Implement abstract method to convert a slice to a BaseVector, Implement RealNumberVector over BaseVector instead of over Vec<T>

src/linalg/mod.rs

@@ -83,6 +83,21 @@ pub trait BaseVector<T: RealNumber>: Clone + Debug {
         self.len() == 0
     }
 
+    /// Create a new vector from a &[T]
+    /// ```
+    /// use smartcore::linalg::naive::dense_matrix::*;
+    /// let slice: &[f64] = &[0., 0.5, 2., 3., 4.];
+    /// let a: Vec<f64> = BaseVector::from_slice(slice);
+    /// assert_eq!(a, vec![0., 0.5, 2., 3., 4.]);
+    /// ```
+    fn from_slice(f: &[T]) -> Self {
+        let mut v = Self::zeros(f.len());
+        for (i, elem) in f.iter().enumerate() {
+            v.set(i, *elem);
+        }
+        v
+    }
+
     /// Return a vector with the elements of the one-dimensional array.
     fn to_vec(&self) -> Vec<T>;
 

src/math/vector.rs

@@ -1,13 +1,14 @@
 use crate::math::num::RealNumber;
 use std::collections::HashMap;
 
+use crate::linalg::BaseVector;
 pub trait RealNumberVector<T: RealNumber> {
     fn unique(&self) -> (Vec<T>, Vec<usize>);
 }
 
-impl<T: RealNumber> RealNumberVector<T> for Vec<T> {
+impl<T: RealNumber, V: BaseVector<T>> RealNumberVector<T> for V {
     fn unique(&self) -> (Vec<T>, Vec<usize>) {
-        let mut unique = self.clone();
+        let mut unique = self.to_vec();
         unique.sort_by(|a, b| a.partial_cmp(b).unwrap());
         unique.dedup();
 
@@ -17,8 +18,8 @@ impl<T: RealNumber> RealNumberVector<T> for Vec<T> {
         }
 
         let mut unique_index = Vec::with_capacity(self.len());
-        for e in self {
-            unique_index.push(index[&e.to_i64().unwrap()]);
+        for idx in 0..self.len() {
+            unique_index.push(index[&self.get(idx).to_i64().unwrap()]);
         }
 
         (unique, unique_index)
@@ -27,7 +28,7 @@ impl<T: RealNumber> RealNumberVector<T> for Vec<T> {
 
 #[cfg(test)]
 mod tests {
-    use super::*;
+    use super::RealNumberVector;
 
     #[test]
     fn unique() {

src/naive_bayes/mod.rs

@@ -58,10 +58,7 @@ impl<T: RealNumber, M: Matrix<T>, D: NBDistribution<T, M>> BaseNaiveBayes<T, M,
                 *prediction
             })
             .collect::<Vec<T>>();
-        let mut y_hat = M::RowVector::zeros(rows);
-        for (i, prediction) in predictions.iter().enumerate().take(rows) {
-            y_hat.set(i, *prediction);
-        }
+        let y_hat = M::RowVector::from_slice(&predictions);
         Ok(y_hat)
     }
 }