Class swarmauri_standard.inner_products.FrobeniusRealInnerProduct.FrobeniusRealInnerProduct

swarmauri_standard.inner_products.FrobeniusRealInnerProduct.FrobeniusRealInnerProduct

Bases: InnerProductBase

Implementation of the Frobenius inner product for real-valued matrices.

The Frobenius inner product is defined as the sum of element-wise products of two matrices, which is equivalent to Tr(A^T B) for real matrices.

Attributes

type : Literal["FrobeniusRealInnerProduct"] The specific type identifier for this inner product implementation

type class-attribute instance-attribute

type = 'FrobeniusRealInnerProduct'

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 = INNER_PRODUCT.value

version class-attribute instance-attribute

version = '0.1.0'

compute

compute(a, b)

Compute the Frobenius inner product between two real matrices.

The Frobenius inner product is defined as the sum of element-wise products, which is equivalent to Tr(A^T B) for real matrices.

Parameters

a : Union[NDArray, Matrix] The first matrix for inner product calculation b : Union[NDArray, Matrix] The second matrix for inner product calculation

Returns

float The Frobenius inner product value

Raises

ValueError If inputs are not matrices, are not real, or have different shapes

Source code in swarmauri_standard/inner_products/FrobeniusRealInnerProduct.py

def compute(self, a: Union[NDArray, Matrix], b: Union[NDArray, Matrix]) -> float:
    """
    Compute the Frobenius inner product between two real matrices.

    The Frobenius inner product is defined as the sum of element-wise products,
    which is equivalent to Tr(A^T B) for real matrices.

    Parameters
    ----------
    a : Union[NDArray, Matrix]
        The first matrix for inner product calculation
    b : Union[NDArray, Matrix]
        The second matrix for inner product calculation

    Returns
    -------
    float
        The Frobenius inner product value

    Raises
    ------
    ValueError
        If inputs are not matrices, are not real, or have different shapes
    """
    if not isinstance(a, np.ndarray) or not isinstance(b, np.ndarray):
        raise ValueError("Both inputs must be numpy arrays (matrices)")

    logger.debug(
        f"Computing Frobenius inner product between matrices of shape {a.shape} and {b.shape}"
    )

    if a.shape != b.shape:
        raise ValueError(f"Matrix shapes must match: {a.shape} != {b.shape}")

    if np.iscomplexobj(a) or np.iscomplexobj(b):
        raise ValueError("This implementation only supports real-valued matrices")

    # Compute the Frobenius inner product
    # For real matrices, this is equivalent to np.sum(a * b) or np.trace(a.T @ b)
    result = np.sum(a * b)

    logger.debug(f"Frobenius inner product result: {result}")
    return float(result)

check_conjugate_symmetry

check_conjugate_symmetry(a, b)

Check if the Frobenius inner product satisfies conjugate symmetry property.

For real matrices, this simplifies to checking if = .

Parameters

a : Union[NDArray, Matrix] The first matrix b : Union[NDArray, Matrix] The second matrix

Returns

bool True if conjugate symmetry holds, False otherwise

Source code in swarmauri_standard/inner_products/FrobeniusRealInnerProduct.py

def check_conjugate_symmetry(
    self, a: Union[NDArray, Matrix], b: Union[NDArray, Matrix]
) -> bool:
    """
    Check if the Frobenius inner product satisfies conjugate symmetry property.

    For real matrices, this simplifies to checking if <a,b> = <b,a>.

    Parameters
    ----------
    a : Union[NDArray, Matrix]
        The first matrix
    b : Union[NDArray, Matrix]
        The second matrix

    Returns
    -------
    bool
        True if conjugate symmetry holds, False otherwise
    """
    logger.debug(
        f"Checking conjugate symmetry for matrices of shape {a.shape} and {b.shape}"
    )

    # For real matrices, conjugate symmetry means <a,b> = <b,a>
    forward = self.compute(a, b)
    backward = self.compute(b, a)

    # Check if the values are close enough (floating point comparison)
    is_symmetric = np.isclose(forward, backward)

    logger.debug(
        f"Conjugate symmetry check result: {is_symmetric} ({forward} vs {backward})"
    )
    return bool(is_symmetric)

check_linearity_first_argument

check_linearity_first_argument(a1, a2, b, alpha, beta)

Check if the Frobenius inner product satisfies linearity in the first argument.

This checks if = alpha + beta.

Parameters

a1 : Union[NDArray, Matrix] First component of the first argument a2 : Union[NDArray, Matrix] Second component of the first argument b : Union[NDArray, Matrix] The second matrix alpha : float Scalar multiplier for a1 beta : float Scalar multiplier for a2

Returns

bool True if linearity in the first argument holds, False otherwise

Source code in swarmauri_standard/inner_products/FrobeniusRealInnerProduct.py

def check_linearity_first_argument(
    self,
    a1: Union[NDArray, Matrix],
    a2: Union[NDArray, Matrix],
    b: Union[NDArray, Matrix],
    alpha: float,
    beta: float,
) -> bool:
    """
    Check if the Frobenius inner product satisfies linearity in the first argument.

    This checks if <alpha*a1 + beta*a2, b> = alpha*<a1, b> + beta*<a2, b>.

    Parameters
    ----------
    a1 : Union[NDArray, Matrix]
        First component of the first argument
    a2 : Union[NDArray, Matrix]
        Second component of the first argument
    b : Union[NDArray, Matrix]
        The second matrix
    alpha : float
        Scalar multiplier for a1
    beta : float
        Scalar multiplier for a2

    Returns
    -------
    bool
        True if linearity in the first argument holds, False otherwise
    """
    logger.debug(f"Checking linearity with alpha={alpha}, beta={beta}")

    # Validate that all matrices have the same shape
    if a1.shape != a2.shape or a1.shape != b.shape:
        raise ValueError("All matrices must have the same shape")

    # Compute the left side: <alpha*a1 + beta*a2, b>
    left_side = self.compute(alpha * a1 + beta * a2, b)

    # Compute the right side: alpha*<a1, b> + beta*<a2, b>
    right_side = alpha * self.compute(a1, b) + beta * self.compute(a2, b)

    # Check if the values are close enough (floating point comparison)
    is_linear = np.isclose(left_side, right_side)

    logger.debug(
        f"Linearity check result: {is_linear} ({left_side} vs {right_side})"
    )
    return bool(is_linear)

check_positivity

check_positivity(a)

Check if the Frobenius inner product satisfies the positivity property.

This checks if >= 0 and = 0 iff a = 0.

Parameters

a : Union[NDArray, Matrix] The matrix to check positivity for

Returns

bool True if positivity holds, False otherwise

Source code in swarmauri_standard/inner_products/FrobeniusRealInnerProduct.py

def check_positivity(self, a: Union[NDArray, Matrix]) -> bool:
    """
    Check if the Frobenius inner product satisfies the positivity property.

    This checks if <a, a> >= 0 and <a, a> = 0 iff a = 0.

    Parameters
    ----------
    a : Union[NDArray, Matrix]
        The matrix to check positivity for

    Returns
    -------
    bool
        True if positivity holds, False otherwise
    """
    logger.debug(f"Checking positivity for matrix of shape {a.shape}")

    # Compute <a, a>
    inner_product = self.compute(a, a)

    # Check if inner product is non-negative
    is_non_negative = inner_product >= 0

    # Check if inner product is zero iff a is zero
    is_zero_iff_a_zero = (inner_product == 0 and np.all(a == 0)) or (
        inner_product > 0 and not np.all(a == 0)
    )

    result = is_non_negative and is_zero_iff_a_zero

    logger.debug(
        f"Positivity check result: {result} (inner product: {inner_product})"
    )
    return result

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