path: root/README

$Id: README,v 1.6 2005-12-31 21:29:37 matju Exp $

PureUnity

Copyright 2006 by Mathieu Bouchard <matju à artengine point ca>

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.

See file ./COPYING for further informations on licensing terms.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.

+-+-+--+---+-----+--------+-------------+---------------------+
GOALS

  1. To provide a unit-test framework, which also provide benchmarking
     features, all made in Pd for use in Pd.

  2. To provide tests for functionality in internals, externals, abstractions, 
     etc., in a modularized way, in a DRY/OAOO fashion, thus abstracting out
     common features so that many objects share the same test patch for the 
     features that they have in common.

+-+-+--+---+-----+--------+-------------+---------------------+
TEST PROTOCOL

  new:
    create common (reusable) fixtures.

  inlet 0:
    bang:
      run all available tests in that class. individual tests don't have
      to be available through individual methods but may. If they do, the
      names of the methods must match those given in the test results. 

      each test should build its own non-reusable fixtures and reinitialize
      common fixtures, not assuming that the previous tests have left the
      common fixtures in a normal state.

  outlet 0:
    test results. a sequence of lists like:
      list $name $passed? $accuracy $elapsed

    where:
      $name is a symbol
      $passed? is either 0 for failure or 1 for success
      $accuracy is a float proportional to relative error on math
         (if not applicable, use 0)
      $elapsed is a float, the time elapsed in milliseconds
         or it is the symbol "-" if not measured.

    for example:
      list commutative1 1 0 -

    Which means that the 1st test about commutativity passed ($2=1) because it
    was perfectly accurate ($3==0) and that we didn't measure the time ($4=-).

+-+-+--+---+-----+--------+-------------+---------------------+
SEVERITIES (in decreasing order)

 * crash: Segmentation Fault, Bus Error, Illegal Instruction, Infinite Loop, 
   etc.  You can't deal with those errors at the level of the tests. Maybe there 
   should be a way to tell a test object to skip certain tests, by name, in 
   order to be able to perform as many tests as possible while waiting for a 
   fix. It could become possible to rescue from some of those crashes if Pd
   supported exceptions (stack-unwinding).

 * corruption: this may cause future crashes and failures on innocent 
   objects/features. I have no solution for this except to be careful.

 * post(),error(),pd_error(): Gets printed in the console. The problem is that 
   those can't be handled by the test objects, so someone has to read them and 
   interpret them. Also they prevent test objects to ensure that error 
   conditions produce error messages.

 * pd_error2(): I wish this would exist. It would be sort of like pd_error() 
   but it would produce a pd message instead, whose selector would be an 
   error code, designed to be both localizable and [route]able. By default, that 
   message would be sent to the console, but there would be an internal class
   designed to catch those messages. (If stack-unwinding were possible, it would 
   be disabled by default on pd_error2 and could be enabled explicitly 
   by-selector).

 * failure: a test object reports a problem through outlet 0.

 * dropout: a failure in realtimeness... difficult for an object to detect.

 * inaccuracy: a test more or less succeeds but the test detected that the 
   epsilon sucks.

+-+-+--+---+-----+--------+-------------+---------------------+
PROTOCOL FOR [error]

new:
  optional argument which would either be a float
  (e.g. the $0 of the enclosing abstraction) or a pointer.

inlet 0:
  set $scapegoat:
    replaces the originator of the message by $scapegoat, which can be a
    float or a pointer

  error $1 ...:
    causes its arguments to be concatenated, space-separated (may include
    floats), and then sent through pd_error using the appropriate
    originator (scapegoat).

  list $1 ...:
    for future use. would use pd_error2() (see README or previous mail).
    $1 has to be a symbol.

+-+-+--+---+-----+--------+-------------+---------------------+
ACCURACY AND ERROR (in math-related unit tests)

The "absolute error" between a practical result and the expected value
is considered to be the distance between the two value. That is the
absolute value of the difference.

In the case of positions in 2D, 3D, etc., use the L2-Norm which is
a generalized Pythagoras' Theorem: dist^2 = x^2 + y^2 + z^2 + ...
A norm is a distance between something and zero.

Sometimes you have several practical results for one expected value
and must extract a single absolute error out of that. Then you should pick
the largest of the individual absolute errors.

Sometimes you don't have an expected value, you just have several
practical results that you expect to be quite the same. In that case,
the absolute error is the "diameter" of those results. The meaning
of diameter here is: the largest distance between any two results.

If in a single test you must compare 2D errors with 3D errors and 1D
errors, etc., you may have to adjust them by dividing the error by
the square root of N (N is the number of dimensions). In that case,
the resulting value is called a RMS (Root-Mean-Square).

The maximum error introduced by just representing a number as a float
(instead of an exact value) is at most proportional to the magnitude
of the number (e.g. usually 16 million times smaller: about 6 decimals).
Also, often we are only interested in relative error, which is absolute
error divided by the norm of the expected result, because small absolute
errors don't matter much with large results. This is the reason floats
exist in the first place. By default, use relative error as the $accuracy
in Pd tests.

If you don't have an expected result, then compute the relative error as
being the absolute error divided by the norm of the average of practical
results.

In the RMS case of relative error, the norms of expected results should also
be adjusted, but both adjustments cancel because they get divided by each
other. That means: don't divide by the sqrt(N) at all and you'll get an
appropriate result.

+-+-+--+---+-----+--------+-------------+---------------------+
TYPE PREFIXES

Those have to be prefixes in order to be honored by DOLLSYM:
[$1norm] should expand to [fnorm], [lfnorm], [#norm], etc.

Those prefixes are necessary in order to achieve polymorphism through
abstraction arguments.

CURRENT:
  f   float

FUTURE (from PureData):
  ~   signal
  s   symbol
  p   gpointer
  a   anything
  l   list (of whatever)
  lf  list of floats
  ls  list of symbols
  lp  list of pointers

FUTURE (from DesireData):
  t   stringpointer
  L   listpointer
  v   varpointer (instance symbol)

FUTURE (from GridFlow):
  #   grid (of whatever)
  #b  grid of bytes (uint8)
  #s  grid of shorts (int16)
  #i  grid of ints (int32)
  #l  grid of longs (int64)
  #f  grid of floats (float32)
  #d  grid of doubles (float64)
  #r  grid of rubies (VALUE*)

+-+-+--+---+-----+--------+-------------+---------------------+
ETC


(write me!)




If +-test.pd tests [+], it can test for hotness, coldness, it can test
that only one result is produced per hot message, that all results are
float, that a few example additions work, and that with random inputs it
respects commutativity, associativity, invertibility, within appropriate
relative-error bounds, etc.

However +-test.pd can't test that errormessages aren't printed during the
testing. This may be something that we want to check for, and currently
the best way to handle it is to search the console for error messages, and
if there are any, restart the tests in verbose mode and see where the
error happens exactly.

[...]

Floating-point is the scientific notation for numbers that we all
learned on paper in school. Rounding and inaccuracy are two sides
of the same coin. They are required when it is stupid to have perfect
results, that is, when it would mean too many computations for little
gain.

However sometimes we want to make sure that our math is accurate enough.
Many algorithms are data-recursive: each computation uses previous
results. Many of those algorithms have chaotic and/or unstable
behaviours, which means that the inaccuracies may skyrocket instead of
fading out.