Start working on factor graph.

src/lib.rs

@@ -1,6 +1,9 @@
 mod matrix;
+mod factor_graph;
+mod gaussian;
 
 use matrix::Matrix;
+use factor_graph::*;
 
 /// Default initial mean of ratings.
 const MU: f32 = 25.0;
@@ -35,6 +38,38 @@ impl Default for Rating {
     }
 }
 
+
+fn _team_sizes(rating_groups: &[&[Rating]]) -> Vec<usize> {
+    let mut team_sizes = Vec::new();
+
+    for group in rating_groups {
+        let last = team_sizes.last().map(|v| *v).unwrap_or(0);
+
+        team_sizes.push(group.len() + last);
+    }
+
+    team_sizes
+}
+
+fn factor_graph_builders(rating_groups: &[&[Rating]]) {
+    let flatten_ratings = rating_groups
+        .iter()
+        .flat_map(|group| group.iter())
+        .collect::<Vec<_>>();
+
+    let flatten_weights = vec![1.0; flatten_ratings.len()].into_boxed_slice();
+
+    let size = flatten_ratings.len();
+    let group_size = rating_groups.len();
+
+    let rating_vars = (0..size).map(|_| Variable::new()).collect::<Vec<_>>();
+    let perf_vars = (0..size).map(|_| Variable::new()).collect::<Vec<_>>();
+    let team_perf_vars = (0..group_size).map(|_| Variable::new()).collect::<Vec<_>>();
+    let team_diff_vars = (0..group_size - 1).map(|_| Variable::new()).collect::<Vec<_>>();
+
+    let team_sizes = _team_sizes(rating_groups);
+}
+
 fn rate(rating_groups: &[&[Rating]]) {
     let ranks = (0..rating_groups.len()).collect::<Vec<_>>();
     let weights = vec![1.0; rating_groups.len()];