I'm having trouble articulating the difference between Chomsky type 2 (context free languages) and Chomsky type 3 (Regular languages).
Can someone out there give me an answer in plain English? I'm having trouble understanding the whole hierarchy thing.
A Type II grammar is a Type III grammar with a stack
A Type II grammar is basically a Type III grammar with nesting.
Type III grammar (Regular):
Use Case - CSV (Comma Separated Values)
Characteristics:
can be read with a using a FSM (Finite State Machine)
requires no intermediate storage
can be read with Regular Expressions
usually expressed using a 1D or 2D data structure
is flat, meaning no nesting or recursive properties
Ex:
this,is,,"an "" example",\r\n
"of, a",type,"III\n",grammar\r\n
As long as you can figure out all of the rules and edge cases for the above text you can parse CSV.
Type II grammar (Context Free):
Use Case - HTML (Hyper Text Markup Language) or SGML in general
Characteristics:
can be read using a DPDA (Deterministic Pushdown Automata)
will require a stack for intermediate storage
may be expressed as an AST (Abstract Syntax Tree)
may contain nesting and/or recursive properties
HTML could be expressed as a regular grammar:
<h1>Useless Example</h1>
<p>Some stuff written here</p>
<p>Isn't this fun</p>
But it's try parsing this using a FSM:
<body>
<div id=titlebar>
<h1>XHTML 1.0</h1>
<h2>W3C's failed attempt to enforce HTML as a context-free language</h2>
</div>
<p>Back when the web was still pretty boring, the W3C attempted to standardize away the quirkiness of HTML by introducing a strict specification</p
<p>Unfortunately, everybody ignored it.</p>
</body>
See the difference? Imagine you were writing a parser, you could start on an open tag and finish on a closing tag but what happens when you encounter a second opening tag before reaching the closing tag?
It's simple, you push the first opening tag onto a stack and start parsing the second tag. Repeat this process for as many levels of nesting that exist and if the syntax is well-structured, the stack can be un-rolled one layer at a time in the opposite level that it was built
Due to the strict nature of 'pure' context-free languages, they're relatively rare unless they're generated by a program. JSON, is a prime example.
The benefit of context-free languages is that, while very expressive, they're still relatively simple to parse.
But wait, didn't I just say HTML is context-free. Yep, if it is well-formed (ie XHTML).
While XHTML may be considered context-free, the looser-defined HTML would actually considered Type I (Ie Context Sensitive). The reason being, when the parser reaches poorly structured code it actually makes decisions about how to interpret the code based on the surrounding context. For example if an element is missing its closing tags, it would need to determine where that element exists in the hierarchy before it can decide where the closing tag should be placed.
Other features that could make a context-free language context-sensitive include, templates, imports, preprocessors, macros, etc.
In short, context-sensitive languages look a lot like context-free languages but the elements of a context-sensitive languages may be interpreted in different ways depending on the program state.
Disclaimer: I am not formally trained in CompSci so this answer may contain errors or assumptions. If you asked me the difference between a terminal and a non-terminal you'll earn yourself a blank stare. I learned this much by actually building a Type III (Regular) parser and by reading extensively about the rest.
The wikipedia page has a good picture and bullet points.
Roughly, the underlying machine that can describe a regular language does not need memory. It runs as a statemachine (DFA/NFA) on the input. Regular languages can also be expressed with regular expressions.
A language with the "next" level of complexity added to it is a context free language. The underlying machine describing this kind of language will need some memory to be able to represent the languages that are context free and not regular. Note that adding memory to your machine makes it a little more powerful, so it can still express languages (e.g. regular languages) that didn't need the memory to begin with. The underlying machine is typically a push-down automaton.
Type 3 grammars consist of a series of states. They cannot express embedding. For example, a Type 3 grammar cannot require matching parentheses because it has no way to show that the parentheses should be "wrapped around" their contents. This is because, as Derek points out, a Type 3 grammar does not "remember" anything about the previous states that it passed through to get to the current state.
Type 2 grammars consist of a set of "productions" (you can think of them as patterns) that can have other productions embedded within them. Thus, they are recursively defined. A production can only be defined in terms of what it contains, and cannot "see" outside of itself; this is what makes the grammar context-free.
Is there a list of items on the web that you should not use when creating a model or variable?
For example, if I wanted to create apartment listings, naming a model something like Property would be problematic in the future and also confusing since property is a built-in Python function.
I did try Googling this, but couldn't come up with anything.
Thanks!
Rules and constraints about naming depend on the programming language. How an identifier/name is bound depends on the language semantics and its scoping rules: an identifer/name will be bound to different element depending on the scope. Scoping is usally lexical (i.e. static) but some language have dynamic scoping (some variant of lisp).
If names are different, there is no confusion in scoping. If identifiers/names are reused accrossed scopes, an identifier/name might mask another one. This is referred as Shadowing. This is a source of confusion.
Certain reserved names (i.e. keywords) have special meaning. Such keyword can simply be forbidden as names of other elements, or not.
For instance, in Smallatalk self is a keyword. It is still possible to declare a temporary variable self, though. In the scope where the temporary variable is visible, self resolves to the temporary variable, not the usual self that is receiver of the message.
Of course, shadowing can happen between regular names.
Scoping rules take types into consideration as well, and inheritance might introduce shadows.
Another source of confusion related to binding is Method Overloading. In statically typed languages, which method is executed depends on the static types at the call site. In certain cases, overloading makes it confusing to know which method is selected. Both Shadowing and Overloading should avoided to avoid confusions.
If your goal is to translate Python to Javascript, and vice versa, I guess you need to check the scoping rules and keywords of both languages to make sure your translation is not only syntactically correct, but also semantically correct.
Generally, programming languages have 'reserved words' or 'keywords' that you're either not able to use or in some cases are but should stay away from. For Python, you can find that list here.
Most words in most natural languages can have different meanings, according to the context. That's why we use specifiers to make the meaning of a word clear. If in any case you think that some particular identifier may be confusing, you can just add a specifier to make it clear. For example ObjectProperty has probably nothing to do with real estate, even in an application that deals with real estate.
The case you present is no different than using generic identifiers with no attached context. For example a variable named limit or length may have completely different meanings in different programs. Just use identifiers that make sense and document their meaning extensively. Being consistent within your own code base would also be preferable. Do not complicate your life with banned term lists that will never be complete and will only make programming more difficult.
The obvious exceptions are words reserved by your programming language of choice - but then again no decent compiler would allow you to use them anyway...
In every project I've started in languages without type systems, I eventually begin to invent a runtime type system. Maybe the term "type system" is too strong; at the very least, I create a set of type/value-range validators when I'm working with complex data types, and then I feel the need to be paranoid about where data types can be created and modified.
I hadn't thought twice about it until now. As an independent developer, my methods have been working in practice on a number of small projects, and there's no reason they'd stop working now.
Nonetheless, this must be wrong. I feel as if I'm not using dynamically-typed languages "correctly". If I must invent a type system and enforce it myself, I may as well use a language that has types to begin with.
So, my questions are:
Are there existing programming paradigms (for languages without types) that avoid the necessity of using or inventing type systems?
Are there otherwise common recommendations on how to solve the problems that static typing solves in dynamically-typed languages (without sheepishly reinventing types)?
Here is a concrete example for you to consider. I'm working with datetimes and timezones in erlang (a dynamic, strongly typed language). This is a common datatype I work with:
{{Y,M,D},{tztime, {time, HH,MM,SS}, Flag}}
... where {Y,M,D} is a tuple representing a valid date (all entries are integers), tztime and time are atoms, HH,MM,SS are integers representing a sane 24-hr time, and Flag is one of the atoms u,d,z,s,w.
This datatype is commonly parsed from input, so to ensure valid input and a correct parser, the values need to be checked for type correctness, and for valid ranges. Later on, instances of this datatype are compared to each other, making the type of their values all the more important, since all terms compare. From the erlang reference manual
number < atom < reference < fun < port < pid < tuple < list < bit string
Aside from the confsion of static vs. dynamic and strong vs. weak typing:
What you want to implement in your example isn't really solved by most existing static typing systems. Range checks and complications like February 31th and especially parsed input are usually checked during runtime no matter what type system you have.
Your example being in Erlang I have a few recommendations:
Use records. Besides being usefull and helpfull for a whole bunch of reasons, the give you easy runtime type checking without a lot of effort e.g.:
is_same_day(#datetime{year=Y1, month=M1, day=D1},
#datetime{year=Y2, month=M2, day=D2}) -> ...
Effortless only matches for two datetime records. You could even add guards to check for ranges if the source is untrusted. And it conforms to erlangs let it crash method of error handling: if no match is found you get a badmatch, and can handle this on the level where it is apropriate (usually the supervisor level).
Generally write your code that it crashes when the assumptions are not valid
If this doesn't feel static checked enough: use typer and dialyzer to find the kind of errors that can be found statically, whatever remains will be checkd at runtime.
Don't be too restrictive in your functions what "types" you accept, sometimes the added functionality of just doing someting useful even for different inputs is worth more than checking the types and ranges on every function. If you do it where it matters usually you will catch the error early enough for it to be easy fixable. This is especially true for a functionaly language where you allways know where every value comes from.
A lot of good answers, let me add:
Are there existing programming paradigms (for languages without types) that avoid the necessity of using or inventing type systems?
The most important paradigm, especially in Erlang, is this: Assume the type is right, otherwise let it crash. Don't write excessively checking paranoid code, but assume that the input you get is of the right type or the right pattern. Don't write (there are exceptions to this rule, but in general)
foo({tag, ...}) -> do_something(..);
foo({tag2, ...}) -> do_something_else(..);
foo(Otherwise) ->
report_error(Otherwise),
try to fix problem here...
Kill the last clause and have it crash right away. Let a supervisor and other processes do the cleanup (you can use monitors() for janitorial processes to know when a crash has occurred).
Do be precise however. Write
bar(N) when is_integer(N) -> ...
baz([]) -> ...
baz(L) when is_list(L) -> ...
if the function is known only to work with integers or lists respectively. Yes, it is a runtime check but the goal is to convey information to the programmer. Also, HiPE tend to utilize the hint for optimization and eliminate the type check if possible. Hence, the price may be less than what you think it is.
You choose an untyped/dynamically-typed language so the price you have to pay is that type checking and errors from clashes will happen at runtime. As other posts hint, a statically typed language is not exempt from doing some checks as well - the type system is (usually) an approximation of a proof of correctness. In most static languages you often get input which you can't trust. This input is transformed at the "border" of the application and then converted to an internal format. The conversion serves to mark trust: From now on, the thing has been validated and we can assume certain things about it. The power and correctness of this assumption is directly tied to its type signature and how good the programmer is with juggling the static types of the language.
Are there otherwise common recommendations on how to solve the problems that static typing solves in dynamically-typed languages (without sheepishly reinventing types)?
Erlang has the dialyzer which can be used to statically analyze and infer types of your programs. It will not come up with as many type errors as a type checker in e.g., Ocaml, but it won't "cry wolf" either: An error from the dialyzer is provably an error in the program. And it won't reject a program which may be working ok. A simple example is:
and(true, true) -> true;
and(true, _) -> false;
and(false, _) -> false.
The invocation and(true, greatmistake) will return false, yet a static type system will reject the program because it will infer from the first line that the type signature takes a boolean() value as the 2nd parameter. The dialyzer will accept this function in contrast and give it the signature (boolean(), term()) -> boolean(). It can do this, because there is no need to protect a priori for an error. If there is a mistake, the runtime system has a type check that will capture it.
In order for a statically-typed language to match the flexibility of a dynamically-typed one, I think it would need a lot, perhaps infinitely many, features.
In the Haskell world, one hears a lot of sophisticated, sometimes to the point of being scary, teminology. Type classes. Parametric polymorphism. Generalized algebraic data types. Type families. Functional dependencies. The Ωmega programming language takes it even further, with the website listing "type-level functions" and "level polymorphism", among others.
What are all these? Features added to static typing to make it more flexible. These features can be really cool, and tend to be elegant and mind-blowing, but are often difficult to understand. Learning curve aside, type systems often fail to model real-world problems elegantly. A particularly good example of this is interacting with other languages (a major motivation for C# 4's dynamic feature).
Dynamically-typed languages give you the flexibility to implement your own framework of rules and assumptions about data, rather than be constrained by the ever-limited static type system. However, "your own framework" won't be machine-checked, meaning the onus is on you to ensure your "type system" is safe and your code is well-"typed".
One thing I've found from learning Haskell is that I can carry lessons learned about strong typing and sound reasoning over to weaker-typed languages, such as C and even assembly, and do the "type checking" myself. Namely, I can prove that sections of code are correct in and of themselves, by bearing in mind the rules my functions and values are supposed to follow, and the assumptions I am allowed to make about other functions and values. When debugging, I go through and check things again, and think through whether or not my approach is sound.
The bottom line: dynamic typing puts more flexibility at your fingertips. On the other hand, statically-typed languages tend to be more efficient (by orders of magnitude), and good static type systems drastically cut down on debugging time by letting the computer do much of it for you. If you want the benefits of both, install a static type checker in your brain by learning decent, strongly-typed languages.
Sometimes data need validation. Validating any data received from the network is almost always a good idea — especially data from a public network. Being paranoid here is only good. If something resembling a static type system helps this in the least painful way, so be it. There's a reason why Erlang allows type annotations. Even pattern matching can be seen as just a kind of dynamic type checking; nevertheless, it's a central feature of the language. The very structure of data is its 'type' in Erlang.
The good thing is that you can custom-tailor your 'type system' to your needs, make it flexible and smart, while type systems of OO languages typically have fixed features. When data structures you use are immutable, once you've validated such a structure, you're safe to assume it conforms your restrictions, just like with static typing.
There's no point in being ready to process any kind of data at any point of a program, dynamically-typed or not. A 'dynamic type' is essentially a union of all possible types; limiting it to a useful subset is a valid way to program.
A statically typed language detects type errors at compile time. A dynamically typed language detects them at runtime. There are some modest restrictions on what one can write in a statically typed language such that all type errors can be caught at compile time.
But yes, you still have types even in a dynamically typed language, and that's a good thing. The problem is you wander into lots of runtime checks to ensure that you have the types you think you do, since the compiler hasn't taken care of that for you.
Erlang has a very nice tool for specifying and statically verifying lots of types -- dialyzer: Erlang type system, for references.
So don't reinvent types, use the typing tools that Erlang already provides, to handle the types that already exist in your program (but which you haven't yet specified).
And this on its own won't eliminate range checks, unfortunately. Without lots of special sauce you really have to enforce this on your own by convention (and smart constructors, etc. to help), or fall back to runtime checks, or both.
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I keep hearing this term tossed around in several different contexts. What is it?
Declarative programming is when you write your code in such a way that it describes what you want to do, and not how you want to do it. It is left up to the compiler to figure out the how.
Examples of declarative programming languages are SQL and Prolog.
The other answers already do a fantastic job explaining what declarative programming is, so I'm just going to provide some examples of why that might be useful.
Context Independence
Declarative Programs are context-independent. Because they only declare what the ultimate goal is, but not the intermediary steps to reach that goal, the same program can be used in different contexts. This is hard to do with imperative programs, because they often depend on the context (e.g. hidden state).
Take yacc as an example. It's a parser generator aka. compiler compiler, an external declarative DSL for describing the grammar of a language, so that a parser for that language can automatically be generated from the description. Because of its context independence, you can do many different things with such a grammar:
Generate a C parser for that grammar (the original use case for yacc)
Generate a C++ parser for that grammar
Generate a Java parser for that grammar (using Jay)
Generate a C# parser for that grammar (using GPPG)
Generate a Ruby parser for that grammar (using Racc)
Generate a tree visualization for that grammar (using GraphViz)
simply do some pretty-printing, fancy-formatting and syntax highlighting of the yacc source file itself and include it in your Reference Manual as a syntactic specification of your language
And many more …
Optimization
Because you don't prescribe the computer which steps to take and in what order, it can rearrange your program much more freely, maybe even execute some tasks in parallel. A good example is a query planner and query optimizer for a SQL database. Most SQL databases allow you to display the query that they are actually executing vs. the query that you asked them to execute. Often, those queries look nothing like each other. The query planner takes things into account that you wouldn't even have dreamed of: rotational latency of the disk platter, for example or the fact that some completely different application for a completely different user just executed a similar query and the table that you are joining with and that you worked so hard to avoid loading is already in memory anyway.
There is an interesting trade-off here: the machine has to work harder to figure out how to do something than it would in an imperative language, but when it does figure it out, it has much more freedom and much more information for the optimization stage.
Loosely:
Declarative programming tends towards:-
Sets of declarations, or declarative statements, each of which has meaning (often in the problem domain) and may be understood independently and in isolation.
Imperative programming tends towards:-
Sequences of commands, each of which perform some action; but which may or may not have meaning in the problem domain.
As a result, an imperative style helps the reader to understand the mechanics of what the system is actually doing, but may give little insight into the problem that it is intended to solve. On the other hand, a declarative style helps the reader to understand the problem domain and the approach that the system takes towards the solution of the problem, but is less informative on the matter of mechanics.
Real programs (even ones written in languages that favor the ends of the spectrum, such as ProLog or C) tend to have both styles present to various degrees at various points, to satisfy the varying complexities and communication needs of the piece. One style is not superior to the other; they just serve different purposes, and, as with many things in life, moderation is key.
Here's an example.
In CSS (used to style HTML pages), if you want an image element to be 100 pixels high and 100 pixels wide, you simply "declare" that that's what you want as follows:
#myImageId {
height: 100px;
width: 100px;
}
You can consider CSS a declarative "style sheet" language.
The browser engine that reads and interprets this CSS is free to make the image appear this tall and this wide however it wants. Different browser engines (e.g., the engine for IE, the engine for Chrome) will implement this task differently.
Their unique implementations are, of course, NOT written in a declarative language but in a procedural one like Assembly, C, C++, Java, JavaScript, or Python. That code is a bunch of steps to be carried out step by step (and might include function calls). It might do things like interpolate pixel values, and render on the screen.
I am sorry, but I must disagree with many of the other answers. I would like to stop this muddled misunderstanding of the definition of declarative programming.
Definition
Referential transparency (RT) of the sub-expressions is the only required attribute of a declarative programming expression, because it is the only attribute which is not shared with imperative programming.
Other cited attributes of declarative programming, derive from this RT. Please click the hyperlink above for the detailed explanation.
Spreadsheet example
Two answers mentioned spreadsheet programming. In the cases where the spreadsheet programming (a.k.a. formulas) does not access mutable global state, then it is declarative programming. This is because the mutable cell values are the monolithic input and output of the main() (the entire program). The new values are not written to the cells after each formula is executed, thus they are not mutable for the life of the declarative program (execution of all the formulas in the spreadsheet). Thus relative to each other, the formulas view these mutable cells as immutable. An RT function is allowed to access immutable global state (and also mutable local state).
Thus the ability to mutate the values in the cells when the program terminates (as an output from main()), does not make them mutable stored values in the context of the rules. The key distinction is the cell values are not updated after each spreadsheet formula is performed, thus the order of performing the formulas does not matter. The cell values are updated after all the declarative formulas have been performed.
Declarative programming is the picture, where imperative programming is instructions for painting that picture.
You're writing in a declarative style if you're "Telling it what it is", rather than describing the steps the computer should take to get to where you want it.
When you use XML to mark-up data, you're using declarative programming because you're saying "This is a person, that is a birthday, and over there is a street address".
Some examples of where declarative and imperative programming get combined for greater effect:
Windows Presentation Foundation uses declarative XML syntax to describe what a user interface looks like, and what the relationships (bindings) are between controls and underlying data structures.
Structured configuration files use declarative syntax (as simple as "key=value" pairs) to identify what a string or value of data means.
HTML marks up text with tags that describe what role each piece of text has in relation to the whole document.
Declarative Programming is programming with declarations, i.e. declarative sentences. Declarative sentences have a number of properties that distinguish them from imperative sentences. In particular, declarations are:
commutative (can be reordered)
associative (can be regrouped)
idempotent (can repeat without change in meaning)
monotonic (declarations don't subtract information)
A relevant point is that these are all structural properties and are orthogonal to subject matter. Declarative is not about "What vs. How". We can declare (represent and constrain) a "how" just as easily as we declare a "what". Declarative is about structure, not content. Declarative programming has a significant impact on how we abstract and refactor our code, and how we modularize it into subprograms, but not so much on the domain model.
Often, we can convert from imperative to declarative by adding context. E.g. from "Turn left. (... wait for it ...) Turn Right." to "Bob will turn left at intersection of Foo and Bar at 11:01. Bob will turn right at the intersection of Bar and Baz at 11:06." Note that in the latter case the sentences are idempotent and commutative, whereas in the former case rearranging or repeating the sentences would severely change the meaning of the program.
Regarding monotonic, declarations can add constraints which subtract possibilities. But constraints still add information (more precisely, constraints are information). If we need time-varying declarations, it is typical to model this with explicit temporal semantics - e.g. from "the ball is flat" to "the ball is flat at time T". If we have two contradictory declarations, we have an inconsistent declarative system, though this might be resolved by introducing soft constraints (priorities, probabilities, etc.) or leveraging a paraconsistent logic.
Describing to a computer what you want, not how to do something.
imagine an excel page. With columns populated with formulas to calculate you tax return.
All the logic is done declared in the cells, the order of the calculation is by determine by formula itself rather than procedurally.
That is sort of what declarative programming is all about. You declare the problem space and the solution rather than the flow of the program.
Prolog is the only declarative language I've use. It requires a different kind of thinking but it's good to learn if just to expose you to something other than the typical procedural programming language.
I have refined my understanding of declarative programming, since Dec 2011 when I provided an answer to this question. Here follows my current understanding.
The long version of my understanding (research) is detailed at this link, which you should read to gain a deep understanding of the summary I will provide below.
Imperative programming is where mutable state is stored and read, thus the ordering and/or duplication of program instructions can alter the behavior (semantics) of the program (and even cause a bug, i.e. unintended behavior).
In the most naive and extreme sense (which I asserted in my prior answer), declarative programming (DP) is avoiding all stored mutable state, thus the ordering and/or duplication of program instructions can NOT alter the behavior (semantics) of the program.
However, such an extreme definition would not be very useful in the real world, since nearly every program involves stored mutable state. The spreadsheet example conforms to this extreme definition of DP, because the entire program code is run to completion with one static copy of the input state, before the new states are stored. Then if any state is changed, this is repeated. But most real world programs can't be limited to such a monolithic model of state changes.
A more useful definition of DP is that the ordering and/or duplication of programming instructions do not alter any opaque semantics. In other words, there are not hidden random changes in semantics occurring-- any changes in program instruction order and/or duplication cause only intended and transparent changes to the program's behavior.
The next step would be to talk about which programming models or paradigms aid in DP, but that is not the question here.
It's a method of programming based around describing what something should do or be instead of describing how it should work.
In other words, you don't write algorithms made of expressions, you just layout how you want things to be. Two good examples are HTML and WPF.
This Wikipedia article is a good overview: http://en.wikipedia.org/wiki/Declarative_programming
Since I wrote my prior answer, I have formulated a new definition of the declarative property which is quoted below. I have also defined imperative programming as the dual property.
This definition is superior to the one I provided in my prior answer, because it is succinct and it is more general. But it may be more difficult to grok, because the implication of the incompleteness theorems applicable to programming and life in general are difficult for humans to wrap their mind around.
The quoted explanation of the definition discusses the role pure functional programming plays in declarative programming.
Declarative vs. Imperative
The declarative property is weird, obtuse, and difficult to capture in a technically precise definition that remains general and not ambiguous, because it is a naive notion that we can declare the meaning (a.k.a semantics) of the program without incurring unintended side effects. There is an inherent tension between expression of meaning and avoidance of unintended effects, and this tension actually derives from the incompleteness theorems of programming and our universe.
It is oversimplification, technically imprecise, and often ambiguous to define declarative as “what to do” and imperative as “how to do”. An ambiguous case is the “what” is the “how” in a program that outputs a program— a compiler.
Evidently the unbounded recursion that makes a language Turing complete, is also analogously in the semantics— not only in the syntactical structure of evaluation (a.k.a. operational semantics). This is logically an example analogous to Gödel's theorem— “any complete system of axioms is also inconsistent”. Ponder the contradictory weirdness of that quote! It is also an example that demonstrates how the expression of semantics does not have a provable bound, thus we can't prove2 that a program (and analogously its semantics) halt a.k.a. the Halting theorem.
The incompleteness theorems derive from the fundamental nature of our universe, which as stated in the Second Law of Thermodynamics is “the entropy (a.k.a. the # of independent possibilities) is trending to maximum forever”. The coding and design of a program is never finished— it's alive!— because it attempts to address a real world need, and the semantics of the real world are always changing and trending to more possibilities. Humans never stop discovering new things (including errors in programs ;-).
To precisely and technically capture this aforementioned desired notion within this weird universe that has no edge (ponder that! there is no “outside” of our universe), requires a terse but deceptively-not-simple definition which will sound incorrect until it is explained deeply.
Definition:
The declarative property is where there can exist only one possible set of statements that can express each specific modular semantic.
The imperative property3 is the dual, where semantics are inconsistent under composition and/or can be expressed with variations of sets of statements.
This definition of declarative is distinctively local in semantic scope, meaning that it requires that a modular semantic maintain its consistent meaning regardless where and how it's instantiated and employed in global scope. Thus each declarative modular semantic should be intrinsically orthogonal to all possible others— and not an impossible (due to incompleteness theorems) global algorithm or model for witnessing consistency, which is also the point of “More Is Not Always Better” by Robert Harper, Professor of Computer Science at Carnegie Mellon University, one of the designers of Standard ML.
Examples of these modular declarative semantics include category theory functors e.g. the Applicative, nominal typing, namespaces, named fields, and w.r.t. to operational level of semantics then pure functional programming.
Thus well designed declarative languages can more clearly express meaning, albeit with some loss of generality in what can be expressed, yet a gain in what can be expressed with intrinsic consistency.
An example of the aforementioned definition is the set of formulas in the cells of a spreadsheet program— which are not expected to give the same meaning when moved to different column and row cells, i.e. cell identifiers changed. The cell identifiers are part of and not superfluous to the intended meaning. So each spreadsheet result is unique w.r.t. to the cell identifiers in a set of formulas. The consistent modular semantic in this case is use of cell identifiers as the input and output of pure functions for cells formulas (see below).
Hyper Text Markup Language a.k.a. HTML— the language for static web pages— is an example of a highly (but not perfectly3) declarative language that (at least before HTML 5) had no capability to express dynamic behavior. HTML is perhaps the easiest language to learn. For dynamic behavior, an imperative scripting language such as JavaScript was usually combined with HTML. HTML without JavaScript fits the declarative definition because each nominal type (i.e. the tags) maintains its consistent meaning under composition within the rules of the syntax.
A competing definition for declarative is the commutative and idempotent properties of the semantic statements, i.e. that statements can be reordered and duplicated without changing the meaning. For example, statements assigning values to named fields can be reordered and duplicated without changed the meaning of the program, if those names are modular w.r.t. to any implied order. Names sometimes imply an order, e.g. cell identifiers include their column and row position— moving a total on spreadsheet changes its meaning. Otherwise, these properties implicitly require global consistency of semantics. It is generally impossible to design the semantics of statements so they remain consistent if randomly ordered or duplicated, because order and duplication are intrinsic to semantics. For example, the statements “Foo exists” (or construction) and “Foo does not exist” (and destruction). If one considers random inconsistency endemical of the intended semantics, then one accepts this definition as general enough for the declarative property. In essence this definition is vacuous as a generalized definition because it attempts to make consistency orthogonal to semantics, i.e. to defy the fact that the universe of semantics is dynamically unbounded and can't be captured in a global coherence paradigm.
Requiring the commutative and idempotent properties for the (structural evaluation order of the) lower-level operational semantics converts operational semantics to a declarative localized modular semantic, e.g. pure functional programming (including recursion instead of imperative loops). Then the operational order of the implementation details do not impact (i.e. spread globally into) the consistency of the higher-level semantics. For example, the order of evaluation of (and theoretically also the duplication of) the spreadsheet formulas doesn't matter because the outputs are not copied to the inputs until after all outputs have been computed, i.e. analogous to pure functions.
C, Java, C++, C#, PHP, and JavaScript aren't particularly declarative.
Copute's syntax and Python's syntax are more declaratively coupled to
intended results, i.e. consistent syntactical semantics that eliminate the extraneous so one can readily
comprehend code after they've forgotten it. Copute and Haskell enforce
determinism of the operational semantics and encourage “don't repeat
yourself” (DRY), because they only allow the pure functional paradigm.
2 Even where we can prove the semantics of a program, e.g. with the language Coq, this is limited to the semantics that are expressed in the typing, and typing can never capture all of the semantics of a program— not even for languages that are not Turing complete, e.g. with HTML+CSS it is possible to express inconsistent combinations which thus have undefined semantics.
3 Many explanations incorrectly claim that only imperative programming has syntactically ordered statements. I clarified this confusion between imperative and functional programming. For example, the order of HTML statements does not reduce the consistency of their meaning.
Edit: I posted the following comment to Robert Harper's blog:
in functional programming ... the range of variation of a variable is a type
Depending on how one distinguishes functional from imperative
programming, your ‘assignable’ in an imperative program also may have
a type placing a bound on its variability.
The only non-muddled definition I currently appreciate for functional
programming is a) functions as first-class objects and types, b)
preference for recursion over loops, and/or c) pure functions— i.e.
those functions which do not impact the desired semantics of the
program when memoized (thus perfectly pure functional
programming doesn't exist in a general purpose denotational semantics
due to impacts of operational semantics, e.g. memory
allocation).
The idempotent property of a pure function means the function call on
its variables can be substituted by its value, which is not generally
the case for the arguments of an imperative procedure. Pure functions
seem to be declarative w.r.t. to the uncomposed state transitions
between the input and result types.
But the composition of pure functions does not maintain any such
consistency, because it is possible to model a side-effect (global
state) imperative process in a pure functional programming language,
e.g. Haskell's IOMonad and moreover it is entirely impossible to
prevent doing such in any Turing complete pure functional programming
language.
As I wrote in 2012 which seems to the similar consensus of
comments in your recent blog, that declarative programming is an
attempt to capture the notion that the intended semantics are never
opaque. Examples of opaque semantics are dependence on order,
dependence on erasure of higher-level semantics at the operational
semantics layer (e.g. casts are not conversions and reified generics
limit higher-level semantics), and dependence on variable values
which can not be checked (proved correct) by the programming language.
Thus I have concluded that only non-Turing complete languages can be
declarative.
Thus one unambiguous and distinct attribute of a declarative language
could be that its output can be proven to obey some enumerable set of
generative rules. For example, for any specific HTML program (ignoring
differences in the ways interpreters diverge) that is not scripted
(i.e. is not Turing complete) then its output variability can be
enumerable. Or more succinctly an HTML program is a pure function of
its variability. Ditto a spreadsheet program is a pure function of its
input variables.
So it seems to me that declarative languages are the antithesis of
unbounded recursion, i.e. per Gödel's second incompleteness
theorem self-referential theorems can't be proven.
Lesie Lamport wrote a fairytale about how Euclid might have
worked around Gödel's incompleteness theorems applied to math proofs
in the programming language context by to congruence between types and
logic (Curry-Howard correspondence, etc).
Declarative programming is "the act of programming in languages that conform to the mental model of the developer rather than the operational model of the machine".
The difference between declarative and imperative programming is well
illustrated by the problem of parsing structured data.
An imperative program would use mutually recursive functions to consume input
and generate data. A declarative program would express a grammar that defines
the structure of the data so that it can then be parsed.
The difference between these two approaches is that the declarative program
creates a new language that is more closely mapped to the mental model of the
problem than is its host language.
It may sound odd, but I'd add Excel (or any spreadsheet really) to the list of declarative systems. A good example of this is given here.
I'd explain it as DP is a way to express
A goal expression, the conditions for - what we are searching for. Is there one, maybe or many?
Some known facts
Rules that extend the know facts
...and where there is a deduct engine usually working with a unification algorithm to find the goals.
As far as I can tell, it started being used to describe programming systems like Prolog, because prolog is (supposedly) about declaring things in an abstract way.
It increasingly means very little, as it has the definition given by the users above. It should be clear that there is a gulf between the declarative programming of Haskell, as against the declarative programming of HTML.
A couple other examples of declarative programming:
ASP.Net markup for databinding. It just says "fill this grid with this source", for example, and leaves it to the system for how that happens.
Linq expressions
Declarative programming is nice because it can help simplify your mental model* of code, and because it might eventually be more scalable.
For example, let's say you have a function that does something to each element in an array or list. Traditional code would look like this:
foreach (object item in MyList)
{
DoSomething(item);
}
No big deal there. But what if you use the more-declarative syntax and instead define DoSomething() as an Action? Then you can say it this way:
MyList.ForEach(DoSometing);
This is, of course, more concise. But I'm sure you have more concerns than just saving two lines of code here and there. Performance, for example. The old way, processing had to be done in sequence. What if the .ForEach() method had a way for you to signal that it could handle the processing in parallel, automatically? Now all of a sudden you've made your code multi-threaded in a very safe way and only changed one line of code. And, in fact, there's a an extension for .Net that lets you do just that.
If you follow that link, it takes you to a blog post by a friend of mine. The whole post is a little long, but you can scroll down to the heading titled "The Problem" _and pick it up there no problem.*
It depends on how you submit the answer to the text. Overall you can look at the programme at a certain view but it depends what angle you look at the problem. I will get you started with the programme:
Dim Bus, Car, Time, Height As Integr
Again it depends on what the problem is an overall. You might have to shorten it due to the programme. Hope this helps and need the feedback if it does not.
Thank You.