some more thinking...

planner/src/constraint.rs

@@ -8,6 +8,22 @@
 //! by a key of type `K`.
 //! A constraint owns references to actual values assigned,
 //! used to perform checks.
+//!
+//!
+//! The problem is to clarify the way Constraints operate.
+//! Do they compute their status from some data on demand ?
+//! Do they keep their status updated by watching the Variables
+//! they act on ?
+//! Worse, do they have superpowers ?
+//! Could they filter on a variable domain, according to some other variable
+//! state ? This would mean that constraints won't judge a result, but guide
+//! the solving process to avoid erroring paths, like a constraint-driven
+//! solving. This would be powerfull but maybe far too complex...
+//!
+//! On the other hand, we can implement a simple Observer pattern, with strong
+//! coupling to [`Problem`](crate::solver::Problem).
+//! Because of this, we can safely play with private fields of Problem, and in
+//! return, provide a public api to build specific constraints.
 
 #[derive(Debug, Copy, Clone, PartialEq, Eq)]
 pub enum Status {
@@ -93,7 +109,7 @@ mod tests {
         use super::Constraint;
 
         let domain = Domain::new(vec![1, 2, 3]);
-        let mut problem = Problem::build()
+        let problem = Problem::build()
             .add_variable("Left", domain.all(), None)
             .add_variable("Right", domain.all(), None)
             .add_constraint(Constraint::new(vec![&"Left", &"Right"]))

planner/src/rules.rs

@@ -1,16 +1,56 @@
 //! Rules used by the `planner`
 //! A rule is a constraint on valid solutions, but also provides insights
 //! and eventually inferences to optimize the solving process.
+//!
+//! * Basic repartition
+//!    * All different meals
+//!    * Map recipes categories to each meals
+//!    * (Eating a dish over two days (leftovers))
+//! * Nutritional values
+//!    * Per day : according to user profile (man: 2000kcal, woman: 1800kcal)
+//!    * Per meal : some meals should have higher nutrional values than others
+//!
+//! * Ingredients
+//!    * Per week : should use most of a limited set of ingredients (excluding
+//!                 condiments, ...)
+//!    * To consume : must use a small set of ingredients (leftovers)
+//!
+//!
+//! Price
+//! - Per week : should restrict ingredients cost to a given amount
 
-// Nutritional values
-// - Per day : according to user profile (man: 2000kcal, woman: 1800kcal)
-// - Per meal : some meals should have higher nutrional values than others
+enum Status {
+    Ok,
+    Violated,
+}
 
-// Ingredients
-// - Per week : should use most of a limited set of ingredients (excluding
-// condiments, ...)
-// - To consume : must use a small set of ingredients (leftovers)
-//
+trait Rule {
+    type Key;
+    type Value;
 
-// Price
-// - Per week : should restrict ingredients cost to a given amount
+    fn status(&self, state: (Vec<&Self::Key>, Vec<&Self::Value>));
+    fn update(&self, idx: usize, value: Option<Self::Value>) -> Option<Filter>;
+};
+
+struct AllDifferentMeals;
+
+impl Rule for AllDifferentMeals {
+    type State = Vec<Recipe>;
+
+    fn status(&self, _: Self::State) -> Status {
+        Status::Ok // Always enforced by update rule
+    }
+
+    fn update(&self, _: Self::State) -> Option<Filter> {
+        // Returns a filter excluding this value from domain.
+        // so that it is impossible to select the same meal twice.
+        None
+    }
+}
+struct FilterRecipeByMeals; // Essentially work on domain
+
+struct NutritionalByDayAverageReq;
+struct NutritionalByMealAverageValues;
+
+struct IngredientsInFridge;
+struct IngredientsMustUse;

planner/src/solver.rs

@@ -105,6 +105,46 @@ impl<V: fmt::Debug> fmt::Debug for Domain<V> {
     }
 }
 
+/// Or we can have a much more complex version of Domain.
+/// We want to retrieve a filtered domain for each variable.
+/// Filters will be static (filter by category,...) or dynamic
+/// (inserted by rules updates).
+///
+/// For every variable, we can retrieve its filtered values (values,
+/// filtered by all globals, filtered by one local).
+/// Plus, set a dynamic filter that will apply to all other variables.
+/// Of course, it also affects this variable, but considering that dynamic
+/// filters are cleared and repopulated on every assign, this side-effect
+/// can never occur.
+struct SDomain<V, Filter> {
+    values: Vec<V>,
+    global_filters: Vec<Filter>, // Globals are dynamic Filters
+    local_filters: Vec<Filter>, // Locals are static Filters
+}
+
+impl<V, F> SDomain<V, F> {
+    fn new(values: Vec<V>) -> Self {
+        Self {
+            values,
+            global_filters: Vec::new(),
+            local_filters: Vec::new(),
+        }
+    }
+
+    /// Returns the current domain values for a variable by index
+    fn get(&self, idx: usize) -> DomainValues<V> {
+        self.values
+            .iter()
+            .collect()
+    }
+
+    /// Adds a dynamic filter to globals, identified by its setter's id
+    /// /!\ Previously set filters are overriden, hence dynamic
+    fn set_global(&mut self, setter: usize, filter: F) {
+        self.global_filters[setter] = filter;
+    }
+}
+
 
 //pub type Constraint<'a,V> = fn(&Variables<'a,V>) -> bool;
 
@@ -116,7 +156,7 @@ impl<V: fmt::Debug> fmt::Debug for Domain<V> {
 
 pub struct Problem<'p, V, K> {
     keys: Vec<K>,
-    /// The initial assignements map
+    /// The initial assignements
     variables: Variables<'p, V>,
     /// Each variable has its associated domain
     domains: Vec<DomainValues<'p,V>>,
@@ -139,18 +179,19 @@ impl<'p, V: PartialEq, K: Eq + Hash + Clone> Problem<'p, V, K> {
     /// Returns all possible Updates for next assignements, prepended with
     /// a Clear to ensure the variable is unset before when leaving the branch.
     fn _push_updates(&self) -> Option<Vec<Assignment<'p,V>>> {
-        // TODO: should be able to inject a choosing strategy
-        if let Some(key) = self._next_assign() {
+        if let Some(idx) = self._next_assign() {
+            // TODO: Domain will filter possible values for us
+            // let values = self.domain.get(idx);
             let domain_values = self.domains
-                                    .get(key)
+                                    .get(idx)
                                     .expect("No domain for variable !");
+            // TODO: handle case of empty domain.values
             assert!(!domain_values.is_empty());
             // Push a clear assignment first, just before going up the stack.
-            let mut updates = vec![Assignment::Clear(key.clone())];
-            // TODO: should be able to filter domain values (inference, pertinence)
+            let mut updates = vec![Assignment::Clear(idx.clone())];
             domain_values.iter().for_each(|value| {
                 updates.push(
-                    Assignment::Update(key, *value)
+                    Assignment::Update(idx, *value)
                 );
             });
             Some(updates)
@@ -160,6 +201,7 @@ impl<'p, V: PartialEq, K: Eq + Hash + Clone> Problem<'p, V, K> {
     }
 
     fn _next_assign(&self) -> Option<usize> {
+        // TODO: should be able to inject a choosing strategy
         self.variables.iter()
             .enumerate()
             .find_map(|(idx, val)| {
@@ -180,6 +222,28 @@ impl<'p, V: PartialEq, K: Eq + Hash + Clone> Problem<'p, V, K> {
         &self.keys[idx]
     }
 
+    fn _get_solution(&self) -> Solution<'p, V, K> {
+        // Returns the current state wrapped in a Solution type.
+        self.keys.iter().cloned()
+            .zip(self.variables.iter().cloned())
+            .collect()
+    }
+
+    /// Assigns a new value to the given index, then calls update
+    /// on every constraints.
+    fn _assign(&mut self, idx: usize, value: Option<&'p V>) {
+        self.variables[idx] = value;
+        let var_key = &self.keys[idx];
+        // TODO: manage dynamic filters on Domain
+        // let filters: Filter::Chain = self.constraints.iter_mut().map([...]).collect();
+        // self.domain.set_global(idx, filters);
+        self.constraints.iter_mut()
+                        .for_each(|c| {
+                            c.update(&var_key, value);
+                        });
+
+    }
+
     fn _solve(&mut self, limit: Option<usize>) -> Vec<Solution<'p, V, K>> {
         let mut solutions: Vec<Solution<V, K>> = vec![];
         let mut stack: Vec<Assignment<'p, V>> = vec![];
@@ -204,35 +268,19 @@ impl<'p, V: PartialEq, K: Eq + Hash + Clone> Problem<'p, V, K> {
             match node.unwrap() {
                 Assignment::Update(idx, val) => {
                     // Assign the variable and open new branches, if any.
-                    self.variables[idx] = Some(val);
-                    {
-                        let v_key = &self.keys[idx];
-                        self.constraints.iter_mut().for_each(|cons| {
-                            cons.update(&v_key, Some(val));
-                        });
-                    }
-                    // TODO: handle case of empty domain.values
+                    self._assign(idx, Some(val));
                     if let Some(mut nodes) = self._push_updates() {
                         stack.append(&mut nodes);
                     } else {
                         // Assignements are completed
                         if self._is_valid() {
-                            solutions.push(
-                                // Builds Solution
-                                self.keys.iter().cloned()
-                                    .zip(self.variables.iter().cloned())
-                                    .collect()
-                            );
+                            solutions.push(self._get_solution());
                         };
                     };
                 },
                 Assignment::Clear(idx) => {
                     // We are closing this branch, unset the variable
-                    self.variables[idx] = None;
-                    let v_key = &self.keys[idx];
-                    self.constraints.iter_mut().for_each(|cons| {
-                        cons.update(&v_key, None);
-                    });
+                    self._assign(idx, None);
                 },
             };
         };
@@ -268,11 +316,11 @@ impl<'p, V, K: Eq + Hash + Clone> ProblemBuilder<'p, V, K> {
             })
     }
 
-    pub fn add_variable(mut self, name: K, domain: Vec<&'p V>, value: Option<&'p V>) -> Self
+    pub fn add_variable(mut self, name: K, static_filter: Vec<&'p V>, initial: Option<&'p V>) -> Self
     {
         self.0.keys.push(name);
-        self.0.variables.push(value);
-        self.0.domains.push(domain);
+        self.0.variables.push(initial);
+        self.0.domains.push(static_filter);
         self
     }
 
@@ -293,7 +341,7 @@ mod tests {
     fn test_solver_find_pairs() {
         use super::*;
         let domain = Domain::new(vec![1,2,3]);
-        let mut problem: Problem<_, _> = Problem::build()
+        let problem: Problem<_, _> = Problem::build()
             .add_variable(String::from("Left"), domain.all(), None)
             .add_variable(String::from("Right"), domain.all(), None)
             .finish();
@@ -311,7 +359,7 @@ mod tests {
     fn test_solver_find_pairs_with_initial() {
         use super::*;
         let domain = Domain::new(vec![1,2,3]);
-        let mut problem: Problem<_, _> = Problem::build()
+        let problem: Problem<_, _> = Problem::build()
             .add_variable("Left".to_string(), domain.all(), None)
             .add_variable("Right".to_string(), domain.all(), Some(&2))
             .finish();