v1.2

Example_dynamic.m

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+%%%
+% NGPCA: Clustering high-dimensional and non-stationary data streams
+% Please cite this code using the following DOI: https://doi.org/10.1016/j.patcog.2023.110030
+%%%
+
+%% 
+clear variables
+close all
+addpath('ngpca', 'data');
+% Reproducability
+rng(0)
+
+%% Load the ring-line-square and vortex data sets
+load rls.mat -ASCII;
+vortex = vortex(6*pi, 120, 20, 4000);
+
+%% 
+% Create an NGPCA object with hard-clustering. Set the data vector to the 
+% rls data initialize the units. Start the training processes and swap the 
+% distribution towards the vortex mid training
+ngpca = NGPCA('softhard', 1);
+figure;
+data = rls;
+scatter(data(:,1), data(:,2),5,'o','MarkerFaceAlpha',.2,'MarkerEdgeAlpha',.2);
+hold on;
+axis equal;
+axis manual;
+ngpca = init_units(ngpca, data, 19, 'iterations', 40000, 'PCADimensionality', 2);
+for i = 1:ngpca.iterations
+    % Sample a random data point in each iteration
+    ngpca = fit_single(ngpca, data(ceil(size(data,1) .* rand), :));
+    % Needed to overwrite the ngpca object if plotted in a loop to delete
+    % units from previous plot
+    if mod(i,100) == 0
+        ngpca = ngpca.draw();
+    end
+    if i == 20000
+        data = vortex;
+        scatter(data(:,1), data(:,2),5,'o','MarkerFaceAlpha',.2,'MarkerEdgeAlpha',.2);
+    end
+end