Class swarmauri_standard.norms.GeneralLpNorm.GeneralLpNorm

swarmauri_standard.norms.GeneralLpNorm.GeneralLpNorm

Bases: NormBase

General Lp norm implementation with parameter p in (1, ∞).

This class implements the Lp norm for various magnitudes of p on real-valued functions. The Lp norm of a vector x is defined as (sum(|x_i|^p))^(1/p) for finite p > 1.

Attributes

type : Literal["GeneralLpNorm"] The type identifier for this norm. p : float The parameter p for the Lp norm. Must be finite and greater than 1. resource : str, optional The resource type, defaults to NORM.

type class-attribute instance-attribute

type = 'GeneralLpNorm'

p class-attribute instance-attribute

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

resource class-attribute instance-attribute

resource = Field(default=NORM.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/norms/GeneralLpNorm.py

@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

compute

compute(x)

Compute the Lp norm of the input.

Parameters

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

Returns

float The computed Lp 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/GeneralLpNorm.py

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

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

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

    Raises
    ------
    TypeError
        If the input type is not supported.
    ValueError
        If the norm cannot be computed for the given input.
    """
    try:
        # Convert input to numpy array
        x_array = self._convert_to_array(x)

        # Compute Lp norm: (sum(|x_i|^p))^(1/p)
        norm_value = np.sum(np.abs(x_array) ** self.p) ** (1 / self.p)

        logger.debug(f"Computed Lp norm with p={self.p}: {norm_value}")
        return float(norm_value)
    except Exception as e:
        logger.error(f"Error computing Lp norm: {str(e)}")
        raise

check_non_negativity

check_non_negativity(x)

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

The Lp 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, False otherwise.

Source code in swarmauri_standard/norms/GeneralLpNorm.py

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

    The Lp 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, False otherwise.
    """
    try:
        norm_value = self.compute(x)
        result = norm_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_definiteness

check_definiteness(x)

Check if the Lp 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, False otherwise.

Source code in swarmauri_standard/norms/GeneralLpNorm.py

def check_definiteness(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> bool:
    """
    Check if the Lp 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, False otherwise.
    """
    try:
        x_array = self._convert_to_array(x)
        norm_value = self.compute(x)

        # Check if norm is 0 iff x is 0 (all elements are 0)
        is_zero = np.allclose(x_array, 0)
        norm_is_zero = np.isclose(norm_value, 0)

        result = (is_zero and norm_is_zero) or (not is_zero and not norm_is_zero)
        logger.debug(f"Definiteness check result: {result}")
        return result
    except Exception as e:
        logger.error(f"Error in definiteness check: {str(e)}")
        return False

check_triangle_inequality

check_triangle_inequality(x, y)

Check if the Lp 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, False otherwise.

Raises

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

Source code in swarmauri_standard/norms/GeneralLpNorm.py

def check_triangle_inequality(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    y: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
) -> bool:
    """
    Check if the Lp 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, False otherwise.

    Raises
    ------
    TypeError
        If the inputs are not of the same type or cannot be added.
    """
    try:
        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 TypeError(
                "Inputs must have the same shape for triangle inequality check"
            )

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

        # Compute norm of sum
        sum_array = x_array + y_array
        norm_sum = np.sum(np.abs(sum_array) ** self.p) ** (1 / self.p)

        # Check triangle inequality
        result = (
            norm_sum <= norm_x + norm_y + 1e-10
        )  # Small epsilon for numerical stability
        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

check_absolute_homogeneity

check_absolute_homogeneity(x, scalar)

Check if the Lp 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, False otherwise.

Raises

TypeError If the input cannot be scaled by the scalar.

Source code in swarmauri_standard/norms/GeneralLpNorm.py

def check_absolute_homogeneity(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    scalar: float,
) -> bool:
    """
    Check if the Lp 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, False otherwise.

    Raises
    ------
    TypeError
        If the input cannot be scaled by the scalar.
    """
    try:
        x_array = self._convert_to_array(x)

        # Compute norm of x
        norm_x = self.compute(x)

        # Compute norm of scaled x
        scaled_array = scalar * x_array
        norm_scaled = np.sum(np.abs(scaled_array) ** self.p) ** (1 / self.p)

        # Check absolute homogeneity
        expected = abs(scalar) * norm_x
        result = np.isclose(norm_scaled, expected, rtol=1e-10, atol=1e-10)
        logger.debug(f"Absolute homogeneity check result: {result}")
        return result
    except Exception as e:
        logger.error(f"Error in absolute homogeneity check: {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