Class swarmauri_standard.seminorms.TraceSeminorm.TraceSeminorm

swarmauri_standard.seminorms.TraceSeminorm.TraceSeminorm

Bases: SeminormBase

Trace seminorm implementation that computes the trace norm of a matrix.

This seminorm uses the trace of a matrix in its computation without guaranteeing positive-definiteness. It calculates the sum of singular values of a matrix, which is equal to the trace of the square root of A*A^T.

Attributes

type : Literal["TraceSeminorm"] The type identifier for this seminorm.

type class-attribute instance-attribute

type = 'TraceSeminorm'

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 trace seminorm of the input matrix.

For a matrix input, this computes the sum of singular values, which is equivalent to the trace norm.

Parameters

x : InputType The input to compute the seminorm for. Should be a matrix or matrix-like object that supports the trace operation.

Returns

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

Raises

TypeError If the input type does not support trace operation ValueError If the computation cannot be performed on the given input

Source code in swarmauri_standard/seminorms/TraceSeminorm.py

def compute(self, x: InputType) -> float:
    """
    Compute the trace seminorm of the input matrix.

    For a matrix input, this computes the sum of singular values,
    which is equivalent to the trace norm.

    Parameters
    ----------
    x : InputType
        The input to compute the seminorm for. Should be a matrix or
        matrix-like object that supports the trace operation.

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

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

    # Handle matrix inputs
    if isinstance(x, IMatrix):
        # Compute trace norm (nuclear norm) as sum of singular values
        try:
            # Get numpy representation for computation
            matrix_data = x.to_numpy()
            # Calculate singular values
            singular_values = np.linalg.svd(matrix_data, compute_uv=False)
            # Trace norm is the sum of singular values
            return float(np.sum(singular_values))
        except Exception as e:
            logger.error(f"Error computing trace seminorm: {str(e)}")
            raise ValueError(f"Failed to compute trace seminorm: {str(e)}")

    # Handle numpy arrays directly
    elif isinstance(x, np.ndarray):
        if x.ndim >= 2:  # Must be a matrix-like array
            try:
                singular_values = np.linalg.svd(x, compute_uv=False)
                return float(np.sum(singular_values))
            except Exception as e:
                logger.error(
                    f"Error computing trace seminorm for numpy array: {str(e)}"
                )
                raise ValueError(
                    f"Failed to compute trace seminorm for numpy array: {str(e)}"
                )
        else:
            raise TypeError("Input numpy array must have at least 2 dimensions")

    # If input is a vector, treat it as a column matrix
    elif isinstance(x, IVector):
        try:
            vector_data = x.to_numpy()
            # Reshape to column matrix
            matrix_data = vector_data.reshape(-1, 1)
            singular_values = np.linalg.svd(matrix_data, compute_uv=False)
            return float(np.sum(singular_values))
        except Exception as e:
            logger.error(f"Error computing trace seminorm for vector: {str(e)}")
            raise ValueError(
                f"Failed to compute trace seminorm for vector: {str(e)}"
            )

    else:
        logger.error(f"Unsupported input type for trace seminorm: {type(x)}")
        raise TypeError(
            f"Trace seminorm requires a matrix or matrix-like input, got {type(x)}"
        )

check_triangle_inequality

check_triangle_inequality(x, y)

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

The triangle inequality for trace norm 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/TraceSeminorm.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 for trace norm 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)}"
    )

    # Ensure we can add the inputs
    try:
        # Convert inputs to numpy arrays if they're IMatrix or IVector
        x_data = x.to_numpy() if hasattr(x, "to_numpy") else np.array(x)
        y_data = y.to_numpy() if hasattr(y, "to_numpy") else np.array(y)

        # Check if shapes are compatible for addition
        if x_data.shape != y_data.shape:
            logger.warning(
                f"Incompatible shapes for triangle inequality check: {x_data.shape} vs {y_data.shape}"
            )
            return False

        # Compute the sum
        sum_data = x_data + y_data

        # Compute norms
        x_norm = self.compute(x)
        y_norm = self.compute(y)
        sum_norm = self.compute(sum_data)

        # Check triangle inequality
        # Add a small epsilon for floating point comparison
        epsilon = 1e-10
        return sum_norm <= x_norm + y_norm + epsilon

    except Exception as e:
        logger.error(f"Error checking triangle inequality: {str(e)}")
        raise ValueError(f"Failed to check triangle inequality: {str(e)}")

check_scalar_homogeneity

check_scalar_homogeneity(x, alpha)

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

The scalar homogeneity for trace norm 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/TraceSeminorm.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 for trace norm 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:
        # Convert alpha to a complex number to handle all numeric types
        alpha_complex = complex(alpha)
        alpha_abs = abs(alpha_complex)

        # Convert input to numpy array if it's IMatrix or IVector
        x_data = x.to_numpy() if hasattr(x, "to_numpy") else np.array(x)

        # Compute scaled input
        scaled_data = alpha_complex * x_data

        # Compute norms
        x_norm = self.compute(x)
        scaled_norm = self.compute(scaled_data)

        # Check scalar homogeneity
        # Add a small epsilon for floating point comparison
        epsilon = 1e-10
        return abs(scaled_norm - alpha_abs * x_norm) < epsilon

    except Exception as e:
        logger.error(f"Error checking scalar homogeneity: {str(e)}")
        raise ValueError(f"Failed to check scalar homogeneity: {str(e)}")

is_degenerate

is_degenerate(x)

Check if the seminorm is degenerate for the given input.

A seminorm is degenerate if there exists a non-zero input for which the seminorm is zero.

Parameters

x : InputType The input to check for degeneracy

Returns

bool True if the seminorm is degenerate for the input, 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/TraceSeminorm.py

def is_degenerate(self, x: InputType) -> bool:
    """
    Check if the seminorm is degenerate for the given input.

    A seminorm is degenerate if there exists a non-zero input for which
    the seminorm is zero.

    Parameters
    ----------
    x : InputType
        The input to check for degeneracy

    Returns
    -------
    bool
        True if the seminorm is degenerate for the input, 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 degeneracy for input of type {type(x)}")

    try:
        # Convert input to numpy array if it's IMatrix or IVector
        x_data = x.to_numpy() if hasattr(x, "to_numpy") else np.array(x)

        # Check if input is non-zero
        if not np.any(x_data):
            # Zero input is not interesting for degeneracy check
            return False

        # Compute the seminorm
        norm_value = self.compute(x)

        # If input is non-zero but norm is zero, the seminorm is degenerate
        epsilon = 1e-10  # Small threshold for floating point comparison
        return norm_value < epsilon

    except Exception as e:
        logger.error(f"Error checking degeneracy: {str(e)}")
        raise ValueError(f"Failed to check degeneracy: {str(e)}")

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