Enum TensorExpr

#[non_exhaustive]
pub enum TensorExpr<A, B = A> {
    Binary(BoxTensor<A>, BoxTensor<B>, BinaryOp),
    BinaryScalar(BoxTensor<A>, B, BinaryOp),
    Unary(BoxTensor<A>, UnaryOp),
    Matmul(BoxTensor<A>, BoxTensor<B>),
    Sigmoid(BoxTensor<A>),
    Shape(ReshapeExpr<A>),
}

Variants (Non-exhaustive)

Non-exhaustive enums could have additional variants added in future. Therefore, when matching against variants of non-exhaustive enums, an extra wildcard arm must be added to account for any future variants.

Binary(BoxTensor<A>, BoxTensor<B>, BinaryOp)

BinaryScalar(BoxTensor<A>, B, BinaryOp)

Unary(BoxTensor<A>, UnaryOp)

Matmul(BoxTensor<A>, BoxTensor<B>)

Sigmoid(BoxTensor<A>)

Shape(ReshapeExpr<A>)

Implementations

impl<A, B> TensorExpr<A, B>

pub fn binary(lhs: TensorBase<A>, rhs: TensorBase<B>, op: BinaryOp) -> Self

pub fn binary_scalar(lhs: TensorBase<A>, rhs: B, op: BinaryOp) -> Self

pub fn binary_scalar_c( lhs: TensorBase<A>, rhs: Complex<A>, op: BinaryOp, ) -> TensorExpr<A, Complex<A>>

pub fn broadcast(tensor: TensorBase<A>, shape: Shape) -> Self

pub fn matmul(lhs: TensorBase<A>, rhs: TensorBase<B>) -> Self

pub fn reshape(tensor: TensorBase<A>, shape: Shape) -> Self

pub fn shape(expr: ReshapeExpr<A>) -> Self

pub fn sigmoid(tensor: TensorBase<A>) -> Self

pub fn swap_axes(tensor: TensorBase<A>, swap: Axis, with: Axis) -> Self

pub fn transpose(tensor: TensorBase<A>) -> Self

pub fn unary(tensor: TensorBase<A>, op: UnaryOp) -> Self

pub fn lhs(self) -> Option<TensorBase<A>>

pub fn rhs(self) -> Option<TensorBase<B>>

pub fn view(&self) -> TensorExpr<&A, &B>

pub fn view_mut(&mut self) -> TensorExpr<&mut A, &mut B>

Trait Implementations

impl<A: Clone, B: Clone> Clone for TensorExpr<A, B>

fn clone(&self) -> TensorExpr<A, B>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<A: Debug, B: Debug> Debug for TensorExpr<A, B>

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

Formats the value using the given formatter.

impl<'de, A, B> Deserialize<'de> for TensorExpr<A, B>

where A: Deserialize<'de>, B: Deserialize<'de>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<A, B> From<ReshapeExpr<A>> for TensorExpr<A, B>

fn from(expr: ReshapeExpr<A>) -> Self

Converts to this type from the input type.

impl<T> From<TensorExpr<T>> for BackpropOp<T>

fn from(op: TensorExpr<T>) -> Self

Converts to this type from the input type.

impl<A: Hash, B: Hash> Hash for TensorExpr<A, B>

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl<A: PartialEq, B: PartialEq> PartialEq for TensorExpr<A, B>

fn eq(&self, other: &TensorExpr<A, B>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<A, B> Serialize for TensorExpr<A, B>

where A: Serialize, B: Serialize,

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<A: Eq, B: Eq> Eq for TensorExpr<A, B>

impl<A, B> StructuralPartialEq for TensorExpr<A, B>