compute_fourier_basis Assertion Error about pygsp HOT 7 CLOSED

It's indeed a numerical issue then (your largest eigenvalue is marginally above 2). We'll relax the assertion. In the mean time you can just comment it. Thanks for reporting!

from pygsp.

This assertion is only done if the Laplacian is normalized.

if self.lap_type == 'normalized':
     # Spectrum bounded by [0, 2].
     assert self._e[-1] <= 2

In this case, the spectrum has to be bounded by 2 from the theory. So if your weight matrix is non-negative and symmetric, it should always pass this test.

from pygsp.

Thank you so much for the reply; I really appreciate it.
Hmmm... this is strange, then. The minimum weight in my weight matrix is 1 and G.is_directed() returns False. Any other possible reasons this would fail?

Edit: I tried this:

Gg=pygsp.graphs.Graph(nx.adjacency_matrix(G),lap_type='normalized')
e, U = np.linalg.eigh(Gg.L.toarray())
print e[-1]
Gg.compute_fourier_basis(recompute=True)

This printed 2.0 followed by the assertion error assert self._e[-1] <= 2

Also, I have the following:

print "largest eigenvalue:", Gg._e[-1]
print "Gg._e[-1] == 2.0", Gg._e[-1] == 2.0
print "2.0 <= 2", 2.0 <= 2
print "Gg._e[-1] <= 2", Gg._e[-1] <= 2
print "float(Gg._e[-1]) == 2.0", float(Gg._e[-1]) == 2.0
print "float(Gg._e[-1])", float(Gg._e[-1])

The print output of the above is:
largest eigenvalue: 2.0
Gg._e[-1] == 2.0 False
2.0 <= 2 True
Gg._e[-1] <= 2 False
float(Gg._e[-1]) == 2.0 False
float(Gg._e[-1]) 2.0

I'm pretty confused now. It seems like when I print Gg._e[-1], its value is 2.0, yet the comparisons aren't working.

from pygsp.

It really looks like a numerical error. We should relax slightly the assertion. I will do it this afternoon.
Thanks for reporting this bug.

from pygsp.

Could you print for us self._e[-1]-2?

from pygsp.

It prints out 4.84057238737e-14.

from pygsp.

That was fixed in 58421d5.

from pygsp.