Enum TensorView 

pub enum TensorView<'a> {
    F32(TypedTensorView<'a, f32>),
    F64(TypedTensorView<'a, f64>),
    I32(TypedTensorView<'a, i32>),
    I64(TypedTensorView<'a, i64>),
    Bool(TypedTensorView<'a, bool>),
    C32(TypedTensorView<'a, Complex<f32>>),
    C64(TypedTensorView<'a, Complex<f64>>),
}

Dynamic read-only borrowed tensor view.

TensorView keeps dtype erased while borrowing typed view metadata and storage. Use TypedTensorView directly when the scalar type is statically known.

Examples

use tenferro_tensor::{DType, TensorView, TypedTensorView};

let data = [1_i32, 2, 3, 4];
let typed = TypedTensorView::from_slice([2, 2], [1, 2], 0, &data)?;
let view = TensorView::I32(typed);

assert_eq!(view.dtype(), DType::I32);
assert_eq!(view.shape(), &[2, 2]);

Variants

F32(TypedTensorView<'a, f32>)

F64(TypedTensorView<'a, f64>)

I32(TypedTensorView<'a, i32>)

I64(TypedTensorView<'a, i64>)

Bool(TypedTensorView<'a, bool>)

C32(TypedTensorView<'a, Complex<f32>>)

C64(TypedTensorView<'a, Complex<f64>>)

Implementations

impl<'a> TensorView<'a>

pub fn f32(shape: &'a [usize], data: &'a [f32]) -> Result<Self>

Create a dynamic f32 view over compact column-major host data.

Examples

use tenferro_tensor::{DType, TensorView};

let data = [1.0_f32, 2.0];
let view = TensorView::f32(&[2], &data)?;
assert_eq!(view.dtype(), DType::F32);

pub fn f64(shape: &'a [usize], data: &'a [f64]) -> Result<Self>

Create a dynamic f64 view over compact column-major host data.

Examples

use tenferro_tensor::{DType, TensorView};

let data = [1.0_f64, 2.0];
let view = TensorView::f64(&[2], &data)?;
assert_eq!(view.dtype(), DType::F64);

pub fn i64(shape: &'a [usize], data: &'a [i64]) -> Result<Self>

Create a dynamic i64 view over compact column-major host data.

Examples

use tenferro_tensor::{DType, TensorView};

let data = [1_i64, 2];
let view = TensorView::i64(&[2], &data)?;
assert_eq!(view.dtype(), DType::I64);

pub fn i32(shape: &'a [usize], data: &'a [i32]) -> Result<Self>

Create a dynamic i32 view over compact column-major host data.

Examples

use tenferro_tensor::{DType, TensorView};

let data = [1_i32, 2];
let view = TensorView::i32(&[2], &data)?;
assert_eq!(view.dtype(), DType::I32);

pub fn bool(shape: &'a [usize], data: &'a [bool]) -> Result<Self>

Create a dynamic bool view over compact column-major host data.

Examples

use tenferro_tensor::{DType, TensorView};

let data = [true, false];
let view = TensorView::bool(&[2], &data)?;
assert_eq!(view.dtype(), DType::Bool);

pub fn c32(shape: &'a [usize], data: &'a [Complex32]) -> Result<Self>

Create a dynamic Complex32 view over compact column-major host data.

Examples

use num_complex::Complex32;
use tenferro_tensor::{DType, TensorView};

let data = [Complex32::new(1.0, 2.0)];
let view = TensorView::c32(&[1], &data)?;
assert_eq!(view.dtype(), DType::C32);

pub fn c64(shape: &'a [usize], data: &'a [Complex64]) -> Result<Self>

Create a dynamic Complex64 view over compact column-major host data.

Examples

use num_complex::Complex64;
use tenferro_tensor::{DType, TensorView};

let data = [Complex64::new(1.0, 2.0)];
let view = TensorView::c64(&[1], &data)?;
assert_eq!(view.dtype(), DType::C64);

pub fn dtype(&self) -> DType

pub fn shape(&self) -> &[usize]

pub fn to_tensor(&self) -> Result<Tensor>

Materialize this host view into an owned tensor.

This method has no backend context and does not download backend buffers. Use a backend-specific TensorViewCanonicalization method or an explicit device transfer before materializing backend views on the host.

Examples

use tenferro_tensor::{DType, TensorView};

let data = [1.0_f64, 2.0];
let view = TensorView::f64(&[2], &data)?;
let tensor = view.to_tensor()?;
assert_eq!(tensor.dtype(), DType::F64);

Trait Implementations

impl<'a> Clone for TensorView<'a>

fn clone(&self) -> TensorView<'a>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<'a> Debug for TensorView<'a>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.