def set_workload(self, W):
marg = approximation.marginals_approx(W)
self.workload = marg
d = len(self.domain)
A = np.arange(2**d)
weights = marg.weight_vector()
self.dphi = np.array(
[np.dot(weights**2, self.mult[A | b]) for b in range(2**d)])
import approximation
import implicit
eps = 0.1
X, Xs = pickle.load(
open('/home/ryan/Desktop/privbayes_data/census-%.1f.pkl' % eps, 'rb'))
diffs = [(X - Y).sum(axis=3).flatten() for Y in Xs]
dims = [[0], [1], [2], [4]]
sf1 = CensusSF1().project_and_merge(dims)
approx = CensusSF1Approx().project_and_merge(dims)
proj = CensusSF1Projected().project_and_merge(dims)
proj2 = CensusSF1().unique_project().project_and_merge(dims)
kifer = CensusKifer().project_and_merge(dims)
marginals = approximation.marginals_approx(sf1)
eye = [np.eye(n) for n in sf1.domain]
t0 = time.time()
A_sf1 = optimize.restart_union_kron(sf1, 50, [1, 1, 8, 10])
t1 = time.time()
A_approx = optimize.restart_union_kron(approx, 50, [1, 1, 8, 10])
t2 = time.time()
A_proj = optimize.restart_kron(proj, 50, [1, 1, 8, 10])
t3 = time.time()
A_kifer = optimize.restart_kron(kifer, 50, [1, 1, 1, 10])
t4 = time.time()
A_proj2 = optimize.restart_kron(proj2, 50, [1, 1, 8, 10])
A_marg, A1_marg = optimize.restart_marginals(marginals, 50)
err1 = sf1.expected_error(eye, eps)