# Plot matrix containing negatives in confusionchart() style

10 views (last 30 days)
Christopher McCausland on 26 Jul 2023
Hello,
I have a matrix of numbers which includes negitives. I would like to plot this in the same graphical style as confusionchart() so all figures look consistent. I have had a look inside the function with open confusionchart() however I do not beleive the specific code is visable.
Is there an easy way to replicate this? If not I can code it manually, however I don't want to spend lots of time on this if there is a pre-existing solotion.
Kind regards,
Christopher

Shubham on 26 Jul 2023
Hi Christopher,
If you want to plot a matrix of numbers with negative values in the same graphical style as `confusionchart()`, you can use the `imagesc()` function in MATLAB. `imagesc()` is commonly used to visualize matrices as images, with a color scale representing the values.
Here's an example of how you can use `imagesc()` to plot a matrix with negative values:
% Example matrix with negative values
matrix = [1 2 3; -1 -2 -3; 4 5 6];
% Plot the matrix using imagesc()
figure;
imagesc(matrix);
% Set the color map to match confusionchart()
colormap(parula); % You can choose a different colormap if desired
colorbar;
In this example, `imagesc()` is used to plot the `matrix` with negative values. The `colormap()` function is then used to set the color map to match the style of `confusionchart()`. You can choose a different colormap if you prefer.
By using `imagesc()` and setting the appropriate colormap, you can achieve a consistent graphical style similar to `confusionchart()` for plotting matrices with negative values.
Shubham on 27 Jul 2023
Edited: Shubham on 27 Jul 2023
Can you please explain in detail about your question on colour map? I did not get it.
Christopher McCausland on 4 Aug 2023
Edited: Christopher McCausland on 4 Aug 2023
Hi Shubham.
What I had meant was to exactly mirror how a plot from confusionchat() looks like. Including the red-white-blue colour scheme and black/white contrast writting. For a differance graph of two matricies. I may change the colourgraph in the future as I am not convinced that this is clear, given that a reduction in confusion is marked in red but this is beside the point. For anyone in the future, here is the code to do so. I don't have the time to wrap it into a function so please forgive the untidy code.
% Sample data
data = [1350, -943, -440, 536, -503;
319, 1439, -875, 138, -1021;
-64, 70, 791, -113, -684;
-168, -341, -373, 1832, -950;
-206, -1026, -337, -352, 1921];
% Calculate the maximum and minimum values for setting the colormap range
cmax = max(data(:));
cmin = min(data(:));
% Create the heatmap
figure;
imagesc(data);
offset = 400; % Change the contrast point for black/white text
% Given colormap values
cmMap = [ 0 0.4500 0.7400
0.0079 0.4543 0.7420
0.0157 0.4587 0.7441
0.0236 0.4630 0.7461
0.0315 0.4673 0.7482
0.0394 0.4717 0.7502
0.0472 0.4760 0.7523
0.0551 0.4803 0.7543
0.0630 0.4846 0.7564
0.0709 0.4890 0.7584
0.0787 0.4933 0.7605
0.0866 0.4976 0.7625
0.0945 0.5020 0.7646
0.1024 0.5063 0.7666
0.1102 0.5106 0.7687
0.1181 0.5150 0.7707
0.1260 0.5193 0.7728
0.1339 0.5236 0.7748
0.1417 0.5280 0.7769
0.1496 0.5323 0.7789
0.1575 0.5366 0.7809
0.1654 0.5409 0.7830
0.1732 0.5453 0.7850
0.1811 0.5496 0.7871
0.1890 0.5539 0.7891
0.1969 0.5583 0.7912
0.2047 0.5626 0.7932
0.2126 0.5669 0.7953
0.2205 0.5713 0.7973
0.2283 0.5756 0.7994
0.2362 0.5799 0.8014
0.2441 0.5843 0.8035
0.2520 0.5886 0.8055
0.2598 0.5929 0.8076
0.2677 0.5972 0.8096
0.2756 0.6016 0.8117
0.2835 0.6059 0.8137
0.2913 0.6102 0.8157
0.2992 0.6146 0.8178
0.3071 0.6189 0.8198
0.3150 0.6232 0.8219
0.3228 0.6276 0.8239
0.3307 0.6319 0.8260
0.3386 0.6362 0.8280
0.3465 0.6406 0.8301
0.3543 0.6449 0.8321
0.3622 0.6492 0.8342
0.3701 0.6535 0.8362
0.3780 0.6579 0.8383
0.3858 0.6622 0.8403
0.3937 0.6665 0.8424
0.4016 0.6709 0.8444
0.4094 0.6752 0.8465
0.4173 0.6795 0.8485
0.4252 0.6839 0.8506
0.4331 0.6882 0.8526
0.4409 0.6925 0.8546
0.4488 0.6969 0.8567
0.4567 0.7012 0.8587
0.4646 0.7055 0.8608
0.4724 0.7098 0.8628
0.4803 0.7142 0.8649
0.4882 0.7185 0.8669
0.4961 0.7228 0.8690
0.5039 0.7272 0.8710
0.5118 0.7315 0.8731
0.5197 0.7358 0.8751
0.5276 0.7402 0.8772
0.5354 0.7445 0.8792
0.5433 0.7488 0.8813
0.5512 0.7531 0.8833
0.5591 0.7575 0.8854
0.5669 0.7618 0.8874
0.5748 0.7661 0.8894
0.5827 0.7705 0.8915
0.5906 0.7748 0.8935
0.5984 0.7791 0.8956
0.6063 0.7835 0.8976
0.6142 0.7878 0.8997
0.6220 0.7921 0.9017
0.6299 0.7965 0.9038
0.6378 0.8008 0.9058
0.6457 0.8051 0.9079
0.6535 0.8094 0.9099
0.6614 0.8138 0.9120
0.6693 0.8181 0.9140
0.6772 0.8224 0.9161
0.6850 0.8268 0.9181
0.6929 0.8311 0.9202
0.7008 0.8354 0.9222
0.7087 0.8398 0.9243
0.7165 0.8441 0.9263
0.7244 0.8484 0.9283
0.7323 0.8528 0.9304
0.7402 0.8571 0.9324
0.7480 0.8614 0.9345
0.7559 0.8657 0.9365
0.7638 0.8701 0.9386
0.7717 0.8744 0.9406
0.7795 0.8787 0.9427
0.7874 0.8831 0.9447
0.7953 0.8874 0.9468
0.8031 0.8917 0.9488
0.8110 0.8961 0.9509
0.8189 0.9004 0.9529
0.8268 0.9047 0.9550
0.8346 0.9091 0.9570
0.8425 0.9134 0.9591
0.8504 0.9177 0.9611
0.8583 0.9220 0.9631
0.8661 0.9264 0.9652
0.8740 0.9307 0.9672
0.8819 0.9350 0.9693
0.8898 0.9394 0.9713
0.8976 0.9437 0.9734
0.9055 0.9480 0.9754
0.9134 0.9524 0.9775
0.9213 0.9567 0.9795
0.9291 0.9610 0.9816
0.9370 0.9654 0.9836
0.9449 0.9697 0.9857
0.9528 0.9740 0.9877
0.9606 0.9783 0.9898
0.9685 0.9827 0.9918
0.9764 0.9870 0.9939
0.9843 0.9913 0.9959
0.9921 0.9957 0.9980
1.0000 1.0000 1.0000
1.0000 1.0000 1.0000
0.9988 0.9947 0.9929
0.9976 0.9894 0.9858
0.9965 0.9842 0.9787
0.9953 0.9789 0.9717
0.9941 0.9736 0.9646
0.9929 0.9683 0.9575
0.9917 0.9631 0.9504
0.9906 0.9578 0.9433
0.9894 0.9525 0.9362
0.9882 0.9472 0.9291
0.9870 0.9420 0.9220
0.9858 0.9367 0.9150
0.9846 0.9314 0.9079
0.9835 0.9261 0.9008
0.9823 0.9209 0.8937
0.9811 0.9156 0.8866
0.9799 0.9103 0.8795
0.9787 0.9050 0.8724
0.9776 0.8998 0.8654
0.9764 0.8945 0.8583
0.9752 0.8892 0.8512
0.9740 0.8839 0.8441
0.9728 0.8787 0.8370
0.9717 0.8734 0.8299
0.9705 0.8681 0.8228
0.9693 0.8628 0.8157
0.9681 0.8576 0.8087
0.9669 0.8523 0.8016
0.9657 0.8470 0.7945
0.9646 0.8417 0.7874
0.9634 0.8365 0.7803
0.9622 0.8312 0.7732
0.9610 0.8259 0.7661
0.9598 0.8206 0.7591
0.9587 0.8154 0.7520
0.9575 0.8101 0.7449
0.9563 0.8048 0.7378
0.9551 0.7995 0.7307
0.9539 0.7943 0.7236
0.9528 0.7890 0.7165
0.9516 0.7837 0.7094
0.9504 0.7784 0.7024
0.9492 0.7731 0.6953
0.9480 0.7679 0.6882
0.9469 0.7626 0.6811
0.9457 0.7573 0.6740
0.9445 0.7520 0.6669
0.9433 0.7468 0.6598
0.9421 0.7415 0.6528
0.9409 0.7362 0.6457
0.9398 0.7309 0.6386
0.9386 0.7257 0.6315
0.9374 0.7204 0.6244
0.9362 0.7151 0.6173
0.9350 0.7098 0.6102
0.9339 0.7046 0.6031
0.9327 0.6993 0.5961
0.9315 0.6940 0.5890
0.9303 0.6887 0.5819
0.9291 0.6835 0.5748
0.9280 0.6782 0.5677
0.9268 0.6729 0.5606
0.9256 0.6676 0.5535
0.9244 0.6624 0.5465
0.9232 0.6571 0.5394
0.9220 0.6518 0.5323
0.9209 0.6465 0.5252
0.9197 0.6413 0.5181
0.9185 0.6360 0.5110
0.9173 0.6307 0.5039
0.9161 0.6254 0.4969
0.9150 0.6202 0.4898
0.9138 0.6149 0.4827
0.9126 0.6096 0.4756
0.9114 0.6043 0.4685
0.9102 0.5991 0.4614
0.9091 0.5938 0.4543
0.9079 0.5885 0.4472
0.9067 0.5832 0.4402
0.9055 0.5780 0.4331
0.9043 0.5727 0.4260
0.9031 0.5674 0.4189
0.9020 0.5621 0.4118
0.9008 0.5569 0.4047
0.8996 0.5516 0.3976
0.8984 0.5463 0.3906
0.8972 0.5410 0.3835
0.8961 0.5357 0.3764
0.8949 0.5305 0.3693
0.8937 0.5252 0.3622
0.8925 0.5199 0.3551
0.8913 0.5146 0.3480
0.8902 0.5094 0.3409
0.8890 0.5041 0.3339
0.8878 0.4988 0.3268
0.8866 0.4935 0.3197
0.8854 0.4883 0.3126
0.8843 0.4830 0.3055
0.8831 0.4777 0.2984
0.8819 0.4724 0.2913
0.8807 0.4672 0.2843
0.8795 0.4619 0.2772
0.8783 0.4566 0.2701
0.8772 0.4513 0.2630
0.8760 0.4461 0.2559
0.8748 0.4408 0.2488
0.8736 0.4355 0.2417
0.8724 0.4302 0.2346
0.8713 0.4250 0.2276
0.8701 0.4197 0.2205
0.8689 0.4144 0.2134
0.8677 0.4091 0.2063
0.8665 0.4039 0.1992
0.8654 0.3986 0.1921
0.8642 0.3933 0.1850
0.8630 0.3880 0.1780
0.8618 0.3828 0.1709
0.8606 0.3775 0.1638
0.8594 0.3722 0.1567
0.8583 0.3669 0.1496
0.8571 0.3617 0.1425
0.8559 0.3564 0.1354
0.8547 0.3511 0.1283
0.8535 0.3458 0.1213
0.8524 0.3406 0.1142
0.8512 0.3353 0.1071
0.8500 0.3300 0.1000];
% Set the colormap for the current figure
colormap(flip(cmMap));
% Add text values to each segment
textStrings = num2str(data(:), '%d'); % Convert elements to string
textStrings = strtrim(cellstr(textStrings)); % Remove any space padding
[x, y] = meshgrid(1:size(data, 2), 1:size(data, 1)); % start the text grid
hStrings = text(x(:), y(:), textStrings(:), 'HorizontalAlignment', 'center');
midValue = offset + mean([cmin, cmax]);
textColors = repmat(data(:) > midValue, 1, 3);
set(hStrings, {'Color'}, num2cell(textColors, 2));
% Set custom labels on both axes
xticks(1:size(data, 2));
yticks(1:size(data, 1));
xticklabels({'N1', 'N2', 'N3', 'W', 'R'});
yticklabels({'N1', 'N2', 'N3', 'W', 'R'});
% Set axis labels and title
xlabel('Predicted Class');
ylabel('True Class');
title('Confusion Matrix');
% Set colorbar
colorbar;
% Remove tick marks on both axes
ax = gca;
ax.TickLength = [0, 0];

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