forked from M-Labs/nalgebra
Moving functions into impl for Vector<N,D,S>
This commit is contained in:
Nathan
parent
28525bfc20
commit
36feddb8c2
@ -5,138 +5,125 @@ use std::cmp;
|
||||
use storage::Storage;
|
||||
use {zero, Real, Vector, VectorN, U1};
|
||||
|
||||
/// Returns the convolution of the target vector and a kernel
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `vector` - A Vector with size > 0
|
||||
/// * `kernel` - A Vector with size > 0
|
||||
///
|
||||
/// # Errors
|
||||
/// Inputs must statisfy `vector.len() >= kernel.len() > 0`.
|
||||
///
|
||||
pub fn convolve_full<N, D1, D2, S1, S2>(
|
||||
vector: Vector<N, D1, S1>,
|
||||
kernel: Vector<N, D2, S2>,
|
||||
) -> VectorN<N, DimDiff<DimSum<D1, D2>, U1>>
|
||||
where
|
||||
N: Real,
|
||||
D1: DimAdd<D2>,
|
||||
D2: DimAdd<D1, Output = DimSum<D1, D2>>,
|
||||
DimSum<D1, D2>: DimSub<U1>,
|
||||
S1: Storage<N, D1>,
|
||||
S2: Storage<N, D2>,
|
||||
DefaultAllocator: Allocator<N, DimDiff<DimSum<D1, D2>, U1>>,
|
||||
{
|
||||
let vec = vector.len();
|
||||
let ker = kernel.len();
|
||||
impl<N: Real, D1: Dim, S1: Storage<N, D1>> Vector<N, D1, S1> {
|
||||
/// Returns the convolution of the target vector and a kernel
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `kernel` - A Vector with size > 0
|
||||
///
|
||||
/// # Errors
|
||||
/// Inputs must statisfy `vector.len() >= kernel.len() > 0`.
|
||||
///
|
||||
pub fn convolve_full<D2, S2>(
|
||||
&self,
|
||||
kernel: Vector<N, D2, S2>,
|
||||
) -> VectorN<N, DimDiff<DimSum<D1, D2>, U1>>
|
||||
where
|
||||
D1: DimAdd<D2>,
|
||||
D2: DimAdd<D1, Output = DimSum<D1, D2>>,
|
||||
DimSum<D1, D2>: DimSub<U1>,
|
||||
S2: Storage<N, D2>,
|
||||
DefaultAllocator: Allocator<N, DimDiff<DimSum<D1, D2>, U1>>,
|
||||
{
|
||||
let vec = self.len();
|
||||
let ker = kernel.len();
|
||||
|
||||
if ker == 0 || ker > vec {
|
||||
panic!("convolve_full expects `vector.len() >= kernel.len() > 0`, received {} and {} respectively.",vec,ker);
|
||||
}
|
||||
if ker == 0 || ker > vec {
|
||||
panic!("convolve_full expects `self.len() >= kernel.len() > 0`, received {} and {} respectively.",vec,ker);
|
||||
}
|
||||
|
||||
let result_len = vector.data.shape().0.add(kernel.data.shape().0).sub(U1);
|
||||
let mut conv = VectorN::zeros_generic(result_len, U1);
|
||||
let result_len = self.data.shape().0.add(kernel.data.shape().0).sub(U1);
|
||||
let mut conv = VectorN::zeros_generic(result_len, U1);
|
||||
|
||||
for i in 0..(vec + ker - 1) {
|
||||
let u_i = if i > vec { i - ker } else { 0 };
|
||||
let u_f = cmp::min(i, vec - 1);
|
||||
for i in 0..(vec + ker - 1) {
|
||||
let u_i = if i > vec { 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)];
|
||||
if u_i == u_f {
|
||||
conv[i] += self[u_i] * kernel[(i - u_i)];
|
||||
} else {
|
||||
for u in u_i..(u_f + 1) {
|
||||
if i - u < ker {
|
||||
conv[i] += self[u] * kernel[(i - u)];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
conv
|
||||
}
|
||||
conv
|
||||
}
|
||||
/// Returns the convolution of the target vector and a kernel
|
||||
/// The output convolution consists only of those elements that do not rely on the zero-padding.
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `kernel` - A Vector with size > 0
|
||||
///
|
||||
///
|
||||
/// # Errors
|
||||
/// Inputs must statisfy `self.len() >= kernel.len() > 0`.
|
||||
///
|
||||
pub fn convolve_valid<D2, S2>(&self, kernel: Vector<N, D2, S2>,
|
||||
) -> VectorN<N, DimDiff<DimSum<D1, U1>, D2>>
|
||||
where
|
||||
D1: DimAdd<U1>,
|
||||
D2: Dim,
|
||||
DimSum<D1, U1>: DimSub<D2>,
|
||||
S2: Storage<N, D2>,
|
||||
DefaultAllocator: Allocator<N, DimDiff<DimSum<D1, U1>, D2>>,
|
||||
{
|
||||
let vec = self.len();
|
||||
let ker = kernel.len();
|
||||
|
||||
/// Returns the convolution of the vector and a kernel
|
||||
/// The output convolution consists only of those elements that do not rely on the zero-padding.
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `vector` - A Vector with size > 0
|
||||
/// * `kernel` - A Vector with size > 0
|
||||
///
|
||||
///
|
||||
/// # Errors
|
||||
/// Inputs must statisfy `vector.len() >= kernel.len() > 0`.
|
||||
///
|
||||
pub fn convolve_valid<N, D1, D2, S1, S2>(
|
||||
vector: Vector<N, D1, S1>,
|
||||
kernel: Vector<N, D2, S2>,
|
||||
) -> VectorN<N, DimDiff<DimSum<D1, U1>, D2>>
|
||||
where
|
||||
N: Real,
|
||||
D1: DimAdd<U1>,
|
||||
D2: Dim,
|
||||
DimSum<D1, U1>: DimSub<D2>,
|
||||
S1: Storage<N, D1>,
|
||||
S2: Storage<N, D2>,
|
||||
DefaultAllocator: Allocator<N, DimDiff<DimSum<D1, U1>, D2>>,
|
||||
{
|
||||
let vec = vector.len();
|
||||
let ker = kernel.len();
|
||||
|
||||
if ker == 0 || ker > vec {
|
||||
panic!("convolve_valid expects `vector.len() >= kernel.len() > 0`, received {} and {} respectively.",vec,ker);
|
||||
}
|
||||
|
||||
let result_len = vector.data.shape().0.add(U1).sub(kernel.data.shape().0);
|
||||
let mut conv = VectorN::zeros_generic(result_len, U1);
|
||||
|
||||
for i in 0..(vec - ker + 1) {
|
||||
for j in 0..ker {
|
||||
conv[i] += vector[i + j] * kernel[ker - j - 1];
|
||||
if ker == 0 || ker > vec {
|
||||
panic!("convolve_valid expects `self.len() >= kernel.len() > 0`, received {} and {} respectively.",vec,ker);
|
||||
}
|
||||
}
|
||||
conv
|
||||
}
|
||||
|
||||
/// Returns the convolution of the vector and a kernel
|
||||
/// The output convolution is the same size as vector, centered with respect to the ‘full’ output.
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `vector` - A Vector with size > 0
|
||||
/// * `kernel` - A Vector with size > 0
|
||||
///
|
||||
/// # Errors
|
||||
/// Inputs must statisfy `vector.len() >= kernel.len() > 0`.
|
||||
pub fn convolve_same<N, D1, D2, S1, S2>(
|
||||
vector: Vector<N, D1, S1>,
|
||||
kernel: Vector<N, D2, S2>,
|
||||
) -> VectorN<N, DimMaximum<D1, D2>>
|
||||
where
|
||||
N: Real,
|
||||
D1: DimMax<D2>,
|
||||
D2: DimMax<D1, Output = DimMaximum<D1, D2>>,
|
||||
S1: Storage<N, D1>,
|
||||
S2: Storage<N, D2>,
|
||||
DefaultAllocator: Allocator<N, DimMaximum<D1, D2>>,
|
||||
{
|
||||
let vec = vector.len();
|
||||
let ker = kernel.len();
|
||||
let result_len = self.data.shape().0.add(U1).sub(kernel.data.shape().0);
|
||||
let mut conv = VectorN::zeros_generic(result_len, U1);
|
||||
|
||||
if ker == 0 || ker > vec {
|
||||
panic!("convolve_same expects `vector.len() >= kernel.len() > 0`, received {} and {} respectively.",vec,ker);
|
||||
}
|
||||
|
||||
let result_len = vector.data.shape().0.max(kernel.data.shape().0);
|
||||
let mut conv = VectorN::zeros_generic(result_len, U1);
|
||||
|
||||
for i in 0..vec {
|
||||
for j in 0..ker {
|
||||
let val = if i + j < 1 || i + j >= vec + 1 {
|
||||
zero::<N>()
|
||||
} else {
|
||||
vector[i + j - 1]
|
||||
};
|
||||
conv[i] += val * kernel[ker - j - 1];
|
||||
for i in 0..(vec - ker + 1) {
|
||||
for j in 0..ker {
|
||||
conv[i] += self[i + j] * kernel[ker - j - 1];
|
||||
}
|
||||
}
|
||||
conv
|
||||
}
|
||||
|
||||
/// Returns the convolution of the targetvector and a kernel
|
||||
/// The output convolution is the same size as vector, centered with respect to the ‘full’ output.
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `kernel` - A Vector with size > 0
|
||||
///
|
||||
/// # Errors
|
||||
/// Inputs must statisfy `self.len() >= kernel.len() > 0`.
|
||||
pub fn convolve_same<D2, S2>(&self, kernel: Vector<N, D2, S2>) -> VectorN<N, DimMaximum<D1, D2>>
|
||||
where
|
||||
D1: DimMax<D2>,
|
||||
D2: DimMax<D1, Output = DimMaximum<D1, D2>>,
|
||||
S2: Storage<N, D2>,
|
||||
DefaultAllocator: Allocator<N, DimMaximum<D1, D2>>,
|
||||
{
|
||||
let vec = self.len();
|
||||
let ker = kernel.len();
|
||||
|
||||
if ker == 0 || ker > vec {
|
||||
panic!("convolve_same expects `self.len() >= kernel.len() > 0`, received {} and {} respectively.",vec,ker);
|
||||
}
|
||||
|
||||
let result_len = self.data.shape().0.max(kernel.data.shape().0);
|
||||
let mut conv = VectorN::zeros_generic(result_len, U1);
|
||||
|
||||
for i in 0..vec {
|
||||
for j in 0..ker {
|
||||
let val = if i + j < 1 || i + j >= vec + 1 {
|
||||
zero::<N>()
|
||||
} else {
|
||||
self[i + j - 1]
|
||||
};
|
||||
conv[i] += val * kernel[ker - j - 1];
|
||||
}
|
||||
}
|
||||
conv
|
||||
}
|
||||
conv
|
||||
}
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
use na::linalg::{convolve_full,convolve_valid,convolve_same};
|
||||
use na::{Vector2,Vector3,Vector4,Vector5,DVector};
|
||||
use std::panic;
|
||||
|
||||
@ -13,13 +12,13 @@ use std::panic;
|
||||
fn convolve_same_check(){
|
||||
// Static Tests
|
||||
let actual_s = Vector4::from_vec(vec![1.0,4.0,7.0,10.0]);
|
||||
let expected_s = convolve_same(Vector4::new(1.0,2.0,3.0,4.0), Vector2::new(1.0,2.0));
|
||||
let expected_s = Vector4::new(1.0,2.0,3.0,4.0).convolve_same(Vector2::new(1.0,2.0));
|
||||
|
||||
assert!(relative_eq!(actual_s, expected_s, epsilon = 1.0e-7));
|
||||
|
||||
// Dynamic Tests
|
||||
let actual_d = DVector::from_vec(vec![1.0,4.0,7.0,10.0]);
|
||||
let expected_d = convolve_same(DVector::from_vec(vec![1.0,2.0,3.0,4.0]),DVector::from_vec(vec![1.0,2.0]));
|
||||
let expected_d = DVector::from_vec(vec![1.0,2.0,3.0,4.0]).convolve_same(DVector::from_vec(vec![1.0,2.0]));
|
||||
|
||||
assert!(relative_eq!(actual_d, expected_d, epsilon = 1.0e-7));
|
||||
|
||||
@ -27,19 +26,19 @@ fn convolve_same_check(){
|
||||
// These really only apply to dynamic sized vectors
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_same(DVector::from_vec(vec![1.0,2.0]), DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
DVector::from_vec(vec![1.0,2.0]).convolve_same(DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
}).is_err()
|
||||
);
|
||||
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_same(DVector::<f32>::from_vec(vec![]), DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
DVector::<f32>::from_vec(vec![]).convolve_same(DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
}).is_err()
|
||||
);
|
||||
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_same(DVector::from_vec(vec![1.0,2.0,3.0,4.0]),DVector::<f32>::from_vec(vec![]));
|
||||
DVector::from_vec(vec![1.0,2.0,3.0,4.0]).convolve_same(DVector::<f32>::from_vec(vec![]));
|
||||
}).is_err()
|
||||
);
|
||||
}
|
||||
@ -50,13 +49,13 @@ fn convolve_same_check(){
|
||||
fn convolve_full_check(){
|
||||
// Static Tests
|
||||
let actual_s = Vector5::new(1.0,4.0,7.0,10.0,8.0);
|
||||
let expected_s = convolve_full(Vector4::new(1.0,2.0,3.0,4.0), Vector2::new(1.0,2.0));
|
||||
let expected_s = Vector4::new(1.0,2.0,3.0,4.0).convolve_full(Vector2::new(1.0,2.0));
|
||||
|
||||
assert!(relative_eq!(actual_s, expected_s, epsilon = 1.0e-7));
|
||||
|
||||
// Dynamic Tests
|
||||
let actual_d = DVector::from_vec(vec![1.0,4.0,7.0,10.0,8.0]);
|
||||
let expected_d = convolve_full(DVector::from_vec(vec![1.0,2.0,3.0,4.0]), DVector::from_vec(vec![1.0,2.0]));
|
||||
let expected_d = DVector::from_vec(vec![1.0,2.0,3.0,4.0]).convolve_full(DVector::from_vec(vec![1.0,2.0]));
|
||||
|
||||
assert!(relative_eq!(actual_d, expected_d, epsilon = 1.0e-7));
|
||||
|
||||
@ -64,19 +63,19 @@ fn convolve_full_check(){
|
||||
// These really only apply to dynamic sized vectors
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_full(DVector::from_vec(vec![1.0,2.0]), DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
DVector::from_vec(vec![1.0,2.0]).convolve_full(DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
}).is_err()
|
||||
);
|
||||
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_full(DVector::<f32>::from_vec(vec![]), DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
DVector::<f32>::from_vec(vec![]).convolve_full(DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
}).is_err()
|
||||
);
|
||||
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_full(DVector::from_vec(vec![1.0,2.0,3.0,4.0]),DVector::<f32>::from_vec(vec![]));
|
||||
DVector::from_vec(vec![1.0,2.0,3.0,4.0]).convolve_full(DVector::<f32>::from_vec(vec![]));
|
||||
}).is_err()
|
||||
);
|
||||
}
|
||||
@ -87,13 +86,13 @@ fn convolve_full_check(){
|
||||
fn convolve_valid_check(){
|
||||
// Static Tests
|
||||
let actual_s = Vector3::from_vec(vec![4.0,7.0,10.0]);
|
||||
let expected_s = convolve_valid( Vector4::new(1.0,2.0,3.0,4.0), Vector2::new(1.0,2.0));
|
||||
let expected_s = Vector4::new(1.0,2.0,3.0,4.0).convolve_valid( Vector2::new(1.0,2.0));
|
||||
|
||||
assert!(relative_eq!(actual_s, expected_s, epsilon = 1.0e-7));
|
||||
|
||||
// Dynamic Tests
|
||||
let actual_d = DVector::from_vec(vec![4.0,7.0,10.0]);
|
||||
let expected_d = convolve_valid(DVector::from_vec(vec![1.0,2.0,3.0,4.0]), DVector::from_vec(vec![1.0,2.0]));
|
||||
let expected_d = DVector::from_vec(vec![1.0,2.0,3.0,4.0]).convolve_valid(DVector::from_vec(vec![1.0,2.0]));
|
||||
|
||||
assert!(relative_eq!(actual_d, expected_d, epsilon = 1.0e-7));
|
||||
|
||||
@ -101,19 +100,19 @@ fn convolve_valid_check(){
|
||||
// These really only apply to dynamic sized vectors
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_valid(DVector::from_vec(vec![1.0,2.0]), DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
DVector::from_vec(vec![1.0,2.0]).convolve_valid(DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
}).is_err()
|
||||
);
|
||||
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_valid(DVector::<f32>::from_vec(vec![]), DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
DVector::<f32>::from_vec(vec![]).convolve_valid(DVector::from_vec(vec![1.0,2.0,3.0,4.0]));
|
||||
}).is_err()
|
||||
);
|
||||
|
||||
assert!(
|
||||
panic::catch_unwind(|| {
|
||||
convolve_valid(DVector::from_vec(vec![1.0,2.0,3.0,4.0]),DVector::<f32>::from_vec(vec![]));
|
||||
DVector::from_vec(vec![1.0,2.0,3.0,4.0]).convolve_valid(DVector::<f32>::from_vec(vec![]));
|
||||
}).is_err()
|
||||
);
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user