Class swarmauri_standard.inner_products.TraceFormWeightedInnerProduct.TraceFormWeightedInnerProduct

swarmauri_standard.inner_products.TraceFormWeightedInnerProduct.TraceFormWeightedInnerProduct

TraceFormWeightedInnerProduct(weight_matrix=None, **kwargs)

Bases: InnerProductBase

Matrix-based inner product where trace is modulated by an external weight matrix.

This class implements an inner product calculation between matrices where the inner product is defined as trace(A^T * W * B), where W is a weight matrix. The weight matrix modulates the importance of different elements in the matrices.

Attributes

type : Literal["TraceFormWeightedInnerProduct"] The type identifier for this inner product implementation resource : str The resource type identifier, defaulting to INNER_PRODUCT weight_matrix : np.ndarray The weight matrix used to modulate the inner product calculation

Initialize the TraceFormWeightedInnerProduct with an optional weight matrix.

Parameters

weight_matrix : Optional[np.ndarray], default=None The weight matrix to use for inner product calculations. If None, an identity matrix will be used.

Source code in swarmauri_standard/inner_products/TraceFormWeightedInnerProduct.py

def __init__(self, weight_matrix: Optional[np.ndarray] = None, **kwargs):
    """
    Initialize the TraceFormWeightedInnerProduct with an optional weight matrix.

    Parameters
    ----------
    weight_matrix : Optional[np.ndarray], default=None
        The weight matrix to use for inner product calculations.
        If None, an identity matrix will be used.
    """
    if weight_matrix is None:
        # Default to identity matrix if no weight matrix is provided
        weight_matrix = np.eye(1)
        logger.info("No weight matrix provided, using identity matrix.")
    else:
        # Ensure the weight matrix is a numpy array
        weight_matrix = np.array(weight_matrix)
        logger.info(
            f"Initialized with weight matrix of shape {weight_matrix.shape}"
        )
    kwargs["weight_matrix"] = weight_matrix
    super().__init__(**kwargs)

type class-attribute instance-attribute

type = 'TraceFormWeightedInnerProduct'

weight_matrix instance-attribute

weight_matrix

model_config class-attribute instance-attribute

model_config = ConfigDict(
    arbitrary_types_allowed=True,
    json_encoders={ndarray: lambda v: tolist()},
)

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 weighted trace inner product between two matrices.

The inner product is defined as trace(A^T * W * B), where W is the weight matrix. For complex matrices, we use the conjugate transpose A^H instead of transpose A^T.

Parameters

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

Returns

float The inner product value

Raises

ValueError If the input objects are not matrices or have incompatible dimensions

Source code in swarmauri_standard/inner_products/TraceFormWeightedInnerProduct.py

def compute(
    self, a: Union[Vector, Matrix, Callable], b: Union[Vector, Matrix, Callable]
) -> float:
    """
    Compute the weighted trace inner product between two matrices.

    The inner product is defined as trace(A^T * W * B), where W is the weight matrix.
    For complex matrices, we use the conjugate transpose A^H instead of transpose A^T.

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

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

    Raises
    ------
    ValueError
        If the input objects are not matrices or have incompatible dimensions
    """
    logger.debug(
        f"Computing weighted trace inner product between {type(a)} and {type(b)}"
    )

    # Convert inputs to numpy arrays if they aren't already
    if not isinstance(a, np.ndarray):
        a = np.array(a)
    if not isinstance(b, np.ndarray):
        b = np.array(b)

    # Check if dimensions are compatible
    if (
        a.shape[0] != self.weight_matrix.shape[0]
        or b.shape[0] != self.weight_matrix.shape[1]
    ):
        error_msg = (
            f"Incompatible dimensions: a.shape={a.shape}, "
            f"weight_matrix.shape={self.weight_matrix.shape}, b.shape={b.shape}"
        )
        logger.error(error_msg)
        raise ValueError(error_msg)

    # Compute the weighted trace inner product
    try:
        # For complex matrices, use conjugate transpose (Hermitian)
        if (
            np.iscomplexobj(a)
            or np.iscomplexobj(b)
            or np.iscomplexobj(self.weight_matrix)
        ):
            # Calculate A^H * W * B (conjugate transpose)
            weighted_product = np.matmul(
                a.conj().T, np.matmul(self.weight_matrix, b)
            )
        else:
            # Calculate A^T * W * B (regular transpose for real matrices)
            weighted_product = np.matmul(a.T, np.matmul(self.weight_matrix, b))

        # Take the trace
        result = np.trace(weighted_product)
        logger.debug(f"Inner product result: {result}")
        return result if np.iscomplexobj(result) else float(result)
    except Exception as e:
        logger.error(f"Error computing inner product: {str(e)}")
        raise

check_conjugate_symmetry

check_conjugate_symmetry(a, b)

Check if the inner product satisfies the conjugate symmetry property: = * (complex conjugate).

For the weighted trace inner product, this property holds if the weight matrix is Hermitian (equal to its own conjugate transpose).

Parameters

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

Returns

bool True if conjugate symmetry holds, False otherwise

Source code in swarmauri_standard/inner_products/TraceFormWeightedInnerProduct.py

def check_conjugate_symmetry(
    self, a: Union[Vector, Matrix, Callable], b: Union[Vector, Matrix, Callable]
) -> bool:
    """
    Check if the inner product satisfies the conjugate symmetry property:
    <a, b> = <b, a>* (complex conjugate).

    For the weighted trace inner product, this property holds if the weight matrix is Hermitian
    (equal to its own conjugate transpose).

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

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

    # First, check if the weight matrix is Hermitian
    is_hermitian = np.allclose(
        self.weight_matrix, self.weight_matrix.T.conj(), rtol=1e-5, atol=1e-8
    )

    if not is_hermitian:
        logger.debug(
            "Weight matrix is not Hermitian, conjugate symmetry does not hold"
        )
        return False

    # If weight matrix is Hermitian, compute both inner products to verify
    try:
        ip_ab = self.compute(a, b)
        ip_ba = self.compute(b, a)

        # For complex matrices, we need to take the complex conjugate
        ip_ba_conj = np.conj(ip_ba)

        # Use more relaxed tolerances for complex numbers
        result = np.isclose(ip_ab, ip_ba_conj, rtol=1e-4, atol=1e-6)

        logger.debug(
            f"Conjugate symmetry values: <a,b>={ip_ab}, <b,a>*={ip_ba_conj}, diff={abs(ip_ab - ip_ba_conj)}"
        )
        return bool(result)
    except Exception as e:
        logger.error(f"Error checking conjugate symmetry: {str(e)}")
        return False

check_linearity_first_argument

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

Check if the inner product satisfies linearity in the first argument: = alpha + beta.

Parameters

a1 : Union[Vector, Matrix, Callable] First component of the first argument a2 : Union[Vector, Matrix, Callable] Second component of the first argument b : Union[Vector, Matrix, Callable] 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/TraceFormWeightedInnerProduct.py

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

    Parameters
    ----------
    a1 : Union[Vector, Matrix, Callable]
        First component of the first argument
    a2 : Union[Vector, Matrix, Callable]
        Second component of the first argument
    b : Union[Vector, Matrix, Callable]
        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 in first argument with alpha={alpha}, beta={beta}"
    )

    try:
        # Convert inputs to numpy arrays if they aren't already
        if not isinstance(a1, np.ndarray):
            a1 = np.array(a1)
        if not isinstance(a2, np.ndarray):
            a2 = np.array(a2)
        if not isinstance(b, np.ndarray):
            b = np.array(b)

        # Check if dimensions are compatible
        if a1.shape != a2.shape:
            logger.error(
                f"Incompatible dimensions: a1.shape={a1.shape}, a2.shape={a2.shape}"
            )
            return False

        # Compute left-hand side: <alpha*a1 + beta*a2, b>
        linear_combination = alpha * a1 + beta * a2
        lhs = self.compute(linear_combination, b)

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

        result = np.isclose(lhs, rhs)
        logger.debug(f"Linearity check result: {result}")
        return bool(result)
    except Exception as e:
        logger.error(f"Error checking linearity: {str(e)}")
        return False

check_positivity

check_positivity(a)

Check if the inner product satisfies the positivity property: >= 0 and = 0 iff a = 0.

For the weighted trace inner product, this property holds if the weight matrix is positive semi-definite.

Parameters

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

Returns

bool True if positivity holds, False otherwise

Source code in swarmauri_standard/inner_products/TraceFormWeightedInnerProduct.py

def check_positivity(self, a: Union[Vector, Matrix, Callable]) -> bool:
    """
    Check if the inner product satisfies the positivity property:
    <a, a> >= 0 and <a, a> = 0 iff a = 0.

    For the weighted trace inner product, this property holds if the weight matrix is positive semi-definite.

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

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

    # First, check if the weight matrix is positive semi-definite
    try:
        # Compute eigenvalues of the weight matrix
        eigenvalues = np.linalg.eigvalsh(self.weight_matrix)
        is_psd = np.all(eigenvalues >= -1e-10)  # Allow for small numerical errors

        if not is_psd:
            logger.debug(
                "Weight matrix is not positive semi-definite, positivity does not hold"
            )
            return False

        # Convert input to numpy array if it isn't already
        if not isinstance(a, np.ndarray):
            a = np.array(a)

        # Check if a is zero
        is_zero = np.allclose(a, np.zeros_like(a))

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

        # Check if inner product is non-negative
        is_non_negative = (
            inner_product >= -1e-10
        )  # Allow for small numerical errors

        # Check if <a, a> = 0 iff a = 0
        if is_zero:
            is_zero_condition = np.isclose(inner_product, 0)
        else:
            is_zero_condition = inner_product > 0

        result = is_non_negative and is_zero_condition
        logger.debug(f"Positivity check result: {result}")
        return bool(result)
    except Exception as e:
        logger.error(f"Error checking positivity: {str(e)}")
        return False

set_weight_matrix

set_weight_matrix(weight_matrix)

Set a new weight matrix for the inner product calculations.

Parameters

weight_matrix : np.ndarray The new weight matrix to use

Source code in swarmauri_standard/inner_products/TraceFormWeightedInnerProduct.py

def set_weight_matrix(self, weight_matrix: np.ndarray) -> None:
    """
    Set a new weight matrix for the inner product calculations.

    Parameters
    ----------
    weight_matrix : np.ndarray
        The new weight matrix to use
    """
    logger.info(f"Setting new weight matrix of shape {weight_matrix.shape}")
    self.weight_matrix = np.array(weight_matrix)

get_weight_matrix

get_weight_matrix()

Get the current weight matrix.

Returns

np.ndarray The current weight matrix

Source code in swarmauri_standard/inner_products/TraceFormWeightedInnerProduct.py

def get_weight_matrix(self) -> np.ndarray:
    """
    Get the current weight matrix.

    Returns
    -------
    np.ndarray
        The current weight matrix
    """
    return self.weight_matrix

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