TODO.lisp

   1 ;;; -*- mode: lisp -*-
   2
   3 ;;; Time-stamp: <2008-12-03 07:42:18 tony>
   4 ;;; Creation:   <2008-09-08 08:06:30 tony>
   5 ;;; File:       TODO.lisp
   6 ;;; Author:     AJ Rossini <blindglobe@gmail.com>
   7 ;;; Copyright:  (c) 2007-2008, AJ Rossini <blindglobe@gmail.com>.  BSD.
   8 ;;; Purpose:    demonstrations of how one might use CLS.
   9
  10 ;;; What is this talk of 'release'? Klingons do not make software
  11 ;;; 'releases'.  Our software 'escapes', leaving a bloody trail of
  12 ;;; designers and quality assurance people in its wake.
  13
  14 ;;; This file contains the current challenges to solve, including a
  15 ;;; description of the setup and the work to solve....
  16
  17 ;;; SET UP
  18
  19 (in-package :cl-user)
  20 ;;(asdf:oos 'asdf:compile-op 'lispstat)
  21 ;;(asdf:oos 'asdf:load-op 'lispstat)
  22
  23 (in-package :lisp-stat-unittests)
  24
  25 (describe (run-tests :suite 'lisp-stat-ut))
  26 (run-tests :suite 'lisp-stat-ut)
  27 ;; tests = 68, failures = 12, errors = 7
  28
  29 (in-package :ls-user)
  30
  31 ;;; FIXME: Example: currently not relevant, yet
  32 #|
  33   (describe
  34     (lift::run-test
  35       :test-case  'lisp-stat-unittests::create-proto
  36       :suite 'lisp-stat-unittests::lisp-stat-ut-proto))
  37 |#
  38
  39 :;; FIXME: data frames and structural inheritance
  40 ;;
  41 ;; Serious flaw -- need to consider that we are not really well
  42 ;; working with the data structures, in that Luke created compound as
  43 ;; a base class, which turns out to be slightly backward if we are to
  44 ;; maintain the numerical structures as well as computational
  45 ;; efficiency.
  46
  47
  48
  49 ;;; FIXME: Regression modeling
  50 (progn
  51
  52   (defparameter m nil
  53     "holding variable.")
  54   ;; need to make vectors and matrices from the lists...
  55   (def m (regression-model (list iron aluminum) absorbtion :print nil))
  56
  57   (defparameter *indep-vars-1-matrix*
  58     (make-matrix 1 (length iron)
  59                  :initial-contents
  60                  (list (mapcar #'(lambda (x) (coerce x 'double-float))
  61                                iron))))
  62   ;; *indep-vars-1-matrix*
  63
  64   (defparameter *indep-vars-2-matrix*
  65     (make-matrix 2 (length iron)
  66                  :initial-contents
  67                  (list
  68                   (mapcar #'(lambda (x) (coerce x 'double-float))
  69                           iron)
  70                   (mapcar #'(lambda (x) (coerce x 'double-float))
  71                           aluminum))))
  72   ;; *indep-vars-2-matrix*
  73
  74
  75   ;; FAILS due to coercion issues; it just isn't lispy, it's R'y.
  76   ;; (defparameter *dep-var* (make-vector (length absorbtion)
  77   ;;                                 :initial-contents (list absorbtion)))
  78   ;; BUT this should be the right type.
  79   (defparameter *dep-var*
  80     (make-vector (length absorbtion)
  81                  :type :row
  82                  :initial-contents
  83                  (list
  84                   (mapcar #'(lambda (x) (coerce x 'double-float))
  85                           absorbtion))))
  86   ;; *dep-var*
  87
  88
  89   (defparameter *dep-var-int*
  90     (make-vector (length absorbtion)
  91                  :type :row
  92                  :element-type 'integer
  93                  :initial-contents (list absorbtion)))
  94
  95   (typep *dep-var* 'matrix-like) ; => T
  96   (typep *dep-var* 'vector-like) ; => T
  97
  98   (typep *indep-vars-1-matrix* 'matrix-like) ; => T
  99   (typep *indep-vars-1-matrix* 'vector-like) ; => T
 100   (typep *indep-vars-2-matrix* 'matrix-like) ; => T
 101   (typep *indep-vars-2-matrix* 'vector-like) ; => F
 102
 103   (def m1 (regression-model-new *indep-vars-1-matrix* *dep-var* ))
 104   (def m2 (regression-model-new *indep-vars-2-matrix* *dep-var* ))
 105
 106   iron
 107   ;; following fails, need to ensure that we work on list elts, not just
 108   ;; elts within a list:
 109   ;; (coerce iron 'real)
 110
 111   ;; the following is a general list-conversion coercion approach -- is
 112   ;; there a more efficient way?
 113   (mapcar #'(lambda (x) (coerce x 'double-float)) iron)
 114
 115   (coerce 1 'real)
 116
 117   (send m :compute)
 118   (send m :sweep-matrix)
 119   (format t "~%~A~%" (send m :sweep-matrix))
 120
 121   ;; need to get multiple-linear regression working (simple linear regr
 122   ;; works)... to do this, we need to redo the whole numeric structure,
 123   ;; I'm keeping these in as example of brokenness...
 124
 125   (send m :basis) ;; this should be positive?
 126   (send m :coef-estimates)
 127
 128
 129   )
 130
 131
 132 (progn ;; FIXME: Need to clean up data examples, licenses, attributions, etc.
 133
 134   ;; The following breaks because we should use a package to hold
 135   ;; configuration details, and this would be the only package outside
 136   ;; of packages.lisp, as it holds the overall defsystem structure.
 137   (load-data "iris.lsp")  ;; (the above partially fixed).
 138   (variables)
 139   diabetes
 140   )
 141
 142 (progn
 143   ;; FIXME: Data.Frames probably deserve to be related to lists --
 144   ;; either lists of cases, or lists of variables.  We probably do not
 145   ;; want to mix them, but want to be able to convert between such
 146   ;; structures.
 147
 148   (defparameter *my-case-data*
 149     '((:cases
 150        (:case1 Y Med  3.4 5)
 151        (:case2 N Low  3.2 3)
 152        (:case3 Y High 3.1 4))
 153       (:var-names (list "Response" "Level" "Pressure" "Size"))))
 154
 155   *my-case-data*
 156
 157   (elt *my-case-data* 1)
 158   (elt *my-case-data* 0)
 159   (elt *my-case-data* 2) ;; error
 160   (elt (elt *my-case-data* 0) 1)
 161   (elt (elt *my-case-data* 0) 0)
 162   (elt (elt (elt *my-case-data* 0) 1) 0)
 163   (elt (elt (elt *my-case-data* 0) 1) 1)
 164   (elt (elt (elt *my-case-data* 0) 1) 2)
 165   (elt (elt *my-case-data* 0) 3)
 166
 167   )
 168
 169
 170 (progn ;; FIXME: read data from CSV file.  To do.
 171
 172   ;; challenge is to ensure that we get mixed arrays when we want them,
 173   ;; and single-type (simple) arrays in other cases.
 174
 175   (defparameter *csv-num* (read-csv "Data/example-num.csv" :type 'numeric))
 176   (defparameter *csv-mix* (read-csv "Data/example-mixed.csv" :type 'data))
 177
 178   ;; The handling of these types should be compariable to what we do for
 179   ;; matrices, but without the numerical processing.  i.e. mref, bind2,
 180   ;; make-dataframe, and the class structure should be similar.
 181
 182   ;; With numerical data, there should be a straightforward mapping from
 183   ;; the data.frame to a matrix.   With categorical data (including
 184   ;; dense categories such as doc-strings, as well as sparse categories
 185   ;; such as binary data), we need to include metadata about ordering,
 186   ;; coding, and such.  So the structures should probably consider
 187
 188   ;; Using the CSV file:
 189
 190   (asdf:oos 'asdf:compile-op 'csv :force t)
 191   (asdf:oos 'asdf:load-op 'parse-number)
 192   (asdf:oos 'asdf:load-op 'csv)
 193   (fare-csv:read-csv-file "Data/example-numeric.csv")
 194
 195   ;; but I think the cl-csv package is broken, need to use the dsv-style
 196   ;; package.
 197   )