Class swarmauri_standard.inner_products.EuclideanInnerProduct.EuclideanInnerProduct

swarmauri_standard.inner_products.EuclideanInnerProduct.EuclideanInnerProduct

Bases: InnerProductBase

Euclidean Inner Product implementation for real-valued vectors.

This class implements the standard dot product used in Euclidean geometry for real vector spaces. It computes the L2 inner product over real-valued, finite-dimensional vectors.

Attributes

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

type class-attribute instance-attribute

type = 'EuclideanInnerProduct'

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 Euclidean inner product (dot product) between two vectors.

Parameters

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

Returns

float The inner product value

Raises

ValueError If inputs are not compatible for dot product computation TypeError If inputs are not numeric arrays or vectors

Source code in swarmauri_standard/inner_products/EuclideanInnerProduct.py

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

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

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

    Raises
    ------
    ValueError
        If inputs are not compatible for dot product computation
    TypeError
        If inputs are not numeric arrays or vectors
    """
    logger.debug(
        f"Computing Euclidean inner product between {type(a)} and {type(b)}"
    )

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

        # Check if inputs are numeric
        if not np.issubdtype(a_array.dtype, np.number) or not np.issubdtype(
            b_array.dtype, np.number
        ):
            raise TypeError("Input vectors must contain numeric values")

        # Check if inputs are finite
        if not np.all(np.isfinite(a_array)) or not np.all(np.isfinite(b_array)):
            raise ValueError("Input vectors must contain only finite values")

        # Check if inputs have compatible dimensions for dot product
        if a_array.shape != b_array.shape:
            raise ValueError(
                f"Input vectors must have the same shape. Got {a_array.shape} and {b_array.shape}"
            )

        # Compute the dot product
        result = np.sum(a_array * b_array)
        logger.debug(f"Euclidean inner product result: {result}")
        return float(result)

    except Exception as e:
        logger.error(f"Error computing Euclidean inner product: {str(e)}")
        raise

check_conjugate_symmetry

check_conjugate_symmetry(a, b)

Check if the Euclidean inner product satisfies the conjugate symmetry property: = for real vectors.

Parameters

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

Returns

bool True if conjugate symmetry holds, False otherwise

Source code in swarmauri_standard/inner_products/EuclideanInnerProduct.py

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

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

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

    try:
        # Compute inner products in both directions
        ab_product = self.compute(a, b)
        ba_product = self.compute(b, a)

        # For real vectors, the products should be exactly equal
        is_symmetric = np.isclose(ab_product, ba_product)

        logger.debug(
            f"Conjugate symmetry check result: {is_symmetric} (<a,b>={ab_product}, <b,a>={ba_product})"
        )
        return bool(is_symmetric)

    except Exception as e:
        logger.error(f"Error checking conjugate symmetry: {str(e)}")
        raise

check_linearity_first_argument

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

Check if the Euclidean 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 vector 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/EuclideanInnerProduct.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 Euclidean 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 vector
    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
        a1_array = np.array(a1) if not isinstance(a1, np.ndarray) else a1
        a2_array = np.array(a2) if not isinstance(a2, np.ndarray) else a2

        # Check if a1 and a2 have the same shape
        if a1_array.shape != a2_array.shape:
            raise ValueError(
                f"a1 and a2 must have the same shape. Got {a1_array.shape} and {a2_array.shape}"
            )

        # Compute left side of the equation: <alpha*a1 + beta*a2, b>
        combined = alpha * a1_array + beta * a2_array
        left_side = self.compute(combined, b)

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

        # Check if both sides are equal (within numerical precision)
        is_linear = np.isclose(left_side, right_side)

        logger.debug(
            f"Linearity check result: {is_linear} (left={left_side}, right={right_side})"
        )
        return bool(is_linear)

    except Exception as e:
        logger.error(f"Error checking linearity: {str(e)}")
        raise

check_positivity

check_positivity(a)

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

Parameters

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

Returns

bool True if positivity holds, False otherwise

Source code in swarmauri_standard/inner_products/EuclideanInnerProduct.py

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

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

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

    try:
        # Convert input to numpy array
        a_array = np.array(a) if not isinstance(a, np.ndarray) else a

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

        # Check if <a, a> >= 0
        is_nonnegative = self_product >= 0

        # Check if <a, a> = 0 iff a = 0
        is_zero_iff_a_zero = (self_product == 0 and np.allclose(a_array, 0)) or (
            self_product > 0 and not np.allclose(a_array, 0)
        )

        result = is_nonnegative and is_zero_iff_a_zero
        logger.debug(
            f"Positivity check result: {result} (self-product={self_product})"
        )
        return result

    except Exception as e:
        logger.error(f"Error checking positivity: {str(e)}")
        raise

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