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dkohl
2012-03-12 20:52:50 -04:00
parent 6e2507f92e
commit 9235ff8d0f
4 changed files with 125 additions and 96 deletions
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13
src/dkohl/bayes/builders/FoodNetBuilder.java
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@@ -221,12 +221,12 @@ public class FoodNetBuilder {
public static BayesNet createDishNet(Survey survey, RecipeBook recipeBook, int startIndex, int endIndex) { public static BayesNet createDishNet(Survey survey, RecipeBook recipeBook, int startIndex, int endIndex) {
BayesNet net = new BayesNet(VARIABLES); BayesNet net = new BayesNet(VARIABLES);
net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN), vegi(survey)); // net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN), vegi(survey));
//net.setDistribution(new Variable(SOMEONE_ALLERGIC_NUTS, DOMAIN), allergicNuts(survey)); //net.setDistribution(new Variable(SOMEONE_ALLERGIC_NUTS, DOMAIN), allergicNuts(survey));
net.setDistribution(new Variable(CONTAINS_MEAT, DOMAIN), meat()); net.setDistribution(new Variable(CONTAINS_MEAT, DOMAIN), meat());
//net.setDistribution(new Variable(CONTAINS_NUTS, DOMAIN), nuts()); //net.setDistribution(new Variable(CONTAINS_NUTS, DOMAIN), nuts());
net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN), vegi(survey));
net.setDistribution(new Variable(CONTAINS_VEGETABLE, DOMAIN), net.setDistribution(new Variable(CONTAINS_VEGETABLE, DOMAIN),
vegetables()); vegetables());
net.setDistribution(new Variable(CONTAINS_BEEF, DOMAIN), beef()); net.setDistribution(new Variable(CONTAINS_BEEF, DOMAIN), beef());
@@ -238,10 +238,11 @@ public class FoodNetBuilder {
net.setDistribution(new Variable(TASTE, RATING_DOMAIN), taste()); net.setDistribution(new Variable(TASTE, RATING_DOMAIN), taste());
Ontology ontology = createOntology(); Ontology ontology = createOntology();
for (String category : ontology.getClasses()) { // for (String category : ontology.getClasses()) {
net.connect(category, SOMEONE_VEGETARIAN); // net.connect(category, SOMEONE_VEGETARIAN);
/*net.connect(category, SOMEONE_ALLERGIC_NUTS);*/ // /*net.connect(category, SOMEONE_ALLERGIC_NUTS);*/
} // }
net.connect(SOMEONE_VEGETARIAN, CONTAINS_MEAT);
for (String thing : OBSERVED) { for (String thing : OBSERVED) {
net.connect(thing, ontology.getInheritance().get(thing)); net.connect(thing, ontology.getInheritance().get(thing));

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writeup/P2 Proposal.log
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@@ -70,30 +70,24 @@ scale of 1 to 10, 10 being favorite and 1 being least favorite'. Furthermore,
%daniel is here %daniel is here
\paragraph*{Knowledge Engineering} \paragraph*{Knowledge Engineering}
We model the diners' various taste preferences using
a Bayes net. We model the taste
We model the diners' various taste preferences preferences using
a Bayes net. In the first layer in Figure \ref{img:bayes_net}
we capture control variables such as vegarian or allergy to nuts.
These were modeled as boolean variables, with 'true' indicating the presence of a constraint.
Our model consists of 4 layers,
each modeling a different aspect of taste or dietary requirement.
\begin{description} \begin{description}
\item[Layer 1] General meal preferences such as \item[Layer 1] The first layer models a general preference towards
being vegetarian or being allergic to nuts.
\item[Layer 2] The second layer models a general preference towards
different food categories like vegetables or meat. different food categories like vegetables or meat.
As one can see, the food categories are dependent As one can see, the food categories are dependent
on the general meal preference. For example on the general meal preference. For example
being vegetarian will exclude meat and will being vegetarian will exclude meat and will
support vegetables. support vegetables.
\item[Layer 3] Specific flavors and ingredients. Each ingredient is conditioned \item[Layer 2] Specific flavors and ingredients. Each ingredient is conditioned
by the food category to which it belongs. by the food category to which it belongs.
\end{description} \end{description}
If we need to model hard constraints, like
The overall net is shown in Figure \ref{img:bayes_net}. The overall net is shown in Figure \ref{img:bayes_net}.
Given a recipe with a list of ingredients $I = i_1,...,i_n$ Given a recipe with a list of ingredients $I = i_1,...,i_n$
and a Bayesian network capturing user preferences and a Bayesian network capturing user preferences