Graph multiresolution: Kron pyramid¶
In this demonstration file, we show how to reduce a graph using the PyGSP. Then we apply the pyramid to simple signal. To start open a python shell (IPython is recommended here) and import the required packages. You would probably also import numpy as you will need it to create matrices and arrays.
>>> import numpy as np >>> from pygsp import graphs, reduction
For this demo we will be using a sensor graph with 512 nodes.
>>> G = graphs.Sensor(512, distribute=True) >>> G.compute_fourier_basis()
The function graph_multiresolution computes the graph pyramid for you:
>>> levels = 5 >>> Gs = reduction.graph_multiresolution(G, levels, sparsify=False)
Next, we will compute the fourier basis of our different graph layers:
>>> for gr in Gs: ... gr.compute_fourier_basis()
Those that were already computed are returning with an error, meaning that nothing happened. Let’s now create two signals and a filter, resp f, f2 and g:
>>> f = np.ones((G.N)) >>> f[np.arange(G.N//2)] = -1 >>> f = f + 10*Gs.U[:, 7]
>>> f2 = np.ones((G.N, 2)) >>> f2[np.arange(G.N//2)] = -1
>>> g = [lambda x: 5./(5 + x)]
We will run the analysis of the two signals on the pyramid and obtain a coarse approximation for each layer, with decreasing number of nodes. Additionally, we will also get prediction errors at each node at every layer.
>>> ca, pe = reduction.pyramid_analysis(Gs, f, h_filters=g, method='exact') >>> ca2, pe2 = reduction.pyramid_analysis(Gs, f2, h_filters=g, method='exact')
Given the pyramid, the coarsest approximation and the prediction errors, we will now reconstruct the original signal on the full graph.
>>> f_pred, _ = reduction.pyramid_synthesis(Gs, ca[levels], pe, method='exact') >>> f_pred2, _ = reduction.pyramid_synthesis(Gs, ca2[levels], pe2, method='exact')
Here are the final errors for each signal after reconstruction.
>>> err = np.linalg.norm(f_pred-f)/np.linalg.norm(f) >>> err2 = np.linalg.norm(f_pred2-f2)/np.linalg.norm(f2) >>> assert (err < 1e-10) & (err2 < 1e-10)