Fixing type traits based on feedback, `convolve_full` still broken
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Nathan
parent
b08c2ad70d
commit
9f52019385
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@ -1,71 +1,110 @@
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use storage::Storage;
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use {zero, DVector, Dim, Dynamic, Matrix, Real, VecStorage, Vector, U1, Add};
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use base::allocator::Allocator;
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use base::default_allocator::DefaultAllocator;
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use base::dimension::{DimAdd, DimDiff, DimMax, DimMaximum, DimName, DimSub, DimSum,Dim};
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use std::cmp;
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use storage::Storage;
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use {zero, Real, Vector, VectorN, U1};
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impl<N: Real, D1: Dim, S1: Storage<N,D1>> Vector<N,D1,S1>{
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/// Returns the convolution of the vector and a kernel
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///
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/// # Arguments
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///
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/// * `vector` - A Vector with size > 0
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/// * `kernel` - A Vector with size > 0
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///
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/// # Note:
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/// This function is commutative. If kernel > vector,
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/// they will swap their roles as in
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/// (self, kernel) = (kernel,self)
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///
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/// # Example
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///
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/// ```
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/// let vec = Vector3::new(1.0,2.0,3.0);
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/// let ker = Vector2::new(0.4,0.6);
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/// let convolve = convolve_full(vec,ker);
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/// ```
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pub fn convolve_full<N, D1, D2, S1, S2>(
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vector: Vector<N, D1, S1>,
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kernel: Vector<N, D2, S2>,
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) -> VectorN<N, DimDiff<DimSum<D1, D2>, U1>>
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where
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N: Real,
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D1: DimAdd<D2>,
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D2: DimAdd<D1, Output = DimSum<D1, D2>>,
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DimSum<D1, D2>: DimSub<U1>,
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S1: Storage<N, D1>,
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S2: Storage<N, D2>,
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DimSum<D1, D2>: Dim,
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DefaultAllocator: Allocator<N, DimDiff<DimSum<D1, D2>, U1>>,
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{
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let vec = vector.len();
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let ker = kernel.len();
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/// Returns the convolution of the vector and a kernel
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///
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/// # Arguments
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///
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/// * `self` - A DVector with size D > 0
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/// * `kernel` - A DVector with size D > 0
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///
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/// # Note:
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/// This function is commutative. If D_kernel > D_vector,
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/// they will swap their roles as in
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/// (self, kernel) = (kernel,self)
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///
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/// # Example
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///
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/// ```
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///
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/// ```
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pub fn convolve_full<D2: Dim, S2: Storage<N, D2>>(&self, kernel: Vector<N, D2, S2>) -> Vector<N,Add<D1,D2>,Add<S1,S2>>
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{
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let vec = self.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 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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// if ker > vec {
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// return kernel::convolve_full(vector);
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// }
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let result_len = vector.data.shape().0.add(kernel.data.shape().0).sub(U1);
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let mut conv = VectorN::zeros_generic(result_len, U1);
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let newlen = vec + ker - 1;
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let mut conv = DVector::<N>::zeros(newlen);
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for i in 0..(vec + ker - 1) {
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let u_i = if i > vec { i - ker } else { 0 };
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let u_f = cmp::min(i, vec - 1);
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for i in 0..newlen {
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let u_i = if i > ker { i - ker } else { 0 };
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let u_f = cmp::min(i, vec - 1);
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if u_i == u_f {
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conv[i] += self[u_i] * kernel[(i - u_i)];
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} else {
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for u in u_i..(u_f + 1) {
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if i - u < ker {
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conv[i] += self[u] * kernel[(i - u)];
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}
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if u_i == u_f {
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conv[i] += vector[u_i] * kernel[(i - u_i)];
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} else {
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for u in u_i..(u_f + 1) {
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if i - u < ker {
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conv[i] += vector[u] * kernel[(i - u)];
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}
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}
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}
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// conv
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}
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conv
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}
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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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///
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/// Returns the convolution of the vector and a kernel
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/// The output convolution consists only of those elements that do not rely on the zero-padding.
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/// # Arguments
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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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/// * `vector` - A Vector with size > 0
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/// * `kernel` - A Vector with size > 0
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///
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/// # Note:
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/// This function is commutative. If kernel > vector,
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/// they will swap their roles as in
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/// (self, kernel) = (kernel,self)
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///
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/// # Example
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///
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/// ```
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/// let vec = Vector3::new(1.0,2.0,3.0);
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/// let ker = Vector2::new(0.4,0.6);
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/// let convolve = convolve_valid(vec,ker);
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/// ```
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pub fn convolve_valid<N, D1, D2, S1, S2>(
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vector: Vector<N, D1, S1>,
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kernel: Vector<N, D2, S2>,
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) -> VectorN<N, DimSum<DimDiff<D1, D2>, U1>>
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where
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N: Real,
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D1: DimSub<D2>,
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D2: DimSub<D1, Output = DimDiff<D1, D2>>,
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DimDiff<D1, D2>: DimAdd<U1>,
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S1: Storage<N, D1>,
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S2: Storage<N, D2>,
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DimDiff<D1, D2>: DimName,
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DefaultAllocator: Allocator<N, DimSum<DimDiff<D1, D2>, U1>>
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{
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let vec = vector.len();
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let ker = kernel.len();
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@ -76,12 +115,10 @@ pub fn convolve_valid<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E
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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 result_len = vector.data.shape().0.sub(kernel.data.shape().0).add(U1);
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let mut conv = VectorN::zeros_generic(result_len, U1);
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let newlen = vec - ker + 1;
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let mut conv = DVector::<R>::zeros(newlen);
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for i in 0..newlen {
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for i in 0..(vec - ker + 1) {
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for j in 0..ker {
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conv[i] += vector[i + j] * kernel[ker - j - 1];
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}
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@ -89,13 +126,38 @@ pub 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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///
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/// Returns the convolution of the vector and a kernel
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/// The output convolution is the same size as vector, centered with respect to the ‘full’ output.
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/// # Arguments
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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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/// * `vector` - A Vector with size > 0
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/// * `kernel` - A Vector with size > 0
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///
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/// # Note:
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/// This function is commutative. If kernel > vector,
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/// they will swap their roles as in
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/// (self, kernel) = (kernel,self)
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///
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/// # Example
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///
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/// ```
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/// let vec = Vector3::new(1.0,2.0,3.0);
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/// let ker = Vector2::new(0.4,0.6);
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/// let convolve = convolve_same(vec,ker);
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/// ```
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pub fn convolve_same<N, D1, D2, S1, S2>(
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vector: Vector<N, D1, S1>,
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kernel: Vector<N, D2, S2>,
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) -> VectorN<N, DimMaximum<D1, D2>>
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where
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N: Real,
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D1: DimMax<D2>,
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D2: DimMax<D1, Output = DimMaximum<D1, D2>>,
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S1: Storage<N, D1>,
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S2: Storage<N, D2>,
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DimMaximum<D1, D2>: Dim,
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DefaultAllocator: Allocator<N, DimMaximum<D1, D2>>,
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{
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let vec = vector.len();
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let ker = kernel.len();
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@ -107,12 +169,13 @@ pub fn convolve_same<R: Real, D: Dim, E: Dim, S: Storage<R, D>, Q: Storage<R, E>
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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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let result_len = vector.data.shape().0.max(kernel.data.shape().0);
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let mut conv = VectorN::zeros_generic(result_len, U1);
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for i in 0..vec {
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for j in 0..ker {
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let val = if i + j < 1 || i + j >= vec + 1 {
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zero::<R>()
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zero::<N>()
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} else {
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vector[i + j - 1]
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};
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@ -121,4 +184,3 @@ pub 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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conv
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}
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@ -1,5 +1,7 @@
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#[allow(unused_imports)] // remove after fixing unit test
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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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#[allow(unused_imports)]
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use na::{Vector2,Vector3,Vector4,Vector5,DVector};
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//
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// Should mimic calculations in Python's scipy library
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@ -10,40 +12,70 @@ use na::{Vector2,Vector4,DVector};
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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 vec_s = Vector4::new(1.0,2.0,3.0,4.0);
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let ker_s = 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 actual_s = Vector4::from_vec(vec![1.0,4.0,7.0,10.0]);
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let expected = convolve_same(vec,ker);
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let expected_s = convolve_same(vec_s,ker_s);
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let expected_s_r = convolve_same(ker_s,vec_s);
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assert!(relative_eq!(actual, expected, epsilon = 1.0e-7));
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assert!(relative_eq!(actual_s, expected_s, epsilon = 1.0e-7));
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assert!(relative_eq!(actual_s, expected_s_r, epsilon = 1.0e-7));
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let vec_d = DVector::from_vec(4,vec![1.0,2.0,3.0,4.0]);
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let ker_d = DVector::from_vec(2,vec![1.0,2.0]);
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let actual_d = DVector::from_vec(4,vec![1.0,4.0,7.0,10.0]);
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let expected_d = convolve_same(vec_d.clone(),ker_d.clone());
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let expected_d_r = convolve_same(ker_d,vec_d);
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assert!(relative_eq!(actual_d, expected_d, epsilon = 1.0e-7));
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assert!(relative_eq!(actual_d, expected_d_r, epsilon = 1.0e-7));
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}
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// >>> convolve([1,2,3,4],[1,2],"valid")
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// >>> convolve([1,2,3,4],[1,2],"full")
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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 vec_s = Vector4::new(1.0,2.0,3.0,4.0);
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let ker_s = 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 actual_s = Vector5::new(1.0,4.0,7.0,10.0,8.0);
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let expected = convolve_full(vec,ker);
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let expected_s = convolve_full(vec_s,ker_s);
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let expected_s_r = convolve_full(ker_s,vec_s);
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assert!(relative_eq!(actual, expected, epsilon = 1.0e-7));
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assert!(relative_eq!(actual_s, expected_s, epsilon = 1.0e-7));
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assert!(relative_eq!(actual_s, expected_s_r, epsilon = 1.0e-7));
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let vec_d = DVector::from_vec(4,vec![1.0,2.0,3.0,4.0]);
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let ker_d = DVector::from_vec(2,vec![1.0,2.0]);
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let actual_d = DVector::from_vec(5,vec![1.0,4.0,7.0,10.0,8.0]);
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let expected_d = convolve_full(vec_d.clone(),ker_d.clone());
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let expected_d_r = convolve_full(ker_d,vec_d);
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assert!(relative_eq!(actual_d, expected_d, epsilon = 1.0e-7));
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assert!(relative_eq!(actual_d, expected_d_r, 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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// #[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 actual = Vector3::from_vec(vec![4.0,7.0,10.0]);
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let expected = convolve_valid(vec,ker);
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// let expected1 = convolve_valid(vec, ker);
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// let expected2 = convolve_valid(ker, vec);
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assert!(relative_eq!(actual, expected, epsilon = 1.0e-7));
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}
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// assert!(relative_eq!(actual, expected1, epsilon = 1.0e-7));
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// assert!(relative_eq!(actual, expected2, epsilon = 1.0e-7));
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// }
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