This is simple. First, estimate kernel density parameters from data vector, using fitdist:
pd = fitdist(X,'kernel');
Use this parameters to generate random samples, where 100x1 is the desired random samples:
Y = random(pd, [100,1]);
or Y = pd.random(100,1);
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