feat: puts ndarray and nalgebra bindings behind feature flags

src/linalg/ndarray_bindings.rs

@@ -336,7 +336,9 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
 #[cfg(test)]
 mod tests {
     use super::*;
-    use ndarray::{arr1, arr2, Array2};
+    use crate::ensemble::random_forest_regressor::*;
+    use crate::linear::logistic_regression::*;
+    use ndarray::{arr1, arr2, Array1, Array2};
 
     #[test]
     fn vec_get_set() {
@@ -654,4 +656,92 @@ mod tests {
         a.abs_mut();
         assert_eq!(a, expected);
     }
+
+    #[test]
+    fn lr_fit_predict_iris() {
+        let x = arr2(&[
+            [5.1, 3.5, 1.4, 0.2],
+            [4.9, 3.0, 1.4, 0.2],
+            [4.7, 3.2, 1.3, 0.2],
+            [4.6, 3.1, 1.5, 0.2],
+            [5.0, 3.6, 1.4, 0.2],
+            [5.4, 3.9, 1.7, 0.4],
+            [4.6, 3.4, 1.4, 0.3],
+            [5.0, 3.4, 1.5, 0.2],
+            [4.4, 2.9, 1.4, 0.2],
+            [4.9, 3.1, 1.5, 0.1],
+            [7.0, 3.2, 4.7, 1.4],
+            [6.4, 3.2, 4.5, 1.5],
+            [6.9, 3.1, 4.9, 1.5],
+            [5.5, 2.3, 4.0, 1.3],
+            [6.5, 2.8, 4.6, 1.5],
+            [5.7, 2.8, 4.5, 1.3],
+            [6.3, 3.3, 4.7, 1.6],
+            [4.9, 2.4, 3.3, 1.0],
+            [6.6, 2.9, 4.6, 1.3],
+            [5.2, 2.7, 3.9, 1.4],
+        ]);
+        let y: Array1<f64> = arr1(&[
+            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);
+
+        let y_hat = lr.predict(&x);
+
+        let error: f64 = y
+            .into_iter()
+            .zip(y_hat.into_iter())
+            .map(|(&a, &b)| (a - b).abs())
+            .sum();
+
+        assert!(error <= 1.0);
+    }
+
+    #[test]
+    fn my_fit_longley_ndarray() {
+        let x = arr2(&[
+            [234.289, 235.6, 159., 107.608, 1947., 60.323],
+            [259.426, 232.5, 145.6, 108.632, 1948., 61.122],
+            [258.054, 368.2, 161.6, 109.773, 1949., 60.171],
+            [284.599, 335.1, 165., 110.929, 1950., 61.187],
+            [328.975, 209.9, 309.9, 112.075, 1951., 63.221],
+            [346.999, 193.2, 359.4, 113.27, 1952., 63.639],
+            [365.385, 187., 354.7, 115.094, 1953., 64.989],
+            [363.112, 357.8, 335., 116.219, 1954., 63.761],
+            [397.469, 290.4, 304.8, 117.388, 1955., 66.019],
+            [419.18, 282.2, 285.7, 118.734, 1956., 67.857],
+            [442.769, 293.6, 279.8, 120.445, 1957., 68.169],
+            [444.546, 468.1, 263.7, 121.95, 1958., 66.513],
+            [482.704, 381.3, 255.2, 123.366, 1959., 68.655],
+            [502.601, 393.1, 251.4, 125.368, 1960., 69.564],
+            [518.173, 480.6, 257.2, 127.852, 1961., 69.331],
+            [554.894, 400.7, 282.7, 130.081, 1962., 70.551],
+        ]);
+        let y = arr1(&[
+            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
+            114.2, 115.7, 116.9,
+        ]);
+
+        let expected_y: Vec<f64> = vec![
+            85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.,
+        ];
+
+        let y_hat = RandomForestRegressor::fit(
+            &x,
+            &y,
+            RandomForestRegressorParameters {
+                max_depth: None,
+                min_samples_leaf: 1,
+                min_samples_split: 2,
+                n_trees: 1000,
+                mtry: Option::None,
+            },
+        )
+        .predict(&x);
+
+        for i in 0..y_hat.len() {
+            assert!((y_hat[i] - expected_y[i]).abs() < 1.0);
+        }
+    }
 }