GMM - gaussian mixture as summation of pdf

Question

Azzam Albalawi 2023-3-27

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1936074-gmm-gaussian-mixture-as-summation-of-pdf

回答： the cyclist 2023-3-28

I will provide my code that do GMM Modeling, I need to plot the gaussian mixture as summation of pdf and lay down the scatter of data on top of the summation of pdf:

clear variables;
% Load CSV dataset
data = csvread('reduced_dataset_10.csv');
% Prepare the dataset
X = data; % Select relevant features
X_norm = zscore(X); % Normalize the dat
% Define the maximum number of Gaussians to consider
max_k = 10;
% Fit GMM models with different numbers of Gaussians and replicates
models = cell(max_k, 1);
for k = 1:max_k
    models{k} = fitgmdist(X_norm, k, 'Replicates', 5);
end
% Calculate AIC for each model
AIC = zeros(max_k, 1);
for k = 1:max_k
    AIC(k) = models{k}.AIC;
end
% Choose the model with the lowest AIC
[~, best_k] = min(AIC);
best_model = models{best_k};
% Get the PDFs, Gaussians, means, and standard deviations
pdfs = @(x)pdf(best_model, x); % PDFs
gaussians = @(x)best_model.pdf(x); % Gaussians
% Get the means and standard deviations
means = best_model.mu; % Means
stds = sqrt(best_model.Sigma);
% Display the best fit number of Gaussians
fprintf('Best fit number of Gaussians: %d\n', best_k);
% Plot the means and standard deviations in a 3D plot with an improved viewing angle
figure;
hold on;
for i = 1:size(means,1)
    for j = 1:size(means,2)
        plot3(means(i,j), j, i, 'ro', 'MarkerSize', 10, 'MarkerFaceColor', 'r');
        plot3([means(i,j)-stds(i,j) means(i,j)+stds(i,j)], [j j], [i i], 'b', 'LineWidth', 2);
    end
end
xlabel('Feature Mean');
ylabel('Gaussian Component');
zlabel('Feature Number');
set(gca, 'YTick', 1:size(means,2), 'YTickLabel', {'1', '2', '3', '4', '5', '6', '7', '8', '9', '10'});
view(60, 30); % Set the viewing angle to 45 degrees azimuth and 30 degrees elevation
clear k X X_norm;

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the cyclist 2023-3-28

Can you upload the data? You can use the paper clip icon in the INSERT section of the toolbar.

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Answer 1

the cyclist 2023-3-28

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1936074-gmm-gaussian-mixture-as-summation-of-pdf#answer_1202379

It will be easier to help when we see the data, but the general idea would be to sum the pdfs of your best_model, weighted by the ComponentProportion property.