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