comparisonPlot
Class: matlab.perftest.TimeResult
Package: matlab.perftest
Create plot to compare baseline and measurement test results
Syntax
Description
comparisonPlot(
creates a plot that visually compares each Baseline,Measurement)TimeResult object in the
arrays Baseline and Measurement. The comparison is
based on the minimum of sample measurement times.
comparisonPlot(
specifies the statistic applied to the sample measurement times of each
Baseline,Measurement,stat)TimeResult object in Baseline and
Measurement.
comparisonPlot(___,
creates a comparison plot with additional options specified by one or more
Name,Value)Name,Value pair arguments. For example,
comparisonPlot(Baseline,Measurement,'Scale','linear') creates a plot
with a linear scale for the x- and y-axes.
cp = comparisonPlot(___) returns the
ComparisonPlot object, specified as an instance of the matlab.unittest.measurement.chart.ComparisonPlot class. Use cp
to modify the properties of a specific comparison plot after you create it.
Input Arguments
Examples
Visually Compare Sorting Algorithm Performance
Visualize the computational complexity of two sorting algorithms, bubble sort and merge sort, which sort list elements in ascending order. Bubble sort is a simple sorting algorithm that repeatedly steps through a list, compares adjacent pairs of elements, and swaps elements if they are in the wrong order. Merge sort is a "divide and conquer" algorithm that takes advantage of the ease of merging sorted sublists into a new sorted list.
In your current folder, save the following code in
bubbleSort.m.
function y = bubbleSort(x) % Sorting algorithm with O(n^2) complexity n = length(x); swapped = true; while swapped swapped = false; for i = 2:n if x(i-1) > x(i) temp = x(i-1); x(i-1) = x(i); x(i) = temp; swapped = true; end end end y=x; end
Save the following code in mergeSort.m.
function y = mergeSort(x) % Sorting algorithm with O(n*logn) complexity y = x; % A list of one element is considered sorted if length(x) > 1 mid = floor(length(x)/2); L = x(1:mid); R = x((mid+1):end); % Sort left and right sublists recursively L = mergeSort(L); R = mergeSort(R); % Merge the sorted left (L) and right (R) sublists i = 1; j = 1; k = 1; while i <= length(L) && j <= length(R) if L(i) < R(j) y(k) = L(i); i = i + 1; else y(k) = R(j); j = j + 1; end k = k + 1; end % At this point, either L or R is empty while i <= length(L) y(k) = L(i); i = i + 1; k = k + 1; end while j <= length(R) y(k) = R(j); j = j + 1; k = k + 1; end end end
Create the following parameterized test class, TestSort, which
compares the performance of the bubble sort and merge sort algorithms. The
len property contains the number of list elements you want to
test.
classdef TestSort < matlab.perftest.TestCase properties Data SortedData end properties(ClassSetupParameter) % Create 25 logarithmically spaced values between 10^2 and 10^4 len = num2cell(round(logspace(2,4,25))); end methods(TestClassSetup) function ClassSetup(testCase,len) orig = rng; testCase.addTeardown(@rng,orig) rng('default') testCase.Data = rand(1,len); testCase.SortedData = sort(testCase.Data); end end methods(Test) function testBubbleSort(testCase) while testCase.keepMeasuring y = bubbleSort(testCase.Data); end testCase.verifyEqual(y,testCase.SortedData); end function testMergeSort(testCase) while testCase.keepMeasuring y = mergeSort(testCase.Data); end testCase.verifyEqual(y,testCase.SortedData); end end end
Run performance tests for all test elements that correspond to the
'testBubbleSort' method, and save the results in the
Baseline array. Your results might vary from the results
shown.
Baseline = runperf('TestSort','ProcedureName','testBubbleSort');
Running TestSort .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... ..... Done TestSort __________
Run performance tests for all elements that correspond to the
'testMergeSort' method, and save the results in the
Measurement array.
Measurement = runperf('TestSort','ProcedureName','testMergeSort');
Running TestSort .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... Done TestSort __________
Visually compare the minimum of the MeasuredTime column in the
Samples table for each corresponding pair of
Baseline and Measurement objects.
comparisonPlot(Baseline,Measurement);
In this comparison plot, most data points are blue because they are below the shaded similarity region. This result indicates the superior performance of merge sort for the majority of test cases. However, for small enough lists, bubble sort performs better than or comparable to merge sort, as shown by the orange and gray points on the plot.
As a comparison summary, the plot reports a 78% performance improvement due to merge sort. This value is the geometric mean of the improvement percentages corresponding to all data points. If the comparison plot contained invalid data points, a comparison summary would not be generated.
You can press or hover over any data point to view detailed information about the time measurement results being compared.
To study the worst-case sorting algorithm performance for different list lengths, create a comparison plot based on the maximum of sample measurement times.
comparisonPlot(Baseline,Measurement,'max');
Reduce SimilarityTolerance to 0.01 when
comparing the maximum of sample measurement times. Return the
ComparisonPlot object in cp so that you can modify
its properties later.
cp = comparisonPlot(Baseline,Measurement,'max','SimilarityTolerance',0.01);
