#[cfg(test)]
use super::super::test::ErrorTensor;
use crate::math::{
write_tensor_rank_0, Tensor, TensorArray, TensorRank0, TensorRank1, TensorRank2, TensorVec,
};
use std::{
fmt::{Display, Formatter, Result},
ops::{Add, AddAssign, Div, DivAssign, Index, IndexMut, Mul, MulAssign, Sub, SubAssign},
};
#[derive(Clone, Debug)]
pub struct TensorRank1Vec<const D: usize, const I: usize>(Vec<TensorRank1<D, I>>);
impl<const D: usize, const I: usize> Display for TensorRank1Vec<D, I> {
fn fmt(&self, f: &mut Formatter) -> Result {
write!(f, "\x1B[s")?;
write!(f, "[[")?;
self.iter().enumerate().try_for_each(|(i, tensor_rank_1)| {
tensor_rank_1
.iter()
.try_for_each(|entry| write_tensor_rank_0(f, entry))?;
if i + 1 < self.len() {
writeln!(f, "\x1B[2D],")?;
write!(f, "\x1B[u")?;
write!(f, "\x1B[{}B [", i + 1)?;
}
Ok(())
})?;
write!(f, "\x1B[2D]]")
}
}
#[cfg(test)]
impl<const D: usize, const I: usize> ErrorTensor for TensorRank1Vec<D, I> {
fn error(
&self,
comparator: &Self,
tol_abs: &TensorRank0,
tol_rel: &TensorRank0,
) -> Option<usize> {
let error_count = self
.iter()
.zip(comparator.iter())
.map(|(entry, comparator_entry)| {
entry
.iter()
.zip(comparator_entry.iter())
.filter(|(&entry_i, &comparator_entry_i)| {
&(entry_i - comparator_entry_i).abs() >= tol_abs
&& &(entry_i / comparator_entry_i - 1.0).abs() >= tol_rel
})
.count()
})
.sum();
if error_count > 0 {
Some(error_count)
} else {
None
}
}
fn error_fd(&self, comparator: &Self, epsilon: &TensorRank0) -> Option<(bool, usize)> {
let error_count = self
.iter()
.zip(comparator.iter())
.map(|(entry, comparator_entry)| {
entry
.iter()
.zip(comparator_entry.iter())
.filter(|(&entry_i, &comparator_entry_i)| {
&(entry_i / comparator_entry_i - 1.0).abs() >= epsilon
&& (&entry_i.abs() >= epsilon || &comparator_entry_i.abs() >= epsilon)
})
.count()
})
.sum();
if error_count > 0 {
let auxillary = self
.iter()
.zip(comparator.iter())
.map(|(entry, comparator_entry)| {
entry
.iter()
.zip(comparator_entry.iter())
.filter(|(&entry_i, &comparator_entry_i)| {
&(entry_i / comparator_entry_i - 1.0).abs() >= epsilon
&& &(entry_i - comparator_entry_i).abs() >= epsilon
&& (&entry_i.abs() >= epsilon
|| &comparator_entry_i.abs() >= epsilon)
})
.count()
})
.sum::<usize>()
> 0;
Some((auxillary, error_count))
} else {
None
}
}
}
impl<const D: usize> From<TensorRank1Vec<D, 0>> for TensorRank1Vec<D, 1> {
fn from(tensor_rank_1_vec: TensorRank1Vec<D, 0>) -> Self {
tensor_rank_1_vec
.iter()
.map(|tensor_rank_1| tensor_rank_1.into())
.collect()
}
}
impl<const D: usize, const I: usize> FromIterator<TensorRank1<D, I>> for TensorRank1Vec<D, I> {
fn from_iter<Ii: IntoIterator<Item = TensorRank1<D, I>>>(into_iterator: Ii) -> Self {
Self(Vec::from_iter(into_iterator))
}
}
impl<const D: usize, const I: usize> Index<usize> for TensorRank1Vec<D, I> {
type Output = TensorRank1<D, I>;
fn index(&self, index: usize) -> &Self::Output {
&self.0[index]
}
}
impl<const D: usize, const I: usize> IndexMut<usize> for TensorRank1Vec<D, I> {
fn index_mut(&mut self, index: usize) -> &mut Self::Output {
&mut self.0[index]
}
}
impl<const D: usize, const I: usize> TensorRank1Vec<D, I> {
pub fn dot(&self, tensors: &Self) -> TensorRank0 {
self.iter()
.zip(tensors.iter())
.map(|(entry, tensor)| entry * tensor)
.sum()
}
}
impl<const D: usize, const I: usize> TensorVec for TensorRank1Vec<D, I> {
type Item = TensorRank1<D, I>;
type Slice<'a> = &'a [[TensorRank0; D]];
fn append(&mut self, other: &mut Self) {
self.0.append(&mut other.0)
}
fn is_empty(&self) -> bool {
self.0.is_empty()
}
fn len(&self) -> usize {
self.0.len()
}
fn new(slice: Self::Slice<'_>) -> Self {
slice
.iter()
.map(|slice_entry| Self::Item::new(*slice_entry))
.collect()
}
fn push(&mut self, item: Self::Item) {
self.0.push(item)
}
fn zero(len: usize) -> Self {
(0..len).map(|_| super::zero()).collect()
}
}
impl<const D: usize, const I: usize> Tensor for TensorRank1Vec<D, I> {
type Item = TensorRank1<D, I>;
fn get_at(&self, indices: &[usize]) -> &TensorRank0 {
&self[indices[0]][indices[1]]
}
fn get_at_mut(&mut self, indices: &[usize]) -> &mut TensorRank0 {
&mut self[indices[0]][indices[1]]
}
fn iter(&self) -> impl Iterator<Item = &Self::Item> {
self.0.iter()
}
fn iter_mut(&mut self) -> impl Iterator<Item = &mut Self::Item> {
self.0.iter_mut()
}
}
impl<const D: usize, const I: usize> Div<TensorRank0> for TensorRank1Vec<D, I> {
type Output = Self;
fn div(mut self, tensor_rank_0: TensorRank0) -> Self::Output {
self /= &tensor_rank_0;
self
}
}
impl<const D: usize, const I: usize> Div<&TensorRank0> for TensorRank1Vec<D, I> {
type Output = Self;
fn div(mut self, tensor_rank_0: &TensorRank0) -> Self::Output {
self /= tensor_rank_0;
self
}
}
impl<const D: usize, const I: usize> DivAssign<TensorRank0> for TensorRank1Vec<D, I> {
fn div_assign(&mut self, tensor_rank_0: TensorRank0) {
self.iter_mut().for_each(|entry| *entry /= &tensor_rank_0);
}
}
impl<const D: usize, const I: usize> DivAssign<&TensorRank0> for TensorRank1Vec<D, I> {
fn div_assign(&mut self, tensor_rank_0: &TensorRank0) {
self.iter_mut().for_each(|entry| *entry /= tensor_rank_0);
}
}
impl<const D: usize, const I: usize> Mul<TensorRank0> for TensorRank1Vec<D, I> {
type Output = Self;
fn mul(mut self, tensor_rank_0: TensorRank0) -> Self::Output {
self *= &tensor_rank_0;
self
}
}
impl<const D: usize, const I: usize> Mul<&TensorRank0> for TensorRank1Vec<D, I> {
type Output = Self;
fn mul(mut self, tensor_rank_0: &TensorRank0) -> Self::Output {
self *= tensor_rank_0;
self
}
}
impl<const D: usize, const I: usize> Mul<&TensorRank0> for &TensorRank1Vec<D, I> {
type Output = TensorRank1Vec<D, I>;
fn mul(self, tensor_rank_0: &TensorRank0) -> Self::Output {
self.iter().map(|self_i| self_i * tensor_rank_0).collect()
}
}
impl<const D: usize, const I: usize> MulAssign<TensorRank0> for TensorRank1Vec<D, I> {
fn mul_assign(&mut self, tensor_rank_0: TensorRank0) {
self.iter_mut().for_each(|entry| *entry *= &tensor_rank_0);
}
}
impl<const D: usize, const I: usize> MulAssign<&TensorRank0> for TensorRank1Vec<D, I> {
fn mul_assign(&mut self, tensor_rank_0: &TensorRank0) {
self.iter_mut().for_each(|entry| *entry *= tensor_rank_0);
}
}
impl<const D: usize, const I: usize> Add for TensorRank1Vec<D, I> {
type Output = Self;
fn add(mut self, tensor_rank_1_vec: Self) -> Self::Output {
self += tensor_rank_1_vec;
self
}
}
impl<const D: usize, const I: usize> Add<&Self> for TensorRank1Vec<D, I> {
type Output = Self;
fn add(mut self, tensor_rank_1_vec: &Self) -> Self::Output {
self += tensor_rank_1_vec;
self
}
}
impl<const D: usize, const I: usize> AddAssign for TensorRank1Vec<D, I> {
fn add_assign(&mut self, tensor_rank_1_vec: Self) {
self.iter_mut()
.zip(tensor_rank_1_vec.iter())
.for_each(|(self_entry, tensor_rank_1)| *self_entry += tensor_rank_1);
}
}
impl<const D: usize, const I: usize> AddAssign<&Self> for TensorRank1Vec<D, I> {
fn add_assign(&mut self, tensor_rank_1_vec: &Self) {
self.iter_mut()
.zip(tensor_rank_1_vec.iter())
.for_each(|(self_entry, tensor_rank_1)| *self_entry += tensor_rank_1);
}
}
impl<const D: usize, const I: usize, const J: usize> Mul<TensorRank1Vec<D, J>>
for TensorRank1Vec<D, I>
{
type Output = TensorRank2<D, I, J>;
fn mul(self, tensor_rank_1_vec: TensorRank1Vec<D, J>) -> Self::Output {
self.iter()
.zip(tensor_rank_1_vec.iter())
.map(|(self_entry, tensor_rank_1_vec_entry)| {
TensorRank2::dyad(self_entry, tensor_rank_1_vec_entry)
})
.sum()
}
}
impl<const D: usize, const I: usize, const J: usize> Mul<&TensorRank1Vec<D, J>>
for TensorRank1Vec<D, I>
{
type Output = TensorRank2<D, I, J>;
fn mul(self, tensor_rank_1_vec: &TensorRank1Vec<D, J>) -> Self::Output {
self.iter()
.zip(tensor_rank_1_vec.iter())
.map(|(self_entry, tensor_rank_1_vec_entry)| {
TensorRank2::dyad(self_entry, tensor_rank_1_vec_entry)
})
.sum()
}
}
impl<const D: usize, const I: usize, const J: usize> Mul<TensorRank1Vec<D, J>>
for &TensorRank1Vec<D, I>
{
type Output = TensorRank2<D, I, J>;
fn mul(self, tensor_rank_1_vec: TensorRank1Vec<D, J>) -> Self::Output {
self.iter()
.zip(tensor_rank_1_vec.iter())
.map(|(self_entry, tensor_rank_1_vec_entry)| {
TensorRank2::dyad(self_entry, tensor_rank_1_vec_entry)
})
.sum()
}
}
impl<const D: usize, const I: usize, const J: usize> Mul<&TensorRank1Vec<D, J>>
for &TensorRank1Vec<D, I>
{
type Output = TensorRank2<D, I, J>;
fn mul(self, tensor_rank_1_vec: &TensorRank1Vec<D, J>) -> Self::Output {
self.iter()
.zip(tensor_rank_1_vec.iter())
.map(|(self_entry, tensor_rank_1_vec_entry)| {
TensorRank2::dyad(self_entry, tensor_rank_1_vec_entry)
})
.sum()
}
}
impl<const D: usize, const I: usize> Sub for TensorRank1Vec<D, I> {
type Output = Self;
fn sub(mut self, tensor_rank_1_vec: Self) -> Self::Output {
self -= tensor_rank_1_vec;
self
}
}
impl<const D: usize, const I: usize> Sub<&Self> for TensorRank1Vec<D, I> {
type Output = Self;
fn sub(mut self, tensor_rank_1_vec: &Self) -> Self::Output {
self -= tensor_rank_1_vec;
self
}
}
impl<const D: usize, const I: usize> SubAssign for TensorRank1Vec<D, I> {
fn sub_assign(&mut self, tensor_rank_1_vec: Self) {
self.iter_mut()
.zip(tensor_rank_1_vec.iter())
.for_each(|(self_entry, tensor_rank_1)| *self_entry -= tensor_rank_1);
}
}
impl<const D: usize, const I: usize> SubAssign<&Self> for TensorRank1Vec<D, I> {
fn sub_assign(&mut self, tensor_rank_1_vec: &Self) {
self.iter_mut()
.zip(tensor_rank_1_vec.iter())
.for_each(|(self_entry, tensor_rank_1)| *self_entry -= tensor_rank_1);
}
}