How I categorize a features?

Do you mean that you have numerical values, and you want to treat those as categorical instead? You can convert numeric to categorical using the categorical function.

x = 1:5
x = 1×5
     1     2     3     4     5
c = categorical(x)
c = 1×5 categorical array
     1      2      3      4      5 

You said "roughly" equal in value, so maybe you need to do some rounding first?

x = [1.1 2.2 2.9 3.8 5.1]
x = 1×5
    1.1000    2.2000    2.9000    3.8000    5.1000
c = categorical(round(x))
c = 1×5 categorical array
     1      2      3      4      5 

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the cyclist 2022-5-14

在 MATLAB Online 中打开

When I wrote this answer, I hadn't noticed that your values are not 1,2,3,4,5, but rather 10^-3 times that. So, you'll need to round differently:

x = [1.1 2.2 2.9 3.8 5.1]*1.e-3
x = 1×5
    0.0011    0.0022    0.0029    0.0038    0.0051
rx = round(x,3)
rx = 1×5
    0.0010    0.0020    0.0030    0.0040    0.0050
c = categorical(rx)
c = 1×5 categorical array
     0.001      0.002      0.003      0.004      0.005 

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Answer 2

Image Analyst 2022-5-14

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1719175-how-i-categorize-a-features#answer_964055

在 MATLAB Online 中打开

You can add a tiny bit of noise then recompute the histogram edges such that the bins will be equal percentages (heights). Like this:

data = [zeros(1, 1580), ones(1, 50), 2*ones(1, 70), 2*ones(1, 50), 3*ones(1, 40), 4*ones(1, 25), 4.7*ones(1, 10)]/1000;

subplot(2, 1, 1);

[counts, edges] = histcounts(data);

bar(edges(1:end-1), counts);

grid on;

title('Uneven Bars', 'FontSize', 20);

% Now add a tiny bit of noise and sort

noisyData = data + 0.000001 * rand(size(data));

sortedData = sort(noisyData);

% Get cdf

c = cumsum(sortedData);

c = rescale(c, 0, 100); % Convert to percent.

% Find 6 bins

numBins = 6;

indexes = round(linspace(1, length(data), numBins+1))

indexes = 1×7

1 305 609 913 1217 1521 1825

edges2 = sortedData(indexes)

edges2 = 1×7

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0047

subplot(2, 1, 2);

counts2 = histcounts(noisyData, edges2)

counts2 = 1×6

304 304 304 304 304 305

bar(edges2(1:end-1), counts2);

grid on;

title('Even Bars', 'FontSize', 20);

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the cyclist 2022-5-14

I'll point out here that @Image Analyst seems to have interpreted your phrase "as the amount at witch are they roughly equal in value" to mean you want the bar heights to be equal.

I interpreted that differently, and took it to to mean that you wanted your data values to be equal (rather than "roughly equal"), which is why our two approaches are very different.

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