def power_iteration_4(Y, X, U, S, iterations, v0):
v = CuckooVector({})
v.add(v0)
for i in range(iterations):
v1 = CuckooVector({})
for (x,y) in zip(X,Y):
v1.add_scale_dict(x, v.dot_dict(y))
v2 = CuckooVector({})
for (x,y) in zip(X,Y):
v2.add_scale_dict(y, v1.dot_dict(x))
for (u,s) in zip(U,S):
print(-s * u.dot(v))
v2.add_scale(u, -s * u.dot(v))
mu = v2.norm(2)
v2.scale(1.0/mu)
v = v2
return mu,v
X += sn
P += sn[:-n]
F += sn[n:]
pickle.dump((X,P,F), open(out_dir + 'xpf.pcl', 'wb', -1))
#(X,F,P) = pickle.load(open('xpf.pcl', 'rb'))
rnd = np.random
start = time.time()
# Form an initial vector for power iteration by taking a randomly
# weighted sum of training examples.
v0 = CuckooVector({})
for x in X:
v0.add_scale_dict(x, rnd.normal(0.0, 5.0))
U = []
Ud = []
S = []
# Compute 1000 singular vectors of future-past covariance
# by computing the eigen vectors of FP^TPF^T using power iteration
# with deflation.
for i in range(1000):
print(i)
mu,u = mat_utils.power_iteration_4(F, P, U, S, 10, v0)
S.append(mu)
U.append(u)
# Convert new singular vector from Cuckoovec to dictionary
