API Reference - Cores - AutomaticDifferentiationTensor

Constructors

new()


AutomaticDifferentiationTensor.new(tensor: tensor, PartialDerivativeFunction: Function, inputTensorArray: tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • tensor: The tensor to be used by the automatic differentiation tensor object.

  • PartialDerivativeFunction (Optional): The partial derivative function to be multiplied with initialPartialFirstDerivativeTensor. Must supply the initialPartialFirstDerivativeTensor argument to the function.

  • inputTensorArray (Optional): The input tensor objects that was used to generate the current tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

sin()


AutomaticDifferentiationTensor.sin(tensor: tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • tensor: The tensor to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

cos()


AutomaticDifferentiationTensor.cos(tensor: tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • tensor: The tensor to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

tan()


AutomaticDifferentiationTensor.tan(tensor: tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • tensor: The tensor to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

exponent()


AutomaticDifferentiationTensor.exponent(tensor: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • tensor: The tensor to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

logarithm()


AutomaticDifferentiationTensor.logarithm(numberTensor: number/tensor/AutomaticDifferentiationTensorObject, baseTensor: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • numberTensor: The tensor to be used by the automatic differentiation tensor object.

  • baseTensor: The tensor to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

clamp()


AutomaticDifferentiationTensor.clamp(tensor: tensor/AutomaticDifferentiationTensorObject, upperBoundTensor: number/tensor/AutomaticDifferentiationTensorObject upperBoundTensor: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

maximum()


AutomaticDifferentiationTensor.maximum(tensor1: number/tensor/AutomaticDifferentiationTensorObject, tensor2: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • tensor: The tensor to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

minimum()

Parameters:

  • tensor: The tensor to be used by the automatic differentiation tensor object.

AutomaticDifferentiationTensor.minimum(tensor1: number/tensor/AutomaticDifferentiationTensorObject, tensor2: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

Functions

add()


AutomaticDifferentiationTensor:add(AutomaticDifferentiationTensor: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • AutomaticDifferentiationTensor: The tensor object to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

subtract()


AutomaticDifferentiationTensor:subtract(AutomaticDifferentiationTensor: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • AutomaticDifferentiationTensor: The tensor object to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

multiply()


AutomaticDifferentiationTensor:multiply(AutomaticDifferentiationTensor: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • AutomaticDifferentiationTensor: The tensor object to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

divide()


AutomaticDifferentiationTensor:divide(AutomaticDifferentiationTensor: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • AutomaticDifferentiationTensor: The tensor object to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

power()


AutomaticDifferentiationTensor:power(AutomaticDifferentiationTensor: number/tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • AutomaticDifferentiationTensor: The exponent tensor object to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

sum()


AutomaticDifferentiationTensor:sum(dimension: number): AutomaticDifferentiationTensorObject

Parameters:

  • dimension: The dimension to sum across the tensor.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

dotProduct()


AutomaticDifferentiationTensor:dotProduct(AutomaticDifferentiationTensor: tensor/AutomaticDifferentiationTensorObject): AutomaticDifferentiationTensorObject

Parameters:

  • AutomaticDifferentiationTensor: The tensor to be used by the automatic differentiation tensor object.

Returns

  • AutomaticDifferentiationTensorObject: The generated automatic differentiation tensor object.

getTensor()


AutomaticDifferentiationTensor:getTensor(doNotDeepCopy: boolean): tensor

Parameters:

  • doNotDeepCopy: Set whether or not to deep copy the tensor.

Returns:

  • tensor: The tensor generated by the automatic differentiation tensor object.

setTensor()


AutomaticDifferentiationTensor:getTensor(tensor: tensor, doNotDeepCopy: boolean)

Parameters:

  • tensor: The tensor to be used by the automatic differentiation tensor object.

  • doNotDeepCopy: Set whether or not to deep copy the tensor.

getTotalPartialFirstDerivativeTensor()


AutomaticDifferentiationTensor:getTotalPartialFirstDerivativeTensor(doNotDeepCopy: boolean): tensor

Parameters:

  • doNotDeepCopy: Set whether or not to deep copy the tensor.

Returns:

  • totalPartialFirstDerivativeTensor: The total of partial first derivative tensor generated by the automatic differentiation tensor object.

setTotalPartialFirstDerivativeTensor()


AutomaticDifferentiationTensor:setTotalPartialFirstDerivativeTensor(totalPartialFirstDerivativeTensor: tensor, doNotDeepCopy: boolean)

Parameters:

  • totalPartialFirstDerivativeTensor: The total of partial first derivative tensor to be used by the automatic differentiation tensor object.

  • doNotDeepCopy: Set whether or not to deep copy the tensor.

destroy()


AutomaticDifferentiationTensor:destroy(areDescendantsDestroyed: boolean)

Parameters:

  • areDescendantsDestroyed: Set whether or not to destroy the descendants of the current automatic differentiation tensor object.