Enum TensorExpr

#[non_exhaustive]
pub enum TensorExpr<A, B = A> {
    Binary(Box<TensorBase<A>>, Box<TensorBase<B>>, BinaryOp),
    BinaryScalar(Box<TensorBase<A>>, B, BinaryOp),
    Unary(Box<TensorBase<A>>, UnaryOp),
    Matmul(Box<TensorBase<A>>, Box<TensorBase<B>>),
    Sigmoid(Box<TensorBase<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(Box<TensorBase<A>>, Box<TensorBase<B>>, BinaryOp)

BinaryScalar(Box<TensorBase<A>>, B, BinaryOp)

Unary(Box<TensorBase<A>>, UnaryOp)

Matmul(Box<TensorBase<A>>, Box<TensorBase<B>>)

Sigmoid(Box<TensorBase<A>>)

Shape(ReshapeExpr<A>)

Implementations

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

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

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

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) -> TensorExpr<A, B>

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

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

pub fn shape(expr: ReshapeExpr<A>) -> TensorExpr<A, B>

pub fn sigmoid(tensor: TensorBase<A>) -> TensorExpr<A, B>

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

pub fn transpose(tensor: TensorBase<A>) -> TensorExpr<A, B>

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

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, B> Clone for TensorExpr<A, B>

where A: Clone, B: Clone,

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

Returns a copy of the value.

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

Performs copy-assignment from source.

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

where A: Debug, B: Debug,

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

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<TensorExpr<A, B>, <__D as Deserializer<'de>>::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>) -> TensorExpr<A, B>

Converts to this type from the input type.

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

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

Converts to this type from the input type.

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

where A: Hash, B: Hash,

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

where __H: Hasher,

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, B> PartialEq for TensorExpr<A, B>

where A: PartialEq, B: PartialEq,

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 as Serializer>::Ok, <__S as Serializer>::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

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

where A: Eq, B: Eq,

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