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The bottom pane of each wizard page displays a row of navigation buttons for moving between the wizard's pages and for closing the wizard Different types of pages require different navigation buttons Figure 77 shows an example of navigation buttons for a wizard page

The destroy method checks whether the peer eld is zero, and sets the peer eld to zero after calling the overloaded destroy native method It is de ned as a synchronized method to avoid race conditions 129

Having resolved how to represent the grammar, let's write a function to read it:



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// read a grammar from a given input stream Grammar read_grammar(istream& in) { Grammar ret; string line; // read the input while (getline(in, line)) { // split the input into words vector<string> entry = split(line);

This document is created with the unregistered version of CHM2PDF Pilot if (!entryempty()) // use the category to store the associated rule ret[entry[0]]push_back( Rule(entrybegin() + 1, entryend())); } return ret; }





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In Figure 77, notice the horizontal separator between the bottom pane and the two upper panes The separator indicates that the bottom pane's navigation buttons relate to the entire wizard, not just to the contents of the current page The bottom pane can contain the following navigation buttons: Back, Next, Last, Cancel, Help, Finish, and Close No single page contains all these buttons; the correct set of buttons depends on the type of the page Table 17 describes each navigation button that can be displayed in the bottom pane

The above code will not work as you might expect, however The virtual machine will never garbage collect any KeyInput instances unless you call destroy explicitly The KeyInput constructor creates a JNI global reference to the KeyInput instance The global reference prevents the KeyInput instance from being garbage collected You can overcome this problem by using a weak global reference instead of a global reference:

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The function will read from an input stream and generate a Grammar as output The while loop looks like many that we've seen before: It reads a line at a time from in and stores what it read in line The while terminates when we run out of input or encounter invalid data The body of the while is astonishingly concise We use the split function from 611/103 to break the input into words, and store the resulting vector in a variable called entry If entry is empty, we saw a blank input line, so we disregard it Otherwise, we know that the first element in entry will be the category that we are defining We use this element as the index into ret The expression ret[entry[0]] yields the object of type Rule_collection that is associated with the category in entry[0], remembering that a Rule_collection is a vector, each element of which holds a Rule (or, equivalently, a vector<string>) Therefore, ret[entry[0]] is a vector, onto the back of which we push the rule that we just read That rule is in entry, starting with the second element; the first element in entry is the category We construct a new, unnamed Rule, by copying the elements from entry (except for the first element), and push that newly created Rule onto the back of the Rule_collection indexed by ret[entry[0]]

Having read all the input, we must next generate a random sentence We know that our input will be a grammar, and that we want to produce a sentence Our output will be a vector<string> that represents the output sentence That's the easy part The more interesting problem is how the function should work We know that initially we'll need to find a rule that corresponds to <sentence> Moreover, we know that we are going to build our output in pieces, which we will assemble from various rules and parts of rules In principle, we could concatenate those pieces to form our result However, because there is no built-in concatenation operation for vectors, we will start with an empty vector and call push_back repeatedly on it These two constraints starting with <sentence>, and calling push_back repeatedly on an initially empty vector suggest that we are going to want to define our sentence generator in terms of an auxiliary function, which we will call as follows:

vector<string> gen_sentence(const Grammar& g) { vector<string> ret; gen_aux(g, "<sentence>", ret); return ret; }

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