Register Bricks

class fugu.bricks.register_bricks.Register(max_size, initial_value=0, name='Register', output_coding='Undefined', register_label=None, single_set=False)

Bases: Brick

Brick that stores the binary encoding of an non-negative integer. This brick also provides simple controls such as “recall”, “clear”, and “set”

build(graph, metadata, control_nodes, input_lists, input_codings)

Build Register brick.

Parameters:

  • graph – networkx graph to define connections of the computational graph

  • metadata (dict) – dictionary to define the shapes and parameters of the brick

  • control_nodes (dict) –

    dictionary of lists of auxillary networkx nodes. Expected keys:

    ’complete’ - A list of neurons that fire when the brick is done

  • input_lists (list) – list of nodes that will contain input

  • input_coding (list) – list of input coding formats. All coding types supported

Returns:

graph of a computational elements and connections begin: dictionary of output parameters (shape, coding, layers, depth, etc) complete: dictionary of control nodes (‘complete’) output_lists (list): list of output self.output_codings (list): list of coding formats of output

Return type:

graph

class fugu.bricks.register_bricks.Max(default_size=-1, name='Max', output_coding='Undefined')

Bases: Brick

Brick that calculates the maximum value of a collection of values stored as binary registers.

build(graph, metadata, control_nodes, input_lists, input_codings)

Build Register brick.

Parameters:

  • graph – networkx graph to define connections of the computational graph

  • metadata (dict) – dictionary to define the shapes and parameters of the brick

  • control_nodes (dict) –

    dictionary of lists of auxillary networkx nodes. Expected keys:

    ’complete’ - A list of neurons that fire when the brick is done

  • input_lists (list) – list of nodes that will contain input

  • input_coding (list) – list of input coding formats. All coding types supported

Returns:

graph of a computational elements and connections begin: dictionary of output parameters (shape, coding, layers, depth, etc) complete: dictionary of control nodes (‘complete’) output_lists: list of output self.output_codings: list of coding formats of output

Return type:

graph

class fugu.bricks.register_bricks.Addition(register_size=5, name='Addition', output_coding='Undefined')

Bases: Brick

Brick that calculates the sum of two values stored as binary

build(graph, metadata, control_nodes, input_lists, input_codings)

Build Addition brick. :param graph: networkx graph to define connections of the computational graph :param metadata: dictionary to define the shapes and parameters of the brick :type metadata: dict :param control_nodes: dictionary of lists of auxillary networkx nodes.

Expected keys:

‘complete’ - A list of neurons that fire when the brick is done

Parameters:

  • input_lists (list) – list of nodes that will contain input

  • input_coding (list) – list of input coding formats. All coding types supported

Returns:

graph of a computational elements and connections begin (dict): dictionary of output parameters (shape, coding, layers, depth, etc) complete (dict): dictionary of control nodes (‘complete’) output_lists (list): list of output self.output_codings (list): list of coding formats of output

Return type:

graph

Raises:

ValueError – Too many inputs! {} can only support two inputs, received: {}

class fugu.bricks.register_bricks.Subtraction(register_size=5, name='Subtraction', output_coding='Undefined')

Bases: CompoundBrick

Brick that calculates the difference of two values stored as binary values. The brick subtracts the second (Y) input from the first (X), i.e. this brick returns the value of X - Y

build(graph, metadata, control_nodes, input_lists, input_codings)

Build Subtraction brick. :param graph: networkx graph to define connections of the computational graph :param metadata: dictionary to define the shapes and parameters of the brick :param control_nodes: dictionary of lists of auxillary networkx nodes.

Expected keys:

‘complete’ - A list of neurons that fire when the brick is done

Parameters:

  • input_lists – list of nodes that will contain input

  • input_coding – list of input coding formats. All coding types supported

Returns:

graph of a computational elements and connections complete: dictionary of control nodes (‘complete’) begin: dictionary of output parameters (shape, coding, layers, depth, etc) output_lists (list): list of output self.output_codings (list): list of coding formats of output

Return type:

graph