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.