forked from M-Labs/nalgebra
105 lines
2.7 KiB
Rust
105 lines
2.7 KiB
Rust
use storage::Storage;
|
||
use {zero, DVector, Dim, Dynamic, Matrix, Real, VecStorage, Vector, U1};
|
||
use std::cmp;
|
||
|
||
///
|
||
/// The output is the full discrete linear convolution of the inputs
|
||
///
|
||
pub fn convolve_full<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
|
||
vector: Vector<R, D, S>,
|
||
kernel: Vector<R, E, Q>,
|
||
) -> Matrix<R, Dynamic, U1, VecStorage<R, Dynamic, U1>> {
|
||
let vec = vector.len();
|
||
let ker = kernel.len();
|
||
|
||
if vec == 0 || ker == 0 {
|
||
panic!("Convolve's inputs must not be 0-sized. ");
|
||
}
|
||
|
||
if ker > vec {
|
||
return convolve_full(kernel, vector);
|
||
}
|
||
|
||
let newlen = vec + ker - 1;
|
||
|
||
let mut conv = DVector::<R>::zeros(newlen);
|
||
|
||
for i in 0..newlen {
|
||
let u_i = if i > ker { i - ker } else { 0 };
|
||
let u_f = cmp::min(i, vec - 1);
|
||
|
||
if u_i == u_f {
|
||
conv[i] += vector[u_i] * kernel[(i - u_i)];
|
||
} else {
|
||
for u in u_i..(u_f + 1) {
|
||
if i - u < ker {
|
||
conv[i] += vector[u] * kernel[(i - u)];
|
||
}
|
||
}
|
||
}
|
||
}
|
||
conv
|
||
}
|
||
|
||
///
|
||
/// The output convolution consists only of those elements that do not rely on the zero-padding.
|
||
///
|
||
pub fn convolve_valid<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
|
||
vector: Vector<R, D, S>,
|
||
kernel: Vector<R, E, Q>,
|
||
) -> Matrix<R, Dynamic, U1, VecStorage<R, Dynamic, U1>> {
|
||
let vec = vector.len();
|
||
let ker = kernel.len();
|
||
|
||
if vec == 0 || ker == 0 {
|
||
panic!("Convolve's inputs must not be 0-sized. ");
|
||
}
|
||
|
||
if ker > vec {
|
||
return convolve_valid(kernel, vector);
|
||
}
|
||
|
||
let newlen = vec - ker + 1;
|
||
|
||
let mut conv = DVector::<R>::zeros(newlen);
|
||
|
||
for i in 0..newlen {
|
||
for j in 0..ker {
|
||
conv[i] += vector[i + j] * kernel[ker - j - 1];
|
||
}
|
||
}
|
||
conv
|
||
}
|
||
|
||
///
|
||
/// The output convolution is the same size as vector, centered with respect to the ‘full’ output.
|
||
///
|
||
pub fn convolve_same<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>>(
|
||
vector: Vector<R, D, S>,
|
||
kernel: Vector<R, E, Q>,
|
||
) -> Matrix<R, Dynamic, U1, VecStorage<R, Dynamic, U1>> {
|
||
let vec = vector.len();
|
||
let ker = kernel.len();
|
||
|
||
if vec == 0 || ker == 0 {
|
||
panic!("Convolve's inputs must not be 0-sized. ");
|
||
}
|
||
|
||
if ker > vec {
|
||
return convolve_same(kernel, vector);
|
||
}
|
||
|
||
let mut conv = DVector::<R>::zeros(vec);
|
||
|
||
for i in 0..vec {
|
||
for j in 0..ker {
|
||
let val = if i + j < 1 || i + j >= vec + 1 {
|
||
zero::<R>()
|
||
} else {
|
||
vector[i + j - 1]
|
||
};
|
||
conv[i] += val * kernel[ker - j - 1];
|
||
}
|
||
}
|
||
conv
|
||
} |