All that remains is to write average_analysis and optimistic_median_analysis

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623 Grading based on average homework grade

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We would like the average_analysis function to compute the students' grades by using the average homework grade, rather than the median Therefore, the logical first step is to write a function to compute the average of a vector, with the aim of using it instead of median for grade computation:

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double average(const vector<double>& v) { return accumulate(vbegin(), vend(), 00) / vsize(); }

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This function uses accumulate, which, unlike the other library algorithms we've used, is declared in <numeric> As this header's name implies, it offers tools for numeric computation The accumulate function adds the values in the range denoted by its first two arguments, starting the summation with the value given by its third argument The type of the sum is the type of the third argument, so it is crucially important for us to use 00, as we did here, instead of 0 Otherwise, the result would be an int, and any fractional part would be lost Having used accumulate to generate the sum of all the elements in the range, we divide that sum by vsize(), which is the number of elements in the range The result of that division, of course, is the average, which we return to our caller Once we have the average function, we can use it to implement the average_grade function to reflect this alternative grading policy:

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double average_grade(const Student_info& s) { return grade(smidterm, sfinal, average(shomework)); }

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This function uses the average function to compute an overall homework grade, which it then gives to the grade function from 41/52 to use in computing the final grade With this infrastructure in place, the average_analysis function is simplicity itself:

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double average_analysis(const vector<Student_info>& students)

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This document is created with the unregistered version of CHM2PDF Pilot { vector<double> grades; transform(studentsbegin(), studentsend(), back_inserter(grades), average_grade); return median(grades); }

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The only difference between this function and median_analysis ( 622/113) is its name and its use of average_grade instead of grade_aux

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624 Median of the completed homework

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The last analysis scheme, optimistic_median_analysis, gets its name from the optimistic assumption that the students' grades on the homework that they didn't turn in would have been the same as the homework that they did turn in With that assumption, we would like to compute the median of just the homework that each student submitted We'll call this computation an optimistic median, and we'll begin by writing a function to compute it Of course, we have to contend with the possibility that a student did no homework at all, in which case we'll use 0 as the overall homework grade:

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// median of the nonzero elements of shomework, or 0 if no such elements exist double optimistic_median(const Student_info& s) { vector<double> nonzero; remove_copy(shomeworkbegin(), shomeworkend(), back_inserter(nonzero), 0); if (nonzeroempty()) return grade(smidterm, sfinal, 0); else return grade(smidterm, sfinal, median(nonzero)); }

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This function works by extracting the nonzero elements from the homework vector and putting them into a new vector, called nonzero Once we have the nonzero homework grades, we call the version of grade defined in 41/52 to compute the final score based on the median of the homework assignments that were actually submitted The only new idea in this function is how we get values into nonzero, which we do by calling the remove_copy algorithm To understand the call to remove_copy, you may find it useful to know that the library provides "copying" versions of many of the algorithms So, for example, remove_copy does what remove does, but copies its results to an indicated destination The remove function finds all values that match a given value and "removes" those values from the container All the values in the input sequence that are not "removed" will be copied into the destination We'll have more to say shortly about what "remove" means in this context The remove_copy function takes three iterators and a value As with most algorithms, the first two iterators denote the input sequence The third denotes the beginning of the destination for the copy As with copy, the remove_copy algorithm assumes that there is enough space in the destination to hold all the elements that are copied We call back_inserter to grow nonzero as needed We should now be able to see that the effect of the remove_copy call is to copy into nonzero all the nonzero elements in shomework We then check whether v is empty, and if not, we do the normal grade calculation based on the median of the nonzero grades If v is empty, then we use 0 as the homework grade Of course, to complete our analysis, we need to write an analysis function to call our optimistic_median function We

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