src/probability/probability.lisp

   1 ;;; -*- mode: lisp -*-
   2
   3 ;;; Time-stamp: <2010-11-07 11:29:30 tony>
   4 ;;; Creation:   <2010-11-06 01:51:20 tony>
   5 ;;; File:       probability.lisp
   6 ;;; Author:     AJ Rossini <blindglobe@gmail.com>
   7 ;;; Copyright:  (c)2010--, AJ Rossini.  Currently licensed under MIT
   8 ;;;             license.  See file LICENSE.mit in top-level directory
   9 ;;;             for information.
  10 ;;; Purpose:    Probability functions
  11
  12 ;;; What is this talk of 'release'? Klingons do not make software
  13 ;;; 'releases'.  Our software 'escapes', leaving a bloody trail of
  14 ;;; designers and quality assurance people in its wake.
  15
  16 ;;; This organization and structure is new to the 21st Century
  17 ;;; version..   Think, "21st Century Schizoid Man".
  18
  19
  20 ;;; Current computations are handled by leveraging the cl-variates and
  21 ;;; gsll packages, as they have flexibility and the capability to be
  22 ;;; reproducible.  This is just a stub for the interface/API that we
  23 ;;; would like to be able to use.
  24
  25 (in-package :cls-probability)
  26
  27 (defgeneric density (probability-law-instance value))
  28 (defmethod density ((pli probability-law) value))
  29
  30 (defgeneric distribution (probability-law-instance value))
  31
  32 (defgeneric quantile (probability-law-instance value))
  33
  34 (defgeneric interquartile-range (probability-law))
  35
  36 (defgeneric draw-variates (probability-law-instance n))
  37
  38 (defclass probability-parameters ()
  39   ()
  40   :documentation "Virtual class to denote prob parameters")
  41
  42 (defclass probability-law ()
  43   ((density-function
  44     :type prob-function
  45     :documention "density function, if exists")
  46    (mass-function
  47     :type prob-function
  48     :documention "function")
  49    (support-function
  50     :documentation "List of values for discrete mass functions, list of pairs denoting for ranges")
  51    (support-class
  52     :type symbol
  53     :documentation "'REAL, 'DISCRETE'")
  54    (parameters
  55     :type list)
  56    (prng-stream
  57     :type unif-stream
  58     :documentation "current underlying prng stream object, typically Uniform[0,1]"))
  59   (:documentation "sufficient data to compute probabilistic
  60   quantities.  Given the support of the probability law, and a
  61   function mapping the law to the prob result, we can compute, in an
  62   expensive manner, most quantities.  When feasible, we can accelerate
  63   this quite a bit.")
  64   ())
  65
  66
  67 #|
  68
  69 ;; We basically want to support the following style of construct:
  70
  71  (let ((my-abstract-law (make 'probability-law
  72                              :density/mass (gaussian-law
  73                                             :parameters '(:mean 5 :variance 3))
  74                              :seed 1324
  75                              :name "Gaussian(5,3)"
  76                              :documentation "model-distribution, used
  77                              for likelihoods, probabilities of
  78                              asympts, and other somethings."))
  79       (my-empirical-law (make 'probability-law
  80                               :density/mass (empirical-law data-vector-or-data-list)
  81                               :seed 415
  82                               :name "Empirical Law from observations"
  83                               :documentation "based on observations,
  84                                bootstrap/resampling style
  85                                probability.")))
  86   (mean my-law)
  87   (variance my-law)
  88   (standard-deviation my-law)
  89   (draw-variates my-law 10)
  90   ;; one of the following would return a number, the other would return a 'nil
  91   (probability-density-function my-law x)
  92   (probability-mass-function my-law x)
  93   (cumulative-distribution-function my-law x)
  94   (survivorship-function my-law x)
  95   (hazard-function my-law x)
  96   (cumulative-hazard-function my-law x)
  97
  98
  99   (mean my-empirical-law) ; empirical mean
 100   (draw-variates my-empirical-law 10) ; bootstrap (unweighted)
 101   ;; the rest would consist of empirical
 102
 103   ())
 104
 105 |#
 106