Class swarmauri_standard.inner_products.RKHSInnerProduct.RKHSInnerProduct

swarmauri_standard.inner_products.RKHSInnerProduct.RKHSInnerProduct

RKHSInnerProduct(kernel_function, **kwargs)

Bases: InnerProductBase

Implements inner product from a reproducing kernel.

This class induces an inner product via kernel evaluation in a Reproducing Kernel Hilbert Space (RKHS). The kernel function must be positive-definite to ensure that the induced inner product satisfies all properties of an inner product.

Attributes

type : Literal["RKHSInnerProduct"] The type identifier for this inner product implementation resource : str The resource type identifier, defaulting to INNER_PRODUCT kernel_function : Callable The kernel function used to compute the inner product

Initialize the RKHS inner product with a kernel function.

Parameters

kernel_function : Callable A positive-definite kernel function that takes two arguments and returns a scalar value **kwargs Additional keyword arguments

Source code in swarmauri_standard/inner_products/RKHSInnerProduct.py

def __init__(self, kernel_function: Callable, **kwargs):
    """
    Initialize the RKHS inner product with a kernel function.

    Parameters
    ----------
    kernel_function : Callable
        A positive-definite kernel function that takes two arguments
        and returns a scalar value
    **kwargs
        Additional keyword arguments
    """
    kwargs["kernel_function"] = kernel_function
    super().__init__(**kwargs)
    logger.info("Initialized RKHSInnerProduct with kernel function")

type class-attribute instance-attribute

type = 'RKHSInnerProduct'

kernel_function instance-attribute

kernel_function

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 inner product between two objects using the kernel function.

Parameters

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

Returns

float The inner product value computed using the kernel function

Raises

TypeError If the input types are not supported by the kernel function

Source code in swarmauri_standard/inner_products/RKHSInnerProduct.py

def compute(
    self, a: Union[Vector, Matrix, Callable], b: Union[Vector, Matrix, Callable]
) -> float:
    """
    Compute the inner product between two objects using the kernel function.

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

    Returns
    -------
    float
        The inner product value computed using the kernel function

    Raises
    ------
    TypeError
        If the input types are not supported by the kernel function
    """
    logger.debug(f"Computing RKHS inner product between {type(a)} and {type(b)}")
    try:
        result = self.kernel_function(a, b)
        logger.debug(f"Inner product result: {result}")
        return result
    except Exception as e:
        logger.error(f"Error computing RKHS inner product: {str(e)}")
        raise TypeError(f"Inputs not supported by kernel function: {str(e)}")

check_conjugate_symmetry

check_conjugate_symmetry(a, b)

Check if the kernel-induced inner product satisfies the conjugate symmetry property.

For real-valued kernels, this checks if K(a,b) = K(b,a). For complex-valued kernels, this checks if K(a,b) = K(b,a)*.

Parameters

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

Returns

bool True if conjugate symmetry holds, False otherwise

Source code in swarmauri_standard/inner_products/RKHSInnerProduct.py

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

    For real-valued kernels, this checks if K(a,b) = K(b,a).
    For complex-valued kernels, this checks if K(a,b) = K(b,a)*.

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

    Returns
    -------
    bool
        True if conjugate symmetry holds, False otherwise
    """
    logger.debug("Checking conjugate symmetry for RKHS inner product")

    # Compute inner products in both directions
    inner_ab = self.compute(a, b)
    inner_ba = self.compute(b, a)

    # Check if they are equal (for real values) or complex conjugates
    if isinstance(inner_ab, complex) or isinstance(inner_ba, complex):
        # For complex values, check conjugate symmetry
        is_symmetric = np.isclose(inner_ab, np.conj(inner_ba))
    else:
        # For real values, they should be equal
        is_symmetric = np.isclose(inner_ab, inner_ba)

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

check_linearity_first_argument

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

Check if the kernel-induced inner product satisfies linearity in the first argument.

This verifies if K(alphaa1 + betaa2, b) = alphaK(a1, b) + betaK(a2, b). Note: This check may not be applicable for all kernel functions, especially if they don't support linear combinations of inputs directly.

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 object alpha : float Scalar multiplier for a1 beta : float Scalar multiplier for a2

Returns

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

Raises

TypeError If the inputs don't support linear combinations

Source code in swarmauri_standard/inner_products/RKHSInnerProduct.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 kernel-induced inner product satisfies linearity in the first argument.

    This verifies if K(alpha*a1 + beta*a2, b) = alpha*K(a1, b) + beta*K(a2, b).
    Note: This check may not be applicable for all kernel functions, especially
    if they don't support linear combinations of inputs directly.

    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 object
    alpha : float
        Scalar multiplier for a1
    beta : float
        Scalar multiplier for a2

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

    Raises
    ------
    TypeError
        If the inputs don't support linear combinations
    """
    logger.debug(
        f"Checking linearity in first argument with alpha={alpha}, beta={beta}"
    )

    # Check if inputs support linear combinations
    if isinstance(a1, np.ndarray) and isinstance(a2, np.ndarray):
        # Compute the linear combination
        linear_combo = alpha * a1 + beta * a2

        # Compute the left side of the linearity equation
        left_side = self.compute(linear_combo, b)

        # Compute the right side of the linearity equation
        right_side = alpha * self.compute(a1, b) + beta * self.compute(a2, b)

        # Check if they are approximately equal
        is_linear = np.isclose(left_side, right_side)
        logger.debug(f"Linearity check result: {is_linear}")
        return is_linear
    else:
        logger.warning("Linearity check not supported for non-array inputs")
        raise TypeError("Linearity check requires numpy array inputs")

check_positivity

check_positivity(a)

Check if the kernel-induced inner product satisfies the positivity property.

This verifies if K(a, a) >= 0 for all a. For a valid positive-definite kernel, this property should always hold.

Parameters

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

Returns

bool True if positivity holds, False otherwise

Source code in swarmauri_standard/inner_products/RKHSInnerProduct.py

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

    This verifies if K(a, a) >= 0 for all a.
    For a valid positive-definite kernel, this property should always hold.

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

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

    # Compute the inner product of a with itself
    inner_aa = self.compute(a, a)

    # For a valid kernel, K(a,a) should always be non-negative
    is_positive = inner_aa >= 0

    logger.debug(f"Positivity check result: {is_positive}")
    return is_positive

is_positive_definite

is_positive_definite(vectors)

Check if the kernel function is positive definite.

This method constructs the Gram matrix for a set of vectors and checks if it is positive definite by verifying that all eigenvalues are positive.

Parameters

vectors : list A list of vectors to use for constructing the Gram matrix

Returns

bool True if the kernel is positive definite, False otherwise

Source code in swarmauri_standard/inner_products/RKHSInnerProduct.py

def is_positive_definite(self, vectors: list) -> bool:
    """
    Check if the kernel function is positive definite.

    This method constructs the Gram matrix for a set of vectors and
    checks if it is positive definite by verifying that all eigenvalues
    are positive.

    Parameters
    ----------
    vectors : list
        A list of vectors to use for constructing the Gram matrix

    Returns
    -------
    bool
        True if the kernel is positive definite, False otherwise
    """
    logger.debug(
        f"Checking if kernel is positive definite using {len(vectors)} vectors"
    )

    n = len(vectors)
    # Construct the Gram matrix
    gram_matrix = np.zeros((n, n))
    for i in range(n):
        for j in range(n):
            gram_matrix[i, j] = self.compute(vectors[i], vectors[j])

    # Check if the Gram matrix is positive definite
    # A matrix is positive definite if all eigenvalues are positive
    eigenvalues = np.linalg.eigvals(gram_matrix)
    is_pd = np.all(eigenvalues > -1e-10)  # Allow for numerical errors

    logger.debug(f"Positive definiteness check result: {is_pd}")
    return is_pd

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