Class swarmauri_standard.norms.L1ManhattanNorm.L1ManhattanNorm

swarmauri_standard.norms.L1ManhattanNorm.L1ManhattanNorm

Bases: NormBase

Implementation of the L1 (Manhattan) norm.

The L1 norm calculates the sum of the absolute values of vector components. Also known as the Manhattan or Taxicab norm, it represents the distance traveled along grid lines in a city block layout.

Attributes

type : Literal["L1ManhattanNorm"] The type identifier for this norm implementation. resource : str, optional The resource type, defaults to NORM.

type class-attribute instance-attribute

type = 'L1ManhattanNorm'

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 L1 (Manhattan) norm of the input.

Parameters

x : Union[IVector, IMatrix, Sequence, str, float] The input for which to compute the norm.

Returns

float The computed L1 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/L1ManhattanNorm.py

def compute(self, x: Union[IVector, IMatrix, Sequence, str, float]) -> float:
    """
    Compute the L1 (Manhattan) norm of the input.

    Parameters
    ----------
    x : Union[IVector, IMatrix, Sequence, str, float]
        The input for which to compute the norm.

    Returns
    -------
    float
        The computed L1 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 L1 Manhattan norm for input of type: {type(x)}")

    # Handle IVector implementation
    if isinstance(x, IVector):
        return float(sum(abs(val) for val in x.values))

    # Handle IMatrix implementation (flattening the matrix)
    elif isinstance(x, IMatrix):
        return float(sum(abs(val) for row in x.values for val in row))

    # Handle sequence types (lists, tuples, etc.)
    elif isinstance(x, Sequence) and not isinstance(x, str):
        # Convert all elements to float and compute sum of absolute values
        try:
            return float(sum(abs(float(val)) for val in x))
        except (ValueError, TypeError) as e:
            logger.error(
                f"Cannot compute L1 norm for sequence with non-numeric elements: {e}"
            )
            raise TypeError(f"L1 norm requires numeric elements in sequence: {e}")

    # Handle numpy arrays
    elif hasattr(x, "__array__"):  # Check for numpy array compatibility
        try:
            return float(np.sum(np.abs(x)))
        except Exception as e:
            logger.error(f"Error computing L1 norm for numpy array: {e}")
            raise ValueError(
                f"Cannot compute L1 norm for the given numpy array: {e}"
            )

    # Handle unsupported types
    else:
        logger.error(f"Unsupported input type for L1 norm: {type(x)}")
        raise TypeError(f"L1 norm computation not supported for type: {type(x)}")

check_non_negativity

check_non_negativity(x)

Check if the L1 norm satisfies the non-negativity property.

The L1 norm is always non-negative by definition (sum of absolute values).

Parameters

x : Union[IVector, IMatrix, Sequence, str, float] The input to check.

Returns

bool True if the norm is non-negative, always True for L1 norm.

Source code in swarmauri_standard/norms/L1ManhattanNorm.py

def check_non_negativity(
    self, x: Union[IVector, IMatrix, Sequence, str, float]
) -> bool:
    """
    Check if the L1 norm satisfies the non-negativity property.

    The L1 norm is always non-negative by definition (sum of absolute values).

    Parameters
    ----------
    x : Union[IVector, IMatrix, Sequence, str, float]
        The input to check.

    Returns
    -------
    bool
        True if the norm is non-negative, always True for L1 norm.
    """
    try:
        norm_value = self.compute(x)
        logger.debug(f"L1 norm value: {norm_value}, checking non-negativity")
        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 L1 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[IVector, IMatrix, Sequence, str, float] The input to check.

Returns

bool True if the norm satisfies the definiteness property.

Source code in swarmauri_standard/norms/L1ManhattanNorm.py

def check_definiteness(
    self, x: Union[IVector, IMatrix, Sequence, str, float]
) -> bool:
    """
    Check if the L1 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[IVector, IMatrix, Sequence, str, float]
        The input to check.

    Returns
    -------
    bool
        True if the norm satisfies the definiteness property.
    """
    try:
        # Check if all elements are zero
        is_zero = self._is_zero(x)
        norm_value = self.compute(x)

        logger.debug(f"L1 norm value: {norm_value}, is_zero: {is_zero}")

        # The norm is 0 if and only if x is 0
        return (norm_value == 0 and is_zero) or (norm_value > 0 and not 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 L1 norm satisfies the triangle inequality.

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

Parameters

x : Union[IVector, IMatrix, Sequence, str, float] The first input. y : Union[IVector, IMatrix, Sequence, str, float] 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/L1ManhattanNorm.py

def check_triangle_inequality(
    self,
    x: Union[IVector, IMatrix, Sequence, str, float],
    y: Union[IVector, IMatrix, Sequence, str, float],
) -> bool:
    """
    Check if the L1 norm satisfies the triangle inequality.

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

    Parameters
    ----------
    x : Union[IVector, IMatrix, Sequence, str, float]
        The first input.
    y : Union[IVector, IMatrix, Sequence, str, float]
        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:
        # Verify inputs are compatible for addition
        if not self._are_compatible(x, y):
            logger.error("Inputs are not compatible for addition")
            raise TypeError(
                "Inputs must be of the same type and dimension for triangle inequality check"
            )

        # Compute the sum of x and y
        x_plus_y = self._add(x, y)

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

        logger.debug(
            f"Triangle inequality check: norm(x+y)={norm_x_plus_y}, norm(x)+norm(y)={norm_x + norm_y}"
        )

        # Check triangle inequality
        # Allow for small floating-point errors
        return norm_x_plus_y <= (norm_x + norm_y) + 1e-10
    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 L1 norm satisfies the absolute homogeneity property.

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

Parameters

x : Union[IVector, IMatrix, Sequence, str, float] 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/L1ManhattanNorm.py

def check_absolute_homogeneity(
    self, x: Union[IVector, IMatrix, Sequence, str, float], scalar: float
) -> bool:
    """
    Check if the L1 norm satisfies the absolute homogeneity property.

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

    Parameters
    ----------
    x : Union[IVector, IMatrix, Sequence, str, float]
        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:
        # Scale the input by the scalar
        scaled_x = self._scale(x, scalar)

        # Compute norms
        norm_x = self.compute(x)
        norm_scaled_x = self.compute(scaled_x)
        expected_norm = abs(scalar) * norm_x

        logger.debug(
            f"Absolute homogeneity check: norm({scalar}*x)={norm_scaled_x}, |{scalar}|*norm(x)={expected_norm}"
        )

        # Check absolute homogeneity with a small tolerance for floating-point errors
        return abs(norm_scaled_x - expected_norm) < 1e-10
    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