# Distribution¶

class `negmas.helpers.``Distribution`(dtype: str, **kwargs)[source]

Bases: `object`

Parameters
• dtype (str) – Data type of the distribution as a string. It must be one defined in `scipy.stats`

• loc (float) – The location of the distribution (corresponds to mean in Gaussian)

• scale (float) – The _scale of the distribution (corresponds to standard deviation in Gaussian)

• multipliers – An iterable of other distributon to multiply with this one

• adders – An iterable of other utility_priors to add to this one

• **kwargs

Examples

```>>> d2 = Distribution('uniform')
>>> print(d2.mean())
0.5
```
```>>> try:
...     d = Distribution('something crazy')
... except ValueError as e:
...     print(str(e))
Unknown distribution something crazy
```

Attributes Summary

Methods Summary

 `around`([value, range, uncertainty]) Generates a uniform distribution around the input value in the given range with given uncertainty `prob`(val) Returns the probability for the given value `sample`([size])

Attributes Documentation

`loc`
`scale`

Methods Documentation

classmethod `around`(value: float = 0.5, range: Tuple[float, float] = (0.0, 1.0), uncertainty: float = 0.5)negmas.helpers.Distribution[source]

Generates a uniform distribution around the input value in the given range with given uncertainty

Parameters
• value – The value to generate the distribution around

• range – The range of possible values

• uncertainty – The uncertainty level required. 0.0 means no uncertainty and 1.0 means full uncertainty

Returns

Distribution A uniform distribution around `value` with uncertainty (scale) `uncertainty`

`max`()[source]
`mean`()float[source]
`min`()[source]
`prob`(val: float)float[source]

Returns the probability for the given value

`sample`(size: int = 1)numpy.ndarray[source]