Main Content

comparisonPlot

Class: matlab.perftest.TimeResult
Namespace: matlab.perftest

Create plot to compare baseline and measurement test results

expand all in page

Syntax

cp = comparisonPlot(baseline,measurement)
cp = comparisonPlot(baseline,measurement,stat)
cp = comparisonPlot(___,Name,Value)

Description

cp = comparisonPlot(baseline,measurement) creates a plot that visually compares each TimeResult object in the baseline and measurement arrays using the minimum of sample measurement times. The method returns the comparison plot as a matlab.unittest.measurement.chart.ComparisonPlot object.

example

cp = comparisonPlot(baseline,measurement,stat) creates a comparison plot using the specified statistic.

example

cp = comparisonPlot(___,Name,Value) specifies options using one or more name-value arguments in addition to any of the input argument combinations in previous syntaxes. For example, cp = comparisonPlot(baseline,measurement,"Scale","linear") creates a plot with a linear scale for the x- and y-axes.

example

Input Arguments

expand all

Results from running a primary time experiment on a test suite, specified as a matlab.perftest.TimeResult array. baseline must be the same size as measurement.

Results from running a secondary time experiment on a test suite, specified as a matlab.perftest.TimeResult array. measurement must be the same size as baseline.

Statistic applied to the sample measurement times of each TimeResult object in baseline and measurement, specified as "min", "max", "mean", or "median".

Data Types: string | char

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: cp = comparisonPlot(baseline,measurement,SimilarityTolerance=0.05,Scale="linear")

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: cp = comparisonPlot(baseline,measurement,"SimilarityTolerance",0.05,"Scale","linear")

Scale of the x- and y-axes of the comparison plot, specified as "log" or "linear".

Data Types: string | char

Allowable deviation of the statistics ratio from 1, for a pair of similar performance tests, specified as a numeric value between 0 and 1.

SimilarityTolerance specifies the borders of a shaded region in the comparison plot. Data points that fall inside this region represent similar baseline and measurement entries.

Parent container in which to plot, specified as a Figure, Panel, Tab, or TiledChartLayout object.

Examples

expand all

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 a file named bubbleSort.m in your current folder, create the bubbleSort function, which implements the bubble sort algorithm.

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

In a file named mergeSort.m in your current folder, create the mergeSort function, which implements the merge sort algorithm.

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

In a file named SortTest.m in your current folder, create the SortTest parameterized test class, which compares the performance of the bubble sort and merge sort algorithms. The len property of the class contains the numbers of list elements you want to test with.

classdef SortTest < 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 the tests that correspond to the testBubbleSort method and save the results in the baseline array. Your results might vary from the results shown.

baseline = runperf("SortTest","ProcedureName","testBubbleSort");
Running SortTest
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..
Done SortTest
__________

Run performance tests for all the tests that correspond to the testMergeSort method and save the results in the measurement array.

measurement = runperf("SortTest","ProcedureName","testMergeSort");
Running SortTest
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .......... .......... .......... ..........
.......... .....
Done SortTest
__________

Visually compare the minimum of the MeasuredTime column in the Samples table for each corresponding pair of baseline and measurement objects. 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 tests. However, for small enough lists, bubble sort performs better than or comparable to merge sort, as shown by the orange and gray points in the plot. As a comparison summary, the plot reports that merge sort is 80% faster than bubble sort. This value is the geometric mean of the improvement percentages corresponding to all data points.

cp = comparisonPlot(baseline,measurement);

Comparison plot based on the minimum of sample measurement times

You can click or point to any data point to view detailed information about the time measurement results being compared.

Comparison plot based on the minimum of sample measurement times. A data tip displays detailed information about one of the points.

To study the worst-case sorting algorithm performance for different list lengths, create a comparison plot based on the maximum of sample measurement times.

cp = comparisonPlot(baseline,measurement,"max");

Comparison plot based on the maximum of sample measurement times

Reduce similarity tolerance to 0.01 when comparing the maximum of sample measurement times.

cp = comparisonPlot(baseline,measurement,"max","SimilarityTolerance",0.01);

Comparison plot based on the maximum of sample measurement times. The similarity tolerance is 0.01.

Version History

Introduced in R2019b

See Also

Functions

Classes