Distribution

class negmas.preferences.Distribution(*args, **kwargs)[source]

A protocol representing a probability distribution

Attributes Summary

`loc`	Returns the location of the distributon (usually mean)
`max`	Returns the maximum
`min`	Returns the minimum
`scale`	Returns the scale of the distribution (may be std.

Methods Summary

`__call__`(val)	Returns the probability for the given value
`cum_prob`(mn, mx)	Returns the probability for the given range
`is_crisp`()	Returns true if this is a distribution with all probability at one point (delta(v))
`is_gaussian`()	Returns true if this is a gaussian distribution
`is_uniform`()	Returns true if this is a uniform distribution
`mean`()	Finds the mean
`prob`(val)	Returns the probability for the given value
`sample`([size])	Samples `size` elements from the distribution
`type`()	Returns the distribution type (e.g. uniform, normal, ...).

Attributes Documentation

Methods Documentation

__call__(val: float) → float[source]: Returns the probability for the given value

cum_prob(mn: float, mx: float) → float[source]: Returns the probability for the given range

is_crisp() → bool[source]: Returns true if this is a distribution with all probability at one point (delta(v))

is_gaussian() → bool[source]: Returns true if this is a gaussian distribution

is_uniform() → bool[source]: Returns true if this is a uniform distribution

prob(val: float) → float[source]: Returns the probability for the given value

sample(size: int = 1) → Iterable[float][source]: Samples size elements from the distribution

type() → str[source]: Returns the distribution type (e.g. uniform, normal, …)