Scalar

A Scalar is represented either by an OperationNode, or the derived class Scalar.

Scalar can contain strings, ints, floats.

Although it is possible to generate Scalars with the function calls or object construction specified below, the recommended way is to use the methods defined on SystemDSContext.

class systemds.operator.Scalar(sds_context, operation: str, unnamed_input_nodes: Iterable[DAGNode | str | int | float | bool] = None, named_input_nodes: Dict[str, DAGNode | str | int | float | bool] = None, output_type: OutputType = OutputType.DOUBLE, assign: bool = False)

__init__(sds_context, operation: str, unnamed_input_nodes: Iterable[DAGNode | str | int | float | bool] = None, named_input_nodes: Dict[str, DAGNode | str | int | float | bool] = None, output_type: OutputType = OutputType.DOUBLE, assign: bool = False) → Scalar

Create general OperationNode

Parameters:

• sds_context – The SystemDS context for performing the operations

• operation – The name of the DML function to execute

• unnamed_input_nodes – inputs identified by their position, not name

• named_input_nodes – inputs with their respective parameter name

• output_type – type of the output in DML (double, matrix etc.)

• is_python_local_data – if the data is local in python e.g. Numpy arrays

• number_of_outputs – If set to other value than 1 then it is expected that this operation node returns multiple values. If set remember to set the output_types value as well.

• output_types – The types of output in a multi output scenario. Default is None, and means every multi output is a matrix.

abs() → Scalar

Calculate absolute.

Returns:

Scalar representing operation

acos() → Scalar

Calculate arccos.

Returns:

Scalar representing operation

asin() → Scalar

Calculate arcsin.

Returns:

Scalar representing operation

atan() → Scalar

Calculate arctan.

Returns:

Scalar representing operation

code_line(var_name: str, unnamed_input_vars: Sequence[str], named_input_vars: Dict[str, str]) → str

Generates the DML code line equal to the intended action of this node.

Parameters:

• var_name – Name of DML-variable this nodes result should be saved in

• unnamed_input_vars – all strings representing the unnamed parameters

• named_input_vars – all strings representing the named parameters (name value pairs)

Returns:

the DML code line that is equal to this operation

compute(verbose: bool = False, lineage: bool = False)

Get result of this operation. Builds the dml script and executes it in SystemDS, before this method is called all operations are only building the DAG without actually executing (lazy evaluation).

Parameters:

• verbose – Can be activated to print additional information such as created DML-Script

• lineage – Can be activated to print lineage trace till this node

Returns:

the output as an python builtin data type or numpy array

cos() → Scalar

Calculate cos.

Returns:

Scalar representing operation

cosh() → Scalar

Calculate cos.

Returns:

Scalar representing operation

pass_python_data_to_prepared_script(sds, var_name: str, prepared_script: JavaObject) → None

Passes data from python to the prepared script object.

Parameters:

• jvm – the java virtual machine object

• var_name – the variable name the data should get in java

• prepared_script – the prepared script

sin() → Scalar

Calculate sin.

Returns:

Scalar representing operation

sinh() → Scalar

Calculate sin.

Returns:

Scalar representing operation

tan() → Scalar

Calculate tan.

Returns:

Scalar representing operation

tanh() → Scalar

Calculate tan.

Returns:

Scalar representing operation

to_string(**kwargs: Dict[str, DAGNode | str | int | float | bool]) → Scalar

Converts the input to a string representation. :return: Scalar containing the string.