Tag Archives: Recursion

Essays regarding theory and practice of recursive algorithms.

Pan Fried Programming

(Here's the update -- nothing much is new:
MLPTK: http://www.mediafire.com/file/m3u25i445lqkztb/mlptk-2016-12-16.zip
Source Highlight: http://www.mediafire.com/file/ygxb14ie94cwcuy/mlptk-src-hilite-2016-12-16.zip
Book: http://www.mediafire.com/file/vg439qruq3do90q/mlptk-book-2016-12-16.zip
)

Remedial (adj.): intended to rectify, amend, heal.
Panacea (n., myth): goddess of healing, daughter of Aesculapius.
Pansear (n.): Chili's Pokémon.

This remedial lecture will tersely cover a semester's curriculum,
similar to what you have learnt in your high school algebra class,
comprising the fundamentals of programming with synthetic languages
(those that are above machine code).

If you don't know what computer programming is, I would recommend that you study
some tutorials & encyclopedia articles. Much is available on the WWW (Worldwide
Web). The Web is a part of the Internet, and it is the Web you access from your
Web browser when you navigate to a Web page. You could also try one'a them there
"<name of programming language> For Dummies" textbooks: the "For Dummies" books
are excellent "Cliff's Notes"-style crash courses, and each aims to be similar
to a "101" course in the topic advertised.

To make a beginning with any programming language, all you must know is that a
computer computes: your instructions, issued in the program you write, tell the
machine how to progress from its input or initial state to a resultant output or
final state. These instructions look different in different languages -- some
languages require more or fewer -- but every computer program is an algorithm,
or "recipe for computation."

Computers and computer programs can be characterized as finite state automata.
They're like assembly-line robots who know where to weld each sheet of metal.
Also like assembly-line robots, they are "blind" in the sense that they'll kill
you with the soldering iron should you step between it and the sheet.
Computing machines do what they're told, even when it is particularly stupid:
that's why computer viruses, worms, and computer espionage exist.

In simplest terms, the computer's processing units receive some numbers and an
instruction that says what mathematical operation to execute, then operates:
like a calculator. High-level programming languages are more synthetic, like a
human language is, and comprise such ideas as objects (amalgamations of data) &
functions (modular sub-routines). Compilers or interpreters read these languages
and translate them into machine instructions, simplifying the lengthy series of
instructions necessary to make the calculator execute these difficult tasks.

In a high-level language, there are few technical concerns.
You can begin immediately with the abstract concepts.
Here are some:

VARIABLES
As in algebra, a variable is a name that represents a value.
As in solving a system of equations, values are typically assigned to some vars
and the value of the other variables is computed using the values given.
For example, in Javascript:
    var a = 2;
    var b = a + 2;
The variable <b> is now equal to 2 + 2. Similar operations function similarly.
In Javascript and other very-high-level languages, variables aren't only scalars
and can point at any object. They're like placeholders for procedure.
Although "variable" implies a value stored in memory, and "identifier" only its
mnemonic, the words "variable" & "identifier" used loosely mean about the same.
    "Just don't try that with the Captain."
        -- Geordi LaForge, to Data, _Star Trek: the Next Generation._

POINTERS, REFERENCES
These are important ideas that are abstracted away in VHLLs. A pointer stores an
address in memory, for a later indirect read/write or similar operation. In HLLs
a pointer/reference accesses an object directly instead of copying its value.
You'll rarely have to make the distinction in Javascript; but, for example:
    var a = new Array(1, 2, 3); // a[0] == 1, a[1] == 2, a[2] == 3
    var b = a; // Incidentally, b === a, and that is why in the next line...
    b[0] = 555; // ... b[0] == 555, and now a[0] also == 555!
As opposed to:
    var c = new Array(3); // c is a new array of length 3
    c[0] = b[0]; c[1] = b[1]; c[2] = b[2]; // copy scalar values one-by-one
    c[0] = 0; // c[0] == 0, but b[0] remains == a[0], which remains == 555.
    var d = 2;
    var e = d;
    e = 4; // e == 4, but d is still == 2.
As you can see, operating on an object (such as via array subscript operation)
changes the object, even if the object is pointed by multiple variables.
Likewise, objects passed as the argument of a function are passed by reference:
they aren't simply copied, and operating on the argument within the function is
equivalent to changing the object, whose scope is above that of the function.
Some high-level languages, like C, permit you to explicitly specify what is a
pointer or reference, which eliminates some of this confusion but requires more
exacting attention to detail in your design specification.

STATE
The state of a program is the value of all its variables, the current location
within the instruction set, and the environment of the operating system (or the
interpreter). In Javascript, within Web browsers, the browser typically provides
access to some of its state via the Document Object Model.

CONDITIONAL EXECUTION
Heuristics, or "guesswork," could not exist if there were no way to execute some
different code depending on the state of the program. Furthermore there are some
mathematics you can't write as exactly one set of instructions that produces one
semantic value: for instance, a function defined only on an interval, or an even
root of a positive number. In this circumstance, you are writing branches:
    if (5 > 10) { /* of course, the code in this block never happens. */ }
    else if (2 < 0) { /* neither does this, b/c cond'n is also false. */ }
    else { /* but all of this code happens, because the others didn't. */ }
... One of the branches executes, and the others don't.
The part in parentheses is the "conditional statement:" it's evaluated as either
"true" or "false," like in Boolean logic. 

SCOPE
Identifiers are only valid within the block (curly brackets, or { ... }) where
they were declared. Well, they're supposed to, anyway. Therefore, if you declare
a variable inside a function, you can't use it outside of the function or within
another function. Why would you want to, anyway? The next time you invoked the
function, the value of the variables you were using in there would change again.

LOOPS
Computers are great at repetition. Loops repeat a set of instructions: they are
typically written as a prefix, conditional, postfix, and body. For example:
    for (var T = 10; T > 0; T--) { alert("T minus " + T); }
... which counts down from ten to one with annoying alert popups.
While or do-while loops have only conditions & bodies.
A loop is an example of an "iterative algorithm." Each time the loop's body is
executed, it's called an "iteration." In computing fractal geometric patterns,
"iteration" means more like "recursion:" which, see below.

FUNCTIONS
A function is a modular segment of your program: a sequence of computation that
is repeated a few times, or can be reused as part of another computation.
Functions are "invoked," or called by name, with values supplied as arguments,
and return a value, similarly to how functions behave in algebra. When declaring
a function, you'd typically write the name of the function followed by its args
in parentheses and then the function body. For example, again in Javascript:
    function intp (N) { return (N % 1) == 0; } // integer predicate
... which returns true if N is probably an integer, or whole number:
    if (intp(5)) { alert("Yes. 5 is probably an integer."); }
    if (intp(5.55)) { alert("This box never appears..."); }
    else { alert("... but this one does, because 5.55 is a floater."); }
(Floating-point numbers are inaccurate, in Javascript as likewise elsewhere.)

RECURSION
A function that invokes itself is a recursive function. Any function invoking an
other function, which subsequently causes the original function to be invoked
again, causes a recursion-like situation that I think is called "re-entrancy."
It is essential to note that _any_ and _every_ recursive function you can write
for a computer to execute can be rewritten as an iterative algorithm. The proof
of this is complex: it follows from Alan Turing's model of finite state automata
and the read-execute model of arithmetic and logic units (CPUs), and basically
asserts that you'd never be able to execute recursion if you couldn't do it by
reading one instruction at a time. In other words, each time your function calls
itself again, it's simply storing its state in memory temporarily while the
machine executes a fresh copy: after the copy is done, the former state is re-
loaded, and execution proceeds from there. This is achieved with stacks: data
structures that grow in size as more is "pushed" onto them, and shrink when some
is "popped" off of the top.

OBJECTS
An object is a collection of data that comprises a several datum. That is, when
data are related to one another, they can be envisioned as a "shape" or "motion"
that is the sum of its parts. For example, a vector has direction and magnitude;
an individual has a first and last name; a parser has an input stream, a stack,
and a procedure. In Javascript, you'd write something like this:
    function Blah (newz) { if (newz) { this.z = newz; } return this; }
    Blah.prototype = new Object();
    Blah.prototype.z = 555;
    Blah.prototype.tell_me_z = function () { return this.z; }
    var a = new Blah(222), b = new Blah(); // a.z == 222; b.z = 555.
... syntax varies among languages. Essentially an object is a data structure
containing some members ("variables" attached to the object, like Blah::z above)
and, if the object is a class, some methods (functions, like ::tell_me_z).
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Palling around.

Pall (n.): pawl.

I couldn't write last week, and my upgrade to QL has progressed no further.
For reference, I stalled before comparing the efficiency of nested Objects to
that of nested Arrays, which I must test before experimenting further with the
prototype compiler or even refining the design. I intend to do that this month.
In the meantime, here's a snapshot of MLPTK with new experiments included.

http://www.mediafire.com/download/566ln3t1bc5jujp/mlptk-p9k-08apr2016.zip

And a correction to my brief about the grammar ("Saddlebread"): actually, the
InchoateConjugation sequence does not cause differentiation, because the OP_CAT
prevents the original from reducing. Other parts may be inaccurate. I'll revise
the grammar brief and post a new one as soon as I have fixed the QL speed bug.

I took some time out from writing Quadrare Lexema to write some code I've been
meaning to write for a very long time: pal9000, the dissociated companion.
This software design is remarkably similar to the venerable "Eggdrop," whose C
source code is available for download at various locations on the Internets.
Obviously, my code is free and within the Public Domain (as open as open source
can be); you can find pal9000 bundled with today's edition of MLPTK, beneath the
/reference/ directory.

The chatbot is a hardy perennial computer program.
People sometimes say chatbots are artificial intelligence; although they aren't,
exactly, or at least this one isn't, because it doesn't know where it is or what
it's doing (actually it makes some assumptions about itself that are perfectly
wrong) and it doesn't apply the compiler-like technique of categorical learning
because I half-baked the project. Soon, though, I hope...

Nevertheless, mathematics allows us to simulate natural language.
Even a simplistic algorithm like Dissociated Press (see "Internet Jargon File,"
maintained somewhere on the World Wide Web, possibly at Thyrsus Enterprises by
Eric Steven Raymond) can produce humanoid phrases that are like real writing.
Where DisPress fails, naturally, is paragraphs and coherence: as you'll see when
you've researched, it loses track of what it was saying after a few words.

Of course, that can be alleviated with any number of clever tricks; such as:
	1. Use a compiler.
	2. Use a compiler.
	3. Use a compiler.
I haven't done that with p9k, yet, but you can if you want.

Of meaningful significance to chat robots is the Markov chain.
That is a mathematical model, used to describe some physical processes (such as
diffusion), describing a state machine in which the probability of any given
state occurring is dependent only on the next or previous state of the system,
without regard to how that state was encountered.
Natural language, especially that language which occurs during a dream state or
drugged rhapsody (frequently and too often with malicious intent, these are
misinterpreted as the ravings of madmen), can also be modeled with something
like a Markov chain because of the diffusive nature of tangential thought.

The Markov-chain chat robot applies the principle that the state of a finite
automaton can be described in terms of a set of states foregoing the present;
that is, the state of the machine is a sliding window, in which is recorded some
number of states that were encountered before the state existent at the moment.
Each such state is a word (or phrase / sentence / paragraph if you fancy a more
precise approach to artificial intelligence), and the words are strung together
one after another with respect to the few words that fit in the sliding window.
So, it's sort of like a compression algorithm in reverse, and similar to the way
we memorize concepts by relating them to other concepts. "It's a brain. Sorta."

One problem with Markov robots, and another reason why compilers are of import
in the scientific examination of artificial intelligence, is that of bananas.
The Banana Problem describes the fact that, when a Markov chain is traversed, it
"forgets" what state it occupied before the sliding window moved.
Therefore, for any window of width W < 6, the input B A N A N A first produces
state B, then states A and N sequentially forever.
Obviously, the Banana Problem can be solved by widening the window; however, if
you do that, the automaton's memory consumption increases proportionately.

Additionally, very long inputs tend to throw a Markov-'bot for a loop.
You can sorta fix this by increasing the width of the sliding window signifying
which state the automaton presently occupies, but then you run into problems
when the sliding window is too big and it can't think of any suitable phrase
because no known windows (phrases corresponding to the decision tree's depth)
fit the trailing portion of the input.
It's a sticky problem, which is why I mentioned compilers; they're of import to
artificial intelligence, which is news to absolutely no one, because compilers
(and grammar, generally) describe everything we know about the learning process
of everyone on Earth: namely, that intelligent beings construct semantic meaning
by observing their environments and deducing progressively more abstract ideas
via synthesis of observations with abstractions already deduced.
Nevertheless, you'd be hard-pressed to find even a simple random-walk chatbot
that isn't at least amusing.
(See the "dp" module in MLPTK, which implements the vanilla DisPress algorithm.)

My chatbot, pal9000, is inspired by the Dissociated Press & Eggdrop algorithms;
the copy rights of which are held by their authors, who aren't me.
Although p9k was crafted with regard only to the mathematics and not the code,
if my work is an infringement, I'd be happy to expunge it if you want.

Dissociated Press works like this:
	1. Print the first N words (letters? phonemes?) of a body of text.
	2. Then, search for a random occurrence of a word in the corpus
	   which follows the most recently printed N words, and print it.
	3. Ad potentially infinitum, where "last N words" are round-robin.
It is random: therefore, humorously disjointed.

And Eggdrop works like this (AFAICR):
	1. For a given coherence factor, N:
	2. Build a decision tree of depth N from a body of text.
	3. Then, for a given input text:
	4. Feed the input to the decision tree (mmm, roots), and then
	5. Print the least likely response to follow the last N words
	   by applying the Dissociated Press algorithm non-randomly.
	6. Terminate response after its length exceeds some threshold;
	   the exact computation of which I can't recall at the moment.
It is not random: therefore, eerily humanoid. (Cue theremin riff, thundercrash.)

A compiler, such as I imagined above, could probably employ sliding windows (of
width N) to isolate recurring phrases or sentences. Thereby it may automatically
learn how to construct meaningful language without human interaction.
Although I think you'll agree that the simplistic method is pretty effective on
its own; notwithstanding, I'll experiment with a learning design once I've done
QL's code generation method sufficiently that it can translate itself to Python.

Or possibly I'll nick one of the Python compiler compilers that already exists.
(Although that would take all the fun out of it.)

I'll parsimoniously describe how pal9000 blends the two:

First of all, it doesn't (not exactly), but it's close.
Pal9000 learns the exact words you input, then generates a response within some
extinction threshold, with a sliding window whose width is variable and bounded.
Its response is bounded by a maximum length (to solve the Banana Problem).
Because it must by some means know when a response ends "properly," it also
counts the newline character as a word.
These former are departures from Eggdrop.
It also learns from itself (to avoid saying something twice), as does Eggdrop.

In addition, p9k's response isn't necessarily random.
If you use the database I included, or choose the experimental "generator"
response method, p9k produces a response that is simply the most surprising word
it encountered subsequent to the preceding state chain.
This produces responses more often, and they are closer to something you said
before, but of course this is far less surprising and therefore less amusing.
The classical Eggdrop method takes a bit longer to generate any reply; but, when
it does, it drinks Dos Equis.
... Uh, I mean... when it does, the reply is more likely to be worth reading.
After I have experimented to my satisfaction, I'll switch the response method
back to the classic Eggdrop algorithm. Until then, if you'd prefer the Eggdrop
experience, you must delete the included database and regenerate it with the
default values and input a screenplay or something. I think Eggdrop's Web site
has the script for Alien, if you want to use that. Game over, man; game over!

In case you're curious, the algorithmic time complexity of PAL 9000 is somewhere
in the ballpark of O(((1 + MAX_COHERENCE - MIN_COHERENCE) * N) ^ X) per reply,
where N is every unique word ever learnt and X is the extinction threshold.
"It's _SLOW._" It asymptotically approaches O(1) in the best case.

For additional detail, consult /mlptk/reference/PAL9000/readme.txt.

Pal9000 is a prototypical design that implements some strange ideas about how,
exactly, a Markov-'bot should work. As such, some parts are nonfunctional (or,
indeed, malfunction actually) and vestigial. "Oops... I broke the algorithm."
While designing, I altered multiple good ideas that Eggdrop and DisPress did
right the first time, and actually made the whole thing worse on the whole. For
a more classical computer science dish, try downloading & compiling Eggdrop.

On Loggin’.

The post title, "On Loggin'," is a pun on the algorithmic time-complexity of an
ordered binary tree seek operation: O(n * log(n)).

This lecture provides my advice in regard to sorting data by using binary trees.
(From Bouvier's Law Dictionary, Revised 6th Ed (1856) [bouvier]:
 ADVICE, com. law. A letter [...] intended to give notice of [facts contained].)
My lectures are in the Public Domain, and are free of charge.
"By order of the Author." - Mark Twain, _Huckleberry Finn._

A tree is a directed acyclic graph. ("Tree" (1998-11-12) via FOLDOC 30jan2010.)
Other sort methods besides tree sort include quicksort, bubble sort, heap sort,
and insertion sort. ("Sort" (1997-02-12) via FOLDOC, 30th January 2010.)

This pattern can be seen almost everywhere that data are represented as discrete
notional objects: for example, the directory trees contained within the file
systema of your personal computer, the technology/skill trees that are likely to
present themselves in your favorite video game, and in any computer program that
maintains the logical order of a data set as new data are added. These aren't
necessarily treelike data structures; but, because the perceived "shape" of the
data is a tree, they're probably similar if coded in object-oriented language.
I imagine a file system as "what an object even is;" although a computer program
is volatile, whereas files are nonvolatile, the idea is much the same.
See also: object-oriented programming, procedural vs. functional languages, file
systems, {non,}volatile memory such as RAM and EEPROM, sort algorithms.

Of course, treelike structures aren't always the optimum schematic to represent
a hierarchical assortment of data; definitively, like all other object-oriented
designs, they're mere abstractions employed to delineate pieces of a contiguous
unidimensional memory space (the Turing model of a logic machine considers RAM
to be just a big empty pair of set brackets; data are populated & operated-upon
arbitrarily, but the container is merely a set) and to signify correlation.
An object is a collection of related data; which comprise a several datum.
See also: pointers, heap memory, set theory, discrete math, and Boolean logic.

¶ The following (til the next pilcrow) is nearly a verbatim excerpt from FOLDOC.
The data stored in a tree is processed by traversal. The algorithm begins at
root node, transforms or collects or computes against the data, and then repeats
itself for the root's child-nodes ("branches") in some specific order. Three
common traversal orders are pre-order, post-order, and in-order traversal.
For the binary tree illustrated below:
        T
       / \
      I   S
     / \
    D   E
A pre-order traversal visits the nodes in the order T I D E S.
A post-order traversal visits them in the order D E I S T.
An in-order traversal visits them in the order D I E T S.
¶ "Traversal" (2001-10-01) via FOLDOC, ed. 30th January 2010.
See also: recursion, the call stack (subroutine invocation), digraph traversal.

To encode a tree walk via recursive algorithm is straightforward, and is how the
concept of data set traversal is introduced to novitiate computer programmers.
(Well, after they've learned how to loop over an array with "for.")

Succinctly:

    function put (pNode, value) {
        if (pNode.isEmpty()) { pNode.datum = value; }
        else if (value < pNode.datum) { put(pNode.bLeft, value); }
        else if (value > pNode.datum) { put(pNode.bRight, value); }
    } // Ordered insertion.

    function tell (pNode, value) {
        if (value < pNode.datum) { return tell(pNode.bLeft, value); }
        else if (value > pNode.datum) { return tell(pNode.bRight, value); }
        else if (pNode.isEmpty()) { return NULL; }
        return pNode;
    } // Seek pointer to inserted.

_Any_ recursive algorithm can be reduced to an iterative algorithm, provided you
can use variable-length arrays to simulate the function of the call stack, which
stores the arguments to the subroutine you invoked and the address to restore to
the instruction pointer when the subroutine returns. That is, the call stack is
like bookmarks or tabbed indices to tell the routine where it was before a jump.
Replace the "instruction pointer and arguments" model of a stack frame with any
constant-width data set that remembers what you were doing before you jumped to
the next node in the digraph, and, naturally, you can remember the same stuff as
if you had utilized the convenient paradigm that is recursivity. (Stack frames
don't really all have to be the same size, but a definite frame width spares you
from doing yet another algorithm to parse frames. See: network protocol stacks.)

The "stuff you remember" is the state of the finite state automaton who tends to
the mechanism whereby the machine knows which instruction to execute next.
Recursion provides this automaton "for free," because it crops up so frequently;
but, for whatever reason (such as the 3000-call recursion limit in Firefox 3.6),
you might want to write a tree sort without using recursion at all.

Gentlemen, behold...
    corm = GenProtoTok(
        Object,
        function () {
            this.tree = new this.Hop();
            return this;
        },
        "Hopper",
        undefined,

        "locus", undefined,

        "seek", function (u_val) {
            while (this.locus[2] != undefined) {
                if (u_val > this.locus[2]) { this.locus = this.hop(1); }
                else if (u_val == this.locus[2]) { return false; }
                else { this.locus = this.hop(0); } // u < loc[2] || u == undef
            }
            return true;
        }, // end function Hopper::seek

        "tellSorted", function () {
            if (this.tree == undefined) { return undefined; }
            var evaluation = new Array();
            for (var orca = new Array(this.tree), did = new Array(false);
                orca.length>0;
            ) {
                this.locus = orca.pop();
                if (did.pop()) {
                    evaluation.push(this.locus[2]);
                    if (this.locus[1] != undefined) {
                        orca.push(this.locus[1]);
                        did.push(false);
                    }
                } else {
                    orca.push(this.locus);
                    did.push(true);
                    if (this.locus[0] != undefined) {
                        orca.push(this.locus[0]);
                        did.push(false);
                    }
                }
            }
            return evaluation;
        }, // end function Hopper::tellSorted

        "hop", function (index) {
            if (this.locus[index]==undefined) {
                this.locus[index] = new this.Hop();
            }
            return this.locus[index];
        }, // end function Hopper::hop

        "addUnique", function (value) {
            this.locus = this.tree;
            if (this.seek(value)) { this.locus[2] = value; return true; }
            return false;
        }, // end function Hopper::addUnique

        "Hop", GenPrototype(Array, new Array())
    );
... corm!

Here is how corm works: it's an Array retrofitted with a binary tree algorithm.

Each node of the tree is an Array whose members, at these indices, signify:
    0. Left-hand ("less-than") branch pointer.
    1. Right-hand ("greater-than") branch pointer.
    2. Value stored in the node.

Values are added via the corm object's addUnique method, which resets a pointer
to the algorithm's location in the tree and then seeks a place to put the datum.
Seeking causes a node to be created, if no node yet contains the datum; so, the
corm::seek method's name is misleading, but seeking is exactly what it does.

In-order traversal is executed by corm::tellSorted which returns the sorted set.
Recursion is simulated using a state stack whose frames each contain a pointer
to the "previous" node and a boolean value that signifies whether its left child
has been visited. Here is the main loop, step-by-step, in natural English:
    for (var orca = new Array(this.tree), did = new Array(false);
        orca.length>0;
    ) {
"Given the stack, which I consider until it is exhausted..."
        this.locus = orca.pop();
"I am here." -^
        if (did.pop()) {
"If I did the left branch already,"
            evaluation.push(this.locus[2]);
"then this value occurs at this location in the ordered set;"
            if (this.locus[1] != undefined) {
"if the right branch exists,"
                orca.push(this.locus[1]);
"visit the right child on the next loop iteration"
                did.push(false);
"(and, naturally, I haven't visited the right child's left child yet)"
            }
"."
        } else {
"If I haven't done the left branch already,"
            orca.push(this.locus);
"then I have to visit this node again when I come back"
            did.push(true);
"(except I shall have visited the left child by then);"
            if (this.locus[0] != undefined) {
"if the left branch exists,"
                orca.push(this.locus[0]);
"visit the left child on the next loop iteration"
                did.push(false);
"(and, naturally, I haven't visited the left child's left child yet)"
            }
"."
        }
"... I consume pointers and bools, doing all that there stuff"
    }
"."

And that is how to do everything I did with the Hopper data structure
(which is here renamed corm, because I like corm) in quadrare-lexema.js.



Full disclosure: whereas; although I do put on women's clothing; and although
indeed I sleep all night and work all day; and, in addition, despite that I have
been known, of an occasion: to eat eat my lunch, go to the lavatory, pick wild
flowers, hang around in bars, and want to be a girlie; my Wednesdays have not
historically been known to involve shopping or buttered scones, I don't know how
to press a flower, I have neither suspenders nor heels nor a bra (but thanks for
offering), and am not, in fact, a lumberjack.