fix: code and documentation cleanup

src/linalg/lu.rs

@@ -287,7 +287,6 @@ mod tests {
         let expected =
             DenseMatrix::from_2d_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]]);
         let a_inv = a.lu().and_then(|lu| lu.inverse()).unwrap();
-        println!("{}", a_inv);
         assert!(a_inv.approximate_eq(&expected, 1e-4));
     }
 }

src/math/distance/mahalanobis.rs

@@ -108,8 +108,6 @@ impl<T: RealNumber, M: Matrix<T>> Distance<Vec<T>, T> for Mahalanobis<T, M> {
             );
         }
 
-        println!("{}", self.sigmaInv);
-
         let n = x.len();
         let mut z = vec![T::zero(); n];
         for i in 0..n {

src/metrics/cluster_hcv.rs

@@ -5,13 +5,13 @@ use crate::math::num::RealNumber;
 use crate::metrics::cluster_helpers::*;
 
 #[derive(Serialize, Deserialize, Debug)]
-/// Mean Absolute Error
+/// Homogeneity, completeness and V-Measure scores.
 pub struct HCVScore {}
 
 impl HCVScore {
-    /// Computes mean absolute error
+    /// Computes Homogeneity, completeness and V-Measure scores at once.
-    /// * `y_true` - Ground truth (correct) target values.
+    /// * `labels_true` - ground truth class labels to be used as a reference.
-    /// * `y_pred` - Estimated target values.    
+    /// * `labels_pred` - cluster labels to evaluate.    
     pub fn get_score<T: RealNumber, V: BaseVector<T>>(
         &self,
         labels_true: &V,

src/metrics/cluster_helpers.rs

@@ -106,14 +106,17 @@ mod tests {
         let v1 = vec![0.0, 0.0, 1.0, 1.0, 2.0, 0.0, 4.0];
         let v2 = vec![1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0];
 
-        println!("{:?}", contingency_matrix(&v1, &v2));
+        assert_eq!(
+            vec!(vec!(1, 2), vec!(2, 0), vec!(1, 0), vec!(1, 0)),
+            contingency_matrix(&v1, &v2)
+        );
     }
 
     #[test]
     fn entropy_test() {
         let v1 = vec![0.0, 0.0, 1.0, 1.0, 2.0, 0.0, 4.0];
 
-        println!("{:?}", entropy(&v1));
+        assert!((1.2770f32 - entropy(&v1).unwrap()).abs() < 1e-4);
     }
 
     #[test]
@@ -122,6 +125,6 @@ mod tests {
         let v2 = vec![1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0];
         let s: f32 = mutual_info_score(&contingency_matrix(&v1, &v2));
 
-        println!("{}", s);
+        assert!((0.3254 - s).abs() < 1e-4);
     }
 }

src/metrics/mean_absolute_error.rs

@@ -62,8 +62,6 @@ mod tests {
         let score1: f64 = MeanAbsoluteError {}.get_score(&y_pred, &y_true);
         let score2: f64 = MeanAbsoluteError {}.get_score(&y_true, &y_true);
 
-        println!("{}", score1);
-
         assert!((score1 - 0.5).abs() < 1e-8);
         assert!((score2 - 0.0).abs() < 1e-8);
     }

src/metrics/mean_squared_error.rs

@@ -62,8 +62,6 @@ mod tests {
         let score1: f64 = MeanSquareError {}.get_score(&y_pred, &y_true);
         let score2: f64 = MeanSquareError {}.get_score(&y_true, &y_true);
 
-        println!("{}", score1);
-
         assert!((score1 - 0.375).abs() < 1e-8);
         assert!((score2 - 0.0).abs() < 1e-8);
     }

src/metrics/mod.rs

@@ -8,6 +8,7 @@
 //!
 //! * [Classification metrics](struct.ClassificationMetrics.html)
 //! * [Regression metrics](struct.RegressionMetrics.html)
+//! * [Clustering metrics](struct.ClusterMetrics.html)
 //!
 //! Example:
 //! ```
@@ -54,6 +55,7 @@
 pub mod accuracy;
 /// Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
 pub mod auc;
+/// Compute the homogeneity, completeness and V-Measure scores.
 pub mod cluster_hcv;
 pub(crate) mod cluster_helpers;
 /// F1 score, also known as balanced F-score or F-measure.
@@ -126,7 +128,7 @@ impl RegressionMetrics {
 }
 
 impl ClusterMetrics {
-    /// Mean squared error, see [mean squared error](mean_squared_error/index.html).
+    /// Homogeneity and completeness and V-Measure scores at once.
     pub fn hcv_score() -> cluster_hcv::HCVScore {
         cluster_hcv::HCVScore {}
     }
@@ -188,27 +190,29 @@ pub fn r2<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
     RegressionMetrics::r2().get_score(y_true, y_pred)
 }
 
-/// Computes R2 score, see [R2](r2/index.html).
+/// Homogeneity metric of a cluster labeling given a ground truth (range is between 0.0 and 1.0).
-/// * `y_true` - Ground truth (correct) target values.
+/// A cluster result satisfies homogeneity if all of its clusters contain only data points which are members of a single class.
-/// * `y_pred` - Estimated target values.
+/// * `labels_true` - ground truth class labels to be used as a reference.
+/// * `labels_pred` - cluster labels to evaluate.
 pub fn homogeneity_score<T: RealNumber, V: BaseVector<T>>(labels_true: &V, labels_pred: &V) -> T {
     ClusterMetrics::hcv_score()
         .get_score(labels_true, labels_pred)
         .0
 }
 
-/// Computes R2 score, see [R2](r2/index.html).
+///
-/// * `y_true` - Ground truth (correct) target values.
+/// Completeness metric of a cluster labeling given a ground truth (range is between 0.0 and 1.0).
-/// * `y_pred` - Estimated target values.
+/// * `labels_true` - ground truth class labels to be used as a reference.
+/// * `labels_pred` - cluster labels to evaluate.
 pub fn completeness_score<T: RealNumber, V: BaseVector<T>>(labels_true: &V, labels_pred: &V) -> T {
     ClusterMetrics::hcv_score()
         .get_score(labels_true, labels_pred)
         .1
 }
 
-/// Computes R2 score, see [R2](r2/index.html).
+/// The harmonic mean between homogeneity and completeness.
-/// * `y_true` - Ground truth (correct) target values.
+/// * `labels_true` - ground truth class labels to be used as a reference.
-/// * `y_pred` - Estimated target values.
+/// * `labels_pred` - cluster labels to evaluate.
 pub fn v_measure_score<T: RealNumber, V: BaseVector<T>>(labels_true: &V, labels_pred: &V) -> T {
     ClusterMetrics::hcv_score()
         .get_score(labels_true, labels_pred)