	DataPoint point = new DataPoint();
@@ -153,17 +181,19 @@ public class FoodNetBuilder {
 		point.add(build(CONTAINS_TOMATOS, TRUE_VALUE));
 	    } else {
 		point.add(build(CONTAINS_TOMATOS, FALSE_VALUE));
-	    }	}
+	    }	
-	/*
+	}
-	 * if (ingredients.contains(TYPE.GENERIC_NUTS)) {
-	 * point.add(build(CONTAINS_GENERIC_NUTS, TRUE_VALUE)); }
-	 */
-
 	point.add(build(TASTE, "" + weight));
 
 	return point;
     }
 
+    /**
+     * Vegetarian control variable
+     * 
+     * @param survey
+     * @return
+     */
     public static ProbabilityDistribution vegi(Survey survey) {
 	String names[] = {CONTAINS_MEAT, SOMEONE_VEGETARIAN};
 	ProbabilityTable table = new ProbabilityTable(names);
@@ -174,15 +204,8 @@ public class FoodNetBuilder {
 	return table;
     }
     
-    /*
+    /**
-     * public static ProbabilityDistribution allergicNuts(Survey survey) {
+     * Initialize probability distributions
-     * String names[] = { SOMEONE_ALLERGIC_NUTS }; ProbabilityTable table = new
-     * ProbabilityTable(names); if (survey.isDiner("allergic-nuts")) {
-     * table.setProbabilityForAssignment("true;", new Probability(1));
-     * table.setProbabilityForAssignment("false;", new Probability(0)); } else {
-     * table.setProbabilityForAssignment("true;", new Probability(0));
-     * table.setProbabilityForAssignment("false;", new Probability(1)); } return
-     * table; }
      */
     
     public static ProbabilityDistribution beef() {
@@ -203,16 +226,6 @@ public class FoodNetBuilder {
 	return table;
     }
 
-    /*
-     * public static ProbabilityDistribution genericNuts() { String names[] = {
-     * CONTAINS_NUTS, CONTAINS_GENERIC_NUTS }; ProbabilityTable table = new
-     * ProbabilityTable(names); return table; }
-     * 
-     * public static ProbabilityDistribution nuts() { String names[] = {
-     * SOMEONE_ALLERGIC_NUTS, CONTAINS_NUTS }; ProbabilityTable table = new
-     * ProbabilityTable(names); return table; }
-     */
-
     public static ProbabilityDistribution tomatos() {
 	String names[] = { CONTAINS_VEGETABLE, CONTAINS_TOMATOS };
 	ProbabilityTable table = new ProbabilityTable(names);
@@ -242,31 +255,19 @@ public class FoodNetBuilder {
 	    int startIndex, int endIndex) {
 	BayesNet net = new BayesNet(VARIABLES);
 	
-	// net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN),
+	// initialize distributions
-	// vegi(survey));
-	// net.setDistribution(new Variable(SOMEONE_ALLERGIC_NUTS, DOMAIN),
-	// allergicNuts(survey));
-
 	net.setDistribution(new Variable(CONTAINS_MEAT, DOMAIN), meat());
-	// net.setDistribution(new Variable(CONTAINS_NUTS, DOMAIN), nuts());
+	net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN),vegi(survey));
-	net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN),
+	net.setDistribution(new Variable(CONTAINS_VEGETABLE, DOMAIN),vegetables());
-		vegi(survey));
-	net.setDistribution(new Variable(CONTAINS_VEGETABLE, DOMAIN),
-		vegetables());
 	net.setDistribution(new Variable(CONTAINS_BEEF, DOMAIN), beef());
 	net.setDistribution(new Variable(CONTAINS_PORK, DOMAIN), pork());
 	net.setDistribution(new Variable(CONTAINS_POTATOS, DOMAIN), potatos());
 	net.setDistribution(new Variable(CONTAINS_TOMATOS, DOMAIN), tomatos());
-	// net.setDistribution(new Variable(CONTAINS_GENERIC_NUTS, DOMAIN),
-	// genericNuts());
-
 	net.setDistribution(new Variable(TASTE, RATING_DOMAIN), taste());
 
 	Ontology ontology = createOntology();
-	// for (String category : ontology.getClasses()) {
+
-	// net.connect(category, SOMEONE_VEGETARIAN);
+	// Build net
-	// /*net.connect(category, SOMEONE_ALLERGIC_NUTS);*/
-	// }
 	net.connect(SOMEONE_VEGETARIAN, CONTAINS_MEAT);
 
 	for (String thing : OBSERVED) {