Bases: MetricBase
Euclidean metric (L2 distance) implementation.
This class implements the standard Euclidean distance metric, which is the
straight-line distance between two points in Euclidean space, computed as the
square root of the sum of the squared differences between corresponding coordinates.
The Euclidean distance satisfies all metric axioms:
- Non-negativity: d(x,y) ≥ 0
- Identity of indiscernibles: d(x,y) = 0 if and only if x = y
- Symmetry: d(x,y) = d(y,x)
- Triangle inequality: d(x,z) ≤ d(x,y) + d(y,z)
Attributes
type : Literal["EuclideanMetric"]
The specific type of metric.
resource : str, optional
The resource type, defaults to METRIC.
type
class-attribute
instance-attribute
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
owners
class-attribute
instance-attribute
host
class-attribute
instance-attribute
default_logger
class-attribute
logger
class-attribute
instance-attribute
name
class-attribute
instance-attribute
resource
class-attribute
instance-attribute
version
class-attribute
instance-attribute
distance
Calculate the Euclidean distance between two points.
Parameters
x : MetricInput
First point
y : MetricInput
Second point
Returns
float
The Euclidean distance between x and y
Raises
ValueError
If inputs have different dimensions or are incompatible
TypeError
If input types are not supported
Source code in swarmauri_standard/metrics/EuclideanMetric.py
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109 | def distance(self, x: MetricInput, y: MetricInput) -> float:
"""
Calculate the Euclidean distance between two points.
Parameters
----------
x : MetricInput
First point
y : MetricInput
Second point
Returns
-------
float
The Euclidean distance between x and y
Raises
------
ValueError
If inputs have different dimensions or are incompatible
TypeError
If input types are not supported
"""
logger.debug(f"Calculating Euclidean distance between {x} and {y}")
# Handle different input types
if isinstance(x, IVector) and isinstance(y, IVector):
# For vector objects
if len(x) != len(y):
raise ValueError(
f"Vectors must have the same dimension: {len(x)} != {len(y)}"
)
# Get numeric values from vectors
x_values = x.to_numpy()
y_values = y.to_numpy()
# Calculate Euclidean distance
return math.sqrt(
sum((x_i - y_i) ** 2 for x_i, y_i in zip(x_values, y_values))
)
elif (
isinstance(x, Sequence)
and isinstance(y, Sequence)
and not isinstance(x, str)
and not isinstance(y, str)
):
# For general sequences (lists, tuples, etc.)
if len(x) != len(y):
raise ValueError(
f"Sequences must have the same length: {len(x)} != {len(y)}"
)
try:
return math.sqrt(sum((x_i - y_i) ** 2 for x_i, y_i in zip(x, y)))
except (TypeError, ValueError) as e:
logger.error(f"Failed to compute Euclidean distance for sequences: {e}")
raise ValueError(
f"Cannot compute Euclidean distance for sequences with non-numeric elements: {e}"
)
else:
logger.error(
f"Unsupported input types for Euclidean distance: {type(x)} and {type(y)}"
)
raise TypeError(
f"Euclidean distance computation not supported for types {type(x)} and {type(y)}"
)
|
distances
Calculate Euclidean distances between collections of points.
Parameters
x : Union[MetricInput, MetricInputCollection]
First collection of points
y : Union[MetricInput, MetricInputCollection]
Second collection of points
Returns
Union[List[float], IVector, IMatrix]
Matrix or vector of Euclidean distances between points in x and y
Raises
ValueError
If inputs are incompatible
TypeError
If input types are not supported
Source code in swarmauri_standard/metrics/EuclideanMetric.py
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189 | def distances(
self,
x: Union[MetricInput, MetricInputCollection],
y: Union[MetricInput, MetricInputCollection],
) -> Union[List[float], IVector, IMatrix]:
"""
Calculate Euclidean distances between collections of points.
Parameters
----------
x : Union[MetricInput, MetricInputCollection]
First collection of points
y : Union[MetricInput, MetricInputCollection]
Second collection of points
Returns
-------
Union[List[float], IVector, IMatrix]
Matrix or vector of Euclidean distances between points in x and y
Raises
------
ValueError
If inputs are incompatible
TypeError
If input types are not supported
"""
logger.debug("Calculating Euclidean distances between collections")
# Handle different collection types
if isinstance(x, IMatrix) and isinstance(y, IMatrix):
# For matrix objects - compute pairwise distances between rows
if x.shape[1] != y.shape[1]:
raise ValueError(
f"Points must have the same dimension: {x.shape[1]} != {y.shape[1]}"
)
# Create distance matrix
result = [[self.distance(x_row, y_row) for y_row in y] for x_row in x]
return result
elif (
isinstance(x, list)
and isinstance(y, list)
and all(isinstance(item, (list, tuple)) for item in x)
and all(isinstance(item, (list, tuple)) for item in y)
):
# For lists of lists/tuples (representing collections of points)
# Check if all points have the same dimension
x_dims = [len(point) for point in x]
y_dims = [len(point) for point in y]
if len(set(x_dims + y_dims)) != 1:
raise ValueError("All points must have the same dimension")
# Compute pairwise distances
result = [
[self.distance(x_point, y_point) for y_point in y] for x_point in x
]
return result
elif isinstance(x, IVector) and isinstance(y, IVector):
# Single distance between two vectors
return [self.distance(x, y)]
elif isinstance(x, list) and isinstance(y, list):
# If x and y are simple lists (not lists of lists), treat them as individual points
if not any(isinstance(item, (list, tuple)) for item in x + y):
return [self.distance(x, y)]
else:
logger.error("Inconsistent collection structure")
raise ValueError("Inconsistent collection structure")
else:
logger.error(f"Unsupported collection types: {type(x)} and {type(y)}")
raise TypeError(
f"Euclidean distances computation not supported for types {type(x)} and {type(y)}"
)
|
check_non_negativity
check_non_negativity(x, y)
Check if the Euclidean metric satisfies the non-negativity axiom: d(x,y) ≥ 0.
Parameters
x : MetricInput
First point
y : MetricInput
Second point
Returns
bool
True if the axiom is satisfied, which is always the case for Euclidean distance
Source code in swarmauri_standard/metrics/EuclideanMetric.py
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213 | def check_non_negativity(self, x: MetricInput, y: MetricInput) -> bool:
"""
Check if the Euclidean metric satisfies the non-negativity axiom: d(x,y) ≥ 0.
Parameters
----------
x : MetricInput
First point
y : MetricInput
Second point
Returns
-------
bool
True if the axiom is satisfied, which is always the case for Euclidean distance
"""
try:
dist = self.distance(x, y)
logger.debug(f"Checking non-negativity axiom: distance = {dist}")
return dist >= 0 # Euclidean distance is always non-negative
except (TypeError, ValueError) as e:
logger.error(f"Error checking non-negativity: {e}")
return False
|
check_identity_of_indiscernibles
check_identity_of_indiscernibles(x, y)
Check if the Euclidean metric satisfies the identity of indiscernibles axiom:
d(x,y) = 0 if and only if x = y.
Parameters
x : MetricInput
First point
y : MetricInput
Second point
Returns
bool
True if the axiom is satisfied
Source code in swarmauri_standard/metrics/EuclideanMetric.py
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261 | def check_identity_of_indiscernibles(self, x: MetricInput, y: MetricInput) -> bool:
"""
Check if the Euclidean metric satisfies the identity of indiscernibles axiom:
d(x,y) = 0 if and only if x = y.
Parameters
----------
x : MetricInput
First point
y : MetricInput
Second point
Returns
-------
bool
True if the axiom is satisfied
"""
try:
dist = self.distance(x, y)
# Check if distance is zero
is_zero_dist = (
abs(dist) < 1e-10
) # Using small epsilon for floating point comparison
# Check if points are equal
if isinstance(x, IVector) and isinstance(y, IVector):
is_equal = len(x) == len(y) and all(
abs(x_i - y_i) < 1e-10 for x_i, y_i in zip(x, y)
)
elif isinstance(x, Sequence) and isinstance(y, Sequence):
is_equal = len(x) == len(y) and all(
abs(x_i - y_i) < 1e-10 for x_i, y_i in zip(x, y)
)
else:
is_equal = x == y
logger.debug(
f"Checking identity axiom: distance = {dist}, points equal: {is_equal}"
)
# Axiom is satisfied if distance is zero iff points are equal
return (is_zero_dist and is_equal) or (not is_zero_dist and not is_equal)
except (TypeError, ValueError) as e:
logger.error(f"Error checking identity of indiscernibles: {e}")
return False
|
check_symmetry
Check if the Euclidean metric satisfies the symmetry axiom: d(x,y) = d(y,x).
Parameters
x : MetricInput
First point
y : MetricInput
Second point
Returns
bool
True if the axiom is satisfied
Source code in swarmauri_standard/metrics/EuclideanMetric.py
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293 | def check_symmetry(self, x: MetricInput, y: MetricInput) -> bool:
"""
Check if the Euclidean metric satisfies the symmetry axiom: d(x,y) = d(y,x).
Parameters
----------
x : MetricInput
First point
y : MetricInput
Second point
Returns
-------
bool
True if the axiom is satisfied
"""
try:
dist_xy = self.distance(x, y)
dist_yx = self.distance(y, x)
# Check if the distances are equal (within floating point precision)
is_symmetric = abs(dist_xy - dist_yx) < 1e-10
logger.debug(
f"Checking symmetry axiom: d(x,y) = {dist_xy}, d(y,x) = {dist_yx}, symmetric: {is_symmetric}"
)
return is_symmetric
except (TypeError, ValueError) as e:
logger.error(f"Error checking symmetry: {e}")
return False
|
check_triangle_inequality
check_triangle_inequality(x, y, z)
Check if the Euclidean metric satisfies the triangle inequality axiom:
d(x,z) ≤ d(x,y) + d(y,z).
Parameters
x : MetricInput
First point
y : MetricInput
Second point
z : MetricInput
Third point
Returns
bool
True if the axiom is satisfied
Source code in swarmauri_standard/metrics/EuclideanMetric.py
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336 | def check_triangle_inequality(
self, x: MetricInput, y: MetricInput, z: MetricInput
) -> bool:
"""
Check if the Euclidean metric satisfies the triangle inequality axiom:
d(x,z) ≤ d(x,y) + d(y,z).
Parameters
----------
x : MetricInput
First point
y : MetricInput
Second point
z : MetricInput
Third point
Returns
-------
bool
True if the axiom is satisfied
"""
try:
# Calculate the three distances
dist_xy = self.distance(x, y)
dist_yz = self.distance(y, z)
dist_xz = self.distance(x, z)
# Check triangle inequality
satisfies_inequality = (
dist_xz <= dist_xy + dist_yz + 1e-10
) # Adding epsilon for floating point precision
logger.debug(
f"Checking triangle inequality: d(x,z) = {dist_xz}, d(x,y) + d(y,z) = {dist_xy + dist_yz}, "
+ f"inequality satisfied: {satisfies_inequality}"
)
return satisfies_inequality
except (TypeError, ValueError) as e:
logger.error(f"Error checking triangle inequality: {e}")
return False
|
register_model
classmethod
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
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585 | @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
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654 | @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
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24 | @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
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56 | 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
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23 | @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
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55 | 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
|