Class swarmauri_standard.seminorms.CoordinateProjectionSeminorm.CoordinateProjectionSeminorm

swarmauri_standard.seminorms.CoordinateProjectionSeminorm.CoordinateProjectionSeminorm

CoordinateProjectionSeminorm(projection_indices)

Bases: SeminormBase

Seminorm via projection onto a subset of coordinates.

This seminorm ignores certain components of the vector, resulting in possible degeneracy. It projects the input onto a specified subset of coordinates and computes the norm only on those coordinates.

Attributes

type : Literal["CoordinateProjectionSeminorm"] The type identifier for this seminorm projection_indices : Set[int] The set of indices to project onto

Initialize the coordinate projection seminorm.

Parameters

projection_indices : Set[int] The set of indices to project onto. These are the components that will be considered when computing the seminorm, all other components will be ignored.

Raises

ValueError If projection_indices is empty

Source code in swarmauri_standard/seminorms/CoordinateProjectionSeminorm.py

def __init__(self, projection_indices: Set[int]):
    """
    Initialize the coordinate projection seminorm.

    Parameters
    ----------
    projection_indices : Set[int]
        The set of indices to project onto. These are the components that will
        be considered when computing the seminorm, all other components will be ignored.

    Raises
    ------
    ValueError
        If projection_indices is empty
    """
    super().__init__()
    if not projection_indices:
        raise ValueError("Projection indices set cannot be empty")

    self.projection_indices = projection_indices
    logger.info(
        f"Initialized CoordinateProjectionSeminorm with projection indices: {projection_indices}"
    )

type class-attribute instance-attribute

type = 'CoordinateProjectionSeminorm'

projection_indices instance-attribute

projection_indices = projection_indices

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 of the input by projecting onto specified coordinates.

Parameters

x : InputType The input to compute the seminorm for. Can be a vector, matrix, sequence, or numpy array.

Returns

float The seminorm value (non-negative real number)

Raises

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

Source code in swarmauri_standard/seminorms/CoordinateProjectionSeminorm.py

def compute(self, x: InputType) -> float:
    """
    Compute the seminorm of the input by projecting onto specified coordinates.

    Parameters
    ----------
    x : InputType
        The input to compute the seminorm for. Can be a vector, matrix,
        sequence, or numpy array.

    Returns
    -------
    float
        The seminorm value (non-negative real number)

    Raises
    ------
    TypeError
        If the input type is not supported
    ValueError
        If the computation cannot be performed on the given input
    """
    logger.debug(f"Computing projection seminorm for input of type {type(x)}")

    # Handle different input types
    if isinstance(x, IVector):
        return self._compute_vector(x)
    elif isinstance(x, IMatrix):
        return self._compute_matrix(x)
    elif isinstance(x, (list, tuple, np.ndarray)):
        return self._compute_sequence(x)
    else:
        raise TypeError(f"Unsupported input type: {type(x)}")

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/CoordinateProjectionSeminorm.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)}"
    )

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

    # Compute x + y
    if isinstance(x, IVector) and isinstance(y, IVector):
        sum_xy = x + y
    elif isinstance(x, IMatrix) and isinstance(y, IMatrix):
        sum_xy = x + y
    elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
        if len(x) != len(y):
            raise ValueError("Sequences must have the same length for addition")
        sum_xy = [x[i] + y[i] for i in range(len(x))]
    elif isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
        if x.shape != y.shape:
            raise ValueError("Arrays must have the same shape for addition")
        sum_xy = x + y
    else:
        raise TypeError(
            f"Unsupported or incompatible input types: {type(x)} and {type(y)}"
        )

    # Compute norm of the sum
    norm_sum = self.compute(sum_xy)

    # Check triangle inequality
    # Use a small epsilon for floating-point comparison
    epsilon = 1e-10
    return norm_sum <= norm_x + norm_y + epsilon

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/CoordinateProjectionSeminorm.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}"
    )

    # Compute ||x||
    norm_x = self.compute(x)

    # Compute αx
    if isinstance(x, IVector):
        alpha_x = x * alpha
    elif isinstance(x, IMatrix):
        alpha_x = x * alpha
    elif isinstance(x, (list, tuple)):
        alpha_x = [complex(item) * alpha for item in x]
    elif isinstance(x, np.ndarray):
        alpha_x = x * alpha
    else:
        raise TypeError(f"Unsupported input type: {type(x)}")

    # Compute ||αx||
    norm_alpha_x = self.compute(alpha_x)

    # Compute |α|·||x||
    abs_alpha_times_norm_x = abs(complex(alpha)) * norm_x

    # Check scalar homogeneity
    # Use a small epsilon for floating-point comparison
    epsilon = 1e-10
    return abs(norm_alpha_x - abs_alpha_times_norm_x) < epsilon

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