This question stemmed from this (Software Development) textbook question:
A value used to indicate the end of a data stream is called:
a sentinel value.
an end of file (EOF) character.
a flag.
a driver
The correct answer is apparently 1, though I answered 2.
I wasn't able to find a definition in the textbook of an end of file character though I did find the definition of a sentinel value.
Sentinel value (Textbook)
A dummy value used to indicate the end of data within a file. Sentinel is from the word sentry, a sentry being a guard who prevents passage of unauthorised persons.
However, this contradicts what I found on Wikipedia (sources seem legit).
Sentinel value (Wikipedia)
... a special value in the context of an algorithm which uses its presence as a condition of termination, typically in a loop or recursive algorithm
Then the End-of-file definition.
End-of-file (Wikipedia)
... a condition in a computer operating system where no more data can be read from a data source. The data source is usually called a file or stream.
So, from this, it seems the better (or correct?) answer is 2 since the question is asking about a "data stream". Does this mean the textbook definition is wrong or "dumbed down", or is an End-of-file character classed as a sentinel value?
Typically in these Software textbooks, sentinel values are usually associated with file streams and the like.
If I remember correctly, chapters discussing sentinel values generally focused on file handling, so in this case, the answer would be 1.
Related
The starspace documentation is unclear on the parameter 'fileFormat' which takes the value 'labelDoc' or 'fastText'.
I would like to understand intuitively what material difference setting this paramter would have.
Currently, my best guess is that if you set fileFormat to 'fastText' then all tokens in the training file that do not have the prefix '__label__' will be broken down into character-level n-grams as in fastText.
Alternatively, if you set fileFormat to 'labelDoc' then starspace will assume that all tokens are actually labels, and you do not need to prepend '__label__' to the tokens, because they will be recognized as labels anyway.
Is my thinking correct?
The way StarSpace uses the labels highly depends on the trainMode you are using. The labelDoc format is useful when you go for a trainMode that just relies on labels (trainMode 1 through 4) where it may be the same thing to use a fastText format specifying the __label__ prefix but some trainModes benefit from labelDoc format (i.e. trainMode 1 or 3) to use a whole sentence as a label element for that trainMode.
So to clarify that, if you are performing a text classification task(as explained in this example labelDoc wouldn't have any input recognized but on the other hand, as you stated, using fastText format will breakdown all non-labeled text as input and learn to predict the __label__ tags.
And an example for labelDoc format would be developing a content based recommender system (as explained in this example) every tab separated sentence is used at LHS or RHS during training time. But if you go on a collaborative approach (the content of the articles or wherever you sentences come from is not taken in account) it can be trained either with fastText (specifying the __label__ prefix) or labelDoc file format as labels are picked randomly during training time for LHS or RHS. (This second example is explained here).
In both GW-BASIC and QuickBASIC, statements are passed arguments, some of which are optional and can be omitted depending on the statement:
REM Move the text cursor to the specified column and row.
LOCATE row%, column%
REM Move the text cursor to the specified column without changing the row.
LOCATE , column%
In GW-BASIC, the CLEAR statement is rather unusual in that its first "argument" is always omitted:
CLEAR , basicMem
CLEAR , basicMem, basicStack
CLEAR , , basicStack
In QuickBASIC, the basicMem parameter became optional due to the interpreter/runtime managing its own memory:
CLEAR , , basicStack
What I'm wondering is whether that first "argument" ever used for anything prior to GW-BASIC, i.e. something like this was actually useful:
CLEAR missingArg, basicMem, basicStack
REM ^^^^^^^^^^
REM here
That is, was there ever an purposeful non-empty argument before the first comma?
If anybody has any idea, I'd love to know!
What I'm wondering is whether that first "argument" ever used for
anything prior to GW-BASIC, i.e. something like this was actually
useful:
CLEAR missingArg, basicMem, basicStack
REM ^^^^^^^^^^
REM here
That is, was there ever an purposeful non-empty argument before the
first comma?
Yes, there was a first argument, but there was never a 3-argument form that actually made use of it.
Microsoft (originally Micro-Soft) created Altair BASIC. It featured a CLEAR command with no arguments that set all program variables to zero. The 4K version had no strings, so it had no need for managing string space. However, the 8K, Extended, and Disk versions had a CLEAR command that also accepted a single argument of the form CLEAR x. The value x specified the maximum amount of string space available in bytes, with the default at load time of BASIC being 50 bytes in the 8K version and 200 bytes in the Extended and Disk versions until it was changed [source]. That's where the missing first argument came from and what it was used for originally. At the time, however, only that one argument was valid.
Microsoft went on to develop a derivative called "BASIC-80" for several systems, notably the Intel ISIS-II, CP/M, and TEKDOS operating systems. A "Standalone Disk BASIC" version of BASIC-80 was also created that could run on "almost any 8080 or Z80 based disk hardware without an operating system." There was no 4K version of BASIC-80, so it's reasonable to assume all versions of BASIC-80 had strings available as the 8K version of Altair BASIC did. As a result, that string space needed managed. However, it was in BASIC-80 that a second argument was added:
CLEAR [expression![,address]]
expression! was an expression that specified the amount of string space, like in 8K (Altair) BASIC, and address was the maximum address available to BASIC, i.e. the amount of memory available to BASIC, like the argument immediately after the first comma in GW-BASIC.
Eventually, BASIC-80, Release 5.0, was shipped into the world, and it featured the odd syntax instead:
CLEAR [,[expression1][,expression2]]
expression1 was the maximum memory available to BASIC, and expression2 was the amount of stack space. Appendix A: New Features in BASIC-80, Release 5.0 explains why the first argument was dropped:
String space is allocated dynamically, and the first argument in a two-argument CLEAR statement will be ignored.
In other words, CLEAR strSpace!,maxMem would ignore the strSpace! argument in BASIC-80, Release 5.0, so the syntax became CLEAR [,[maxMem][,maxStack]].
QuickBASIC eventually changed the syntax further to just CLEAR [,,stack].
Confusingly, the on-line help system of QuickBASIC 4.5 states the following:
Note: Two commas are used before stack to keep QuickBASIC compatible
with BASICA. BASICA included an additional argument that set the
size of the data segment. Because QuickBASIC automatically manages
the data segment, the first parameter is no longer required.
"The first parameter" mentioned is maxMem as BASICA (and GW-BASIC) used the syntax available with BASIC-80, Release 5.0, rather than the equally missing strSpace! parameter used by pre-5.0 releases of BASIC-80.
Trying to learn lisp (and I guess emacs along with it).
I was wondering how you would go about clearing the output and replacing it.
Could be in a LISP repl, or an emacs buffer.
Something akin to the following in python.
def go(r):
for i in range(r):
sys.stdout.write("\rDoing %i" % i)
sys.stdout.flush()
For common lisp, you are looking for
Functions FINISH-OUTPUT, FORCE-OUTPUT, CLEAR-OUTPUT:
finish-output, force-output, and clear-output exercise control over the internal handling of buffered stream output.
finish-output attempts to ensure that any buffered output sent to output-stream has reached its destination, and then returns.
force-output initiates the emptying of any internal buffers but does not wait for completion or acknowledgment to return.
clear-output attempts to abort any outstanding output operation in progress in order to allow as little output as possible to continue to the destination.
and
Variables *DEBUG-IO*, *ERROR-OUTPUT*, *QUERY-IO*, *STANDARD-INPUT*, *STANDARD-OUTPUT*, *TRACE-OUTPUT*
The value of *debug-io*, called debug I/O, is a stream to be used for interactive debugging purposes.
The value of *error-output*, called error output, is a stream to which warnings and non-interactive error messages should be sent.
The value of *query-io*, called query I/O, is a bidirectional stream to be used when asking questions of the user. The question should be output to this stream, and the answer read from it.
The value of *standard-input*, called standard input, is a stream that is used by many operators as a default source of input when no specific input stream is explicitly supplied.
The value of *standard-output*, called standard output, is a stream that is used by many operators as a default destination for output when no specific output stream is explicitly supplied.
The value of *trace-output*, called trace output, is the stream on which traced functions (see trace) and the time macro print their output.
Emacs Lisp is quite different, you might want to start here: https://www.gnu.org/software/emacs/manual/html_node/elisp/Output-Functions.html
I am defining a variable in the beginning of my source code in MATLAB. Now I would like to know at which lines this variable effects something. In other words, I would like to see all lines in which that variable is read out. This wish does not only include all accesses in the current function, but also possible accesses in sub-functions that use this variable as an input argument. In this way, I can see in a quick way where my change of this variable takes any influence.
Is there any possibility to do so in MATLAB? A graphical marking of the corresponding lines would be nice but a command line output might be even more practical.
You may always use "Find Files" to search for a certain keyword or expression. In my R2012a/Windows version is in Edit > Find Files..., with the keyboard shortcut [CTRL] + [SHIFT] + [F].
The result will be a list of lines where the searched string is found, in all the files found in the specified folder. Please check out the options in the search dialog for more details and flexibility.
Later edit: thanks to #zinjaai, I noticed that #tc88 required that this tool should track the effect of the name of the variable inside the functions/subfunctions. I think this is:
very difficult to achieve. The problem of running trough all the possible values and branching on every possible conditional expression is... well is hard. I think is halting-problem-hard.
in 90% of the case the assumption that the output of a function is influenced by the input is true. But the input and the output are part of the same statement (assigning the result of a function) so looking for where the variable is used as argument should suffice to identify what output variables are affected..
There are perverse cases where functions will alter arguments that are handle-type (because the argument is not copied, but referenced). This side-effect will break the assumption 2, and is one of the main reasons why 1. Outlining the cases when these side effects take place is again, hard, and is better to assume that all of them are modified.
Some other cases are inherently undecidable, because they don't depend on the computer states, but on the state of the "outside world". Example: suppose one calls uigetfile. The function returns a char type when the user selects a file, and a double type for the case when the user chooses not to select a file. Obviously the two cases will be treated differently. How could you know which variables are created/modified before the user deciding?
In conclusion: I think that human intuition, plus the MATLAB Debugger (for run time), and the Find Files (for quick search where a variable is used) and depfun (for quick identification of function dependence) is way cheaper. But I would like to be wrong. :-)
My goal is coming up with a script to track the point a line was added, even if the line is subsequently modified or moved around (both of which confuse traditional vcs 'blame' scripts. I've done some minor background research (see bottom) but didn't find anything useful. I have a concept for how to proceed but the runtime would be atrocious (there's a factorial involved).
The two missing features are tracking edited-in-place lines separate from a deletion-and-addition of that line, and tracking entire functions moved around so they're in different hunks. For those experienced with diff but unfamiliar with the terminology, a subsequence is a contiguous group of + or - lines, with a type of either delete (all -), add (all +), or replace (a combination). I need more information, on moves and edit-in-place lines, vaguely alluded to in an entry on c2: DiffAlgorithm (paragraph starts with "My favorite mode"). Does anyone know what that is? (seems to be based on Tichy, see bottom.)
Here's more info on the two missing features:
no concept of a change on a line, (a fourth type, something like edit-in-place). In this hunk, the parent of 'bc' is 'b' but 'd' is new and isn't a descendant of 'b':
a
-b
+bc
+d
The workaround for this isn't too complicated, if the position of edits is the same (just an expanded version of markup_instraline_changes but comparing edit distance on all equal-sized subsets of old and new lines.
no concept of "moving" code that preserves the ownership of the lines, e.g. this diff shouldn't alter the ownership of "line", although its position changes.
a
-line
c
+line
This could be dealt with in the same way but with much worse runtime (instead of only checking single blocks marked 'replace', you'd need to check Levenshtein distance between all added against all removed lines) and with likely false positives (some, like whitespace-only lines, aren't relevant to my problem).
Research I've done: reading about gestalt pattern matching (Ratcliff and Obershelp, used in Python's difflib) and An O(ND) Difference Algorithm and its Variations (EW Myers).
After posting the question, I found references to Tichy84 which appears to be The string-to-string correction problem with block moves (which I haven't read yet) according to Walter Tichy's paper a year later on RCS
You appear to be interested in origin tracking, the problem of tracing where a line came from.
Ideally, you'd instrument the editor to remember how things were edited, and store the edits with the text in your repository, thus solving the problem trivially, but none of us software engineers seem to be smart enough to implement this simple idea.
As a weak substitute, one can look at a sequence of source code revisions from the repository and reconstruct a "plausible" history of changes. This is what you seem to be doing by proposing the use of "diff". As you've noted, diff doesn't understand the idea of "moving" or "copying".
SD Smart Differencer tools compare source text by parsing the text according to the langauge it is in, discovering the code structures, and computing least-Levensthein differences in terms of programming language constructs (identifiers, expressions, statements, blocks, classes, ...) and abstract editing operators "insert", "delete", "copy", "move" and "rename identifier within a scope". They produce diff-like output, a little richer because they tell you line/column -> line/column with different editing operations.
Obviously the "move" and "copy" edits are the ones most interesting to you in terms of tracking specific lines (well, specific language constructs). Our experience is that code goes through lots of copy and edits, too, which I suspect won't surprise you.
These tools are in Beta, and are presently available for COBOL, Java and C#. Lots of other langauges are in the pipe, because the SmartDifferencer is built on top of a langauge-parameterized infrastructure, DMS Software Reengineering Toolkit, which has quite a number of already existing, robust langauge grammars.
I think the idea of what amount of editing a line that can be done while it remains a descendent of some previously written line is very subjective, and based on context, both things that a computer cannot work with. You'd have to specify some sort of configurable minimum similarity on lines in your program I think... The other problem is that it is entirely possible for two identical lines to be written completely independently (for example incrementing the value of some variable), and this will be be quite a common thing, so your desired algorithm won't really give truthful or useful information about a line quite often.
I would like to suggest an algorithm for this though (which makes tons of hopefully obvious assumptions by the way) so here goes:
Convert both texts to lists of lines
Copy the lists and Strip all whitespace from inside of each line
Delete blank lines from both lists
Repeat
Do a Levenshtein distance from the old to new lists ...
... keeping all intermediate data
Find all lines in the new text that were matched with old lines
Mark the line in both new/old original lists as having been matched
Delete the line from the new text (the copy)
Optional: If some matched lines are in a contiguous sequence ...
... in either original text assign them to a grouping as well!
Until there is nothing left but unmatchable lines in the new text
Group together sequences of unmatched lines in both old and new texts ...
... which are contiguous in the original text
Attribute each with the line match before and after
Run through all groups in old text
If any match before and after attributes with new text groups for each
//If they are inside the same area basically
Concatenate all the lines in both groups (separately and in order)
Include a character to represent where the line breaks are
Repeat
Do a Levenshtein distance on these concatenations
If there are any significantly similar subsequences found
//I can't really define this but basically a high proportion
//of matches throughout all lines involved on both sides
For each matched subsequence
Find suitable newline spots to delimit the subsequence
Mark these lines matched in the original text
//Warning splitting+merging of lines possible
//No 1-to-1 correspondence of lines here!
Delete the subsequence from the new text group concat
Delete also from the new text working list of lines
Until there are no significantly similar subsequences found
Optional: Regroup based on remaining unmatched lines and repeat last step
//Not sure if there's any point in trying that at the moment
Concatenate the ENTIRE list of whitespaced-removed lines in the old text
Concatenate the lines in new text also (should only be unmatched ones left)
//Newline character added in both cases
Repeat
Do Levenshtein distance on these concatenations
Match similar subsequences in the same way as earlier on
//Don't need to worry deleting from list of new lines any more though
//Similarity criteria should be a fair bit stricter here to avoid
// spurious matchings. Already matched lines in old text might have
// even higher strictness, since all of copy/edit/move would be rare
While you still have matchings
//Anything left unmatched in the old text is deleted stuff
//Anything left unmatched in the new text is newly written by the author
Print out some output to show all the comparing results!
Well, hopefully you can see the basics of what I mean with that completely untested algorithm. Find obvious matches first, and verbatim moves of chunks of decreasing size, then compare stuff that's likely to be similar, then look for anything else which is similar, but both modified and moved: probably just coincidentally similar.
Well, if you try implementing this, tell me how it works out, and what details you changed, and what kind of assignments you made to the various variables involved... I expect there will be some test cases where it works brilliantly and others where it just abyssmally fails due to some massive oversight. The idea is that most stuff will be matched before you get to the inefficient final loop, and indeed the previous one