Class swarmauri_standard.seminorms.PointEvaluationSeminorm.PointEvaluationSeminorm

swarmauri_standard.seminorms.PointEvaluationSeminorm.PointEvaluationSeminorm

PointEvaluationSeminorm(point, absolute=True, **kwargs)

Bases: SeminormBase

Seminorm that evaluates a function at a single point.

This seminorm assigns a value by evaluating a function at a fixed coordinate or input. It's useful for measuring the behavior of functions at specific points of interest.

Attributes

type : Literal["PointEvaluationSeminorm"] The type identifier for this seminorm. point : T The point at which to evaluate the function. absolute : bool Whether to take the absolute value of the function evaluation.

Initialize the PointEvaluationSeminorm.

Parameters

point : T The point at which to evaluate the function. absolute : bool, optional Whether to take the absolute value of the function evaluation, by default True. Must be True for a valid seminorm (to ensure non-negativity).

Source code in swarmauri_standard/seminorms/PointEvaluationSeminorm.py

def __init__(self, point: T, absolute: bool = True, **kwargs):
    """
    Initialize the PointEvaluationSeminorm.

    Parameters
    ----------
    point : T
        The point at which to evaluate the function.
    absolute : bool, optional
        Whether to take the absolute value of the function evaluation, by default True.
        Must be True for a valid seminorm (to ensure non-negativity).
    """
    super().__init__(**kwargs)
    self.point = point
    self.absolute = absolute
    logger.debug(
        f"Initialized PointEvaluationSeminorm with point={point}, absolute={absolute}"
    )

type class-attribute instance-attribute

type = 'PointEvaluationSeminorm'

point class-attribute instance-attribute

point = point

absolute class-attribute instance-attribute

absolute = absolute

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=SEMINORM.value)

version class-attribute instance-attribute

version = '0.1.0'

compute

compute(x)

Compute the seminorm by evaluating the input at the specified point.

Parameters

x : InputType The input to evaluate. Must be callable or support item access.

Returns

float The (absolute) value of the function at the specified point.

Raises

TypeError If the input type is not supported. ValueError If the point is not in the domain of the function.

Source code in swarmauri_standard/seminorms/PointEvaluationSeminorm.py

def compute(self, x: InputType) -> float:
    """
    Compute the seminorm by evaluating the input at the specified point.

    Parameters
    ----------
    x : InputType
        The input to evaluate. Must be callable or support item access.

    Returns
    -------
    float
        The (absolute) value of the function at the specified point.

    Raises
    ------
    TypeError
        If the input type is not supported.
    ValueError
        If the point is not in the domain of the function.
    """
    logger.debug(f"Computing point evaluation seminorm for input of type {type(x)}")

    try:
        # Handle different input types
        if callable(x):
            # If x is a function
            result = x(self.point)
        elif isinstance(x, (IVector, IMatrix, list, tuple, np.ndarray)):
            # If x is a vector-like object that supports indexing
            result = x[self.point]
        elif isinstance(x, dict):
            # If x is a dictionary
            result = x[self.point]
        else:
            raise TypeError(
                f"Unsupported input type: {type(x)}. Must be callable or support item access."
            )

        # Convert result to float if possible
        try:
            result_float = float(result)
        except (TypeError, ValueError):
            # If result cannot be converted to float, use its magnitude/norm if available
            if hasattr(result, "norm"):
                result_float = result.norm()
            elif hasattr(result, "__abs__"):
                result_float = abs(result)
            else:
                raise TypeError(
                    f"Cannot convert result {result} to a non-negative real number"
                )

        # Apply absolute value if required
        if self.absolute:
            return abs(result_float)
        return result_float

    except (KeyError, IndexError) as e:
        logger.error(
            f"Point {self.point} is not in the domain of the function: {e}"
        )
        raise ValueError(
            f"Point {self.point} is not in the domain of the function"
        ) from e
    except Exception as e:
        logger.error(f"Error computing point evaluation seminorm: {e}")
        raise

check_triangle_inequality

check_triangle_inequality(x, y)

Check if the triangle inequality property holds for the given inputs.

The triangle inequality states that: ||x + y|| ≤ ||x|| + ||y||

Parameters

x : InputType First input to check y : InputType Second input to check

Returns

bool True if the triangle inequality holds, False otherwise

Raises

TypeError If the input types are not supported or compatible ValueError If the check cannot be performed on the given inputs

Source code in swarmauri_standard/seminorms/PointEvaluationSeminorm.py

def check_triangle_inequality(self, x: InputType, y: InputType) -> bool:
    """
    Check if the triangle inequality property holds for the given inputs.

    The triangle inequality states that:
    ||x + y|| ≤ ||x|| + ||y||

    Parameters
    ----------
    x : InputType
        First input to check
    y : InputType
        Second input to check

    Returns
    -------
    bool
        True if the triangle inequality holds, False otherwise

    Raises
    ------
    TypeError
        If the input types are not supported or compatible
    ValueError
        If the check cannot be performed on the given inputs
    """
    logger.debug(
        f"Checking triangle inequality for inputs of types {type(x)} and {type(y)}"
    )

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

        # For the sum, we need to handle different input types
        if callable(x) and callable(y):
            # For functions, create a new function that is their sum
            def sum_func(p):
                return x(p) + y(p)

            norm_sum = self.compute(sum_func)
        elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
            # For sequences, check if we can add them element-wise
            if len(x) != len(y):
                raise ValueError(
                    "Sequences must have the same length for triangle inequality check"
                )
            sum_seq = [x_i + y_i for x_i, y_i in zip(x, y)]
            norm_sum = self.compute(sum_seq)
        elif isinstance(x, dict) and isinstance(y, dict):
            # For dictionaries, merge them with addition for common keys
            sum_dict = dict(x)
            for key, value in y.items():
                if key in sum_dict:
                    sum_dict[key] += value
                else:
                    sum_dict[key] = value
            norm_sum = self.compute(sum_dict)
        elif hasattr(x, "__add__") and x.__add__(y) is not NotImplemented:
            # For objects that support addition
            norm_sum = self.compute(x + y)
        else:
            raise TypeError(f"Cannot add inputs of types {type(x)} and {type(y)}")

        # Check the triangle inequality
        return norm_sum <= norm_x + norm_y

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

check_scalar_homogeneity

check_scalar_homogeneity(x, alpha)

Check if the scalar homogeneity property holds for the given input and scalar.

The scalar homogeneity states that: ||αx|| = |α|·||x||

Parameters

x : InputType The input to check alpha : T The scalar to multiply by

Returns

bool True if scalar homogeneity holds, False otherwise

Raises

TypeError If the input type is not supported ValueError If the check cannot be performed on the given input

Source code in swarmauri_standard/seminorms/PointEvaluationSeminorm.py

def check_scalar_homogeneity(self, x: InputType, alpha: T) -> bool:
    """
    Check if the scalar homogeneity property holds for the given input and scalar.

    The scalar homogeneity states that:
    ||αx|| = |α|·||x||

    Parameters
    ----------
    x : InputType
        The input to check
    alpha : T
        The scalar to multiply by

    Returns
    -------
    bool
        True if scalar homogeneity holds, False otherwise

    Raises
    ------
    TypeError
        If the input type is not supported
    ValueError
        If the check cannot be performed on the given input
    """
    logger.debug(
        f"Checking scalar homogeneity for input of type {type(x)} with scalar {alpha}"
    )

    try:
        # Compute the seminorm of x
        norm_x = self.compute(x)

        # Create scaled version of x based on input type
        if callable(x):
            # For functions, create a new function that is scaled
            def scaled_func(p):
                return alpha * x(p)

            scaled_x = scaled_func
        elif isinstance(x, (list, tuple)):
            # For sequences, scale each element
            scaled_x = [alpha * item for item in x]
        elif isinstance(x, dict):
            # For dictionaries, scale each value
            scaled_x = {key: alpha * value for key, value in x.items()}
        elif hasattr(x, "__mul__") and x.__mul__(alpha) is not NotImplemented:
            # For objects that support multiplication
            scaled_x = x * alpha
        else:
            raise TypeError(
                f"Cannot scale input of type {type(x)} with scalar {alpha}"
            )

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

        # Check scalar homogeneity (with a small tolerance for floating-point errors)
        return abs(norm_scaled_x - abs(alpha) * norm_x) < 1e-10

    except Exception as e:
        logger.error(f"Error checking scalar homogeneity: {e}")
        raise

to_dict

to_dict()

Convert the seminorm to a dictionary representation.

Returns

Dict[str, T] Dictionary representation of the seminorm

Source code in swarmauri_standard/seminorms/PointEvaluationSeminorm.py

def to_dict(self) -> Dict[str, T]:
    """
    Convert the seminorm to a dictionary representation.

    Returns
    -------
    Dict[str, T]
        Dictionary representation of the seminorm
    """
    return {"type": self.type, "point": self.point, "absolute": self.absolute}

from_dict classmethod

from_dict(data)

Create a PointEvaluationSeminorm from a dictionary representation.

Parameters

data : Dict[str, T] Dictionary representation of the seminorm

Returns

PointEvaluationSeminorm The reconstructed seminorm

Source code in swarmauri_standard/seminorms/PointEvaluationSeminorm.py

@classmethod
def from_dict(cls, data: Dict[str, T]) -> "PointEvaluationSeminorm":
    """
    Create a PointEvaluationSeminorm from a dictionary representation.

    Parameters
    ----------
    data : Dict[str, T]
        Dictionary representation of the seminorm

    Returns
    -------
    PointEvaluationSeminorm
        The reconstructed seminorm
    """
    return cls(point=data["point"], absolute=data.get("absolute", True))

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