Class swarmauri_standard.norms.SobolevNorm.SobolevNorm

swarmauri_standard.norms.SobolevNorm.SobolevNorm

SobolevNorm(**kwargs)

Bases: NormBase

Sobolev norm implementation that combines function and derivative norms.

The Sobolev norm accounts for the smoothness of a function by incorporating the L2 norm of both the function and its derivatives up to a specified order.

Attributes

type : Literal["SobolevNorm"] The type identifier for this norm. order : int The highest derivative order to consider in the norm computation. weights : Dict[int, float] Weights for each derivative order in the norm computation.

Initialize the Sobolev norm with specified parameters.

Parameters

**kwargs Keyword arguments to pass to the parent class constructor. May include 'order' and 'weights' to customize the norm.

Source code in swarmauri_standard/norms/SobolevNorm.py

def __init__(self, **kwargs):
    """
    Initialize the Sobolev norm with specified parameters.

    Parameters
    ----------
    **kwargs
        Keyword arguments to pass to the parent class constructor.
        May include 'order' and 'weights' to customize the norm.
    """
    super().__init__(**kwargs)
    # Ensure weights dictionary has entries for all orders up to self.order
    for i in range(self.order + 1):
        if i not in self.weights:
            self.weights[i] = 1.0

    logger.debug(
        f"Initialized SobolevNorm with order {self.order} and weights {self.weights}"
    )

type class-attribute instance-attribute

type = 'SobolevNorm'

order class-attribute instance-attribute

order = Field(
    default=1,
    description="Highest derivative order to consider",
)

weights class-attribute instance-attribute

weights = Field(
    default_factory=lambda: {0: 1.0, 1: 1.0},
    description="Weights for each derivative order",
)

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 Sobolev norm of the input.

For callable inputs, computes the weighted sum of L2 norms of the function and its derivatives. For other types, falls back to L2 norm of the input itself.

Parameters

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

Returns

float The computed Sobolev norm value.

Raises

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

Source code in swarmauri_standard/norms/SobolevNorm.py

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

    For callable inputs, computes the weighted sum of L2 norms of the function and its derivatives.
    For other types, falls back to L2 norm of the input itself.

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

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

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

    if callable(x):
        return self._compute_for_callable(x)
    elif isinstance(x, (IVector, IMatrix, Sequence)):
        # For non-callable types, use only the function value (0th derivative)
        # with appropriate weight
        logger.debug("Computing Sobolev norm for non-callable using L2 norm")
        return self._compute_l2_norm(x) * self.weights.get(0, 1.0)
    else:
        raise TypeError(
            f"Unsupported input type for Sobolev norm: {type(x).__name__}"
        )

check_non_negativity

check_non_negativity(x)

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

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/SobolevNorm.py

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

    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)
        return norm_value >= 0
    except Exception as e:
        logger.error(f"Error checking non-negativity: {str(e)}")
        return False

check_definiteness

check_definiteness(x)

Check if the Sobolev 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/SobolevNorm.py

def check_definiteness(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> bool:
    """
    Check if the Sobolev 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:
        # For a zero input, the norm should be zero
        if self._is_zero(x):
            norm_value = self.compute(x)
            return abs(norm_value) < 1e-10  # Allow for numerical precision issues

        # For a non-zero input, the norm should be positive
        norm_value = self.compute(x)
        return norm_value > 0
    except Exception as e:
        logger.error(f"Error checking definiteness: {str(e)}")
        return False

check_triangle_inequality

check_triangle_inequality(x, y)

Check if the Sobolev 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 inputs are not of the same type.

Source code in swarmauri_standard/norms/SobolevNorm.py

def check_triangle_inequality(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    y: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
) -> bool:
    """
    Check if the Sobolev 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 inputs are not of the same type.
    """
    # Ensure x and y are of the same type - don't catch this error
    if not isinstance(y, type(x)):
        raise TypeError(
            "Inputs must be of the same type for triangle inequality check"
        )

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

        # For callable functions
        if callable(x) and callable(y):
            # Create a new function representing x + y
            def sum_func(t):
                return x(t) + y(t)

            # For functions with derivatives
            if hasattr(x, "derivative") and hasattr(y, "derivative"):
                # Add derivative method to sum_func
                def create_derivative(func_x, func_y):
                    def derivative_func(t):
                        return func_x.derivative()(t) + func_y.derivative()(t)

                    return derivative_func

                sum_func.derivative = lambda: create_derivative(x, y)

            norm_sum = self.compute(sum_func)

        # For vector-like objects
        elif hasattr(x, "__add__"):
            sum_xy = x + y
            norm_sum = self.compute(sum_xy)

        # For sequences
        elif isinstance(x, Sequence) and isinstance(y, Sequence):
            if len(x) != len(y):
                raise ValueError(
                    "Sequences must have the same length for triangle inequality check"
                )
            sum_xy = [x[i] + y[i] for i in range(len(x))]
            norm_sum = self.compute(sum_xy)

        else:
            raise TypeError(f"Cannot compute sum for type {type(x).__name__}")

        # Check triangle inequality
        logger.debug(f"Triangle inequality check: {norm_sum} <= {norm_x + norm_y}")
        return (
            norm_sum <= norm_x + norm_y + 1e-10
        )  # Allow for numerical precision issues

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

check_absolute_homogeneity

check_absolute_homogeneity(x, scalar)

Check if the Sobolev 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.

Source code in swarmauri_standard/norms/SobolevNorm.py

def check_absolute_homogeneity(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    scalar: float,
) -> bool:
    """
    Check if the Sobolev 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.
    """
    try:
        # Compute norm of x
        norm_x = self.compute(x)

        # For callable functions
        if callable(x):
            # Create a new function representing scalar * x
            def scaled_func(t):
                return scalar * x(t)

            # For functions with derivatives
            if hasattr(x, "derivative"):
                # Add derivative method to scaled_func
                def create_derivative(func_x, scale):
                    def derivative_func(t):
                        return scale * func_x.derivative()(t)

                    return derivative_func

                scaled_func.derivative = lambda: create_derivative(x, scalar)

            norm_scaled = self.compute(scaled_func)

        # For vector-like objects with direct multiplication support
        elif hasattr(x, "__mul__") and not isinstance(x, (list, tuple)):
            scaled_x = x * scalar
            norm_scaled = self.compute(scaled_x)

        # For sequences (lists/tuples)
        elif isinstance(x, Sequence):
            # Handle lists by multiplying each element separately
            scaled_x = [float(scalar) * float(item) for item in x]
            norm_scaled = self.compute(scaled_x)

        else:
            raise TypeError(f"Cannot scale input of type {type(x).__name__}")

        # Check absolute homogeneity
        expected_norm = abs(scalar) * norm_x
        logger.debug(
            f"Absolute homogeneity check: {norm_scaled} == {expected_norm}"
        )
        return abs(norm_scaled - expected_norm) < 1e-10 * (
            1 + abs(expected_norm)
        )  # Relative tolerance

    except Exception as e:
        logger.error(f"Error checking absolute homogeneity: {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