nqs_sdk.bindings.distributions module

class nqs_sdk.bindings.distributions.Distribution(**data)

Bases: BaseModel, ABC

name: str

dtype: str

abstractmethod sample()

Return type:

Any

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class nqs_sdk.bindings.distributions.UniformDistribution(**data)

Bases: Distribution

name: str

min: float

max: float

sample()

Return type:

Union[int, float]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class nqs_sdk.bindings.distributions.PoissonDistribution(**data)

Bases: Distribution

name: str

lam: float

sample()

Return type:

int

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class nqs_sdk.bindings.distributions.NormalDistribution(**data)

Bases: Distribution

name: str

mean: float

std: float

sample()

Return type:

float

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class nqs_sdk.bindings.distributions.ExponentialDistribution(**data)

Bases: Distribution

name: str

lamb: float

sample()

Return type:

float

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class nqs_sdk.bindings.distributions.BinomialDistribution(**data)

Bases: Distribution

name: str

n: int

p: float

sample()

Return type:

int

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].