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.