Class swarmauri_matrix_hamming74.Hamming74Matrix.Hamming74Matrix

swarmauri_matrix_hamming74.Hamming74Matrix.Hamming74Matrix

Hamming74Matrix(data=None)

Bases: MatrixBase

Concrete matrix that models the Hamming (7,4) linear block code.

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def __init__(self, data: Optional[Sequence[Sequence[int]]] = None) -> None:
    source = _DEFAULT_MATRIX if data is None else np.array(data, dtype=int)
    if source.ndim != 2:
        raise ValueError("Hamming74Matrix requires a 2D array of integers")
    self._data = np.mod(source, 2)
    self._metric = HammingMetric()

type class-attribute instance-attribute

type = 'Hamming74Matrix'

shape property

shape

dtype property

dtype

generator_matrix property

generator_matrix

Return the 4x7 generator matrix G.

parity_check_matrix property

parity_check_matrix

Return the 3x7 parity-check matrix H.

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 = Field(default=MATRIX.value)

version class-attribute instance-attribute

version = '0.1.0'

reshape

reshape(shape)

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def reshape(self, shape: Tuple[int, int]) -> "Hamming74Matrix":
    return Hamming74Matrix(self._data.reshape(shape))

tolist

tolist()

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def tolist(self) -> List[List[int]]:
    return self._data.tolist()

row

row(index)

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def row(self, index: int) -> np.ndarray:
    return self._data[index, :].copy()

column

column(index)

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def column(self, index: int) -> np.ndarray:
    return self._data[:, index].copy()

transpose

transpose()

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def transpose(self) -> "Hamming74Matrix":
    return Hamming74Matrix(self._data.T)

encode

encode(message)

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def encode(self, message: Sequence[int]) -> List[int]:
    bits = np.mod(np.array(message, dtype=int), 2)
    if bits.size != 4:
        raise ValueError("Hamming (7,4) encoding expects exactly 4 message bits")
    codeword = np.mod(bits @ self._data, 2)
    return codeword.astype(int).tolist()

syndrome

syndrome(word)

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def syndrome(self, word: Sequence[int]) -> List[int]:
    bits = np.mod(np.array(word, dtype=int), 2)
    if bits.size != 7:
        raise ValueError("Syndrome calculation expects a 7-bit codeword")
    result = np.mod(self.parity_check_matrix @ bits, 2)
    return result.astype(int).tolist()

codewords

codewords()

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def codewords(self) -> List[List[int]]:
    return [self.encode(message) for message in product([0, 1], repeat=4)]

nearest_codeword

nearest_codeword(word)

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def nearest_codeword(self, word: MetricInput) -> List[int]:
    candidate = self._normalise_vector(word)
    if candidate.size != 7:
        raise ValueError("Nearest codeword search expects 7-bit inputs")
    codebook = self.codewords()
    distances = [
        self._metric.distance(candidate.tolist(), codeword) for codeword in codebook
    ]
    index = int(np.argmin(distances))
    return codebook[index]

decode

decode(word)

Decode a noisy 7-bit sequence into (message, codeword).

Source code in swarmauri_matrix_hamming74/Hamming74Matrix.py

def decode(self, word: MetricInput) -> Tuple[List[int], List[int]]:
    """Decode a noisy 7-bit sequence into ``(message, codeword)``."""

    nearest = self.nearest_codeword(word)
    message = nearest[:4]
    return message, nearest

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