Class swarmauri_standard.norms.LInfNorm.LInfNorm

swarmauri_standard.norms.LInfNorm.LInfNorm

Bases: NormBase

L-infinity norm implementation for real-valued functions.

This norm measures the largest absolute value in a function's domain. It requires a bounded domain for proper computation.

Attributes

type : Literal["LInfNorm"] The type identifier for this norm. resource : str, optional The resource type, defaults to NORM. domain_bounds : tuple, optional The bounds of the domain (min, max), defaults to (-1, 1).

type class-attribute instance-attribute

type = 'LInfNorm'

resource class-attribute instance-attribute

resource = Field(default=NORM.value)

domain_bounds class-attribute instance-attribute

domain_bounds = Field(default=(-1, 1))

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_domain_bounds

validate_domain_bounds(v)

Validate that the domain bounds are properly specified.

Parameters

v : tuple The domain bounds to validate.

Returns

tuple The validated domain bounds.

Raises

ValueError If the domain bounds are not properly specified.

Source code in swarmauri_standard/norms/LInfNorm.py

@field_validator("domain_bounds")
def validate_domain_bounds(cls, v):
    """
    Validate that the domain bounds are properly specified.

    Parameters
    ----------
    v : tuple
        The domain bounds to validate.

    Returns
    -------
    tuple
        The validated domain bounds.

    Raises
    ------
    ValueError
        If the domain bounds are not properly specified.
    """
    if not isinstance(v, tuple) or len(v) != 2:
        raise ValueError("Domain bounds must be a tuple of (min, max)")
    if v[0] >= v[1]:
        raise ValueError("Lower bound must be less than upper bound")
    return v

compute

compute(x)

Compute the L-infinity norm of the input.

For vectors, matrices, and sequences, this is the maximum absolute value. For callable functions, this is the maximum absolute value over the domain.

Parameters

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

Returns

float The computed L-infinity 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/LInfNorm.py

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

    For vectors, matrices, and sequences, this is the maximum absolute value.
    For callable functions, this is the maximum absolute value over the domain.

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

    Returns
    -------
    float
        The computed L-infinity 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 L-infinity norm for {type(x)}")

    # Handle different input types
    if isinstance(x, (IVector, IMatrix)):
        # For Vector and Matrix types, use their data representation
        return self._compute_for_sequence(x.data)
    elif isinstance(x, Sequence) and not isinstance(x, str):
        # For sequence types (lists, tuples, etc.)
        return self._compute_for_sequence(x)
    elif isinstance(x, str):
        # For strings, convert to ASCII values
        return self._compute_for_sequence([ord(char) for char in x])
    elif callable(x):
        # For callable functions
        return self._compute_for_function(x)
    else:
        raise TypeError(f"Unsupported input type for L-infinity norm: {type(x)}")

check_non_negativity

check_non_negativity(x)

Check if the L-infinity norm satisfies the non-negativity property.

The L-infinity 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/LInfNorm.py

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

    The L-infinity 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
        return result
    except Exception as e:
        logger.error(f"Error checking non-negativity: {e}")
        return False

check_definiteness

check_definiteness(x)

Check if the L-infinity 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/LInfNorm.py

def check_definiteness(
    self, x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType]
) -> bool:
    """
    Check if the L-infinity 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:
        norm_value = self.compute(x)

        # Check if x is "zero"
        is_zero = False

        if isinstance(x, IVector):
            is_zero = all(v == 0 for v in x.data)
        elif isinstance(x, IMatrix):
            # Fix: Convert numpy array to list before checking
            data_array = np.asarray(x.data)
            is_zero = np.all(data_array == 0)
        elif isinstance(x, Sequence) and not isinstance(x, str):
            # Handle case where sequence might be a numpy array
            if hasattr(x, "__array__") or isinstance(x, np.ndarray):
                is_zero = np.all(np.asarray(x) == 0)
            else:
                is_zero = all(v == 0 for v in x)
        elif isinstance(x, str):
            is_zero = len(x) == 0
        elif callable(x):
            # Sample function at multiple points to check if it's zero
            lower, upper = self.domain_bounds
            sample_points = np.linspace(lower, upper, 100)
            is_zero = all(abs(x(pt)) < 1e-10 for pt in sample_points)

        # The norm is 0 if and only if x is 0
        return (norm_value == 0) == is_zero
    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 L-infinity 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. ValueError If inputs have different dimensions or cannot be combined.

Source code in swarmauri_standard/norms/LInfNorm.py

def check_triangle_inequality(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    y: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
) -> bool:
    """
    Check if the L-infinity 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.
    ValueError
        If inputs have different dimensions or cannot be combined.
    """
    # Handle different input types
    if isinstance(x, IVector) and isinstance(y, type(x)):
        # For Vector types
        z = type(x)(data=[x_val + y_val for x_val, y_val in zip(x.data, y.data)])
    elif isinstance(x, IMatrix) and isinstance(y, type(x)):
        # For Matrix types - use numpy array handling
        x_array = np.asarray(x.data)
        y_array = np.asarray(y.data)
        # Check shapes match
        if x_array.shape != y_array.shape:
            raise ValueError("Matrices must have the same shape")
        sum_array = x_array + y_array
        # Create a new matrix of the same type with the summed data
        z = type(x)(data=sum_array.tolist())
    elif (
        isinstance(x, Sequence)
        and isinstance(y, type(x))
        and not isinstance(x, str)
    ):
        # For sequence types
        if len(x) != len(y):
            raise ValueError("Sequences must have the same length")

        # Handle numpy arrays
        if isinstance(x, np.ndarray) or isinstance(y, np.ndarray):
            z = np.asarray(x) + np.asarray(y)
        else:
            z = [x_val + y_val for x_val, y_val in zip(x, y)]
    elif isinstance(x, str) and isinstance(y, str):
        # For strings, we'll add their ASCII values
        if len(x) != len(y):
            raise ValueError("Strings must have the same length")
        z = "".join(
            chr(min(ord(x_char) + ord(y_char), 255)) for x_char, y_char in zip(x, y)
        )
    elif callable(x) and callable(y):
        # For callable functions
        def z(t):
            return x(t) + y(t)

    else:
        raise TypeError("Inputs must be of the same type and must support addition")

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

        # Check triangle inequality
        # Allow for small numerical errors
        return norm_z <= norm_x + norm_y + 1e-10
    except Exception as e:
        logger.error(f"Error checking triangle inequality: {e}")
        return False

check_absolute_homogeneity

check_absolute_homogeneity(x, scalar)

Check if the L-infinity 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/LInfNorm.py

def check_absolute_homogeneity(
    self,
    x: Union[VectorType, MatrixType, SequenceType, StringType, CallableType],
    scalar: float,
) -> bool:
    """
    Check if the L-infinity 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:
        # Handle different input types
        if isinstance(x, IVector):
            # For Vector types
            scaled_x = type(x)(data=[scalar * val for val in x.data])
        elif isinstance(x, IMatrix):
            # For Matrix types - properly handle numpy arrays
            data_array = np.asarray(x.data)
            scaled_array = scalar * data_array
            scaled_x = type(x)(data=scaled_array.tolist())
        elif isinstance(x, Sequence) and not isinstance(x, str):
            # For sequence types
            if isinstance(x, np.ndarray):
                scaled_x = scalar * x
            else:
                scaled_x = [scalar * val for val in x]
        elif isinstance(x, str):
            # For strings, scaling is not well-defined
            # We'll scale the ASCII values and ensure they're valid (0-255)
            scaled_x = "".join(
                chr(max(0, min(255, int(scalar * ord(char))))) for char in x
            )
        elif callable(x):

            def scaled_x(t):
                return scalar * x(t)

        else:
            raise TypeError(f"Unsupported input type for scaling: {type(x)}")

        # Compute norms
        norm_x = self.compute(x)
        norm_scaled_x = self.compute(scaled_x)

        # Check absolute homogeneity
        expected = abs(scalar) * norm_x
        # Allow for small numerical errors
        return abs(norm_scaled_x - expected) < 1e-10
    except Exception as e:
        logger.error(f"Error checking absolute homogeneity: {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