Function spectral_embedding

pyCombinatorial/algorithm/rss.py:162–189

(L, num_vecs = 3, tol = 1e-6, maxiter = 5000)

# Function: SE
def spectral_embedding(L, num_vecs = 3, tol = 1e-6, maxiter = 5000):
    n = L.shape[0]
    if n <= 2:
        return np.zeros((n, 1), dtype = float)
    num_vecs = int(max(1, min(num_vecs, n - 1)))
    k_req    = min(num_vecs + 3, n - 1)
    k_req    = max(k_req, 2)
    try:
        vals, vecs = eigsh(L, k = k_req, which = 'SM', tol = tol, maxiter = maxiter)
    except ArpackNoConvergence as e:
        if e.eigenvectors is not None and e.eigenvalues is not None:
            vals, vecs = e.eigenvalues, e.eigenvectors
        else:
            k_req2     = max(2, min(k_req - 1, n - 1))
            vals, vecs = eigsh(L, k = k_req2, which = 'SM', tol = tol, maxiter = maxiter)
    idx  = np.argsort(vals)
    vals = vals[idx]
    vecs = vecs[:, idx]
    eps  = 1e-10
    j    = 0
    while j < len(vals) and vals[j] < eps:
        j = j + 1
    if j >= len(vals):
        j = 1
    end = min(j + num_vecs, vecs.shape[1])
    if end <= j:
        end = min(j + 1, vecs.shape[1])
    return vecs[:, j:end].copy()

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