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Recall that an associative array stores a collection of key-value pairs Using [] to access a map element conceals this fact, because we put the key inside the [] and get back the associated value So, for example, counters[s] is an int However, when we iterate over a map, we must have a way to get at both the key and the associated value The map container lets us do so by using a companion library type called pair A pair is a simple data structure that holds two elements, which are named first and second Each element in a map is really a pair, with a first member that contains the key and a second member that contains the associated value When we dereference a map iterator, we obtain a value that is of the pair type associated with the map The pair class can hold values of various types, so when we create a pair, we say what the types of the first and second data members should be For a map that has a key of type K and a value of type V, the associated pair type is pair<const K, V> Note that the pair associated with a map has a key type that is const Because the pair key is const, we are prevented from changing the value of an element's key If the key were not const, we might implicitly change the element's position within the map Accordingly, the key is always const, so that if we dereference a map<string, int> iterator, we get a pair<const string, int> Thus, it->first is the current element's key, and it->second is the associated value Because it is an iterator, *it is an lvalue ( 413/56), and therefore it->first and it->second are also lvalues However, the type of it->first includes const, which prevents us from changing it With this knowledge, we can see that the output statement writes each key (that is, each distinct word from the input), followed by a tab and the corresponding count.

TimerTask A task that can be scheduled for one-time or repeated execution by a Timer MathPI The double value that is closer than any other to pi, the ratio of the

canDoOperation(int) Returns whether the operation specified by the given operation code can be performed

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getSelectedRange() Returns the range of the current selection in coordinates of this viewer s document getSelection() Returns the current selection for this provider getTextWidget() Returns the viewer s text widget isEditable() Returns whether the shown text can be manipulated refresh() Refreshes this viewer completely with information freshly obtained from the viewer s model setDocument(IDocument) Sets the given document as the text viewer s model and updates the presentation accordingly setEditable(boolean) Sets the editable mode setInput(Object) Sets or clears the input for this viewer The TextViewer implementation of this method calls setDocument(IDocument) with the input object if the input object is an instance of IDocument or with null if the input object is not setRedraw(boolean) Enables/disables the redrawing of this text

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setTextColor(Color) Applies the given color to this viewer s selec-

The following example creates a text viewer containing styled text (see Figure 5 12)

Once we know how to count how often words occur in the input, a logical next step is to write a program to generate a cross-reference table that indicates where each word occurs in the input This extension requires several changes to our basic program First, instead of reading a word at a time, we'll need to read a line at a time, so that we can associate line numbers with words Once we're reading lines instead of words, we'll need a way to break each line into its constituent words Fortunately, we already wrote such a function, named split, in 611/103 We can use this function to turn each input line into a vector<string>, from which we can extract each word Rather than using split directly, we're going to make it a parameter to the cross-reference function That way, we leave open the possibility of changing the way we find the words on a line For example, we could pass the find_urls function from 613/105, and use the cross-reference function to see where URLs appear in the input As before, we will use a map with keys that are the distinct words from the input This time, however, we will have to associate a more complicated value with each key Instead of keeping track of how often the word occurs, we want to know all the line numbers on which the word occurred Because any given word may occur on many lines, we will need to store the line numbers in a container When we get a new line number, all we will need to do is append that number to those that we already have for that word Sequential access to the container elements will suffice, so we can use a vector to keep track of line numbers Therefore, we will need a map from string to vector<int> With these preliminaries out of the way, let's look at the code:.

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