Using histogram to find the frequency of combinations within the data set
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I am new to Matlab and just started getting into generating histograms. I understand how to create a histogram that determines the frequency of values..
But how can I create a histogram that is looking for specific combinations of data?
For example I have the following data set:
someData = [1,3,4,4,2,1,1,0]
How would I be able to create a histogram that determines the frequency of one value in the data set is exactly equal to the next value within the same dataset??
In the above example the answer should be 2. (4 repeat and are adjacent to each other. 1 repeats and is adjacent to each other)
Help is MOST appreciated!
回答(1 个)
Image Analyst
2012-8-1
You can do this with the gray level co-occurrence matrix (GLCM). It's done by the function graycomatrix() in the Image Processing Toolbox. It's like a 2D histogram that counts how many times each gray level (numerical value) occurs next to every other gray level (value).
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Image Analyst
2012-8-3
For an average that moves over by 1 every time, use conv().
dataSet = [1,4,6,2,2,7,8,4,4]
slidingAverage = conv(dataSet, [1 1 1]/3, 'valid')
If you want to move in "jumps" of 3, then you'll have to use blockproc(). Here's a 2D demo for blockproc that you can probably easily adapt to 1D signals if you need to:
function blockproc_demo()
try
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
workspace; % Make sure the workspace panel is showing.
fontSize = 20;
% Change the current folder to the folder of this m-file.
if(~isdeployed)
cd(fileparts(which(mfilename)));
end
% Read in standard MATLAB demo image.
grayImage = imread('cameraman.tif');
[rows columns numberOfColorChannels] = size(grayImage);
% Display the original image.
subplot(2, 2, 1);
imshow(grayImage, []);
caption = sprintf('Original Image\n%d by %d pixels', ...
rows, columns);
title(caption, 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize'));
set(gcf, 'name','Image Analysis Demo', 'numbertitle','off')
% Block process the image.
windowSize = 3;
% Each 3x3 block will get replaced by one value.
% Output image will be smaller by a factor of windowSize.
myFilterHandle = @myFilter;
blockyImage = blockproc(grayImage,[windowSize windowSize], myFilterHandle);
[rowsP columnsP numberOfColorChannelsP] = size(blockyImage);
% Display the processed image.
% It is smaller, but the display routine imshow() replicates
% the image so that it looks bigger than it really is.
subplot(2, 2, 2);
imshow(blockyImage, []);
caption = sprintf('Image Processed in %d by %d Blocks\n%d by %d pixels\nCustom Box Filter', ...
windowSize, windowSize, rowsP, columnsP);
title(caption, 'FontSize', fontSize);
% Now let's do it an alternate way where we use an anonymous function.
% We'll take the standard deviation in the blocks.
windowSize = 8;
myFilterHandle2 = @(block_struct) ...
std2(block_struct.data) * ones(size(block_struct.data));
blockyImageSD = blockproc(grayImage, [windowSize windowSize], myFilterHandle2);
[rowsSD columnsSD numberOfColorChannelsSD] = size(blockyImageSD);
subplot(2, 2, 4);
imshow(blockyImageSD, []);
caption = sprintf('Image Processed in %d by %d Blocks\n%d by %d pixels\nAnonymous Standard Deviation Filter', ...
windowSize, windowSize, rowsSD, columnsSD);
title(caption, 'FontSize', fontSize);
% Note: the image size of blockyImageSD is 256x256, NOT smaller.
% That's because we're returning an 8x8 array instead of a scalar.
uiwait(msgbox('Done with demo'));
catch ME
errorMessage = sprintf('Error in blockproc_demo():\n\nError Message:\n%s', ME.message);
uiwait(warndlg(errorMessage));
end
return;
% Takes one 3x3 block of image data and multiplies it
% element by element by the kernel and
% returns a single value.
function singleValue = myFilter(blockStruct)
try
% Assign default value.
% Will be used near sides of image (due to boundary effects),
% or in the case of errors, etc.
singleValue = 0;
% Create a 2D filter.
kernel = [0 0.2 0; 0.2 0.2 0.2; 0 0.2 0];
% kernel = ones(blockStruct.blockSize); % Box filter.
% Make sure filter size matches image block size.
if any(blockStruct.blockSize ~= size(kernel))
% If any of the dimensions don't match.
% You'll get here near the edges,
% if the image is not a multiple of the block size.
% warndlg('block size does not match kernel size');
return;
end
% Size matches if we get here, so we're okay.
% Extract our block out of the structure.
array3x3 = blockStruct.data;
% Do the filtering. Multiply by kernel and sum.
singleValue = sum(sum(double(array3x3) .* kernel));
catch ME
% Some kind of problem...
errorMessage = sprintf('Error in myFilter():\n\nError Message:\n%s', ME.message);
% uiwait(warndlg(errorMessage));
fprintf(1, '%s\n', errorMessage);
end
return;
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