API Reference - AutomaticDifferentiationTensor

Constructors

new() 


AutomaticDifferentiationTensor.new{tensor: tensor, PartialFirstDerivativeFunction: function, inputTensorArray: {tensor}}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is stored inside the automatic differentiation tensor.

  • PartialFirstDerivativeFunction: The function ths is involved in creating the automatic differentiation tensor.

  • inputTensorArray: An array containing the tensors that are involved in creating the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

coerce() 


AutomaticDifferentiationTensor.coerce{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that will be coerced.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

stack() 


AutomaticDifferentiationTensor.stack{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • …: A list of tensors that will be stacked to form a new automatic differentiation tensor object.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

createTensor() 


AutomaticDifferentiationTensor.createTensor{dimensionSizeArray: {number}, allValues: number}: AutomaticDifferentiationTensor

Parameters:

  • dimensionSizeArray: The dimension size array for the automatic differentiation tensor.

  • allValues: The values to be set for the automatic differentiation tensor.

createRandomNormalTensor() 


AutomaticDifferentiationTensor.createRandomNormalTensor{dimensionSizeArray: {number}, mean: number, standardDeviation: number}: AutomaticDifferentiationTensor

Parameters:

  • dimensionSizeArray: The dimension size array for the automatic differentiation tensor.

  • mean: The mean for the generated values.

  • standardDeviation: The standard deviation for the generated values.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

createRandomUniformTensor() 


AutomaticDifferentiationTensor.createRandomNormalTensor{dimensionSizeArray: {number}, minimumValue: number, maximumValue: number}: AutomaticDifferentiationTensor

Parameters:

  • dimensionSizeArray: The dimension size array for the automatic differentiation tensor.

  • minimumValue: The minimum value for the generated values.

  • maximumValue: The maximum value for the generated values.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

radian() 


AutomaticDifferentiationTensor.radian{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

degree() 


AutomaticDifferentiationTensor.degree{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

sin() 


AutomaticDifferentiationTensor.sin{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

cos() 


AutomaticDifferentiationTensor.cos{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

tan() 


AutomaticDifferentiationTensor.tan{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

exponent() 


AutomaticDifferentiationTensor.exponent{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

logarithm() 


AutomaticDifferentiationTensor.logarithm{numberTensor: tensor, baseTensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • numberTensor: The number tensor that is used by the automatic differentiation tensor.

  • baseTensor: The base tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

clamp() 


AutomaticDifferentiationTensor.clamp{tensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

  • upperBondTensor: The upper bound tensor that is stored inside the automatic differentiation tensor.

  • lowerBondTensor: The lower bound tensor that is stored inside the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

maximum() 


AutomaticDifferentiationTensor.maximum{...: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

minimum() 


AutomaticDifferentiationTensor.minimum{...: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

add() 


AutomaticDifferentiationTensor.add{...: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

subtract() 


AutomaticDifferentiationTensor.subtract{...: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

multiply() 


AutomaticDifferentiationTensor.multiply{...: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

divide() 


AutomaticDifferentiationTensor.divide{...: tensor}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

concatenate() 


AutomaticDifferentiationTensor.concatenate{...: tensor, dimension: number}: AutomaticDifferentiationTensor

Parameters:

  • tensor: The tensor that is used by the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

Arithmetic Functions

findMinimumValue() 


AutomaticDifferentiationTensor:findMinimumValue{}: AutomaticDifferentiationTensor

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

findMaximumValue() 


AutomaticDifferentiationTensor:findMaximumValue{}: AutomaticDifferentiationTensor

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

sum() 


AutomaticDifferentiationTensor:sum{dimension: number}: AutomaticDifferentiationTensor

Parameters:

  • dimension: The dimension of calculating the sum along an axis. Can be empty. [Default: None]

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

unaryMinus() 


AutomaticDifferentiationTensor:unaryMinus{}: AutomaticDifferentiationTensor

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

absolute() 


AutomaticDifferentiationTensor:absolute{}: AutomaticDifferentiationTensor

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

power() 


AutomaticDifferentiationTensor:power{otherTensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • otherTensor: The tensor to be used as an exponent.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

dotProduct() 


AutomaticDifferentiationTensor:dotProduct{otherTensor: tensor}: AutomaticDifferentiationTensor

Parameters:

  • otherTensor: The tensor to be used for dot product operation.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

transpose() 


AutomaticDifferentiationTensor:transpose{dimensionArray: {number}}: AutomaticDifferentiationTensor

Parameters:

  • dimensionArray: An array containing the dimensions to transpose the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

flatten() 


AutomaticDifferentiationTensor:flatten{dimensionArray: {number}}: AutomaticDifferentiationTensor

Parameters:

  • dimensionArray: An array containing the dimensions to flatten the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

reshape() 


AutomaticDifferentiationTensor:reshape{dimensionSizeArray: {number}}: AutomaticDifferentiationTensor

Parameters:

  • dimensionSizeArray: An array containing the dimension sizes to reshape the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

permute() 


AutomaticDifferentiationTensor:permute{dimensionArray: {number}}: AutomaticDifferentiationTensor

Parameters:

  • dimensionArray: An array containing the dimensions to permute the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

mean() 


AutomaticDifferentiationTensor:mean{dimension: number}: AutomaticDifferentiationTensor

Parameters:

  • dimension: The dimension of calculating the mean along an axis. Can be empty. [Default: None]

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

standardDeviation() 


AutomaticDifferentiationTensor:standardDeviation{dimension: number}: AutomaticDifferentiationTensor

Parameters:

  • dimension: The dimension of calculating the standard deviation along an axis. Can be empty. [Default: None]

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

zScoreNormalization() 


AutomaticDifferentiationTensor:zScoreNormalization{dimension: number}: AutomaticDifferentiationTensor

Parameters:

  • dimension: The dimension of calculating the z-score normalization along an axis. Can be empty. [Default: None]

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

expandDimensionSizes() 


AutomaticDifferentiationTensor:expandDimensionSizes{targetDimensionSizeArray: {number}}: AutomaticDifferentiationTensor

Parameters:

  • targetDimensionSizeArray: The target dimension sizes to add to the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

expandNumberOfDimensions() 


AutomaticDifferentiationTensor:expandNumberOfDimensions{dimensionSizeToAddArray: {number}}: AutomaticDifferentiationTensor

Parameters:

  • dimensionSizeToAddArray: The dimension size to add to the automatic differentiation tensor.

Returns:

  • AutomaticDifferentiationTensor: The generated automatic differentiation tensor object.

Non-Arithmetic Functions

getDimensionSizeArray() 


AutomaticDifferentiationTensor:getDimensionSizeArray{}: number

Returns:

  • dimensionSizeArray: The dimension size array for the automatic differentiation tensor.

differentiate() 


AutomaticDifferentiationTensors:differentiate{firstDerivativeTensor: tensor}

Parameters:

  • firstDerivativeTensor: The tensor to be used for calculating chain rule first derivative tensors.

copy() 


AutomaticDifferentiationTensor:copy{}: AutomaticDifferentiationTensor

Returns:

  • AutomaticDifferentiationTensor: 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 that is stored inside the automatic differentiation tensor.

setTensor() 


AutomaticDifferentiationTensor:setTensor{tensor: tensor, doNotDeepCopy: boolean}

Parameters:

  • tensor: The tensor that is will be stored inside the automatic differentiation tensor.

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

getTotalFirstDerivativeTensor() 


AutomaticDifferentiationTensor:getTotalFirstDerivativeTensor{doNotDeepCopy: boolean}: tensor

Parameters:

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

Returns:

  • tensor: The total first derivative tensor that is stored inside the automatic differentiation tensor.

setTotalFirstDerivativeTensor() 


AutomaticDifferentiationTensor:setTotalFirstDerivativeTensor{tensor: tensor, doNotDeepCopy: boolean}

Parameters:

  • tensor: The total first derivative tensor that is will be stored inside the automatic differentiation tensor.

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

destroy() 


AutomaticDifferentiationTensor:destroy{areDescendantsDestroyed: boolean, destroyFirstInputTensor: boolean}

Parameters:

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

  • destroyFirstInputTensor: Set whether or not to destroy the very first tensors that are used as inputs.

isAutomaticDifferentiationTensor() 


AutomaticDifferentiationTensor:isAutomaticDifferentiationTensor{}: boolean

Returns:

  • isAutomaticDifferentiationTensor: A boolean that indicates if the object is an automatic differentiation tensor object.