Class swarmauri_standard.pseudometrics.ZeroPseudometric.ZeroPseudometric

swarmauri_standard.pseudometrics.ZeroPseudometric.ZeroPseudometric

Bases: PseudometricBase

Trivial pseudometric that always returns 0 regardless of input.

This pseudometric assigns zero distance between all points, making it a valid mathematical structure that satisfies all pseudometric axioms: 1. Non-negativity: d(x,y) = 0 ≥ 0 2. Symmetry: d(x,y) = 0 = d(y,x) 3. Triangle inequality: d(x,z) = 0 ≤ d(x,y) + d(y,z) = 0 + 0 = 0

While mathematically valid, this pseudometric doesn't provide any meaningful discrimination between different inputs.

Attributes

type : Literal["ZeroPseudometric"] The type identifier for this class

type class-attribute instance-attribute

type = 'ZeroPseudometric'

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

resource = Field(default=PSEUDOMETRIC.value)

version class-attribute instance-attribute

version = '0.1.0'

distance

distance(x, y)

Calculate the pseudometric distance between two objects, always returning 0.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object (not used in calculation) y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object (not used in calculation)

Returns

float Always returns 0.0

Source code in swarmauri_standard/pseudometrics/ZeroPseudometric.py

def distance(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> float:
    """
    Calculate the pseudometric distance between two objects, always returning 0.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object (not used in calculation)
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object (not used in calculation)

    Returns
    -------
    float
        Always returns 0.0
    """
    logger.debug(
        f"Computing ZeroPseudometric distance between objects of types {type(x)} and {type(y)}"
    )
    return 0.0

distances

distances(xs, ys)

Calculate the pairwise distances between two collections of objects.

For ZeroPseudometric, this creates a matrix of zeros with dimensions len(xs) × len(ys).

Parameters

xs : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]] The first collection of objects ys : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]] The second collection of objects

Returns

List[List[float]] A matrix of zeros with dimensions len(xs) × len(ys)

Source code in swarmauri_standard/pseudometrics/ZeroPseudometric.py

def distances(
    self,
    xs: Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]],
    ys: Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]],
) -> List[List[float]]:
    """
    Calculate the pairwise distances between two collections of objects.

    For ZeroPseudometric, this creates a matrix of zeros with dimensions
    len(xs) × len(ys).

    Parameters
    ----------
    xs : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]]
        The first collection of objects
    ys : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]]
        The second collection of objects

    Returns
    -------
    List[List[float]]
        A matrix of zeros with dimensions len(xs) × len(ys)
    """
    logger.debug(
        f"Computing pairwise ZeroPseudometric distances between {len(xs)} and {len(ys)} objects"
    )
    # Create a matrix of zeros with dimensions len(xs) × len(ys)
    return [[0.0 for _ in range(len(ys))] for _ in range(len(xs))]

check_non_negativity

check_non_negativity(x, y)

Check if the distance function satisfies the non-negativity property.

For ZeroPseudometric, this is always true since 0 ≥ 0.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object (not used in calculation) y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object (not used in calculation)

Returns

bool Always returns True

Source code in swarmauri_standard/pseudometrics/ZeroPseudometric.py

def check_non_negativity(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> bool:
    """
    Check if the distance function satisfies the non-negativity property.

    For ZeroPseudometric, this is always true since 0 ≥ 0.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object (not used in calculation)
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object (not used in calculation)

    Returns
    -------
    bool
        Always returns True
    """
    logger.debug(
        f"Checking non-negativity for ZeroPseudometric with objects of types {type(x)} and {type(y)}"
    )
    # Non-negativity is always satisfied since 0 ≥ 0
    return True

check_symmetry

check_symmetry(x, y, tolerance=1e-10)

Check if the distance function satisfies the symmetry property.

For ZeroPseudometric, this is always true since d(x,y) = 0 = d(y,x).

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object (not used in calculation) y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object (not used in calculation) tolerance : float, optional The tolerance for floating-point comparisons, by default 1e-10 (not used)

Returns

bool Always returns True

Source code in swarmauri_standard/pseudometrics/ZeroPseudometric.py

def check_symmetry(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    tolerance: float = 1e-10,
) -> bool:
    """
    Check if the distance function satisfies the symmetry property.

    For ZeroPseudometric, this is always true since d(x,y) = 0 = d(y,x).

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object (not used in calculation)
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object (not used in calculation)
    tolerance : float, optional
        The tolerance for floating-point comparisons, by default 1e-10 (not used)

    Returns
    -------
    bool
        Always returns True
    """
    logger.debug(
        f"Checking symmetry for ZeroPseudometric with objects of types {type(x)} and {type(y)}"
    )
    # Symmetry is always satisfied since d(x,y) = 0 = d(y,x)
    return True

check_triangle_inequality

check_triangle_inequality(x, y, z, tolerance=1e-10)

Check if the distance function satisfies the triangle inequality.

For ZeroPseudometric, this is always true since: d(x,z) = 0 ≤ d(x,y) + d(y,z) = 0 + 0 = 0

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object (not used in calculation) y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object (not used in calculation) z : Union[VectorType, MatrixType, Sequence[T], str, Callable] The third object (not used in calculation) tolerance : float, optional The tolerance for floating-point comparisons, by default 1e-10 (not used)

Returns

bool Always returns True

Source code in swarmauri_standard/pseudometrics/ZeroPseudometric.py

def check_triangle_inequality(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    z: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    tolerance: float = 1e-10,
) -> bool:
    """
    Check if the distance function satisfies the triangle inequality.

    For ZeroPseudometric, this is always true since:
    d(x,z) = 0 ≤ d(x,y) + d(y,z) = 0 + 0 = 0

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object (not used in calculation)
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object (not used in calculation)
    z : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The third object (not used in calculation)
    tolerance : float, optional
        The tolerance for floating-point comparisons, by default 1e-10 (not used)

    Returns
    -------
    bool
        Always returns True
    """
    logger.debug(
        f"Checking triangle inequality for ZeroPseudometric with objects of types {type(x)}, {type(y)}, and {type(z)}"
    )
    # Triangle inequality is always satisfied since d(x,z) = 0 ≤ d(x,y) + d(y,z) = 0 + 0 = 0
    return True

check_weak_identity

check_weak_identity(x, y)

Check if the distance function satisfies the weak identity property.

For ZeroPseudometric, this is always true since d(x,y) = 0 for all x and y, which is consistent with the definition of a pseudometric (allowing d(x,y) = 0 when x ≠ y).

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object (not used in calculation) y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object (not used in calculation)

Returns

bool Always returns True

Source code in swarmauri_standard/pseudometrics/ZeroPseudometric.py

def check_weak_identity(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> bool:
    """
    Check if the distance function satisfies the weak identity property.

    For ZeroPseudometric, this is always true since d(x,y) = 0 for all x and y,
    which is consistent with the definition of a pseudometric (allowing d(x,y) = 0 when x ≠ y).

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object (not used in calculation)
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object (not used in calculation)

    Returns
    -------
    bool
        Always returns True
    """
    logger.debug(
        f"Checking weak identity for ZeroPseudometric with objects of types {type(x)} and {type(y)}"
    )
    # Weak identity is always satisfied since d(x,y) = 0 for all x and y
    return True

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger