feat: simplifies LR API

src/classification/logistic_regression.rs

@@ -122,23 +122,24 @@ impl<'a, M: Matrix> ObjectiveFunction<M> for MultiClassObjectiveFunction<'a, M>
 
 impl<M: Matrix> LogisticRegression<M> {
 
-    pub fn fit(x: &M, y: &M) -> LogisticRegression<M>{
+    pub fn fit(x: &M, y: &M::RowVector) -> LogisticRegression<M>{
 
+        let y_m = M::from_row_vector(y.clone());
         let (x_nrows, num_attributes) = x.shape();
-        let (_, y_nrows) = y.shape();
+        let (_, y_nrows) = y_m.shape();
 
         if x_nrows != y_nrows {
             panic!("Number of rows of X doesn't match number of rows of Y");
         }
         
-        let classes = y.unique();        
+        let classes = y_m.unique();        
 
         let k = classes.len();        
 
         let mut yi: Vec<usize> = vec![0; y_nrows];
 
         for i in 0..y_nrows {
-            let yc = y.get(0, i); 
+            let yc = y_m.get(0, i); 
             let j = classes.iter().position(|c| yc == *c).unwrap();            
             yi[i] = classes.iter().position(|c| yc == *c).unwrap();
         }
@@ -190,19 +191,19 @@ impl<M: Matrix> LogisticRegression<M> {
         
     }
 
-    pub fn predict(&self, x: &M) -> M {
+    pub fn predict(&self, x: &M) -> M::RowVector {
         if self.num_classes == 2 {            
             let (nrows, _) = x.shape();
             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())            
+            M::from_vec(1, nrows, y_hat.iter().map(|y_hat| self.classes[if y_hat.sigmoid() > 0.5 { 1 } else { 0 }]).collect()).to_row_vector()         
 
         } else {
             let (nrows, _) = x.shape();
             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())
+            M::from_vec(1, nrows, class_idxs.iter().map(|class_idx| self.classes[*class_idx]).collect()).to_row_vector()
         }
     }
 
@@ -236,8 +237,7 @@ impl<M: Matrix> LogisticRegression<M> {
 mod tests {    
     use super::*; 
     use crate::linalg::naive::dense_matrix::DenseMatrix;       
-    use crate::linalg::ndarray_bindings;
-    use ndarray::{arr2, Array};
+    use ndarray::{arr1, arr2, Array};
 
     #[test]
     fn multiclass_objective_f() { 
@@ -339,7 +339,7 @@ mod tests {
             &[10., -2.],
             &[ 8.,  2.],
             &[ 9.,  0.]]);
-        let y = DenseMatrix::vector_from_array(&[0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.]);
+        let y = vec![0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.];
 
         let lr = LogisticRegression::fit(&x, &y);
 
@@ -351,7 +351,7 @@ mod tests {
 
         let y_hat = lr.predict(&x);                
 
-        assert_eq!(y_hat, DenseMatrix::vector_from_array(&[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]));
+        assert_eq!(y_hat, vec![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]);
         
 
     }
@@ -380,13 +380,13 @@ mod tests {
             &[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 = DenseMatrix::vector_from_array(&[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]);
+        let y =vec![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);                        
 
-        assert_eq!(y_hat, DenseMatrix::vector_from_array(&[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]));
+        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]);
         
 
     }
@@ -414,15 +414,13 @@ mod tests {
             [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 y = 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);         
 
-        println!("{:?}", lr);
+        let y_hat = lr.predict(&x);                        
 
-        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]);
+        assert_eq!(y_hat, arr1(&[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]));
         
     }
 

src/linalg/mod.rs

@@ -6,6 +6,12 @@ pub mod ndarray_bindings;
 
 pub trait Matrix: Clone + Debug {  
 
+    type RowVector: Clone + Debug;
+
+    fn from_row_vector(vec: Self::RowVector) -> Self;
+
+    fn to_row_vector(self) -> Self::RowVector;
+
     fn from_array(nrows: usize, ncols: usize, values: &[f64]) -> Self;
     
     fn from_vec(nrows: usize, ncols: usize, values: Vec<f64>) -> Self;

src/linalg/naive/dense_matrix.rs

@@ -109,6 +109,16 @@ impl Into<Vec<f64>> for DenseMatrix {
 
 impl Matrix for DenseMatrix {   
     
+    type RowVector = Vec<f64>;
+
+    fn from_row_vector(vec: Self::RowVector) -> Self{
+        DenseMatrix::from_vec(1, vec.len(), vec)
+    }
+
+    fn to_row_vector(self) -> Self::RowVector{
+        self.to_raw_vector()
+    }
+
     fn from_array(nrows: usize, ncols: usize, values: &[f64]) -> DenseMatrix {
         DenseMatrix::from_vec(nrows, ncols, Vec::from(values)) 
     }
@@ -968,6 +978,15 @@ impl Matrix for DenseMatrix {
 mod tests {    
     use super::*; 
 
+    #[test]
+    fn from_to_row_vec() { 
+
+        let vec = vec![ 1.,  2.,  3.];
+        assert_eq!(DenseMatrix::from_row_vector(vec.clone()), DenseMatrix::from_vec(1, 3, vec![1., 2., 3.]));
+        assert_eq!(DenseMatrix::from_row_vector(vec.clone()).to_row_vector(), vec![1., 2., 3.]);
+
+    }
+
     #[test]
     fn qr_solve_mut() { 
 

src/linalg/ndarray_bindings.rs

@@ -1,9 +1,20 @@
 use std::ops::Range;
 use crate::linalg::{Matrix};
-use ndarray::{Array, ArrayBase, OwnedRepr, Ix2, Axis, stack, s};
+use ndarray::{Array, ArrayBase, OwnedRepr, Ix2, Ix1, Axis, stack, s};
 
 impl Matrix for ArrayBase<OwnedRepr<f64>, Ix2>
 {
+    type RowVector = ArrayBase<OwnedRepr<f64>, Ix1>;
+
+    fn from_row_vector(vec: Self::RowVector) -> Self{
+        let vec_size = vec.len();
+        vec.into_shape((1, vec_size)).unwrap()
+    }
+
+    fn to_row_vector(self) -> Self::RowVector{
+        let vec_size = self.nrows() * self.ncols();
+        self.into_shape(vec_size).unwrap()
+    }
 
     fn from_array(nrows: usize, ncols: usize, values: &[f64]) -> Self {
         Array::from_shape_vec((nrows, ncols), values.to_vec()).unwrap()     
@@ -248,7 +259,16 @@ impl Matrix for ArrayBase<OwnedRepr<f64>, Ix2>
 #[cfg(test)]
 mod tests {    
     use super::*; 
-    use ndarray::{arr2, Array2};
+    use ndarray::{arr1, arr2, Array2};
+
+    #[test]
+    fn from_to_row_vec() { 
+
+        let vec = arr1(&[ 1.,  2.,  3.]);
+        assert_eq!(Array2::from_row_vector(vec.clone()), arr2(&[[1., 2., 3.]]));
+        assert_eq!(Array2::from_row_vector(vec.clone()).to_row_vector(), arr1(&[1., 2., 3.]));
+
+    }
 
     #[test]
     fn add_mut() {