API Reference - Cores - SymbolicDifferentiationTensor
Constructors
add()
SymbolicDifferentiationTensorObject.add(...: tensor): SymbolicDifferentiationTensorObject
Parameters:
- tensor: The tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
subtract()
SymbolicDifferentiationTensorObject.subtract(...: tensor): SymbolicDifferentiationTensorObject
Parameters:
- tensor: The tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
multiply()
SymbolicDifferentiationTensorObject.multiply(...: tensor): SymbolicDifferentiationTensorObject
Parameters:
- tensor: The tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
divide()
SymbolicDifferentiationTensorObject.divide(...: tensor): SymbolicDifferentiationTensorObject
Parameters:
- tensor: The tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
power()
SymbolicDifferentiationTensorObject.power(baseTensor: tensor, exponentTensor: tensor): SymbolicDifferentiationTensorObject
Parameters:
-
baseTensor: The base tensor to be used by the symbolic differentiation tensor object.
-
exponentTensor: The exponent tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
log()
SymbolicDifferentiationTensorObject.log(numberTensor: tensor, baseTensor: tensor): SymbolicDifferentiationTensorObject
Parameters:
-
numberTensor: The number tensor to be used by the symbolic differentiation tensor object.
-
baseTensor: The base tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
sin()
SymbolicDifferentiationTensorObject.sin(radianTensor: tensor): SymbolicDifferentiationTensorObject
Parameters:
- radianTensor: The radian tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
cos()
SymbolicDifferentiationTensorObject.cos(radianTensor: tensor): SymbolicDifferentiationTensorObject
Parameters:
- radianTensor: The radian tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
tan()
SymbolicDifferentiationTensorObject.tan(radianTensor: tensor): SymbolicDifferentiationTensorObject
Parameters:
- radianTensor: The radian tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
dotProduct()
SymbolicDifferentiationTensorObject.dotProduct(tensor1: tensor, tensor2: tensor): SymbolicDifferentiationTensorObject
Parameters:
- tensor: The tensor to be used by the symbolic differentiation tensor object.
Returns
- SymbolicDifferentiationTensorObject: The generated symbolic differentiation tensor object.
Functions
getTensor()
SymbolicDifferentiationTensor:getTensor(doNotDeepCopy: boolean): tensor
Parameters:
- doNotDeepCopy: Set whether or not to deep copy the tensor.
Returns:
- tensor: The tensor generated by the symbolic differentiation tensor object.
getTensor()
SymbolicDifferentiationTensor:getTensor(tensor: tensor, doNotDeepCopy: boolean)
Parameters:
-
tensor: The tensor to be used by the symbolic differentiation tensor object.
-
doNotDeepCopy: Set whether or not to deep copy the tensor.
getFirstDerivativeTensorTable()
SymbolicDifferentiationTensor:getFirstDerivativeTensor(...: tensorToBeRespectedTo): tensor
Parameters:
- tensorToBeRespectedTo: The tensor to be used for generating first derivative tensor.
Returns:
- firstDerivativeTensor: The first derivative tensor in respect to arguments used in the function.