Moved test file to lingal folder, wrote tests based on github ticket request (scipy reference)
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@ -1,20 +1,25 @@
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extern crate nalgebra as na;
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use na::storage::Storage;
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use na::{zero, DVector, Dim, Dynamic, Matrix, Real, VecStorage, Vector, U1};
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use storage::Storage;
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use {zero, DVector, Dim, Dynamic, Matrix, Real, VecStorage, Vector, U1};
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use std::cmp;
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enum ConvolveMode {
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Full,
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Valid,
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Same,
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}
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fn convolve_full<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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///
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/// The output is the full discrete linear convolution of the inputs
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///
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pub fn convolve_full<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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vector: Vector<R, D, S>,
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kernel: Vector<R, E, Q>,
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) -> Matrix<R, Dynamic, U1, VecStorage<R, Dynamic, U1>> {
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let vec = vector.len();
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let ker = kernel.len();
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if vec == 0 || ker == 0 {
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panic!("Convolve's inputs must not be 0-sized. ");
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}
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if ker > vec {
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return convolve_full(kernel, vector);
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}
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let newlen = vec + ker - 1;
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let mut conv = DVector::<R>::zeros(newlen);
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@ -36,12 +41,24 @@ fn convolve_full<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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conv
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}
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fn convolve_valid<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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///
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/// The output consists only of those elements that do not rely on the zero-padding.
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///
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pub fn convolve_valid<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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vector: Vector<R, D, S>,
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kernel: Vector<R, E, Q>,
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) -> Matrix<R, Dynamic, U1, VecStorage<R, Dynamic, U1>> {
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let vec = vector.len();
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let ker = kernel.len();
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if vec == 0 || ker == 0 {
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panic!("Convolve's inputs must not be 0-sized. ");
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}
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if ker > vec {
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return convolve_valid(kernel, vector);
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}
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let newlen = vec - ker + 1;
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let mut conv = DVector::<R>::zeros(newlen);
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@ -54,13 +71,24 @@ fn convolve_valid<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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conv
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}
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fn convolve_same<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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///
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/// The output is the same size as in1, centered with respect to the ‘full’ output.
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///
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pub fn convolve_same<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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vector: Vector<R, D, S>,
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kernel: Vector<R, E, Q>,
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) -> Matrix<R, Dynamic, U1, VecStorage<R, Dynamic, U1>> {
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let vec = vector.len();
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let ker = kernel.len();
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if vec == 0 || ker == 0 {
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panic!("Convolve's inputs must not be 0-sized. ");
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}
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if ker > vec {
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return convolve_same(kernel, vector);
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}
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let mut conv = DVector::<R>::zeros(vec);
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for i in 0..vec {
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@ -74,24 +102,4 @@ fn convolve_same<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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}
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}
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conv
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}
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fn convolve<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
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vector: Vector<R, D, S>,
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kernel: Vector<R, E, Q>,
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mode: Option<ConvolveMode>,
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) -> Matrix<R, Dynamic, U1, VecStorage<R, Dynamic, U1>> {
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if kernel.len() > vector.len() {
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return convolve(kernel, vector, mode);
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}
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match mode.unwrap_or(ConvolveMode::Full) {
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ConvolveMode::Full => return convolve_full(vector, kernel),
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ConvolveMode::Valid => return convolve_valid(vector, kernel),
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ConvolveMode::Same => return convolve_same(vector, kernel),
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}
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}
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fn main() {
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}
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}
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@ -17,6 +17,7 @@ mod solve;
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mod svd;
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mod symmetric_eigen;
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mod symmetric_tridiagonal;
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mod convolution;
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//// FIXME: Not complete enough for publishing.
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//// This handles only cases where each eigenvalue has multiplicity one.
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@ -33,3 +34,4 @@ pub use self::schur::*;
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pub use self::svd::*;
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pub use self::symmetric_eigen::*;
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pub use self::symmetric_tridiagonal::*;
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pub use self::convolution::*;
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@ -0,0 +1,49 @@
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use na::linalg::{convolve_full,convolve_valid,convolve_same};
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use na::{Vector2,Vector4,DVector};
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//
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// Should mimic calculations in Python's scipy library
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// >>>from scipy.signal import convolve
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//
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// >>> convolve([1,2,3,4],[1,2],"same")
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// array([ 1, 4, 7, 10])
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#[test]
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fn convolve_same_check(){
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let vec = Vector4::new(1.0,2.0,3.0,4.0);
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let ker = Vector2::new(1.0,2.0);
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let actual = DVector::from_vec(4, vec![1.0,4.0,7.0,10.0]);
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let expected = convolve_same(vec,ker);
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assert!(relative_eq!(actual, expected, epsilon = 1.0e-7));
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}
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// >>> convolve([1,2,3,4],[1,2],"valid")
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// array([ 1, 4, 7, 10, 8])
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#[test]
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fn convolve_full_check(){
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let vec = Vector4::new(1.0,2.0,3.0,4.0);
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let ker = Vector2::new(1.0,2.0);
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let actual = DVector::from_vec(5, vec![1.0,4.0,7.0,10.0,8.0]);
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let expected = convolve_full(vec,ker);
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assert!(relative_eq!(actual, expected, epsilon = 1.0e-7));
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}
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// >>> convolve([1,2,3,4],[1,2],"valid")
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// array([ 4, 7, 10])
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#[test]
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fn convolve_valid_check(){
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let vec = Vector4::new(1.0,2.0,3.0,4.0);
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let ker = Vector2::new(1.0,2.0);
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let actual = DVector::from_vec(3, vec![4.0,7.0,10.0]);
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let expected = convolve_valid(vec,ker);
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assert!(relative_eq!(actual, expected, epsilon = 1.0e-7));
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}
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@ -11,3 +11,4 @@ mod real_schur;
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mod solve;
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mod svd;
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mod tridiagonal;
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mod convolution;
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