Class swarmauri_standard.pseudometrics.LpPseudometric.LpPseudometric

swarmauri_standard.pseudometrics.LpPseudometric.LpPseudometric

LpPseudometric(
    p=2.0, domain=None, coordinates=None, epsilon=1e-10
)

Bases: PseudometricBase

Lp-style pseudometric without point separation.

This class defines a pseudometric over function space using Lp integration, possibly on a subset of coordinates or domain. It allows for calculating distances between functions or vectors based on the Lp norm of their differences, but may not distinguish between all distinct inputs.

Attributes

type : Literal["LpPseudometric"] Type identifier for this component p : float The parameter p for the Lp pseudometric (must be in [1, ∞]) domain : Optional[Tuple[float, float]] The domain interval [a, b] to integrate over coordinates : Optional[List[int]] Specific coordinates to include in the distance calculation epsilon : float Small value used for numerical stability

Initialize an Lp pseudometric.

Parameters

p : float, optional The parameter p for the Lp pseudometric, by default 2.0 domain : Optional[Tuple[float, float]], optional The domain interval [a, b] to integrate over, by default None coordinates : Optional[List[int]], optional Specific coordinates to include in the distance calculation, by default None epsilon : float, optional Small value used for numerical stability, by default 1e-10

Raises

ValueError If p is not in the range [1, ∞] ValueError If domain is specified but not a valid interval ValueError If coordinates contains negative indices

Source code in swarmauri_standard/pseudometrics/LpPseudometric.py

def __init__(
    self,
    p: float = 2.0,
    domain: Optional[Tuple[float, float]] = None,
    coordinates: Optional[List[int]] = None,
    epsilon: float = 1e-10,
):
    """
    Initialize an Lp pseudometric.

    Parameters
    ----------
    p : float, optional
        The parameter p for the Lp pseudometric, by default 2.0
    domain : Optional[Tuple[float, float]], optional
        The domain interval [a, b] to integrate over, by default None
    coordinates : Optional[List[int]], optional
        Specific coordinates to include in the distance calculation, by default None
    epsilon : float, optional
        Small value used for numerical stability, by default 1e-10

    Raises
    ------
    ValueError
        If p is not in the range [1, ∞]
    ValueError
        If domain is specified but not a valid interval
    ValueError
        If coordinates contains negative indices
    """
    super().__init__()

    # Validate p parameter
    if p < 1:
        raise ValueError(f"Parameter p must be at least 1, got {p}")

    # Validate domain if provided
    if domain is not None:
        if len(domain) != 2 or domain[0] >= domain[1]:
            raise ValueError(
                f"Domain must be a valid interval [a, b] where a < b, got {domain}"
            )

    # Validate coordinates if provided
    if coordinates is not None:
        if any(c < 0 for c in coordinates):
            raise ValueError("Coordinates must contain non-negative indices")

    self.p = p
    self.domain = domain
    self.coordinates = coordinates
    self.epsilon = epsilon

    logger.info(
        f"Initialized LpPseudometric with p={p}, domain={domain}, coordinates={coordinates}"
    )

type class-attribute instance-attribute

type = 'LpPseudometric'

p instance-attribute

p = p

domain instance-attribute

domain = domain

coordinates instance-attribute

coordinates = coordinates

epsilon instance-attribute

epsilon = epsilon

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 Lp pseudometric distance between two objects.

Parameters

x : InputType The first object y : InputType The second object

Returns

float The Lp pseudometric distance between x and y

Raises

TypeError If inputs are of incompatible types ValueError If inputs have incompatible dimensions

Source code in swarmauri_standard/pseudometrics/LpPseudometric.py

def distance(self, x: InputType, y: InputType) -> float:
    """
    Calculate the Lp pseudometric distance between two objects.

    Parameters
    ----------
    x : InputType
        The first object
    y : InputType
        The second object

    Returns
    -------
    float
        The Lp pseudometric distance between x and y

    Raises
    ------
    TypeError
        If inputs are of incompatible types
    ValueError
        If inputs have incompatible dimensions
    """
    logger.debug(
        f"Calculating Lp pseudometric distance with p={self.p} between inputs of types {type(x)} and {type(y)}"
    )

    try:
        x_arr = self._convert_to_array(x)
        y_arr = self._convert_to_array(y)

        # Check if dimensions are compatible
        if _NP_AVAILABLE and hasattr(x_arr, "shape") and hasattr(y_arr, "shape"):
            if x_arr.shape != y_arr.shape:
                raise ValueError(
                    f"Inputs must have the same shape: {x_arr.shape} vs {y_arr.shape}"
                )
        else:  # pragma: no cover - numpy fallback
            if isinstance(x_arr[0], (list, tuple)) != isinstance(
                y_arr[0], (list, tuple)
            ):
                raise ValueError("Inputs must have the same shape")
            if isinstance(x_arr[0], (list, tuple)):
                if len(x_arr) != len(y_arr) or any(
                    len(a) != len(b) for a, b in zip(x_arr, y_arr)
                ):
                    raise ValueError("Inputs must have the same shape")
            else:
                if len(x_arr) != len(y_arr):
                    raise ValueError("Inputs must have the same shape")

        # Filter coordinates if specified
        if self.coordinates is not None:
            x_arr = self._filter_coordinates(x_arr)
            y_arr = self._filter_coordinates(y_arr)

        # Calculate the difference
        if _NP_AVAILABLE and hasattr(x_arr, "__sub__"):
            diff = np.abs(x_arr - y_arr)
        else:  # pragma: no cover - numpy fallback
            diff = [abs(a - b) for a, b in zip(x_arr, y_arr)]

        # Apply normalization factor for callable inputs (domain integration)
        scaling_factor = 1.0
        if callable(x) and callable(y) and self.domain is not None:
            # For continuous functions, we need to scale by domain width
            domain_width = self.domain[1] - self.domain[0]
            # The scaling depends on number of sample points (default is 100)
            scaling_factor = domain_width / len(diff)

        # Handle special cases for common p values with scaling
        if math.isclose(self.p, 1.0):
            total = (
                np.sum(diff * scaling_factor)
                if _NP_AVAILABLE
                else sum(d * scaling_factor for d in diff)
            )
            return float(total)
        elif math.isclose(self.p, 2.0):
            total = (
                np.sum((diff**2) * scaling_factor)
                if _NP_AVAILABLE
                else sum((d**2) * scaling_factor for d in diff)
            )
            return float(np.sqrt(total) if _NP_AVAILABLE else math.sqrt(total))
        elif (_NP_AVAILABLE and np.isinf(self.p)) or (
            not _NP_AVAILABLE and math.isinf(self.p)
        ):
            return float(np.max(diff) if _NP_AVAILABLE else max(diff))
        else:
            total = (
                np.sum((diff**self.p) * scaling_factor)
                if _NP_AVAILABLE
                else sum((d**self.p) * scaling_factor for d in diff)
            )
            return float((total) ** (1.0 / self.p))

    except Exception as e:
        logger.error(f"Error calculating Lp pseudometric distance: {str(e)}")
        raise

distances

distances(xs, ys)

Calculate the pairwise distances between two collections of objects.

Parameters

xs : Sequence[InputType] The first collection of objects ys : Sequence[InputType] The second collection of objects

Returns

List[List[float]] A matrix of distances where distances[i][j] is the distance between xs[i] and ys[j]

Raises

TypeError If inputs contain incompatible types ValueError If inputs have incompatible dimensions

Source code in swarmauri_standard/pseudometrics/LpPseudometric.py

def distances(
    self, xs: Sequence[InputType], ys: Sequence[InputType]
) -> List[List[float]]:
    """
    Calculate the pairwise distances between two collections of objects.

    Parameters
    ----------
    xs : Sequence[InputType]
        The first collection of objects
    ys : Sequence[InputType]
        The second collection of objects

    Returns
    -------
    List[List[float]]
        A matrix of distances where distances[i][j] is the distance between xs[i] and ys[j]

    Raises
    ------
    TypeError
        If inputs contain incompatible types
    ValueError
        If inputs have incompatible dimensions
    """
    logger.debug(
        f"Calculating pairwise Lp pseudometric distances between {len(xs)} and {len(ys)} objects"
    )

    result = []
    for i, x in enumerate(xs):
        row = []
        for j, y in enumerate(ys):
            try:
                row.append(self.distance(x, y))
            except Exception as e:
                logger.error(
                    f"Error calculating distance between xs[{i}] and ys[{j}]: {str(e)}"
                )
                raise
        result.append(row)

    return result

check_non_negativity

check_non_negativity(x, y)

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

Parameters

x : InputType The first object y : InputType The second object

Returns

bool True if d(x,y) ≥ 0, False otherwise

Source code in swarmauri_standard/pseudometrics/LpPseudometric.py

def check_non_negativity(self, x: InputType, y: InputType) -> bool:
    """
    Check if the distance function satisfies the non-negativity property.

    Parameters
    ----------
    x : InputType
        The first object
    y : InputType
        The second object

    Returns
    -------
    bool
        True if d(x,y) ≥ 0, False otherwise
    """
    try:
        dist = self.distance(x, y)
        return dist >= 0
    except Exception as e:
        logger.error(f"Error checking non-negativity: {str(e)}")
        return False

check_symmetry

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

Check if the distance function satisfies the symmetry property.

Parameters

x : InputType The first object y : InputType The second object tolerance : float, optional The tolerance for floating-point comparisons, by default 1e-10

Returns

bool True if d(x,y) = d(y,x) within tolerance, False otherwise

Source code in swarmauri_standard/pseudometrics/LpPseudometric.py

def check_symmetry(
    self, x: InputType, y: InputType, tolerance: float = 1e-10
) -> bool:
    """
    Check if the distance function satisfies the symmetry property.

    Parameters
    ----------
    x : InputType
        The first object
    y : InputType
        The second object
    tolerance : float, optional
        The tolerance for floating-point comparisons, by default 1e-10

    Returns
    -------
    bool
        True if d(x,y) = d(y,x) within tolerance, False otherwise
    """
    try:
        dist_xy = self.distance(x, y)
        dist_yx = self.distance(y, x)
        return abs(dist_xy - dist_yx) <= tolerance
    except Exception as e:
        logger.error(f"Error checking symmetry: {str(e)}")
        return False

check_triangle_inequality

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

Check if the distance function satisfies the triangle inequality.

Parameters

x : InputType The first object y : InputType The second object z : InputType The third object tolerance : float, optional The tolerance for floating-point comparisons, by default 1e-10

Returns

bool True if d(x,z) ≤ d(x,y) + d(y,z) within tolerance, False otherwise

Source code in swarmauri_standard/pseudometrics/LpPseudometric.py

def check_triangle_inequality(
    self, x: InputType, y: InputType, z: InputType, tolerance: float = 1e-10
) -> bool:
    """
    Check if the distance function satisfies the triangle inequality.

    Parameters
    ----------
    x : InputType
        The first object
    y : InputType
        The second object
    z : InputType
        The third object
    tolerance : float, optional
        The tolerance for floating-point comparisons, by default 1e-10

    Returns
    -------
    bool
        True if d(x,z) ≤ d(x,y) + d(y,z) within tolerance, False otherwise
    """
    try:
        dist_xz = self.distance(x, z)
        dist_xy = self.distance(x, y)
        dist_yz = self.distance(y, z)

        # Account for floating-point errors with tolerance
        return dist_xz <= dist_xy + dist_yz + tolerance
    except Exception as e:
        logger.error(f"Error checking triangle inequality: {str(e)}")
        return False

check_weak_identity

check_weak_identity(x, y)

Check if the distance function satisfies the weak identity property.

In a pseudometric, d(x,y) = 0 is allowed even when x ≠ y. This method verifies that this property is properly handled.

Parameters

x : InputType The first object y : InputType The second object

Returns

bool True if the pseudometric properly handles the weak identity property

Source code in swarmauri_standard/pseudometrics/LpPseudometric.py

def check_weak_identity(self, x: InputType, y: InputType) -> bool:
    """
    Check if the distance function satisfies the weak identity property.

    In a pseudometric, d(x,y) = 0 is allowed even when x ≠ y.
    This method verifies that this property is properly handled.

    Parameters
    ----------
    x : InputType
        The first object
    y : InputType
        The second object

    Returns
    -------
    bool
        True if the pseudometric properly handles the weak identity property
    """
    # For Lp pseudometric, weak identity is satisfied when:
    # 1. d(x,x) = 0 for any x
    # 2. If objects differ only outside the measured domain/coordinates, d(x,y) = 0

    try:
        # Check if d(x,x) = 0
        if not math.isclose(self.distance(x, x), 0, abs_tol=self.epsilon):
            return False

        # Check if d(y,y) = 0
        if not math.isclose(self.distance(y, y), 0, abs_tol=self.epsilon):
            return False

        # If d(x,y) = 0, then the weak identity property is satisfied
        # for these particular x and y
        if math.isclose(self.distance(x, y), 0, abs_tol=self.epsilon):
            return True

        # If we're only measuring certain coordinates and the objects are
        # identical in those coordinates but different elsewhere, d(x,y) should be 0
        x_arr = self._convert_to_array(x)
        y_arr = self._convert_to_array(y)

        if self.coordinates is not None:
            x_filtered = self._filter_coordinates(x_arr)
            y_filtered = self._filter_coordinates(y_arr)

            # If the filtered arrays are equal but the original arrays are not,
            # then we have a case of weak identity
            return np.array_equal(x_filtered, y_filtered) and not np.array_equal(
                x_arr, y_arr
            )

        # If we have a domain restriction for callable functions,
        # we can't easily check this property in the general case

        return False
    except Exception as e:
        logger.error(f"Error checking weak identity: {str(e)}")
        return False

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