update f128! macro name to F128! since newer version of Rust have f12…

…8 (unstable) type

Cargo.toml

@@ -19,7 +19,7 @@ rand = ">=0.8.5"
 nalgebra = ">=0.31.4"
 rug = { version = ">=1.18.0", features = ["float", "integer"] }
 sysinfo = ">=0.28.1"
-search_trail = ">=0.1.2"
+search_trail = ">=0.1.3"
 peak_alloc = ">=0.2.0"
 chrono = ">=0.4.31"
 rayon = ">=1.8.0"

src/common.rs

@@ -13,12 +13,12 @@
 //
 //You should have received a copy of the GNU Affero General Public License
 //along with this program.  If not, see <http://www.gnu.org/licenses/>.
-macro_rules! f128 {
+macro_rules! F128 {
     ($v:expr) => {
         Float::with_val(113, $v)
     };
 }
 
 pub const FLOAT_CMP_THRESHOLD: f64 = 0.0000001;
 
-pub(crate) use f128;
+pub(crate) use F128;

src/diagrams/dac/dac.rs

@@ -170,7 +170,7 @@ impl<R> Dac<R>
 
     pub fn solution(&self) -> Solution {
         let p = self.circuit_probability().to_f64();
-        Solution::new(f128!(p), f128!(p), self.compile_time)
+        Solution::new(F128!(p), F128!(p), self.compile_time)
     }
 
     /// Updates the values of the distributions to the given values
@@ -190,9 +190,9 @@ impl<R> Dac<R>
     /// Resets the path value of each node
     pub fn zero_paths(&mut self) {
         for node in (0..self.nodes.len()-1).map(NodeIndex) {
-            self[node].set_path_value(f128!(0.0));
+            self[node].set_path_value(F128!(0.0));
         }
-        self.nodes.last_mut().unwrap().set_path_value(f128!(1.0));
+        self.nodes.last_mut().unwrap().set_path_value(F128!(1.0));
     }
 
     /// Evaluates the circuits, layer by layer (starting from the input distribution, then layer 0)

src/diagrams/dac/node.rs

@@ -69,7 +69,7 @@ impl<R> Node<R>
             number_inputs: 0,
             layer: 0,
             to_remove: true,
-            path_value: f128!(1.0),
+            path_value: F128!(1.0),
         }
     }
 
@@ -86,7 +86,7 @@ impl<R> Node<R>
             number_inputs: 0,
             layer: 0,
             to_remove: true,
-            path_value: f128!(1.0),
+            path_value: F128!(1.0),
         }
     }
 
@@ -103,7 +103,7 @@ impl<R> Node<R>
             number_inputs: 0,
             layer: 0,
             to_remove: true,
-            path_value: f128!(1.0),
+            path_value: F128!(1.0),
         }
     }
 
@@ -120,7 +120,7 @@ impl<R> Node<R>
             number_inputs: 0,
             layer: 0,
             to_remove: true,
-            path_value: f128!(1.0),
+            path_value: F128!(1.0),
         }
     }
 

src/diagrams/semiring.rs

@@ -24,7 +24,7 @@
 use rug::Float;
 #[cfg(feature = "tensor")]
 use tch::Tensor;
-use crate::common::f128;
+use crate::common::F128;
 use std::ops::{AddAssign, MulAssign};
 use rug::Assign;
 
@@ -58,7 +58,7 @@ pub trait SemiRing: AddAssign + MulAssign + Send + Sized + std::fmt::Display {
 impl SemiRing for Float {
 
     fn from_f64(value: f64) -> Self {
-        f128!(value)
+        F128!(value)
     }
 
     fn to_f64(&self) -> f64 {

src/learning/learner.rs

@@ -41,7 +41,7 @@ use crate::parser::*;
 use crate::Branching;
 use rayon::prelude::*;
 use super::Learning;
-use crate::common::f128;
+use crate::common::F128;
 use super::utils::*;
 use super::*;
 use rug::{Assign, Float};
@@ -75,7 +75,7 @@ impl <const S: bool> Learner<S> {
         for distribution in distributions.iter() {
             let unsoftmaxed_vector = distribution.iter().map(|p| p.log(std::f64::consts::E)).collect::<Vec<f64>>();
             unsoftmaxed_distributions.push(unsoftmaxed_vector);
-            grads.push(vec![f128!(0.0); distribution.len()]);
+            grads.push(vec![F128!(0.0); distribution.len()]);
         }
         // Retrieves which distributions are learned
         let learned_distributions = learned_distributions_from_cnf(&inputs[0]);
@@ -92,13 +92,13 @@ impl <const S: bool> Learner<S> {
             let d = train_dacs.pop().unwrap();
             let expected = expected_outputs.pop().unwrap();
             train_data.push(d);
-            train_expected.push(f128!(expected));
+            train_expected.push(F128!(expected));
         }
         while !test_dacs.is_empty() {
             let d = test_dacs.pop().unwrap();
             let expected = expected_test.pop().unwrap();
             test_data.push(d);
-            test_expected.push(f128!(expected));
+            test_expected.push(F128!(expected));
         }
         let train_dataset = Dataset::new(train_data, train_expected);
         let test_dataset = Dataset::new(test_data, test_expected);
@@ -201,7 +201,7 @@ impl <const S: bool> Learner<S> {
                             // If it is a product node, we need to divide the path value by the value of the child
                             // This is equivalent to multiplying the values of the other children
                             // If the value of the child is 0, then the path value is simply 0
-                            let mut val = f128!(0.0);
+                            let mut val = F128!(0.0);
                             if self.train[query_id][child].value().to_f64() != 0.0 {
                                 val = path_val.clone() * &value / self.train[query_id][child].value().to_f64();
                             }
@@ -223,7 +223,7 @@ impl <const S: bool> Learner<S> {
                         }
                         // Compute the gradient contribution for the value used in the node 
                         // and all the other possible values of the distribution (derivative of the softmax)
-                        let mut sum_other_w = f128!(0.0);
+                        let mut sum_other_w = F128!(0.0);
                         let child_w = self.get_probability(d, v);
                         for params in (0..self.unsoftmaxed_distributions[d].len()).filter(|i| *i != v) {
                             let weight = self.get_probability(d, params);

src/learning/utils.rs

@@ -23,7 +23,7 @@ use crate::core::components::ComponentExtractor;
 use crate::propagator::Propagator;
 use crate::parser::*;
 use crate::solvers::*;
-use crate::{common::f128, diagrams::{semiring::SemiRing, dac::dac::Dac}};
+use crate::{common::F128, diagrams::{semiring::SemiRing, dac::dac::Dac}};
 
 /// Calculates the softmax (the normalized exponential) function, which is a generalization of the
 /// logistic function to multiple dimensions.
@@ -34,7 +34,7 @@ use crate::{common::f128, diagrams::{semiring::SemiRing, dac::dac::Dac}};
 /// From https://docs.rs/compute/latest/src/compute/functions/statistical.rs.html#43-46
 pub fn softmax(x: &[f64]) -> Vec<Float> {
     let sum_exp: f64 = x.iter().map(|i| i.exp()).sum();
-    x.iter().map(|i| f128!(i.exp() / sum_exp)).collect()
+    x.iter().map(|i| F128!(i.exp() / sum_exp)).collect()
 }
 
 /// Generates a vector of optional Dacs from a list of input files

src/preprocess.rs

@@ -20,7 +20,7 @@ use crate::core::components::{ComponentExtractor, ComponentIndex};
 use crate::core::problem::*;
 use crate::propagator::Propagator;
 use rug::Float;
-use crate::common::f128;
+use crate::common::F128;
 
 pub struct Preprocessor<'b>
 {
@@ -48,7 +48,7 @@ where
     }
 
     pub fn preprocess(&mut self) -> Option<Float> {
-        let mut p = f128!(1.0);
+        let mut p = F128!(1.0);
 
         for variable in self.problem.variables_iter() {
             if self.problem[variable].is_probabilitic() && self.problem[variable].weight().unwrap() == 1.0 {

src/propagator.rs

@@ -47,7 +47,7 @@
 
 use search_trail::{StateManager, UsizeManager, ReversibleUsize};
 
-use crate::common::f128;
+use crate::common::F128;
 use crate::core::components::{ComponentIndex, ComponentExtractor};
 use crate::core::problem::{ClauseIndex, DistributionIndex, Problem, VariableIndex};
 use rug::{Assign, Float};
@@ -81,7 +81,7 @@ impl Propagator {
             assignments: vec![],
             base_assignments: state.manage_usize(0),
             unconstrained_distributions: vec![],
-            propagation_prob: f128!(0.0),
+            propagation_prob: F128!(0.0),
             forced: 0,
         }
     }
@@ -149,7 +149,7 @@ impl Propagator {
         g[distribution].set_unconstrained(state);
         if g[distribution].number_false(state) != 0 {
             self.unconstrained_distributions.push(distribution);
-            let mut p = f128!(0.0);
+            let mut p = F128!(0.0);
             for weight in g[distribution].iter_variables().filter(|v| !g[*v].is_fixed(state)).map(|v| g[v].weight().unwrap()) {
                 p += weight;
             }

src/solvers/solver.rs

@@ -132,7 +132,7 @@ impl<B: BranchingDecision, const S: bool> Solver<B, S> {
             let mut preprocessor = Preprocessor::new(&mut self.problem, &mut self.state, &mut self.propagator, &mut self.component_extractor);
             let preproc = preprocessor.preprocess();
             if preproc.is_none() {
-                return Solution::new(f128!(0.0), f128!(0.0), self.start.elapsed().as_secs());
+                return Solution::new(F128!(0.0), F128!(0.0), self.start.elapsed().as_secs());
             }
             self.preproc_in = Some(preproc.unwrap());
             self.preproc_out = Some(1.0 - self.problem.distributions_iter().map(|d| {
@@ -142,7 +142,7 @@ impl<B: BranchingDecision, const S: bool> Solver<B, S> {
             self.state.restore_state();
             if self.problem.number_clauses() == 0 {
                 let lb = self.preproc_in.clone().unwrap();
-                let ub = f128!(1.0 - self.preproc_out.unwrap());
+                let ub = F128!(1.0 - self.preproc_out.unwrap());
                 return Solution::new(lb, ub, self.start.elapsed().as_secs());
             }
             let max_probability = self.problem.distributions_iter().map(|d| self.problem[d].remaining(&self.state)).product::<f64>();
@@ -185,9 +185,9 @@ impl<B: BranchingDecision, const S: bool> Solver<B, S> {
             self.search_cache.clear();
         }
         self.state.save_state();
-        let mut p_in = f128!(1.0);
-        let mut p_out = f128!(1.0);
-        let mut maximum_probability = f128!(1.0);
+        let mut p_in = F128!(1.0);
+        let mut p_out = F128!(1.0);
+        let mut maximum_probability = F128!(1.0);
         for distribution in self.component_extractor.component_distribution_iter(component) {
             if self.problem[distribution].is_constrained(&self.state) {
                 maximum_probability *= self.problem[distribution].remaining(&self.state);
@@ -206,14 +206,14 @@ impl<B: BranchingDecision, const S: bool> Solver<B, S> {
                 // remaining of the components. This way we always produce a valid lower/upper
                 // bound.
                 if self.start.elapsed().as_secs() >= self.timeout {
-                    return ((f128!(0.0), f128!(0.0)), level - 1);
+                    return ((F128!(0.0), F128!(0.0)), level - 1);
                 }
                 let sub_maximum_probability = self.component_extractor[sub_component].max_probability();
                 assert!(0.0 <= sub_maximum_probability && sub_maximum_probability <= 1.0);
                 let (sub_problem, backtrack_level) = self.get_bounds_from_cache(sub_component, new_bound_factor, level, discrepancy);
                 if backtrack_level != level {
                     self.restore();
-                    return ((f128!(0.0), maximum_probability), backtrack_level);
+                    return ((F128!(0.0), maximum_probability), backtrack_level);
                 }
                 // If any of the component is not fully explored, then so is the node
                 let (sub_p_in, sub_p_out) = sub_problem.bounds();
@@ -265,9 +265,9 @@ impl<B: BranchingDecision, const S: bool> Solver<B, S> {
             self.statistics.or_node();
             let maximum_probability = self.component_extractor[component].max_probability();
             // Stores the accumulated probability of the found models in the sub-problem
-            let mut p_in = f128!(0.0);
+            let mut p_in = F128!(0.0);
             // Stores the accumulated probability of the found non-models in the sub-problem
-            let mut p_out = f128!(0.0);
+            let mut p_out = F128!(0.0);
             // When a sub-problem is UNSAT, this is the factor that must be used for the
             // computation of p_out
             let unsat_factor = maximum_probability / self.problem[distribution].remaining(&self.state);
@@ -296,7 +296,7 @@ impl<B: BranchingDecision, const S: bool> Solver<B, S> {
                             // The clause learning scheme tells us that we need to backtrack
                             // non-chronologically. There are no models in this sub-problem
                             self.restore();
-                            return (SearchCacheEntry::new((f128!(0.0), f128!(maximum_probability)), usize::MAX, Some(distribution)), backtrack_level);
+                            return (SearchCacheEntry::new((F128!(0.0), F128!(maximum_probability)), usize::MAX, Some(distribution)), backtrack_level);
                         }
                     },
                     Ok(_) => {
@@ -320,7 +320,7 @@ impl<B: BranchingDecision, const S: bool> Solver<B, S> {
                             let ((child_p_in, child_p_out), backtrack_level) = self.solve_components(component, level + 1, bound_factor, new_discrepancy);
                             if backtrack_level != level {
                                 self.restore();
-                                return (SearchCacheEntry::new((f128!(0.0), f128!(maximum_probability)), usize::MAX, Some(distribution)), backtrack_level);
+                                return (SearchCacheEntry::new((F128!(0.0), F128!(maximum_probability)), usize::MAX, Some(distribution)), backtrack_level);
                             }
                             p_in += child_p_in * &p;
                             p_out += child_p_out * &p;
@@ -337,7 +337,7 @@ impl<B: BranchingDecision, const S: bool> Solver<B, S> {
             let cache_entry = SearchCacheEntry::new((p_in, p_out), discrepancy, Some(distribution));
             (cache_entry, level)
         } else {
-            (SearchCacheEntry::new((f128!(1.0), f128!(0.0)), usize::MAX, None), level)
+            (SearchCacheEntry::new((F128!(1.0), F128!(0.0)), usize::MAX, None), level)
         }
     }