feat: + cross_validate, trait Predictor, refactoring

src/linalg/mod.rs

@@ -274,6 +274,19 @@ pub trait BaseVector<T: RealNumber>: Clone + Debug {
 
     /// Copies content of `other` vector.
     fn copy_from(&mut self, other: &Self);
+
+    /// Take elements from an array.
+    fn take(&self, index: &[usize]) -> Self {
+        let n = index.len();
+
+        let mut result = Self::zeros(n);
+
+        for i in 0..n {
+            result.set(i, self.get(index[i]));
+        }
+
+        result
+    }
 }
 
 /// Generic matrix type.
@@ -611,6 +624,32 @@ pub trait BaseMatrix<T: RealNumber>: Clone + Debug {
 
     /// Calculates the covariance matrix
     fn cov(&self) -> Self;
+
+    /// Take elements from an array along an axis.
+    fn take(&self, index: &[usize], axis: u8) -> Self {
+        let (n, p) = self.shape();
+
+        let k = match axis {
+            0 => p,
+            _ => n,
+        };
+
+        let mut result = match axis {
+            0 => Self::zeros(index.len(), p),
+            _ => Self::zeros(n, index.len()),
+        };
+
+        for i in 0..index.len() {
+            for j in 0..k {
+                match axis {
+                    0 => result.set(i, j, self.get(index[i], j)),
+                    _ => result.set(j, i, self.get(j, index[i])),
+                };
+            }
+        }
+
+        result
+    }
 }
 
 /// Generic matrix with additional mixins like various factorization methods.
@@ -662,6 +701,8 @@ impl<'a, T: RealNumber, M: BaseMatrix<T>> Iterator for RowIter<'a, T, M> {
 
 #[cfg(test)]
 mod tests {
+    use crate::linalg::naive::dense_matrix::DenseMatrix;
+    use crate::linalg::BaseMatrix;
     use crate::linalg::BaseVector;
 
     #[test]
@@ -684,4 +725,35 @@ mod tests {
 
         assert!((m.var() - 1.25f64).abs() < std::f64::EPSILON);
     }
+
+    #[test]
+    fn vec_take() {
+        let m = vec![1., 2., 3., 4., 5.];
+
+        assert_eq!(m.take(&vec!(0, 0, 4, 4)), vec![1., 1., 5., 5.]);
+    }
+
+    #[test]
+    fn take() {
+        let m = DenseMatrix::from_2d_array(&[
+            &[1.0, 2.0],
+            &[3.0, 4.0],
+            &[5.0, 6.0],
+            &[7.0, 8.0],
+            &[9.0, 10.0],
+        ]);
+
+        let expected_0 = DenseMatrix::from_2d_array(&[&[3.0, 4.0], &[3.0, 4.0], &[7.0, 8.0]]);
+
+        let expected_1 = DenseMatrix::from_2d_array(&[
+            &[2.0, 1.0],
+            &[4.0, 3.0],
+            &[6.0, 5.0],
+            &[8.0, 7.0],
+            &[10.0, 9.0],
+        ]);
+
+        assert_eq!(m.take(&vec!(1, 1, 3), 0), expected_0);
+        assert_eq!(m.take(&vec!(1, 0), 1), expected_1);
+    }
 }

src/linalg/ndarray_bindings.rs

@@ -36,7 +36,7 @@
 //!             1., 1., 1., 1., 1., 1., 1., 1., 1., 1.
 //!         ]);
 //!
-//! let lr = LogisticRegression::fit(&x, &y).unwrap();
+//! let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 //! let y_hat = lr.predict(&x).unwrap();
 //! ```
 use std::iter::Sum;
@@ -917,7 +917,7 @@ mod tests {
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
         ]);
 
-        let lr = LogisticRegression::fit(&x, &y).unwrap();
+        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 
         let y_hat = lr.predict(&x).unwrap();