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DocumentationFunctionalUnary Opscosh_op

cosh_op

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Structs

Struct: CosH

Fields

Methods

fwd(arg0: Array) -> Array
Computes the hyperbolic cosine of the input array element-wise.
Args
  • arg0: Array The input array.
Returns
  • Array - An array containing the hyperbolic cosine of each element in the input array. #### Examples: python a = Array([[1, 2], [3, 4]]) result = cosh(a) print(result) #### Note: This function supports: - Automatic differentiation (forward and reverse modes). - Complex valued arguments.
jvp(primals: List[Array], tangents: List[Array]) -> Array
Computes the Jacobian-vector product for the hyperbolic cosine function.
Args

  • primals: List[Array] A list containing the primal input array.

  • tangents: List[Array] A list containing the tangent vector.

Returns
  • Array - The Jacobian-vector product for the hyperbolic cosine function. #### Note: The Jacobian-vector product for hyperbolic cosine is computed as sinh(x) * dx, where x is the primal input and dx is the tangent vector.

Implements forward-mode automatic differentiation for the hyperbolic cosine function.

vjp(primals: List[Array], grad: Array, out: Array) -> List[Array]
Computes the vector-Jacobian product for the hyperbolic cosine function.
Args

  • primals: List[Array] A list containing the primal input array.

  • grad: Array The gradient of the output with respect to some scalar function.

  • out: Array The output of the forward pass (unused in this function).

Returns
  • List[Array] - A list containing the gradient with respect to the input. #### Note: The vector-Jacobian product for hyperbolic cosine is computed as sinh(x) * grad, where x is the primal input and grad is the incoming gradient.

Implements reverse-mode automatic differentiation for the hyperbolic cosine function.

unary_simd_op(arg0_real: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)], arg0_imag: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)]) -> Tuple[SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)], SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)]]
Low-level function to compute the hyperbolic cosine of a complex number represented as SIMD vectors.
Args

  • arg0_real: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)] The real part of the complex number.

  • arg0_imag: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)] The imaginary part of the complex number.

Returns
  • Tuple[SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)], SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)]] - The real and imaginary parts of the hyperbolic cosine of the complex number as a tuple.
__call__(mut curr: Array, args: List[Array])
Performs the forward pass for element-wise hyperbolic cosine computation of an array.
Args

  • curr: Array The current array to store the result (modified in-place).

  • args: List[Array] A list containing the input array.

Computes the hyperbolic cosine of each element in the input array and stores the result in the current array. Initializes the current array if not already set up.

Note: This function assumes that the shape and data of the args are already set up. If the current array (curr) is not initialized, it computes the shape based on the input array and sets up the data accordingly.

Functions

cosh

cosh(arg0: Array) -> Array
Computes the hyperbolic cosine of the input array element-wise.
Args
  • arg0: Array The input array.
Returns
  • Array - An array containing the hyperbolic cosine of each element in the input array. #### Examples: python a = Array([[1, 2], [3, 4]]) result = cosh(a) print(result) #### Note: This function supports: - Automatic differentiation (forward and reverse modes). - Complex valued arguments.
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