Class swarmauri_standard.metrics.EuclideanMetric.EuclideanMetric

swarmauri_standard.metrics.EuclideanMetric.EuclideanMetric

Bases: MetricBase

Euclidean metric (L2 distance) implementation.

This class implements the standard Euclidean distance metric, which is the straight-line distance between two points in Euclidean space, computed as the square root of the sum of the squared differences between corresponding coordinates.

The Euclidean distance satisfies all metric axioms: - Non-negativity: d(x,y) ≥ 0 - Identity of indiscernibles: d(x,y) = 0 if and only if x = y - Symmetry: d(x,y) = d(y,x) - Triangle inequality: d(x,z) ≤ d(x,y) + d(y,z)

Attributes

type : Literal["EuclideanMetric"] The specific type of metric. resource : str, optional The resource type, defaults to METRIC.

type class-attribute instance-attribute

type = 'EuclideanMetric'

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 = METRIC.value

version class-attribute instance-attribute

version = '0.1.0'

distance

distance(x, y)

Calculate the Euclidean distance between two points.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

float The Euclidean distance between x and y

Raises

ValueError If inputs have different dimensions or are incompatible TypeError If input types are not supported

Source code in swarmauri_standard/metrics/EuclideanMetric.py

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

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

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

    Raises
    ------
    ValueError
        If inputs have different dimensions or are incompatible
    TypeError
        If input types are not supported
    """
    logger.debug(f"Calculating Euclidean distance between {x} and {y}")

    # Handle different input types
    if isinstance(x, IVector) and isinstance(y, IVector):
        # For vector objects
        if len(x) != len(y):
            raise ValueError(
                f"Vectors must have the same dimension: {len(x)} != {len(y)}"
            )

        # Get numeric values from vectors
        x_values = x.to_numpy()
        y_values = y.to_numpy()

        # Calculate Euclidean distance
        return math.sqrt(
            sum((x_i - y_i) ** 2 for x_i, y_i in zip(x_values, y_values))
        )

    elif (
        isinstance(x, Sequence)
        and isinstance(y, Sequence)
        and not isinstance(x, str)
        and not isinstance(y, str)
    ):
        # For general sequences (lists, tuples, etc.)
        if len(x) != len(y):
            raise ValueError(
                f"Sequences must have the same length: {len(x)} != {len(y)}"
            )

        try:
            return math.sqrt(sum((x_i - y_i) ** 2 for x_i, y_i in zip(x, y)))
        except (TypeError, ValueError) as e:
            logger.error(f"Failed to compute Euclidean distance for sequences: {e}")
            raise ValueError(
                f"Cannot compute Euclidean distance for sequences with non-numeric elements: {e}"
            )

    else:
        logger.error(
            f"Unsupported input types for Euclidean distance: {type(x)} and {type(y)}"
        )
        raise TypeError(
            f"Euclidean distance computation not supported for types {type(x)} and {type(y)}"
        )

distances

distances(x, y)

Calculate Euclidean 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 Euclidean distances between points in x and y

Raises

ValueError If inputs are incompatible TypeError If input types are not supported

Source code in swarmauri_standard/metrics/EuclideanMetric.py

def distances(
    self,
    x: Union[MetricInput, MetricInputCollection],
    y: Union[MetricInput, MetricInputCollection],
) -> Union[List[float], IVector, IMatrix]:
    """
    Calculate Euclidean 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 Euclidean distances between points in x and y

    Raises
    ------
    ValueError
        If inputs are incompatible
    TypeError
        If input types are not supported
    """
    logger.debug("Calculating Euclidean distances between collections")

    # Handle different collection types
    if isinstance(x, IMatrix) and isinstance(y, IMatrix):
        # For matrix objects - compute pairwise distances between rows
        if x.shape[1] != y.shape[1]:
            raise ValueError(
                f"Points must have the same dimension: {x.shape[1]} != {y.shape[1]}"
            )

        # Create distance matrix
        result = [[self.distance(x_row, y_row) for y_row in y] for x_row in x]
        return result

    elif (
        isinstance(x, list)
        and isinstance(y, list)
        and all(isinstance(item, (list, tuple)) for item in x)
        and all(isinstance(item, (list, tuple)) for item in y)
    ):
        # For lists of lists/tuples (representing collections of points)

        # Check if all points have the same dimension
        x_dims = [len(point) for point in x]
        y_dims = [len(point) for point in y]

        if len(set(x_dims + y_dims)) != 1:
            raise ValueError("All points must have the same dimension")

        # Compute pairwise distances
        result = [
            [self.distance(x_point, y_point) for y_point in y] for x_point in x
        ]
        return result

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

    elif isinstance(x, list) and isinstance(y, list):
        # If x and y are simple lists (not lists of lists), treat them as individual points
        if not any(isinstance(item, (list, tuple)) for item in x + y):
            return [self.distance(x, y)]
        else:
            logger.error("Inconsistent collection structure")
            raise ValueError("Inconsistent collection structure")

    else:
        logger.error(f"Unsupported collection types: {type(x)} and {type(y)}")
        raise TypeError(
            f"Euclidean distances computation not supported for types {type(x)} and {type(y)}"
        )

check_non_negativity

check_non_negativity(x, y)

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

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True if the axiom is satisfied, which is always the case for Euclidean distance

Source code in swarmauri_standard/metrics/EuclideanMetric.py

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

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

    Returns
    -------
    bool
        True if the axiom is satisfied, which is always the case for Euclidean distance
    """
    try:
        dist = self.distance(x, y)
        logger.debug(f"Checking non-negativity axiom: distance = {dist}")
        return dist >= 0  # Euclidean distance is always non-negative
    except (TypeError, ValueError) as e:
        logger.error(f"Error checking non-negativity: {e}")
        return False

check_identity_of_indiscernibles

check_identity_of_indiscernibles(x, y)

Check if the Euclidean 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

Source code in swarmauri_standard/metrics/EuclideanMetric.py

def check_identity_of_indiscernibles(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the Euclidean 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
    """
    try:
        dist = self.distance(x, y)

        # Check if distance is zero
        is_zero_dist = (
            abs(dist) < 1e-10
        )  # Using small epsilon for floating point comparison

        # Check if points are equal
        if isinstance(x, IVector) and isinstance(y, IVector):
            is_equal = len(x) == len(y) and all(
                abs(x_i - y_i) < 1e-10 for x_i, y_i in zip(x, y)
            )
        elif isinstance(x, Sequence) and isinstance(y, Sequence):
            is_equal = len(x) == len(y) and all(
                abs(x_i - y_i) < 1e-10 for x_i, y_i in zip(x, y)
            )
        else:
            is_equal = x == y

        logger.debug(
            f"Checking identity axiom: distance = {dist}, points equal: {is_equal}"
        )

        # Axiom is satisfied if distance is zero iff points are equal
        return (is_zero_dist and is_equal) or (not is_zero_dist and not is_equal)

    except (TypeError, ValueError) as e:
        logger.error(f"Error checking identity of indiscernibles: {e}")
        return False

check_symmetry

check_symmetry(x, y)

Check if the Euclidean 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

Source code in swarmauri_standard/metrics/EuclideanMetric.py

def check_symmetry(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the Euclidean 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
    """
    try:
        dist_xy = self.distance(x, y)
        dist_yx = self.distance(y, x)

        # Check if the distances are equal (within floating point precision)
        is_symmetric = abs(dist_xy - dist_yx) < 1e-10

        logger.debug(
            f"Checking symmetry axiom: d(x,y) = {dist_xy}, d(y,x) = {dist_yx}, symmetric: {is_symmetric}"
        )
        return is_symmetric

    except (TypeError, ValueError) as e:
        logger.error(f"Error checking symmetry: {e}")
        return False

check_triangle_inequality

check_triangle_inequality(x, y, z)

Check if the Euclidean 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

Source code in swarmauri_standard/metrics/EuclideanMetric.py

def check_triangle_inequality(
    self, x: MetricInput, y: MetricInput, z: MetricInput
) -> bool:
    """
    Check if the Euclidean 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
    """
    try:
        # Calculate the three distances
        dist_xy = self.distance(x, y)
        dist_yz = self.distance(y, z)
        dist_xz = self.distance(x, z)

        # Check triangle inequality
        satisfies_inequality = (
            dist_xz <= dist_xy + dist_yz + 1e-10
        )  # Adding epsilon for floating point precision

        logger.debug(
            f"Checking triangle inequality: d(x,z) = {dist_xz}, d(x,y) + d(y,z) = {dist_xy + dist_yz}, "
            + f"inequality satisfied: {satisfies_inequality}"
        )

        return satisfies_inequality

    except (TypeError, ValueError) as e:
        logger.error(f"Error checking triangle inequality: {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