feat: adds e-SVR

Author: Volodymyr Orlov

src/lib.rs

@@ -88,5 +88,7 @@ pub mod model_selection;
 /// Supervised neighbors-based learning methods
 pub mod neighbors;
 pub(crate) mod optimization;
+/// Support Vector Machines
+pub mod svm;
 /// Supervised tree-based learning methods
 pub mod tree;

src/linalg/mod.rs

@@ -85,6 +85,12 @@ pub trait BaseVector<T: RealNumber>: Clone + Debug {
 
     /// Create new vector of size `len` where each element is set to `value`.
     fn fill(len: usize, value: T) -> Self;
+
+    /// Vector dot product
+    fn dot(&self, other: &Self) -> T;
+
+    /// Returns True if matrices are element-wise equal within a tolerance `error`.
+    fn approximate_eq(&self, other: &Self, error: T) -> bool;
 }
 
 /// Generic matrix type.
@@ -110,6 +116,10 @@ pub trait BaseMatrix<T: RealNumber>: Clone + Debug {
     /// * `row` - row number
     fn get_row_as_vec(&self, row: usize) -> Vec<T>;
 
+    /// Get the `row`'th row
+    /// * `row` - row number
+    fn get_row(&self, row: usize) -> Self::RowVector;
+
     /// Copies a vector with elements of the `row`'th row into `result`
     /// * `row` - row number
     /// * `result` - receiver for the row

src/linalg/naive/dense_matrix.rs

@@ -44,6 +44,32 @@ impl<T: RealNumber> BaseVector<T> for Vec<T> {
     fn fill(len: usize, value: T) -> Self {
         vec![value; len]
     }
+
+    fn dot(&self, other: &Self) -> T {
+        if self.len() != other.len() {
+            panic!("A and B should have the same size");
+        }
+
+        let mut result = T::zero();
+        for i in 0..self.len() {
+            result = result + self[i] * other[i];
+        }
+
+        result
+    }
+
+    fn approximate_eq(&self, other: &Self, error: T) -> bool {
+        if self.len() != other.len() {
+            false
+        } else {
+            for i in 0..other.len() {
+                if (self[i] - other[i]).abs() > error {
+                    return false;
+                }
+            }
+            true
+        }
+    }
 }
 
 /// Column-major, dense matrix. See [Simple Dense Matrix](../index.html).
@@ -371,6 +397,16 @@ impl<T: RealNumber> BaseMatrix<T> for DenseMatrix<T> {
         self.values[col * self.nrows + row]
     }
 
+    fn get_row(&self, row: usize) -> Self::RowVector {
+        let mut v = vec![T::zero(); self.ncols];
+
+        for c in 0..self.ncols {
+            v[c] = self.get(row, c);
+        }
+
+        v
+    }
+
     fn get_row_as_vec(&self, row: usize) -> Vec<T> {
         let mut result = vec![T::zero(); self.ncols];
         for c in 0..self.ncols {
@@ -865,6 +901,21 @@ impl<T: RealNumber> BaseMatrix<T> for DenseMatrix<T> {
 mod tests {
     use super::*;
 
+    #[test]
+    fn vec_dot() {
+        let v1 = vec![1., 2., 3.];
+        let v2 = vec![4., 5., 6.];
+        assert_eq!(32.0, BaseVector::dot(&v1, &v2));
+    }
+
+    #[test]
+    fn vec_approximate_eq() {
+        let a = vec![1., 2., 3.];
+        let b = vec![1. + 1e-5, 2. + 2e-5, 3. + 3e-5];
+        assert!(a.approximate_eq(&b, 1e-4));
+        assert!(!a.approximate_eq(&b, 1e-5));
+    }
+
     #[test]
     fn from_array() {
         let vec = [1., 2., 3., 4., 5., 6.];
@@ -939,6 +990,12 @@ mod tests {
         assert_eq!(result, expected);
     }
 
+    #[test]
+    fn get_row() {
+        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]);
+        assert_eq!(vec![4., 5., 6.], a.get_row(1));
+    }
+
     #[test]
     fn matmul() {
         let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.]]);

src/linalg/nalgebra_bindings.rs

@@ -79,6 +79,20 @@ impl<T: RealNumber + 'static> BaseVector<T> for MatrixMN<T, U1, Dynamic> {
         m.fill(value);
         m
     }
+
+    fn dot(&self, other: &Self) -> T {
+        self.dot(other)
+    }
+
+    fn approximate_eq(&self, other: &Self, error: T) -> bool {
+        if self.shape() != other.shape() {
+            false
+        } else {
+            self.iter()
+                .zip(other.iter())
+                .all(|(a, b)| (*a - *b).abs() <= error)
+        }
+    }
 }
 
 impl<T: RealNumber + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static>
@@ -102,6 +116,10 @@ impl<T: RealNumber + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Su
         self.row(row).iter().map(|v| *v).collect()
     }
 
+    fn get_row(&self, row: usize) -> Self::RowVector {
+        self.row(row).into_owned()
+    }
+
     fn copy_row_as_vec(&self, row: usize, result: &mut Vec<T>) {
         let mut r = 0;
         for e in self.row(row).iter() {
@@ -486,6 +504,21 @@ mod tests {
         assert_eq!(twos, RowDVector::from_vec(vec![2., 2., 2.]));
     }
 
+    #[test]
+    fn vec_dot() {
+        let v1 = RowDVector::from_vec(vec![1., 2., 3.]);
+        let v2 = RowDVector::from_vec(vec![4., 5., 6.]);
+        assert_eq!(32.0, BaseVector::dot(&v1, &v2));
+    }
+
+    #[test]
+    fn vec_approximate_eq() {
+        let a = RowDVector::from_vec(vec![1., 2., 3.]);
+        let noise = RowDVector::from_vec(vec![1e-5, 2e-5, 3e-5]);
+        assert!(a.approximate_eq(&(&noise + &a), 1e-4));
+        assert!(!a.approximate_eq(&(&noise + &a), 1e-5));
+    }
+
     #[test]
     fn get_set_dynamic() {
         let mut m = DMatrix::from_row_slice(2, 3, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
@@ -579,6 +612,12 @@ mod tests {
         assert_eq!(m.get_col_as_vec(1), vec!(2., 5., 8.));
     }
 
+    #[test]
+    fn get_row() {
+        let a = DMatrix::from_row_slice(3, 3, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);
+        assert_eq!(RowDVector::from_vec(vec![4., 5., 6.]), a.get_row(1));
+    }
+
     #[test]
     fn copy_row_col_as_vec() {
         let m = DMatrix::from_row_slice(3, 3, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);

src/linalg/ndarray_bindings.rs

@@ -57,7 +57,7 @@ use crate::linalg::Matrix;
 use crate::linalg::{BaseMatrix, BaseVector};
 use crate::math::num::RealNumber;
 
-impl<T: RealNumber> BaseVector<T> for ArrayBase<OwnedRepr<T>, Ix1> {
+impl<T: RealNumber + ScalarOperand> BaseVector<T> for ArrayBase<OwnedRepr<T>, Ix1> {
     fn get(&self, i: usize) -> T {
         self[i]
     }
@@ -84,6 +84,14 @@ impl<T: RealNumber> BaseVector<T> for ArrayBase<OwnedRepr<T>, Ix1> {
     fn fill(len: usize, value: T) -> Self {
         Array::from_elem(len, value)
     }
+
+    fn dot(&self, other: &Self) -> T {
+        self.dot(other)
+    }
+
+    fn approximate_eq(&self, other: &Self, error: T) -> bool {
+        (self - other).iter().all(|v| v.abs() <= error)
+    }
 }
 
 impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
@@ -109,6 +117,10 @@ impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssi
         self.row(row).to_vec()
     }
 
+    fn get_row(&self, row: usize) -> Self::RowVector {
+        self.row(row).to_owned()
+    }
+
     fn copy_row_as_vec(&self, row: usize, result: &mut Vec<T>) {
         let mut r = 0;
         for e in self.row(row).iter() {
@@ -437,6 +449,21 @@ mod tests {
         assert_eq!(vec![1., 2., 3.], v.to_vec());
     }
 
+    #[test]
+    fn vec_dot() {
+        let v1 = arr1(&[1., 2., 3.]);
+        let v2 = arr1(&[4., 5., 6.]);
+        assert_eq!(32.0, BaseVector::dot(&v1, &v2));
+    }
+
+    #[test]
+    fn vec_approximate_eq() {
+        let a = arr1(&[1., 2., 3.]);
+        let noise = arr1(&[1e-5, 2e-5, 3e-5]);
+        assert!(a.approximate_eq(&(&noise + &a), 1e-4));
+        assert!(!a.approximate_eq(&(&noise + &a), 1e-5));
+    }
+
     #[test]
     fn from_to_row_vec() {
         let vec = arr1(&[1., 2., 3.]);
@@ -678,6 +705,12 @@ mod tests {
         assert_eq!(res, vec![4., 5., 6.]);
     }
 
+    #[test]
+    fn get_row() {
+        let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
+        assert_eq!(arr1(&[4., 5., 6.]), a.get_row(1));
+    }
+
     #[test]
     fn get_col_as_vector() {
         let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);

src/math/num.rs

@@ -6,10 +6,23 @@ use num_traits::{Float, FromPrimitive};
 use rand::prelude::*;
 use std::fmt::{Debug, Display};
 use std::iter::{Product, Sum};
+use std::ops::{AddAssign, DivAssign, MulAssign, SubAssign};
 
 /// Defines real number
 /// <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_CHTML"></script>
-pub trait RealNumber: Float + FromPrimitive + Debug + Display + Copy + Sum + Product {
+pub trait RealNumber:
+    Float
+    + FromPrimitive
+    + Debug
+    + Display
+    + Copy
+    + Sum
+    + Product
+    + AddAssign
+    + SubAssign
+    + MulAssign
+    + DivAssign
+{
     /// Copy sign from `sign` - another real number
     fn copysign(self, sign: Self) -> Self;
 

src/svm/mod.rs

@@ -0,0 +1,25 @@
+//! # Support Vector Machines
+//!
+
+pub mod svr;
+
+use serde::{Deserialize, Serialize};
+
+use crate::linalg::BaseVector;
+use crate::math::num::RealNumber;
+
+/// Kernel
+pub trait Kernel<T: RealNumber, V: BaseVector<T>> {
+    /// Apply kernel function to x_i and x_j
+    fn apply(&self, x_i: &V, x_j: &V) -> T;
+}
+
+/// Linear Kernel
+#[derive(Serialize, Deserialize, Debug)]
+pub struct LinearKernel {}
+
+impl<T: RealNumber, V: BaseVector<T>> Kernel<T, V> for LinearKernel {
+    fn apply(&self, x_i: &V, x_j: &V) -> T {
+        x_i.dot(x_j)
+    }
+}