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Our third implementation is in C++ Since C++ is almost a superset of C, it can be used as if it were C with a few notational conveniences, and our original C version of markov is also a legal C++ program A more appropriate use of C++, however, would be to define classes for the objects in the program, more or less as we did in Java; this would let us hide implementation details We decided to go even further by using the Standard Template Library or STL, since the STL has built-in mechanisms that will do much of what we need The IS0 standard for C++ includes the STL as part of the language definition The STL provides containers such as vectors, lists, and sets, and a family of fundamental algorithms for searching, sorting, inserting, and deleting Using the template features of C++, every STL algorithm works on a variety of containers, including both user-defined types and built-in types like integers Containers are expressed as C++ templates that are instantiated for specific data types; for example, there is a vector container that can be used to make particular types like vector<int> or v e c t o r < s t r i ng> All vector operations, including standard algorithms for sorting, can be used on such data types In addition to a vector container that is similar to Java's Vector, the STL provides a deque container A deque (pronounced "deck") is a double-ended queue that matches what we do with prefixes: it holds NPREF elements, and lets us pop the first element and add a new one to the end, in 0 ( 1) time for both The STL deque is more general than we need, since it permits push and pop at either end, but the performance guarantees make it an obvious choice The STL also provides an explicit map container, based on balanced trees, that stores key-value pairs and provides O(1ogn) retrieval of the value associated with any key Maps might not be as efficient as O(1) hash tables, but it's nice not to have to write any code whatsoever to use them (Some non-standard C++ libraries include a hash or hash-map container whose performance may be better) We also use the built-in comparison functions, which in this case will do string comparisons using the individual strings in the prefix With these components in hand, the code goes together smoothly Here are the declarations:
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typedef deque<stri ng> P r e f i x ; map<Prefix, vector<string> > s t a t e t a b ; // p r e f i x -> s u f f i x e s
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The STL provides a template for deques; the notation dequexstri ng> specializes it to a deque whose elements are strings Since this type appears several times in the program, we used a typedef to give it the name P r e f i x The map type that stores prefixes and suffixes occurs only once, however, so we did not give it a separate name; the map declaration declares a variable s t a t e t a b that is a map from prefixes to vectors of strings This is more convenient than either C or Java, because we don't need to provide a hash function or equals method
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SECTION 3 6
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The main routine initializes the prefix, reads the input (from standard input, called
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c i n in the C++ iostream library), adds a tail, and generates the output, exactly as in
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the earlier versions:
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// markov main: markov-chain random t e x t generation in t mai n(voi d)
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i n t nwords = MAXGEN; Prefix prefix;
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// current i n p u t p r e f i x
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// s e f o r ( i n t i = 0; i < NPREF; i++)t up i n i t i a l p r e f i x x OWR) add (p r e f i , N N O D ; b u i l d ( p r e f i x , cin); add(pref i , N N O D ; x OWR) generate(nwords); r e t u r n 0;
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The function b u i l d uses the iostream library to read the input one word at a time:
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// b u i l d : read i n p u t words, b u i l d s t a t e t a b l e v o i d build(Prefix& p r e f i x , istream& i n ) { s t r i n g buf;
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while ( i n >> buf) add(prefi x, buf) ;
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The string buf will grow as necessary to handle input words of arbitrary length The add function shows more of the advantages of using the STL:
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// add: add word t o s u f f i x l i s t , update p r e f i x v o i d add(Prefix& p r e f i x , const s t r i n g & s)
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i f ( p r e f i x size() == NPREF) { statetabCprefix1 push-back(s) ; p r e f i x pop-f r o n t () ;
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prefixpush-back(s);
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Quite a bit is going on under these apparently simple statements The map container overloads subscripting (the [I operator) to behave as a lookup operation The expression s t a t e t a b[ p r e f i XI a lookup in statetab with p r e f i x as key and returns a does reference to the desired entry; the vector is created if it does not exist already The push-back member functions of vector and deque push a new string onto the back end of the vector or deque; pop-f r o n t pops the first element off the deque Generation is similar to the previous versions:
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// generate: produce output, one word per l i n e void generate(i n t nwords) { Prefix prefix; int i ;
f o r (i = 0 ; i < NPREF; i++) r e s e t i n i t i a l p r e f i x // add(prefix NONWORD); f o r (i = 0 ; i < nwords; i++) { v e c t o r < s t r i ng>& suf = s t a t e t a b [ p r e f i x ] ; const string& w = suf [rand() % suf size()] ; i f (W == NONWORD) break; cout << w << "\nW; p r e f i x pop-f ront () ; // advance p r e f i x push-back(w) ;
Overall, this version seems especially clear and elegant-the code is compact, the data structure is visible and the algorithm is completely transparent Sadly, there is a price to pay: this version runs much slower than the original C version, though it is not the slowest We'll come back to performance measurements shortly
Exercise 3-5 The great strength of the STL is the ease with which one can experiment with different data structures Modify the C++ version of Markov to use various structures to represent the prefix, suffix list, and state table How does performance change for the different structures Exercise 3-6 Write a C++ version that uses only classes and the s t r i n g data type but no other advanced library facilities Compare it in style and speed to the STL versions