feat: documents matrix methods

Author: Volodymyr Orlov

src/algorithm/neighbour/bbd_tree.rs

@@ -337,7 +337,7 @@ mod tests {
 
     #[test]
     fn fit_predict_iris() {
-        let data = DenseMatrix::from_array(&[
+        let data = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
             &[4.7, 3.2, 1.3, 0.2],

src/cluster/kmeans.rs

@@ -20,7 +20,7 @@
 //! use smartcore::cluster::kmeans::*;
 //!
 //! // Iris data
-//! let x = DenseMatrix::from_array(&[
+//! let x = DenseMatrix::from_2d_array(&[
 //!            &[5.1, 3.5, 1.4, 0.2],
 //!            &[4.9, 3.0, 1.4, 0.2],
 //!            &[4.7, 3.2, 1.3, 0.2],
@@ -264,7 +264,7 @@ mod tests {
 
     #[test]
     fn fit_predict_iris() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
             &[4.7, 3.2, 1.3, 0.2],
@@ -298,7 +298,7 @@ mod tests {
 
     #[test]
     fn serde() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
             &[4.7, 3.2, 1.3, 0.2],

src/decomposition/pca.rs

@@ -14,7 +14,7 @@
 //! use smartcore::decomposition::pca::*;
 //!
 //! // Iris data
-//! let iris = DenseMatrix::from_array(&[
+//! let iris = DenseMatrix::from_2d_array(&[
 //!                     &[5.1, 3.5, 1.4, 0.2],
 //!                     &[4.9, 3.0, 1.4, 0.2],
 //!                     &[4.7, 3.2, 1.3, 0.2],
@@ -211,7 +211,7 @@ impl<T: RealNumber, M: Matrix<T>> PCA<T, M> {
             );
         }
 
-        let mut x_transformed = x.dot(&self.projection);
+        let mut x_transformed = x.matmul(&self.projection);
         for r in 0..nrows {
             for c in 0..n_components {
                 x_transformed.sub_element_mut(r, c, self.pmu[c]);
@@ -227,7 +227,7 @@ mod tests {
     use crate::linalg::naive::dense_matrix::*;
 
     fn us_arrests_data() -> DenseMatrix<f64> {
-        DenseMatrix::from_array(&[
+        DenseMatrix::from_2d_array(&[
             &[13.2, 236.0, 58.0, 21.2],
             &[10.0, 263.0, 48.0, 44.5],
             &[8.1, 294.0, 80.0, 31.0],
@@ -285,7 +285,7 @@ mod tests {
     fn decompose_covariance() {
         let us_arrests = us_arrests_data();
 
-        let expected_eigenvectors = DenseMatrix::from_array(&[
+        let expected_eigenvectors = DenseMatrix::from_2d_array(&[
             &[
                 -0.0417043206282872,
                 -0.0448216562696701,
@@ -312,7 +312,7 @@ mod tests {
             ],
         ]);
 
-        let expected_projection = DenseMatrix::from_array(&[
+        let expected_projection = DenseMatrix::from_2d_array(&[
             &[-64.8022, -11.448, 2.4949, -2.4079],
             &[-92.8275, -17.9829, -20.1266, 4.094],
             &[-124.0682, 8.8304, 1.6874, 4.3537],
@@ -394,7 +394,7 @@ mod tests {
     fn decompose_correlation() {
         let us_arrests = us_arrests_data();
 
-        let expected_eigenvectors = DenseMatrix::from_array(&[
+        let expected_eigenvectors = DenseMatrix::from_2d_array(&[
             &[
                 0.124288601688222,
                 -0.0969866877028367,
@@ -421,7 +421,7 @@ mod tests {
             ],
         ]);
 
-        let expected_projection = DenseMatrix::from_array(&[
+        let expected_projection = DenseMatrix::from_2d_array(&[
             &[0.9856, -1.1334, 0.4443, -0.1563],
             &[1.9501, -1.0732, -2.04, 0.4386],
             &[1.7632, 0.746, -0.0548, 0.8347],
@@ -507,7 +507,7 @@ mod tests {
 
     #[test]
     fn serde() {
-        let iris = DenseMatrix::from_array(&[
+        let iris = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
             &[4.7, 3.2, 1.3, 0.2],

src/ensemble/random_forest_classifier.rs

@@ -12,7 +12,7 @@
 //! use smartcore::ensemble::random_forest_classifier::*;
 //!
 //! // Iris dataset
-//! let x = DenseMatrix::from_array(&[
+//! let x = DenseMatrix::from_2d_array(&[
 //!              &[5.1, 3.5, 1.4, 0.2],
 //!              &[4.9, 3.0, 1.4, 0.2],
 //!              &[4.7, 3.2, 1.3, 0.2],
@@ -226,7 +226,7 @@ mod tests {
 
     #[test]
     fn fit_predict_iris() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
             &[4.7, 3.2, 1.3, 0.2],
@@ -270,7 +270,7 @@ mod tests {
 
     #[test]
     fn serde() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
             &[4.7, 3.2, 1.3, 0.2],

src/ensemble/random_forest_regressor.rs

@@ -12,7 +12,7 @@
 //! use smartcore::ensemble::random_forest_regressor::*;
 //!
 //! // Longley dataset (https://www.statsmodels.org/stable/datasets/generated/longley.html)
-//! let x = DenseMatrix::from_array(&[
+//! let x = DenseMatrix::from_2d_array(&[
 //!             &[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],
@@ -184,7 +184,7 @@ mod tests {
 
     #[test]
     fn fit_longley() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_2d_array(&[
             &[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],
@@ -231,7 +231,7 @@ mod tests {
 
     #[test]
     fn serde() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_2d_array(&[
             &[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],

src/lib.rs

@@ -48,7 +48,7 @@
 //! use smartcore::math::distance::*;
 //!
 //! // Turn Rust vectors with samples into a matrix
-//! let x = DenseMatrix::from_array(&[
+//! let x = DenseMatrix::from_2d_array(&[
 //!    &[1., 2.],
 //!    &[3., 4.],
 //!    &[5., 6.],

src/linalg/evd.rs

@@ -793,15 +793,15 @@ mod tests {
 
     #[test]
     fn decompose_symmetric() {
-        let A = DenseMatrix::from_array(&[
+        let A = DenseMatrix::from_2d_array(&[
             &[0.9000, 0.4000, 0.7000],
             &[0.4000, 0.5000, 0.3000],
             &[0.7000, 0.3000, 0.8000],
         ]);
 
         let eigen_values: Vec<f64> = vec![1.7498382, 0.3165784, 0.1335834];
 
-        let eigen_vectors = DenseMatrix::from_array(&[
+        let eigen_vectors = DenseMatrix::from_2d_array(&[
             &[0.6881997, -0.07121225, 0.7220180],
             &[0.3700456, 0.89044952, -0.2648886],
             &[0.6240573, -0.44947578, -0.6391588],
@@ -820,15 +820,15 @@ mod tests {
 
     #[test]
     fn decompose_asymmetric() {
-        let A = DenseMatrix::from_array(&[
+        let A = DenseMatrix::from_2d_array(&[
             &[0.9000, 0.4000, 0.7000],
             &[0.4000, 0.5000, 0.3000],
             &[0.8000, 0.3000, 0.8000],
         ]);
 
         let eigen_values: Vec<f64> = vec![1.79171122, 0.31908143, 0.08920735];
 
-        let eigen_vectors = DenseMatrix::from_array(&[
+        let eigen_vectors = DenseMatrix::from_2d_array(&[
             &[0.7178958, 0.05322098, 0.6812010],
             &[0.3837711, -0.84702111, -0.1494582],
             &[0.6952105, 0.43984484, -0.7036135],
@@ -847,7 +847,7 @@ mod tests {
 
     #[test]
     fn decompose_complex() {
-        let A = DenseMatrix::from_array(&[
+        let A = DenseMatrix::from_2d_array(&[
             &[3.0, -2.0, 1.0, 1.0],
             &[4.0, -1.0, 1.0, 1.0],
             &[1.0, 1.0, 3.0, -2.0],
@@ -857,7 +857,7 @@ mod tests {
         let eigen_values_d: Vec<f64> = vec![0.0, 2.0, 2.0, 0.0];
         let eigen_values_e: Vec<f64> = vec![2.2361, 0.9999, -0.9999, -2.2361];
 
-        let eigen_vectors = DenseMatrix::from_array(&[
+        let eigen_vectors = DenseMatrix::from_2d_array(&[
             &[-0.9159, -0.1378, 0.3816, -0.0806],
             &[-0.6707, 0.1059, 0.901, 0.6289],
             &[0.9159, -0.1378, 0.3816, 0.0806],

src/linalg/lu.rs

@@ -225,11 +225,13 @@ mod tests {
 
     #[test]
     fn decompose() {
-        let a = DenseMatrix::from_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
-        let expected_L = DenseMatrix::from_array(&[&[1., 0., 0.], &[0., 1., 0.], &[0.2, 0.8, 1.]]);
-        let expected_U = DenseMatrix::from_array(&[&[5., 6., 0.], &[0., 1., 5.], &[0., 0., -1.]]);
+        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
+        let expected_L =
+            DenseMatrix::from_2d_array(&[&[1., 0., 0.], &[0., 1., 0.], &[0.2, 0.8, 1.]]);
+        let expected_U =
+            DenseMatrix::from_2d_array(&[&[5., 6., 0.], &[0., 1., 5.], &[0., 0., -1.]]);
         let expected_pivot =
-            DenseMatrix::from_array(&[&[0., 0., 1.], &[0., 1., 0.], &[1., 0., 0.]]);
+            DenseMatrix::from_2d_array(&[&[0., 0., 1.], &[0., 1., 0.], &[1., 0., 0.]]);
         let lu = a.lu();
         assert!(lu.L().approximate_eq(&expected_L, 1e-4));
         assert!(lu.U().approximate_eq(&expected_U, 1e-4));
@@ -238,9 +240,9 @@ mod tests {
 
     #[test]
     fn inverse() {
-        let a = DenseMatrix::from_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
+        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
         let expected =
-            DenseMatrix::from_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]]);
+            DenseMatrix::from_2d_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]]);
         let a_inv = a.lu().inverse();
         println!("{}", a_inv);
         assert!(a_inv.approximate_eq(&expected, 1e-4));