Make verbose an argument for the fit function

examples/abcdef.rs

@@ -16,7 +16,7 @@ fn main() {
 
     model.observe(&["A"], &["B"], 0.0);
 
-    model.fit();
+    model.fit(true);
 
     for player in ["A", "B", "C", "D", "E", "F"] {
         let (mu, sigma) = model.item_score(player, 1.25);
@@ -26,7 +26,7 @@ fn main() {
 
     model.observe(&["C"], &["D"], 0.25);
 
-    model.fit();
+    model.fit(true);
 
     for player in ["A", "B", "C", "D", "E", "F"] {
         let (mu, sigma) = model.item_score(player, 1.25);
@@ -36,7 +36,7 @@ fn main() {
 
     model.observe(&["E"], &["F"], 0.50);
 
-    model.fit();
+    model.fit(true);
 
     for player in ["A", "B", "C", "D", "E", "F"] {
         let (mu, sigma) = model.item_score(player, 1.25);
@@ -46,7 +46,7 @@ fn main() {
 
     model.observe(&["B"], &["C"], 0.75);
 
-    model.fit();
+    model.fit(true);
 
     for player in ["A", "B", "C", "D", "E", "F"] {
         let (mu, sigma) = model.item_score(player, 1.25);
@@ -56,7 +56,7 @@ fn main() {
 
     model.observe(&["D"], &["E"], 1.00);
 
-    model.fit();
+    model.fit(true);
 
     for player in ["A", "B", "C", "D", "E", "F"] {
         let (mu, sigma) = model.item_score(player, 1.25);

examples/kickscore-basics.rs

@@ -31,7 +31,7 @@ fn main() {
     model.observe(&["Jerry"], &["Tom"], 3.0);
     model.observe(&["Jerry"], &["Tom", "Spike"], 3.5);
 
-    model.fit();
+    model.fit(true);
 
     // We can predict a future outcome...
     let (p_win, _p_los) = model.probabilities(&["Jerry"], &["Tom"], 4.0);

examples/nba-history.rs

@@ -69,7 +69,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
         model.observe(&[&winner], &[&loser], t);
     }
 
-    model.fit();
+    model.fit(true);
 
     println!("Probability that CHI beats BOS...");
 

src/model/binary.rs

@@ -102,14 +102,13 @@ impl Binary {
         self.last_t = t;
     }
 
-    pub fn fit(&mut self) -> bool {
+    pub fn fit(&mut self, verbose: bool) -> bool {
         // method="ep", lr=1.0, tol=1e-3, max_iter=100, verbose=False):
 
         let method = FitMethod::Ep;
         let lr = 1.0;
         let tol = 1e-3;
         let max_iter = 100;
-        let verbose = true;
 
         self.last_method = Some(method);
 

src/model/ternary.rs

@@ -119,14 +119,13 @@ impl TernaryModel {
         self.last_t = t;
     }
 
-    pub fn fit(&mut self) -> bool {
+    pub fn fit(&mut self, verbose: bool) -> bool {
         // method="ep", lr=1.0, tol=1e-3, max_iter=100, verbose=False):
 
         let method = FitMethod::Ep;
         let lr = 1.0;
         let tol = 1e-3;
         let max_iter = 100;
-        let verbose = true;
 
         self.last_method = Some(method);
 

tests/binary-1.rs

@@ -23,5 +23,5 @@ fn binary_1() {
     model.observe(&["benjamin"], &["audrey"], 6.0);
     model.observe(&["benjamin"], &["audrey"], 7.0);
 
-    model.fit();
+    model.fit(true);
 }

tests/kickscore-basics.rs

@@ -27,7 +27,7 @@ fn kickscore_basic() {
     model.observe(&["Jerry"], &["Tom"], 3.0);
     model.observe(&["Jerry"], &["Tom", "Spike"], 3.5);
 
-    model.fit();
+    model.fit(true);
 
     let (p_win, _p_los) = model.probabilities(&["Jerry"], &["Tom"], 4.0);
 

tests/nba-history.rs

@@ -73,7 +73,7 @@ fn nba_history() {
         model.observe(&[&winner], &[&loser], t);
     }
 
-    model.fit();
+    model.fit(true);
 
     let (p_win, _) = model.probabilities(
         &["CHI"],