Class swarmauri_standard.metrics.HammingMetric.HammingMetric

swarmauri_standard.metrics.HammingMetric.HammingMetric

Bases: MetricBase

Hamming distance metric implementation.

Hamming distance counts the number of positions at which two sequences differ. It is primarily used for binary/bitwise data and categorical vectors of equal length.

Attributes

type : Literal["HammingMetric"] The type identifier for this metric

type class-attribute instance-attribute

type = 'HammingMetric'

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 = METRIC.value

version class-attribute instance-attribute

version = '0.1.0'

distance

distance(x, y)

Calculate the Hamming distance between two sequences.

The Hamming distance is the number of positions at which the corresponding elements of two sequences are different.

Parameters

x : MetricInput First sequence y : MetricInput Second sequence

Returns

float The Hamming distance between x and y

Raises

ValueError If the sequences have different lengths TypeError If input types are not supported

Source code in swarmauri_standard/metrics/HammingMetric.py

def distance(self, x: MetricInput, y: MetricInput) -> float:
    """
    Calculate the Hamming distance between two sequences.

    The Hamming distance is the number of positions at which the corresponding
    elements of two sequences are different.

    Parameters
    ----------
    x : MetricInput
        First sequence
    y : MetricInput
        Second sequence

    Returns
    -------
    float
        The Hamming distance between x and y

    Raises
    ------
    ValueError
        If the sequences have different lengths
    TypeError
        If input types are not supported
    """
    logger.debug("Calculating Hamming distance between sequences")

    # Check if inputs are sequences
    if not isinstance(x, Sequence) or not isinstance(y, Sequence):
        raise TypeError("Inputs must be sequences")

    # Check if sequences have the same length
    if len(x) != len(y):
        raise ValueError(f"Sequences must have equal length: {len(x)} != {len(y)}")

    # Count positions where elements differ
    distance = sum(xi != yi for xi, yi in zip(x, y))

    logger.debug(f"Hamming distance: {distance}")
    return float(distance)

distances

distances(x, y)

Calculate Hamming distances between collections of sequences.

Parameters

x : Union[MetricInput, MetricInputCollection] First collection of sequences y : Union[MetricInput, MetricInputCollection] Second collection of sequences

Returns

Union[List[float], IVector, IMatrix] Matrix or vector of Hamming distances between sequences in x and y

Raises

ValueError If any pair of sequences have different lengths TypeError If input types are not supported

Source code in swarmauri_standard/metrics/HammingMetric.py

def distances(
    self,
    x: Union[MetricInput, MetricInputCollection],
    y: Union[MetricInput, MetricInputCollection],
) -> Union[List[float], IVector, IMatrix]:
    """
    Calculate Hamming distances between collections of sequences.

    Parameters
    ----------
    x : Union[MetricInput, MetricInputCollection]
        First collection of sequences
    y : Union[MetricInput, MetricInputCollection]
        Second collection of sequences

    Returns
    -------
    Union[List[float], IVector, IMatrix]
        Matrix or vector of Hamming distances between sequences in x and y

    Raises
    ------
    ValueError
        If any pair of sequences have different lengths
    TypeError
        If input types are not supported
    """
    logger.debug("Calculating Hamming distances between collections")

    # Handle numpy arrays
    if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
        # For 1D arrays (single sequences)
        if x.ndim == 1 and y.ndim == 1:
            if x.shape[0] != y.shape[0]:
                raise ValueError(
                    f"Sequences must have equal length: {x.shape[0]} != {y.shape[0]}"
                )
            return float(np.sum(x != y))

        # For 2D arrays (multiple sequences)
        elif x.ndim == 2 and y.ndim == 2:
            if x.shape[1] != y.shape[1]:
                raise ValueError(
                    f"Sequences must have equal length: {x.shape[1]} != {y.shape[1]}"
                )

            # Calculate distances between all pairs
            result = np.zeros((x.shape[0], y.shape[0]))
            for i in range(x.shape[0]):
                for j in range(y.shape[0]):
                    result[i, j] = np.sum(x[i] != y[j])
            return result.tolist()

    # Handle lists and other sequence types
    try:
        # Check if x and y are collections of sequences
        if isinstance(x, Sequence) and isinstance(y, Sequence):
            # If both are single sequences
            if not isinstance(x[0], Sequence) and not isinstance(y[0], Sequence):
                return [self.distance(x, y)]

            # If x is a collection and y is a single sequence
            elif isinstance(x[0], Sequence) and not isinstance(y[0], Sequence):
                return [self.distance(xi, y) for xi in x]

            # If x is a single sequence and y is a collection
            elif not isinstance(x[0], Sequence) and isinstance(y[0], Sequence):
                return [self.distance(x, yi) for yi in y]

            # If both are collections
            else:
                return [[self.distance(xi, yi) for yi in y] for xi in x]
    except (TypeError, IndexError):
        raise TypeError("Inputs must be collections of sequences or sequences")

    raise TypeError("Unsupported input types")

check_non_negativity

check_non_negativity(x, y)

Check if the Hamming metric satisfies the non-negativity axiom: d(x,y) ≥ 0.

Hamming distance always satisfies this axiom as it counts differences, which cannot be negative.

Parameters

x : MetricInput First sequence y : MetricInput Second sequence

Returns

bool True, as Hamming distance is always non-negative

Source code in swarmauri_standard/metrics/HammingMetric.py

def check_non_negativity(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the Hamming metric satisfies the non-negativity axiom: d(x,y) ≥ 0.

    Hamming distance always satisfies this axiom as it counts differences,
    which cannot be negative.

    Parameters
    ----------
    x : MetricInput
        First sequence
    y : MetricInput
        Second sequence

    Returns
    -------
    bool
        True, as Hamming distance is always non-negative
    """
    logger.debug("Checking non-negativity axiom for Hamming distance")
    # Hamming distance is always non-negative as it counts differences
    return True

check_identity_of_indiscernibles

check_identity_of_indiscernibles(x, y)

Check if the Hamming metric satisfies the identity of indiscernibles axiom: d(x,y) = 0 if and only if x = y.

Parameters

x : MetricInput First sequence y : MetricInput Second sequence

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/HammingMetric.py

def check_identity_of_indiscernibles(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the Hamming metric satisfies the identity of indiscernibles axiom:
    d(x,y) = 0 if and only if x = y.

    Parameters
    ----------
    x : MetricInput
        First sequence
    y : MetricInput
        Second sequence

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking identity of indiscernibles axiom for Hamming distance")

    # Calculate distance
    dist = self.distance(x, y)

    # Check if distance is 0 iff x equals y
    sequences_equal = x == y
    distance_zero = dist == 0

    return sequences_equal == distance_zero

check_symmetry

check_symmetry(x, y)

Check if the Hamming metric satisfies the symmetry axiom: d(x,y) = d(y,x).

Parameters

x : MetricInput First sequence y : MetricInput Second sequence

Returns

bool True, as Hamming distance is always symmetric

Source code in swarmauri_standard/metrics/HammingMetric.py

def check_symmetry(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the Hamming metric satisfies the symmetry axiom: d(x,y) = d(y,x).

    Parameters
    ----------
    x : MetricInput
        First sequence
    y : MetricInput
        Second sequence

    Returns
    -------
    bool
        True, as Hamming distance is always symmetric
    """
    logger.debug("Checking symmetry axiom for Hamming distance")

    # Calculate distances both ways
    dist_xy = self.distance(x, y)
    dist_yx = self.distance(y, x)

    # Check if they're equal
    return (
        abs(dist_xy - dist_yx) < 1e-10
    )  # Using small epsilon for float comparison

check_triangle_inequality

check_triangle_inequality(x, y, z)

Check if the Hamming metric satisfies the triangle inequality axiom: d(x,z) ≤ d(x,y) + d(y,z).

Parameters

x : MetricInput First sequence y : MetricInput Second sequence z : MetricInput Third sequence

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/HammingMetric.py

def check_triangle_inequality(
    self, x: MetricInput, y: MetricInput, z: MetricInput
) -> bool:
    """
    Check if the Hamming metric satisfies the triangle inequality axiom:
    d(x,z) ≤ d(x,y) + d(y,z).

    Parameters
    ----------
    x : MetricInput
        First sequence
    y : MetricInput
        Second sequence
    z : MetricInput
        Third sequence

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking triangle inequality axiom for Hamming distance")

    # Calculate the three distances
    dist_xz = self.distance(x, z)
    dist_xy = self.distance(x, y)
    dist_yz = self.distance(y, z)

    # Check if triangle inequality holds
    return (
        dist_xz <= dist_xy + dist_yz + 1e-10
    )  # Small epsilon for float comparison

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