src/unittests/unittests.lisp

   1 ;;; -*- mode: lisp -*-
   2 ;;; Copyright (c) 2007, by A.J. Rossini <blindglobe@gmail.com>
   3 ;;; See COPYRIGHT file for any additional restrictions (BSD license).
   4 ;;; Since 1991, ANSI was finally finished.  Edited for ANSI Common Lisp.
   5
   6 ;;; This is semi-external to lispstat core packages.  The dependency
   7 ;;; should be that lispstat packages are dependencies for the unit
   8 ;;; tests.  However, where they will end up is still to be
   9 ;;; determined.
  10
  11 (in-package :cl-user)
  12
  13 (defpackage :lisp-stat-unittests
  14   (:use :common-lisp :lift :lisp-stat)
  15   (:shadowing-import-from :lisp-stat
  16         slot-value call-method call-next-method ;; objects
  17         expt + - * / ** mod rem abs 1+ 1- log exp sqrt sin cos tan ;; lsmath
  18         asin acos atan sinh cosh tanh asinh acosh atanh float random
  19         truncate floor ceiling round minusp zerop plusp evenp oddp
  20         < <= = /= >= > ;; complex
  21         conjugate realpart imagpart phase
  22         min max logand logior logxor lognot ffloor fceiling
  23         ftruncate fround signum cis)
  24   (:export run-lisp-stat-tests run-lisp-stat-test scoreboard ; exec
  25            almost= almost=lists numerical=)) ; compare
  26
  27 (in-package :lisp-stat-unittests)
  28
  29 ;;; TESTS
  30
  31 (defun run-lisp-stat-tests ()
  32   (run-tests :suite 'lisp-stat-ut))
  33
  34 ;; (run-lisp-stat-tests)
  35
  36
  37 (defun run-lisp-stat-test (&rest x)
  38   (run-test x))
  39
  40
  41 (deftestsuite lisp-stat-ut () ())
  42 (deftestsuite lisp-stat-ut-lin-alg (lisp-stat-ut) ())
  43 (deftestsuite lisp-stat-ut-spec-fns (lisp-stat-ut) ())
  44 (deftestsuite lisp-stat-ut-probdistn (lisp-stat-ut) ())
  45
  46
  47 (defun almost= (a b &key (tol 0.000001))
  48   "Numerically compares 2 values to a tolerance."
  49    (< (abs (- a b)) tol))
  50
  51 (defun almost=lists (a b &key (tol 0.000001))
  52   "Numerically compare 2 lists using almost=."
  53   (if (and (null a) (null b))
  54       t
  55       (and (almost= (car a) (car b) :tol tol)
  56            (almost=lists (cdr a) (cdr b) :tol tol))))
  57
  58
  59
  60 ;; Need to consider a CLOSy approach for almost= to cover the range of
  61 ;; possible data structures that we would like to be equal to a
  62 ;; particular tolerance range.  For example, fill in a shell like:
  63
  64 (defgeneric numerical= (a b &key tol))
  65
  66 (defmethod numerical= ((a real) (b real) &key (tol 0.00001)) ;; real))
  67   ;;(print (format nil " equality pred for real a=~w real b=~w" a b))
  68   (< (abs (- a b)) tol))
  69
  70 ;; can we just worry about reals if integers are a subclass?
  71 (defmethod numerical= ((a integer) (b integer) &key (tol 0.1)) ;; real))
  72   ;;(print (format nil " equality pred for int a=~w int b=~w" a b))
  73   (< (abs (- a b)) tol))
  74
  75 (defmethod numerical= ((a complex) (b complex) &key (tol 0.00001))
  76   ;;(print (format nil " equality pred for cmplx a=~w cmplx b=~w" a b))
  77   (< (abs (- a b)) tol))
  78
  79 (defmethod numerical= ((a sequence) (b sequence) &key (tol 0.00001))
  80   ;; (print (format nil "checking equality for list a ~w list b=~w" a b))
  81   ;; using sequence for lists and vectors, but not arrays.
  82   ;; FIXME++++  This is too slow, too many comparisons!
  83   (if (and (null a) (null b))
  84       t
  85       (if (and (= (length a) (length b))
  86                (> (length a) 0)
  87                (numerical= (car a) (car b) :tol tol))
  88           (progn
  89             (if (= (length (cdr a)) 0)
  90                 t
  91                 (numerical= (cdr a) (cdr b) :tol tol)))
  92           nil)))
  93
  94 ;; To do.
  95
  96 (defmethod numerical= ((a array) (b array) &key (tol 0.00001))
  97   (print (format nil "checking equality for array a ~w and array b=~w" a b))
  98   ;;; FIXME Warning!  Need to generalize past  2-d array!!
  99   (if (/= (array-dimensions a) (array-dimensions b))
 100       nil
 101       (let* ((a-dim (array-dimensions a))
 102              (a-b-elt-eq (loop for i from 0 to (nth 0 a-dim)
 103                                for j from 0 to (nth 1 a-dim)
 104                                collect (numerical= (apply #'aref a (list i j))
 105                                                    (apply #'aref b (list i j))
 106                                                    :tol tol))))
 107         (every #'(lambda (x) x) a-b-elt-eq))))
 108
 109 (deftestsuite lisp-stat-ut-testsupport (lisp-stat-ut)
 110   ()
 111   (:tests
 112    (almost=1 (ensure (almost= 3 3.001 :tol 0.01)))
 113    (almost=2 (ensure (almost= 3 3.01 :tol 0.01)))
 114    (almost=3 (ensure (not (almost= 3 3.1 :tol 0.01))))
 115    (almost=lists1 (ensure (almost=lists nil nil :tol 0.01)))
 116    (almost=lists2 (ensure (almost=lists (list ) (list ) :tol 0.01)))
 117    (almost=lists3 (ensure (almost=lists (list 1.0) (list 1.0) :tol 0.01)))
 118    (almost=lists4 (ensure (almost=lists (list 1.0 1.0) (list 1.0 1.0) :tol 0.01)))
 119    (almost=lists5 (ensure (not (almost=lists (list 1.0 1.0)
 120                                              (list 1.0 1.1) :tol 0.01))))))
 121
 122 (deftestsuite lisp-stat-ut-testsupport2 (lisp-stat-ut)
 123   ()
 124   (:tests
 125    (numerical=1 (ensure (numerical= 3 3.001 :tol 0.01)))
 126    (numerical=1.1 (ensure    (numerical= 2 2)))
 127    (numerical=1.2 (ensure (not (numerical= 2 3))))
 128    (numerical=2 (ensure (numerical= 3 3.01 :tol 0.01)))
 129    (numerical=3 (ensure (not (numerical= 3 3.1 :tol 0.01))))
 130    (numerical=4 (ensure (numerical= nil nil :tol 0.01)))
 131    (numerical=5 (ensure (numerical= (list ) (list ) :tol 0.01)))
 132    (numerical=6 (ensure (numerical= (list 1.0) (list 1.0) :tol 0.01)))
 133    (numerical=7 (ensure (numerical= (list 1.0 1.0) (list 1.0 1.0) :tol 0.01)))
 134    (numerical=7.5 (ensure-error (numerical= 1.0 (list 1.0 1.0) :tol 0.01)))
 135    (numerical=8 (ensure (not (numerical= (list 2.0 2.0 2.2) (list 2.1 2.0 2.2)))))
 136    (numerical=9 (ensure    (numerical= (list 2.1 2.0 2.2) (list 2.1 2.0 2.2)) ))
 137    (numerical=10 (ensure    (numerical= (list 2.1 2.0 2.2 4.2) (list 2.1 2.0 2.2 4.2))))
 138    (numerical=11 (ensure (not (numerical= (list 2.1 2.0 2.3 4.0) (list 2.1 2.0 2.2 4.0)))))
 139    (numerical=12 (ensure (not (numerical= (list 1.0 1.0)
 140                                          (list 1.0 1.1) :tol 0.01))))
 141    (numerical=C1 (ensure (numerical= #C(2 3) #C(2 3))))
 142    (numerical=C2 (ensure (not(numerical= #C(2 3) #C(2 4)))))
 143    (numerical=C3 (ensure (numerical= #C(2 3) #C(3 4) :tol 2)))
 144    (numerical=C4 (ensure (not(numerical= #C(2 3) #C(3 4) :tol 1))))
 145
 146    ;;;; Tests to fix
 147
 148    (numerical=A1 (ensure (numerical= #1A(2 3 4)
 149                                      #1A(2 3 4))))
 150
 151    (numerical=A2 (ensure (numerical= #2A((2 3 4) (1 2 4) (2 4 5))
 152                                      #2A((2 3 4) (1 2 4) (2 4 5)))))
 153
 154    (numerical=A3 (ensure (not (numerical= #2A((2 3 4) (1 2 4) (2 5 4))
 155                                           #2A((2 3 4) (1 2 4) (2 4 5))))))
 156
 157    (numerical=A4 (ensure (not (numerical= #1A(2 2 4)
 158                                           #1A(2 3 4)))))
 159
 160    ))
 161
 162 ;; (describe (run-tests :suite 'lisp-stat-ut-testsupport2))
 163
 164
 165
 166 ;;;; Log-gamma function
 167
 168 (addtest (lisp-stat-ut-spec-fns) log-gamma-fn
 169          (ensure-same
 170           (log-gamma 3.4)
 171           1.0923280596789584
 172           :test 'almost=))
 173
 174
 175 ;;; Probability distributions
 176
 177 ;; This macro should be generalized, but it's a good start now.
 178 ;;(defmacro ProbDistnTests (prefixName
 179 ;;                        quant-params quant-answer
 180 ;;                        cdf-params cdf-answer
 181 ;;                        pmf-params pmf-answer
 182 ;;                        rand-params rand-answer)
 183 ;;  (deftestsuite lisp-stat-ut-probdist-,prefixName (lisp-stat-ut-probdistn)
 184 ;;    ;;  ((  ))
 185 ;;    (:documentation "testing for ,testName distribution results")
 186 ;;    (:test (ensure-same
 187 ;;          (lisp-stat-ut-basics:,testName-quant ,quant-params) ,quant-answer))
 188 ;;    (:test (ensure-same
 189 ;;          (lisp-stat-ut-basics:,testName-cdf ,cdf-params) ,cdf-answer))
 190 ;;    (:test (ensure-same
 191 ;;          (lisp-stat-ut-basics:,testName-pmf ,pmf-params) ,pmf-answer))
 192 ;;    (:test (progn
 193 ;;           (set-seed 234)
 194 ;;           (ensure-same
 195 ;;            (lisp-stat-ut-basics:,testName-rand ,rand-params) ,rand-answer)))))
 196
 197 ;;; Normal distribution
 198
 199 (deftestsuite lisp-stat-ut-probdist-f (lisp-stat-ut-probdistn)
 200   ()
 201   (:documentation "testing for Gaussian distn results")
 202   (:test (ensure-same
 203           (normal-quant 0.95)
 204           1.6448536279366268))
 205   (:test (ensure-same
 206           (normal-cdf 1.3)
 207           0.9031995154143897))
 208   (:test (ensure-same
 209           (normal-dens 1.3)
 210           0.17136859204780736))
 211   (:test (ensure-same
 212           (normal-rand 2)
 213           (list -0.40502015f0 -0.8091404f0)))
 214   (:test (ensure-same
 215           (bivnorm-cdf 0.2 0.4 0.6)
 216           0.4736873734160288)))
 217
 218 ;;;; Cauchy distribution
 219
 220 (deftestsuite lisp-stat-ut-probdist-cauchy (lisp-stat-ut-probdistn)
 221   ()
 222   (:documentation "testing for Cachy-distn results")
 223   (:test (ensure-same
 224           (cauchy-quant 0.95)
 225           6.313751514675031))
 226   (:test (ensure-same
 227           (cauchy-cdf 1.3)
 228           0.7912855998398473))
 229   (:test (ensure-same
 230           (cauchy-dens 1.3)
 231           0.1183308127104695 ))
 232   (:test (ensure-same
 233           (cauchy-rand 2)
 234           (list -1.06224644160405 -0.4524695943939537))))
 235
 236 ;;;; Gamma distribution
 237
 238 (deftestsuite lisp-stat-ut-probdist-gamma (lisp-stat-ut-probdistn)
 239   ()
 240   (:documentation "testing for gamma distn results")
 241   (:test (ensure-same
 242           (gamma-quant 0.95 4.3)
 243           8.178692439291645))
 244   (:test (ensure-same
 245           (gamma-cdf 1.3 4.3)
 246           0.028895150986674906))
 247   (:test (ensure-same
 248           (gamma-dens 1.3 4.3)
 249           0.0731517686447374))
 250   (:test (ensure-same
 251           (gamma-rand 2 4.3)
 252           (list 2.454918912880936 4.081365384357454))))
 253
 254 ;;;; Chi-square distribution
 255
 256 (deftestsuite lisp-stat-ut-probdist-chisq (lisp-stat-ut-probdistn)
 257   ()
 258   (:documentation "testing for Chi-square distn results")
 259   (:test (ensure-same
 260           (chisq-quant 0.95 3)
 261           7.814727903379012))
 262   (:test (ensure-same
 263           (chisq-cdf 1 5)
 264           0.03743422675631789))
 265   (:test (ensure-same
 266           (chisq-dens 1 5)
 267           0.08065690818083521))
 268   (:test (progn
 269            (set-seed 353)
 270            (ensure-same
 271             (chisq-rand 2 4)
 272             (list 1.968535826180572 2.9988646156942997)))))
 273
 274 ;;;; Beta distribution
 275
 276 (deftestsuite lisp-stat-ut-probdist-beta (lisp-stat-ut-probdistn)
 277   ()
 278   (:documentation "testing for beta distn results")
 279   (:test (ensure-same
 280           (beta-quant 0.95 3 2)
 281           0.9023885371149876))
 282   (:test (ensure-same
 283           (beta-cdf 0.4 2 2.4)
 284           0.4247997418541529 ))
 285   (:test (ensure-same
 286           (beta-dens 0.4 2 2.4)
 287           1.5964741858913518 ))
 288   (:test (ensure-same
 289           (beta-rand 2 2 2.4)
 290           (list 0.8014897077282279 0.6516371997922659))))
 291
 292 ;;;; t distribution
 293
 294 (deftestsuite lisp-stat-ut-probdist-t (lisp-stat-ut-probdistn)
 295   ()
 296   (:documentation "testing for t-distn results")
 297   (:test (ensure-same
 298           (t-quant 0.95 3)
 299           2.35336343484194))
 300   (:test (ensure-same
 301           (t-cdf 1 2.3)
 302           0.794733624298342))
 303   (:test (ensure-same
 304           (t-dens 1 2.3)
 305           0.1978163816318102))
 306   (:test (ensure-same
 307           (t-rand 2 2.3)
 308           (list -0.34303672776089306 -1.142505872436518))))
 309
 310 ;;;; F distribution
 311
 312 (deftestsuite lisp-stat-ut-probdist-f (lisp-stat-ut-probdistn)
 313   ()
 314   (:documentation "testing for f-distn results")
 315   (:test (ensure-same
 316           (f-quant 0.95 3 5) 5.409451318117459))
 317   (:test (ensure-same
 318           (f-cdf 1 3.2 5.4)
 319           0.5347130905510765))
 320   (:test (ensure-same
 321           (f-dens 1 3.2 5.4)
 322           0.37551128864591415))
 323   (:test (progn
 324            (set-seed 234)
 325            (ensure-same
 326             (f-rand 2 3 2)
 327             (list 0.7939093442091963 0.07442694152491144)))))
 328
 329 ;;;; Poisson distribution
 330
 331 (deftestsuite lisp-stat-ut-probdist-poisson (lisp-stat-ut-probdistn)
 332   ()
 333   (:documentation "testing for poisson distribution results")
 334   (:test (ensure-same
 335           (poisson-quant 0.95 3.2) 6))
 336   (:test (ensure-same
 337           (poisson-cdf 1 3.2)
 338           0.17120125672252395))
 339   (:test (ensure-same
 340           (poisson-pmf 1 3.2)
 341           0.13043905274097067))
 342   (:test (progn
 343            (set-seed 234)
 344            (ensure-same
 345             (poisson-rand 5 3.2)
 346             (list 2 1 2 0 3)))))
 347
 348 ;; Binomial distribution
 349
 350 (deftestsuite lisp-stat-ut-probdist-binomial (lisp-stat-ut-probdistn)
 351   ()
 352   (:documentation "testing for binomial distribution results")
 353
 354   (:test (ensure-same
 355           (binomial-quant 0.95 3 0.4) ;;; DOESN'T RETURN
 356           ))
 357   (:test (ensure-same
 358           (binomial-quant 0 3 0.4)
 359           ;; -2147483648
 360           ))
 361   (:test (ensure-same
 362           (binomial-cdf 1 3 0.4)
 363           0.6479999999965776))
 364
 365   (:test (ensure-same
 366           (binomial-pmf 1 3 0.4)
 367           0.4320000000226171))
 368   (:test (progn
 369            (set-seed 526)
 370            (ensure-same
 371             (binomial-rand 5 3 0.4)
 372             (list 2 2 0 1 2)))))
 373