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.