Ran cargo fmt [skip ci]

src/bindings.rs

@@ -76,8 +76,16 @@ extern "C" {
     pub fn mf_load_model(path: *const c_char) -> *mut MfModel;
     pub fn mf_destroy_model(model: *mut *mut MfModel);
     pub fn mf_train(prob: *const MfProblem, param: MfParameter) -> *mut MfModel;
-    pub fn mf_train_with_validation(tr: *const MfProblem, va: *const MfProblem, param: MfParameter) -> *mut MfModel;
+    pub fn mf_train_with_validation(
-    pub fn mf_cross_validation(prob: *const MfProblem, nr_folds: c_int, param: MfParameter) -> c_double;
+        tr: *const MfProblem,
+        va: *const MfProblem,
+        param: MfParameter,
+    ) -> *mut MfModel;
+    pub fn mf_cross_validation(
+        prob: *const MfProblem,
+        nr_folds: c_int,
+        param: MfParameter,
+    ) -> c_double;
     pub fn mf_predict(model: *const MfModel, u: c_int, v: c_int) -> c_float;
     pub fn calc_rmse(prob: *const MfProblem, model: *const MfModel) -> c_double;
     pub fn calc_mae(prob: *const MfProblem, model: *const MfModel) -> c_double;

src/model.rs

@@ -65,12 +65,16 @@ impl Model {
 
     /// Returns the latent factors for rows.
     pub fn p_factors(&self) -> &[f32] {
-        unsafe { std::slice::from_raw_parts((*self.model).p, (self.rows() * self.factors()) as usize) }
+        unsafe {
+            std::slice::from_raw_parts((*self.model).p, (self.rows() * self.factors()) as usize)
+        }
     }
 
     /// Returns the latent factors for columns.
     pub fn q_factors(&self) -> &[f32] {
-        unsafe { std::slice::from_raw_parts((*self.model).q, (self.columns() * self.factors()) as usize) }
+        unsafe {
+            std::slice::from_raw_parts((*self.model).q, (self.columns() * self.factors()) as usize)
+        }
     }
 
     /// Returns the latent factors for a row.
@@ -226,14 +230,23 @@ mod tests {
     #[test]
     fn test_loss() {
         let data = generate_data();
-        let model = Model::params().loss(Loss::OneClassL2).quiet(true).fit(&data).unwrap();
+        let model = Model::params()
+            .loss(Loss::OneClassL2)
+            .quiet(true)
+            .fit(&data)
+            .unwrap();
         assert_eq!(model.bias(), 0.0);
     }
 
     #[test]
     fn test_loss_real_kl() {
         let data = generate_data();
-        assert!(Model::params().loss(Loss::RealKL).nmf(true).quiet(true).fit(&data).is_ok());
+        assert!(Model::params()
+            .loss(Loss::RealKL)
+            .nmf(true)
+            .quiet(true)
+            .fit(&data)
+            .is_ok());
     }
 
     #[test]

src/params.rs

@@ -137,35 +137,55 @@ impl Params {
         let param = self.param;
 
         if param.k < 1 {
-            return Err(Error::Parameter("number of factors must be greater than zero".to_string()));
+            return Err(Error::Parameter(
+                "number of factors must be greater than zero".to_string(),
+            ));
         }
 
         if param.nr_threads < 1 {
-            return Err(Error::Parameter("number of threads must be greater than zero".to_string()));
+            return Err(Error::Parameter(
+                "number of threads must be greater than zero".to_string(),
+            ));
         }
 
         if param.nr_bins < 1 || param.nr_bins < param.nr_threads {
-            return Err(Error::Parameter("number of bins must be greater than number of threads".to_string()));
+            return Err(Error::Parameter(
+                "number of bins must be greater than number of threads".to_string(),
+            ));
         }
 
         if param.nr_iters < 1 {
-            return Err(Error::Parameter("number of iterations must be greater than zero".to_string()));
+            return Err(Error::Parameter(
+                "number of iterations must be greater than zero".to_string(),
+            ));
         }
 
-        if param.lambda_p1 < 0.0 || param.lambda_p2 < 0.0 || param.lambda_q1 < 0.0 || param.lambda_q2 < 0.0 {
+        if param.lambda_p1 < 0.0
-            return Err(Error::Parameter("regularization coefficient must be non-negative".to_string()));
+            || param.lambda_p2 < 0.0
+            || param.lambda_q1 < 0.0
+            || param.lambda_q2 < 0.0
+        {
+            return Err(Error::Parameter(
+                "regularization coefficient must be non-negative".to_string(),
+            ));
         }
 
         if param.eta <= 0.0 {
-            return Err(Error::Parameter("learning rate must be greater than zero".to_string()));
+            return Err(Error::Parameter(
+                "learning rate must be greater than zero".to_string(),
+            ));
         }
 
         if matches!(param.fun, Loss::RealKL) && !param.do_nmf {
-            return Err(Error::Parameter("nmf must be set when using generalized KL-divergence".to_string()));
+            return Err(Error::Parameter(
+                "nmf must be set when using generalized KL-divergence".to_string(),
+            ));
         }
 
         if param.alpha < 0.0 {
-            return Err(Error::Parameter("alpha must be a non-negative number".to_string()));
+            return Err(Error::Parameter(
+                "alpha must be a non-negative number".to_string(),
+            ));
         }
 
         Ok(param)