Commit 1817db9e authored by Erik Strand's avatar Erik Strand

Update writeup

parent 9a174bad
......@@ -139,8 +139,8 @@ def em_step():
predicted = np.array([cluster_func(x[n], m) for n in range(N)])
out_var[m] = cw_expectation((y - predicted)**2.0, m) + tiny
for i in range(50):
make_plot(plot_name(i + 1))
......@@ -14,8 +14,12 @@ during the EM iterations.
My code lives [here](
Here's a video of the convergence progress with three clusters.
Here's a video of 50 EM iterations with three clusters. I'm displaying the local linear models over
a $$4 \sigma$$ range. (I don't indicate the output variance in any way, would be nice to add this.)
<video width="480" height="320" controls="controls" muted plays-inline>
<source type="video/mp4" src="../assets/mp4/11_cwm.mp4">
Not surprisingly, for tanh three clusters seems to work best. Any less and the fit degrades, any
more and some clusters end up with needlessly similar local models.
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