Distribution

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

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.

Attributes Documentation

Returns the location of the distributon (usually mean)

Returns the maximum

Returns the minimum

scale

Returns the scale of the distribution (may be std. dev.)

Methods Documentation

__call__(val)[source]

Returns the probability for the given value

abstract cum_prob(mn, mx)[source]

Returns the probability for the given range

Returns true if this is a distribution with all probability at one point (delta(v))

abstract is_gaussian()[source]

Returns true if this is a gaussian distribution

abstract is_uniform()[source]

Returns true if this is a uniform distribution

abstract mean()[source]

Finds the mean

abstract prob(val)[source]

Returns the probability for the given value

abstract sample(size=1)[source]

Samples size elements from the distribution

abstract type()[source]

Returns the distribution type (e.g. uniform, normal, …)