;ò
/’Fc           @   ss   d  Z  d k l Z d k l Z d e e f d „  ƒ  YZ d k l Z d e f d „  ƒ  YZ d f  d	 „  ƒ  YZ d
 S(   s‘  Modelling random variates.

A random variate's model needs to be able to generate `unpredictably'
(chaotically or pseudo-randomly) from the vairate's distribution; it also needs
to be able to perform the integration needed to determine moments (e.g. the
mean) and the probability of the variate falling in any given range of its
permitted values.

$Id: variate.py,v 1.2 2007/03/24 15:57:04 eddy Exp $
(   s
   Integrator(   s   Lazys   Variatec           B   sJ   t  Z e i Z e d „ Z d „  Z d „  Z d „  Z d „  Z	 d „  Z
 RS(   Nc         C   s   |  i | | ƒ g  |  _ d  S(   N(   s   selfs   _Variate__upinits   funcs   widths   _Variate__moments(   s   selfs   funcs   width(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   __init__   s    c         C   sC   |  i | d „ ƒ } | i | ƒ | i | | ƒ | i | ƒ Sd  S(   Nc         C   s   |  | S(   N(   s   xs   j(   s   xs   j(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   <lambda>   s    (   s   selfs   measures   is   gs   befores   ss   betweens   beyond(   s   selfs   is   ss   g(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   __moment   s    c         C   s[   |  i ƒ  } |  i | ƒ |  i | ƒ } t t | | |  i d „ t	 d d | ƒ ƒ ƒ Sd S(   s  Returns expected values of various powers of the variate.

        Single argument, n, will be the length of the tuple returned: its
        entry[i] is the expected value of self.sample()**(1+i).  Thus the first
        entry is the mean, the second is the `mean square' and so on. c         C   s   | |  | ƒ | S(   N(   s   ms   is   ss   t(   s   is   ss   ts   m(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   <lambda>#   s    i   N(   s   selfs   samples   seeds   befores   beyonds   totals   tuples   maps   _Variate__moments   ranges   n(   s   selfs   ns   seeds   total(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   moments   s
     c         C   s   |  i d ƒ d Sd  S(   Ni   i    (   s   selfs   moments(   s   selfs   ignored(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   _lazy_get_mean_&   s    c         C   s+   |  i d ƒ \ |  _ } | |  i d Sd  S(   Ni   (   s   selfs   momentss   means   two(   s   selfs   ignoreds   two(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   _lazy_get_variance_)   s    c         C   s
   t  ‚ d S(   s  Returns a sample value from this distribution.

        Each call returns a fresh value independently of previous calls.
        This method should be over-ridden in derived classes.
        See RatioGenerator for a generic algorithm for implementing samples.
        N(   s   NotImplementedError(   s   self(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   sample-   s     (   s   __name__s
   __module__s
   Integrators   __init__s   _Variate__upinits   Nones   _Variate__moments   momentss   _lazy_get_mean_s   _lazy_get_variance_s   sample(    (    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   Variate   s   					(   s   randoms   Uniformc           B   s2   t  Z e i Z d e d „ Z d „  Z d „  Z RS(   Ni   c         C   sr   | t j o d | f \ } } n | | | | f \ |  _ |  _ |  _ d |  i |  _ |  i |  i	 |  i ƒ d  S(   Ni    f1.0(
   s   his   Nones   los   selfs   mins   maxs   widths   _Uniform__heights   _Uniform__upinits   _Uniform__p(   s   selfs   los   hi(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   __init__9   s
     %c         C   s>   |  i | j  o |  i j  n o |  i Sn d |  i Sd  S(   Ni    (   s   selfs   mins   xs   maxs   _Uniform__height(   s   selfs   x(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   __p?   s    $ c         C   s   t  ƒ  |  i |  i Sd  S(   N(   s   randoms   selfs   widths   min(   s   self(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   sampleC   s    (   s   __name__s
   __module__s   Variates   __init__s   _Uniform__upinits   Nones   _Uniform__ps   sample(    (    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   Uniform7   s   		s   RatioGeneratorc           B   s    t  Z d  Z d „  Z d „  Z RS(   s0  Illustration of a technique.

    If source() generates samples from a distribution g, ratio = (: f(y)/g(y)
    &larr;y :), and (|ratio|source) is bounded below by 0 and above by bound,
    calls to the object returned by RatioGenerator(source, ratio, bound) will be
    samples from the distribution f. c         C   s%   | | | f \ |  _ |  _ |  _ d  S(   N(   s   sources   ratios   bounds   selfs   _RatioGenerator__sources   _RatioGenerator__ratios   _RatioGenerator__bound(   s   selfs   sources   ratios   bound(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   __init__N   s    c         C   sD   x= n o5 |  i ƒ  } |  i | ƒ t ƒ  t j o | Sq q
 Wd S(   s   The ratio test algorithm.i   N(   s   selfs   _RatioGenerator__sources   anss   _RatioGenerator__ratios   randoms   bound(   s   selfs   ans(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   __call__Q   s      (   s   __name__s
   __module__s   __doc__s   __init__s   __call__(    (    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   RatioGeneratorF   s    	N(	   s   __doc__s	   integrates
   Integrators   study.value.lazys   Lazys   Variates   randoms   Uniforms   RatioGenerator(   s   Lazys
   Integrators   randoms   Uniforms   RatioGenerators   Variate(    (    s)   /home/eddy/.sys/py/study/maths/variate.pys   ?
   s   '