Class swarmauri_standard.norms.L2EuclideanNorm.L2EuclideanNorm

swarmauri_standard.norms.L2EuclideanNorm.L2EuclideanNorm

Bases: NormBase

Implementation of the Euclidean (L2) norm.

The Euclidean norm computes the square root of the sum of squares of vector components. It is the most commonly used vector norm and represents the standard notion of distance in Euclidean space.

Attributes

type : Literal["L2EuclideanNorm"] The specific type of norm. resource : str, optional The resource type, defaults to NORM.

type class-attribute instance-attribute

type = 'L2EuclideanNorm'

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=NORM.value)

version class-attribute instance-attribute

version = '0.1.0'

compute

compute(x)

Compute the Euclidean (L2) norm of the input.

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The input for which to compute the norm.

Returns

float The computed Euclidean norm value.

Raises

TypeError If the input type is not supported. ValueError If the norm cannot be computed for the given input.

Source code in swarmauri_standard/norms/L2EuclideanNorm.py

def compute(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> float:
    """
    Compute the Euclidean (L2) norm of the input.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The input for which to compute the norm.

    Returns
    -------
    float
        The computed Euclidean norm value.

    Raises
    ------
    TypeError
        If the input type is not supported.
    ValueError
        If the norm cannot be computed for the given input.
    """
    logger.debug(f"Computing L2 Euclidean norm for input of type {type(x)}")

    # Handle different input types
    if isinstance(x, IVector):
        # Use to_numpy() to get numeric values instead of iterating directly
        vector_data = x.to_numpy()
        return math.sqrt(sum(x_i**2 for x_i in vector_data))
    elif isinstance(x, IMatrix):
        # For matrix objects - treat as flattened vector
        return math.sqrt(sum(x_ij**2 for row in x for x_ij in row))
    elif isinstance(x, Sequence) and not isinstance(x, str):
        # For general sequences (lists, tuples, etc.)
        try:
            return math.sqrt(sum(x_i**2 for x_i in x))
        except (TypeError, ValueError) as e:
            logger.error(f"Failed to compute L2 norm for sequence: {e}")
            raise ValueError(
                f"Cannot compute L2 norm for sequence with non-numeric elements: {e}"
            )
    elif isinstance(x, str):
        # For strings - use ASCII/Unicode values
        return math.sqrt(sum(ord(char) ** 2 for char in x))
    elif callable(x):
        # For functions - not a standard operation, but could implement a custom behavior
        raise TypeError("L2 norm computation for callable objects is not supported")
    else:
        logger.error(f"Unsupported input type for L2 norm: {type(x)}")
        raise TypeError(f"L2 norm computation not supported for type {type(x)}")

check_non_negativity

check_non_negativity(x)

Check if the Euclidean norm satisfies the non-negativity property.

The Euclidean norm is always non-negative by definition.

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The input to check.

Returns

bool True if the norm is non-negative, which is always the case for Euclidean norm.

Source code in swarmauri_standard/norms/L2EuclideanNorm.py

def check_non_negativity(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> bool:
    """
    Check if the Euclidean norm satisfies the non-negativity property.

    The Euclidean norm is always non-negative by definition.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The input to check.

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

check_definiteness

check_definiteness(x)

Check if the Euclidean norm satisfies the definiteness property.

The definiteness property states that the norm of x is 0 if and only if x is 0.

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The input to check.

Returns

bool True if the norm satisfies the definiteness property.

Source code in swarmauri_standard/norms/L2EuclideanNorm.py

def check_definiteness(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> bool:
    """
    Check if the Euclidean norm satisfies the definiteness property.

    The definiteness property states that the norm of x is 0 if and only if x is 0.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The input to check.

    Returns
    -------
    bool
        True if the norm satisfies the definiteness property.
    """
    try:
        # Compute the norm
        norm_value = self.compute(x)

        # Check if the norm is zero
        if (
            abs(norm_value) < 1e-10
        ):  # Using a small epsilon for floating-point comparison
            # Check if all elements are zero
            if isinstance(x, IVector) or (
                isinstance(x, Sequence) and not isinstance(x, str)
            ):
                is_zero = all(abs(element) < 1e-10 for element in x)
            elif isinstance(x, IMatrix):
                is_zero = all(abs(element) < 1e-10 for row in x for element in row)
            elif isinstance(x, str):
                is_zero = len(x) == 0
            else:
                logger.warning(
                    f"Definiteness check not fully implemented for type {type(x)}"
                )
                return False

            logger.debug(
                f"Checking definiteness: norm = {norm_value}, all elements zero: {is_zero}"
            )
            return is_zero
        else:
            # If norm is not zero, then the vector is not zero
            return True
    except (TypeError, ValueError) as e:
        logger.error(f"Error checking definiteness: {e}")
        return False

check_triangle_inequality

check_triangle_inequality(x, y)

Check if the Euclidean norm satisfies the triangle inequality.

The triangle inequality states that norm(x + y) <= norm(x) + norm(y).

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The first input. y : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The second input.

Returns

bool True if the norm satisfies the triangle inequality.

Raises

TypeError If the inputs are not of the same type or cannot be added.

Source code in swarmauri_standard/norms/L2EuclideanNorm.py

def check_triangle_inequality(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    y: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
) -> bool:
    """
    Check if the Euclidean norm satisfies the triangle inequality.

    The triangle inequality states that norm(x + y) <= norm(x) + norm(y).

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The first input.
    y : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The second input.

    Returns
    -------
    bool
        True if the norm satisfies the triangle inequality.

    Raises
    ------
    TypeError
        If the inputs are not of the same type or cannot be added.
    """
    try:
        # Check if inputs are of the same type
        if type(x) is not type(y):
            raise TypeError(
                f"Inputs must be of the same type, got {type(x)} and {type(y)}"
            )

        # Compute norms
        norm_x = self.compute(x)
        norm_y = self.compute(y)

        # Handle different input types for addition
        if isinstance(x, IVector):
            # Vector addition
            if len(x) != len(y):
                raise ValueError("Vectors must have the same dimension")
            z = [x[i] + y[i] for i in range(len(x))]
        elif isinstance(x, IMatrix):
            # Matrix addition
            if x.shape != y.shape:
                raise ValueError("Matrices must have the same shape")
            z = [
                [x[i][j] + y[i][j] for j in range(len(x[0]))] for i in range(len(x))
            ]
        elif isinstance(x, Sequence) and not isinstance(x, str):
            # Sequence addition
            if len(x) != len(y):
                raise ValueError("Sequences must have the same length")
            z = [x[i] + y[i] for i in range(len(x))]
        elif isinstance(x, str):
            # String concatenation (not a standard vector operation)
            z = x + y
        else:
            raise TypeError(
                f"Triangle inequality check not supported for type {type(x)}"
            )

        # Compute norm of the sum
        norm_z = self.compute(z)

        # Check triangle inequality
        result = (
            norm_z <= norm_x + norm_y + 1e-10
        )  # Adding small epsilon for floating-point comparison
        logger.debug(
            f"Triangle inequality check: norm(x+y)={norm_z}, norm(x)+norm(y)={norm_x + norm_y}, result={result}"
        )
        return result

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

check_absolute_homogeneity

check_absolute_homogeneity(x, scalar)

Check if the Euclidean norm satisfies the absolute homogeneity property.

The absolute homogeneity property states that norm(ax) = |a|norm(x) for scalar a.

Parameters

x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType] The input. scalar : float The scalar value.

Returns

bool True if the norm satisfies the absolute homogeneity property.

Raises

TypeError If the input cannot be scaled by the scalar.

Source code in swarmauri_standard/norms/L2EuclideanNorm.py

def check_absolute_homogeneity(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    scalar: float,
) -> bool:
    """
    Check if the Euclidean norm satisfies the absolute homogeneity property.

    The absolute homogeneity property states that norm(a*x) = |a|*norm(x) for scalar a.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
        The input.
    scalar : float
        The scalar value.

    Returns
    -------
    bool
        True if the norm satisfies the absolute homogeneity property.

    Raises
    ------
    TypeError
        If the input cannot be scaled by the scalar.
    """
    try:
        # Compute the norm of x
        norm_x = self.compute(x)

        # Scale the input by the scalar
        if isinstance(x, IVector):
            scaled_x = [scalar * x_i for x_i in x]
        elif isinstance(x, IMatrix):
            scaled_x = [
                [scalar * x[i][j] for j in range(len(x[0]))] for i in range(len(x))
            ]
        elif isinstance(x, Sequence) and not isinstance(x, str):
            scaled_x = [scalar * x_i for x_i in x]
        elif isinstance(x, str):
            if not isinstance(scalar, int) or scalar < 0:
                raise TypeError(
                    "String can only be multiplied by non-negative integers"
                )
            scaled_x = x * scalar
        else:
            raise TypeError(
                f"Absolute homogeneity check not supported for type {type(x)}"
            )

        # Compute the norm of the scaled input
        norm_scaled_x = self.compute(scaled_x)

        # Check absolute homogeneity
        expected = abs(scalar) * norm_x
        result = (
            abs(norm_scaled_x - expected) < 1e-10
        )  # Using small epsilon for floating-point comparison

        logger.debug(
            f"Absolute homogeneity check: norm(a*x)={norm_scaled_x}, |a|*norm(x)={expected}, result={result}"
        )
        return result

    except (TypeError, ValueError) as e:
        logger.error(f"Error checking absolute homogeneity: {e}")
        raise

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