feat: add basic Matrix implementation for ndarray

src/classification/logistic_regression.rs

@@ -193,13 +193,13 @@ impl<M: Matrix> LogisticRegression<M> {
     pub fn predict(&self, x: &M) -> M {
         if self.num_classes == 2 {            
             let (nrows, _) = x.shape();
-            let x_and_bias = x.h_stack(&M::ones(nrows, 1));
+            let x_and_bias = x.v_stack(&M::ones(nrows, 1));
             let y_hat: Vec<f64> = x_and_bias.dot(&self.weights.transpose()).to_raw_vector();
             M::from_vec(1, nrows, y_hat.iter().map(|y_hat| self.classes[if y_hat.sigmoid() > 0.5 { 1 } else { 0 }]).collect())            
 
         } else {
             let (nrows, _) = x.shape();
-            let x_and_bias = x.h_stack(&M::ones(nrows, 1));        
+            let x_and_bias = x.v_stack(&M::ones(nrows, 1));        
             let y_hat = x_and_bias.dot(&self.weights.transpose());            
             let class_idxs = y_hat.argmax();
             M::from_vec(1, nrows, class_idxs.iter().map(|class_idx| self.classes[*class_idx]).collect())
@@ -235,7 +235,9 @@ impl<M: Matrix> LogisticRegression<M> {
 #[cfg(test)]
 mod tests {    
     use super::*; 
-    use crate::linalg::naive::dense_matrix::DenseMatrix;    
+    use crate::linalg::naive::dense_matrix::DenseMatrix;   
+    use crate::linalg::ndarray_bindings;
+    use ndarray::{arr2, Array};
 
     #[test]
     fn multiclass_objective_f() { 
@@ -388,4 +390,40 @@ mod tests {
         
 
     }
+
+    #[test]
+    fn tt() { 
+        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 = Array::from_shape_vec((1, 20), vec![0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]).unwrap();        
+
+        let lr = LogisticRegression::fit(&x, &y);        
+
+        println!("{:?}", lr);
+
+        let y_hat = lr.predict(&x).to_raw_vector();                        
+
+        assert_eq!(y_hat, vec![0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
+        
+    }
+
 }