Mass Functions

Unit Mass Functions

class murasyp.massfuncs.UMFunc(data={})

(Unit) mass functions map states or events to abstract mass values

This class derives from Vector, so its methods apply here as well.

What has changed:

• There is a new constructor. If data is not a Mapping, but is a Hashable Container, then the mass is spread uniformly over its components.

  >>> assert UMFunc('abc') == UMFunc({'a': '1/3', 'c': '1/3', 'b': '1/3'})
  

• Its total mass, i.e., the sum of its values, is one and its domain coincides with its support.

  >>> assert (
  ...     UMFunc({'a': -.6, 'b': 1.2, 'c': 1.4, 'd': 0}) ==
  ...     UMFunc({'a': '-3/10', 'c': '7/10', 'b': '3/5'})
  ... )
  >>> UMFunc({'a': -1, 'b': 1})
  Traceback (most recent call last):
    ...
  ValueError: no UMFunc can be constructed from a Mapping {'a': -1, 'b': 1} with ...
  

• Restriction becomes conditioning, i.e., it includes renormalization and may be impossible if the conditioning event has zero net mass assigned.

  >>> assert (
  ...     UMFunc({'a': -.6, 'b': 1.2, 'c': 1.4}) | {'a', 'b', 'd'} ==
  ...     UMFunc({'a': -1, 'b': 2})
  ... )
  >>> UMFunc({'a': -1, 'b': 1, 'd': 1}) | {'a', 'c', 'd'}
  Traceback (most recent call last):
    ...
  ValueError: ...
  

• Mass functions can be used to to express weighted sums of Gamble values using standard product notation (think ‘expectation’ if the mass function is a PMFunc).

  >>> m = UMFunc({'a': 1.6, 'b': -.6})
  >>> f = Gamble({'a': 13, 'b': -3})
  >>> m * f
  Fraction(113, 5)
  

  Take note, however, that the domain of the gamble acts as a conditioning event (so one can calculate conditional expectations without explicitly calculating conditional mass functions).

  >>> m = UMFunc({'a': '1/3', 'b': '1/6', 'c': '1/2'})
  >>> f = Gamble({'a': 1, 'b': -1, 'c': 0})
  >>> m * f
  Fraction(1, 6)
  >>> m * (f | f.support())
  Fraction(1, 3)
  >>> (m | f.support()) * f
  Fraction(1, 3)
  

• Arithmetic with unit mass functions results in a vector, which may be converted to a unit mass function in case it satisfies the conditions.

  >>> m = UMFunc({'a': 1.7, 'b': -.7})
  >>> n = UMFunc({'a': .5, 'b': .5})
  >>> assert m + n == Vector({'a': '11/5', 'b': '-1/5'})
  >>> assert UMFunc(.5 * m + n / 2) == UMFunc({'a': '11/10', 'b': '-1/10'})
  

Probability Mass Functions

class murasyp.massfuncs.PMFunc(data)

Probability mass functions map states to probability mass

This class derives from UMFunc, so its methods apply here as well.

What has changed:

• Its values have to be nonnegative.

  >>> PMFunc({'a': -1, 'b': 2})
  Traceback (most recent call last):
  ...
  ValueError: no PMFunc can be constructed from a Mapping {'a': -1, 'b': 2} with ...