Update types because change in shape as_array

Signed-off-by: Julius Koskela <julius.koskela@unikie.com>

src/index.rs

@@ -1,8 +1,8 @@
 use super::*;
 use getset::{Getters, MutGetters};
 use std::{
-    ops::{Index, IndexMut, Add, Sub},
-	cmp::Ordering,
+    cmp::Ordering,
+    ops::{Add, Index, IndexMut, Sub},
 };
 
 #[derive(Clone, Copy, Debug, Getters, MutGetters)]
@@ -16,7 +16,6 @@ pub struct TensorIndex<const R: usize> {
 // ---- Construction and Initialization ---------------------------------------
 
 impl<const R: usize> TensorIndex<R> {
-
     pub fn new(shape: TensorShape<R>, indices: [usize; R]) -> Self {
         if !shape.check_indices(indices) {
             panic!("indices out of bounds");
@@ -65,11 +64,9 @@ impl<const R: usize> TensorIndex<R> {
         if self.indices()[0] >= self.shape().get(0) {
             return false;
         }
-		let shape = self.shape().as_array().clone();
+        let shape = self.shape().as_array().clone();
         let mut carry = 1;
-        for (i, &dim_size) in
-            self.indices.iter_mut().zip(&shape).rev()
-        {
+        for (i, &dim_size) in self.indices.iter_mut().zip(&shape).rev() {
             if carry == 1 {
                 *i += 1;
                 if *i >= dim_size {
@@ -159,10 +156,8 @@ impl<const R: usize> TensorIndex<R> {
         }
 
         let mut borrow = true;
-		let shape = self.shape().as_array().clone();
-        for (i, &dim_size) in
-            self.indices_mut().iter_mut().zip(&shape).rev()
-        {
+        let shape = self.shape().as_array().clone();
+        for (i, &dim_size) in self.indices_mut().iter_mut().zip(&shape).rev() {
             if borrow {
                 if *i == 0 {
                     *i = dim_size - 1; // Wrap around to the maximum index of
@@ -346,18 +341,14 @@ impl<const R: usize> IndexMut<usize> for TensorIndex<R> {
     }
 }
 
-impl<const R: usize> From<(TensorShape<R>, [usize; R])>
-    for TensorIndex<R>
-{
+impl<const R: usize> From<(TensorShape<R>, [usize; R])> for TensorIndex<R> {
     fn from((shape, indices): (TensorShape<R>, [usize; R])) -> Self {
         assert!(shape.check_indices(indices));
         Self::new(shape, indices)
     }
 }
 
-impl<const R: usize> From<(TensorShape<R>, usize)>
-    for TensorIndex<R>
-{
+impl<const R: usize> From<(TensorShape<R>, usize)> for TensorIndex<R> {
     fn from((shape, flat_index): (TensorShape<R>, usize)) -> Self {
         let indices = shape.index_from_flat(flat_index).indices;
         Self::new(shape, indices)
@@ -370,9 +361,7 @@ impl<const R: usize> From<TensorShape<R>> for TensorIndex<R> {
     }
 }
 
-impl<T: Value, const R: usize> From<Tensor<T, R>>
-    for TensorIndex<R>
-{
+impl<T: Value, const R: usize> From<Tensor<T, R>> for TensorIndex<R> {
     fn from(tensor: Tensor<T, R>) -> Self {
         Self::zero(tensor.shape().clone())
     }

src/lib.rs

@@ -9,7 +9,7 @@ pub mod shape;
 pub mod tensor;
 pub mod value;
 
-pub use {value::*, axis::*, error::*, index::*, shape::*, tensor::*};
+pub use {axis::*, error::*, index::*, shape::*, tensor::*, value::*};
 
 #[macro_export]
 macro_rules! tensor {
@@ -27,9 +27,9 @@ macro_rules! shape {
 
 #[macro_export]
 macro_rules! index {
-	($tensor:expr) => {
-		TensorIndex::zero($tensor.shape().clone())
-	};
+    ($tensor:expr) => {
+        TensorIndex::zero($tensor.shape().clone())
+    };
     ($tensor:expr, $indices:expr) => {
         TensorIndex::from(($tensor.shape().clone(), $indices))
     };

src/shape.rs

@@ -1,8 +1,8 @@
 use super::*;
+use core::result::Result as SerdeResult;
 use serde::de::{self, Deserialize, Deserializer, SeqAccess, Visitor};
 use serde::ser::{Serialize, SerializeTuple, Serializer};
-use std::fmt::{Result as FmtResult, Formatter};
-use core::result::Result as SerdeResult;
+use std::fmt::{Formatter, Result as FmtResult};
 
 #[derive(Clone, Copy, Debug)]
 pub struct TensorShape<const R: usize>([usize; R]);

src/tensor.rs

@@ -632,7 +632,12 @@ where
     T: Display + Clone,
 {
     fn fmt(&self, f: &mut Formatter<'_>) -> FmtResult {
-        Tensor::<T, R>::fmt_helper(&self.buffer, &self.shape().as_array().clone(), f, 1)
+        Tensor::<T, R>::fmt_helper(
+            &self.buffer,
+            &self.shape().as_array().clone(),
+            f,
+            1,
+        )
     }
 }
 

src/value.rs

@@ -1,9 +1,6 @@
 use num::{Num, One, Zero};
 use serde::{Deserialize, Serialize};
-use std::{
-	fmt::Display,
-	iter::Sum,
-};
+use std::{fmt::Display, iter::Sum};
 
 /// A trait for types that can be used as values in a tensor.
 pub trait Value:
@@ -22,4 +19,4 @@ impl<T> Value for T where
         + Deserialize<'static>
         + Sum
 {
-}
+}