Class swarmauri_standard.metrics.LpMetric.LpMetric

swarmauri_standard.metrics.LpMetric.LpMetric

Bases: MetricBase

Lp metric implementation for measuring distances between points.

This class implements the Lp metric (Minkowski distance) which is a generalization of the Euclidean, Manhattan, and Chebyshev distances. The Lp distance between two points x and y is defined as (sum(|x_i - y_i|^p))^(1/p) for p > 1.

Attributes

type : Literal["LpMetric"] The type identifier for this metric. p : floats The parameter p for the Lp metric. Must be finite and greater than 1. resource : str, optional The resource type, defaults to METRIC.

type class-attribute instance-attribute

type = 'LpMetric'

p class-attribute instance-attribute

p = Field(
    ...,
    description="Parameter p for the Lp metric (must be > 1)",
)

resource class-attribute instance-attribute

resource = Field(default=METRIC.value)

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

version class-attribute instance-attribute

version = '0.1.0'

validate_p

validate_p(v)

Validate that p is greater than 1 and finite.

Parameters

v : float The value to validate.

Returns

float The validated value.

Raises

ValueError If p is not greater than 1 or is not finite.

Source code in swarmauri_standard/metrics/LpMetric.py

@field_validator("p")
def validate_p(cls, v):
    """
    Validate that p is greater than 1 and finite.

    Parameters
    ----------
    v : float
        The value to validate.

    Returns
    -------
    float
        The validated value.

    Raises
    ------
    ValueError
        If p is not greater than 1 or is not finite.
    """
    if v <= 1:
        raise ValueError(f"Parameter p must be greater than 1, got {v}")
    if not np.isfinite(v):
        raise ValueError(f"Parameter p must be finite, got {v}")
    return v

distance

distance(x, y)

Calculate the Lp distance between two points.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

float The Lp distance between x and y

Raises

ValueError If inputs are incompatible with the metric TypeError If input types are not supported

Source code in swarmauri_standard/metrics/LpMetric.py

def distance(self, x: MetricInput, y: MetricInput) -> float:
    """
    Calculate the Lp distance between two points.

    Parameters
    ----------
    x : MetricInput
        First point
    y : MetricInput
        Second point

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

    Raises
    ------
    ValueError
        If inputs are incompatible with the metric
    TypeError
        If input types are not supported
    """
    try:
        # Convert inputs to numpy arrays
        x_array = self._convert_to_array(x)
        y_array = self._convert_to_array(y)

        # Ensure arrays have the same shape
        if x_array.shape != y_array.shape:
            raise ValueError(
                f"Inputs must have the same shape. Got {x_array.shape} and {y_array.shape}"
            )

        # Calculate Lp distance: (sum(|x_i - y_i|^p))^(1/p)
        distance_value = np.sum(np.abs(x_array - y_array) ** self.p) ** (1 / self.p)

        logger.debug(f"Calculated Lp distance with p={self.p}: {distance_value}")
        return float(distance_value)
    except Exception as e:
        logger.error(f"Error calculating Lp distance: {str(e)}")
        raise

distances

distances(x, y)

Calculate Lp distances between collections of points.

Parameters

x : Union[MetricInput, MetricInputCollection] First collection of points y : Union[MetricInput, MetricInputCollection] Second collection of points

Returns

Union[List[float], IVector, IMatrix] Matrix or vector of distances between points in x and y

Raises

ValueError If inputs are incompatible with the metric TypeError If input types are not supported

Source code in swarmauri_standard/metrics/LpMetric.py

def distances(
    self,
    x: Union[MetricInput, MetricInputCollection],
    y: Union[MetricInput, MetricInputCollection],
) -> Union[List[float], IVector, IMatrix]:
    """
    Calculate Lp distances between collections of points.

    Parameters
    ----------
    x : Union[MetricInput, MetricInputCollection]
        First collection of points
    y : Union[MetricInput, MetricInputCollection]
        Second collection of points

    Returns
    -------
    Union[List[float], IVector, IMatrix]
        Matrix or vector of distances between points in x and y

    Raises
    ------
    ValueError
        If inputs are incompatible with the metric
    TypeError
        If input types are not supported
    """
    try:
        # Handle different types of collections
        if isinstance(x, List) and isinstance(y, List):
            # Calculate pairwise distances between lists of points
            result = []
            for xi in x:
                row = []
                for yi in y:
                    row.append(self.distance(xi, yi))
                result.append(row)

            # If one of the inputs is a single point, return a flat list
            if len(x) == 1:
                return result[0]
            elif len(y) == 1:
                return [row[0] for row in result]
            else:
                return result

        elif isinstance(x, IVector) and isinstance(y, IVector):
            # Calculate distance between two vectors
            return self.distance(x, y)

        elif isinstance(x, IMatrix) and isinstance(y, IMatrix):
            # Calculate element-wise distances between matrices
            x_array = x.to_array()
            y_array = y.to_array()

            if x_array.shape != y_array.shape:
                raise ValueError(
                    f"Matrices must have the same shape. Got {x_array.shape} and {y_array.shape}"
                )

            # Calculate element-wise distances
            distances_array = np.sum(
                np.abs(x_array - y_array) ** self.p, axis=-1
            ) ** (1 / self.p)

            # Convert back to appropriate type (assuming matrix implementation has from_array method)
            # This is a simplification - actual implementation depends on the IMatrix implementation
            return distances_array.tolist()

        else:
            # Handle generic case - try to compute distance directly
            return self.distance(x, y)

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

check_non_negativity

check_non_negativity(x, y)

Check if the Lp metric satisfies the non-negativity axiom: d(x,y) ≥ 0.

The Lp metric is always non-negative by definition.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/LpMetric.py

def check_non_negativity(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the Lp metric satisfies the non-negativity axiom: d(x,y) ≥ 0.

    The Lp metric is always non-negative by definition.

    Parameters
    ----------
    x : MetricInput
        First point
    y : MetricInput
        Second point

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    try:
        distance_value = self.distance(x, y)
        result = distance_value >= 0
        logger.debug(f"Non-negativity check result: {result}")
        return result
    except Exception as e:
        logger.error(f"Error in non-negativity check: {str(e)}")
        return False

check_identity_of_indiscernibles

check_identity_of_indiscernibles(x, y)

Check if the Lp metric satisfies the identity of indiscernibles axiom: d(x,y) = 0 if and only if x = y.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/LpMetric.py

def check_identity_of_indiscernibles(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the Lp metric satisfies the identity of indiscernibles axiom:
    d(x,y) = 0 if and only if x = y.

    Parameters
    ----------
    x : MetricInput
        First point
    y : MetricInput
        Second point

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    try:
        distance_value = self.distance(x, y)

        # Convert inputs to numpy arrays for comparison
        x_array = self._convert_to_array(x)
        y_array = self._convert_to_array(y)

        # Check if x and y are equal (all elements are equal)
        are_equal = np.array_equal(x_array, y_array)
        distance_is_zero = np.isclose(distance_value, 0)

        # The axiom is satisfied if:
        # 1. x = y and d(x,y) = 0, or
        # 2. x ≠ y and d(x,y) > 0
        result = (are_equal and distance_is_zero) or (
            not are_equal and not distance_is_zero
        )

        logger.debug(f"Identity of indiscernibles check result: {result}")
        return result
    except Exception as e:
        logger.error(f"Error in identity of indiscernibles check: {str(e)}")
        return False

check_symmetry

check_symmetry(x, y)

Check if the Lp metric satisfies the symmetry axiom: d(x,y) = d(y,x).

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/LpMetric.py

def check_symmetry(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the Lp metric satisfies the symmetry axiom: d(x,y) = d(y,x).

    Parameters
    ----------
    x : MetricInput
        First point
    y : MetricInput
        Second point

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    try:
        distance_xy = self.distance(x, y)
        distance_yx = self.distance(y, x)

        # Check if distances are equal
        result = np.isclose(distance_xy, distance_yx)

        logger.debug(f"Symmetry check result: {result}")
        return result
    except Exception as e:
        logger.error(f"Error in symmetry check: {str(e)}")
        return False

check_triangle_inequality

check_triangle_inequality(x, y, z)

Check if the Lp metric satisfies the triangle inequality axiom: d(x,z) ≤ d(x,y) + d(y,z).

Parameters

x : MetricInput First point y : MetricInput Second point z : MetricInput Third point

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/LpMetric.py

def check_triangle_inequality(
    self, x: MetricInput, y: MetricInput, z: MetricInput
) -> bool:
    """
    Check if the Lp metric satisfies the triangle inequality axiom:
    d(x,z) ≤ d(x,y) + d(y,z).

    Parameters
    ----------
    x : MetricInput
        First point
    y : MetricInput
        Second point
    z : MetricInput
        Third point

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    try:
        distance_xy = self.distance(x, y)
        distance_yz = self.distance(y, z)
        distance_xz = self.distance(x, z)

        # Check if triangle inequality holds
        # Adding small epsilon for numerical stability
        result = distance_xz <= distance_xy + distance_yz + 1e-10

        logger.debug(f"Triangle inequality check result: {result}")
        return result
    except Exception as e:
        logger.error(f"Error in triangle inequality check: {str(e)}")
        return False

get_norm

get_norm()

Get the corresponding Lp norm for this metric.

Returns

GeneralLpNorm The Lp norm with the same p parameter

Source code in swarmauri_standard/metrics/LpMetric.py

def get_norm(self) -> GeneralLpNorm:
    """
    Get the corresponding Lp norm for this metric.

    Returns
    -------
    GeneralLpNorm
        The Lp norm with the same p parameter
    """
    return GeneralLpNorm(p=self.p)

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