def run(backend=SUPPORTED_BACKENDS[0], quiet=True):
m, n, rank = 5, 4, 2
matrix = rnd.randn(m, n)
cost, egrad = create_cost_egrad(backend, matrix, rank)
manifold = FixedRankEmbedded(m, n, rank)
problem = pymanopt.Problem(manifold, cost=cost, egrad=egrad)
if quiet:
problem.verbosity = 0
solver = ConjugateGradient()
left_singular_vectors, singular_values, right_singular_vectors = \
solver.solve(problem)
low_rank_approximation = (left_singular_vectors @ np.diag(singular_values)
@ right_singular_vectors)
if not quiet:
u, s, vt = la.svd(matrix, full_matrices=False)
indices = np.argsort(s)[-rank:]
low_rank_solution = (
u[:, indices] @ np.diag(s[indices]) @ vt[indices, :])
print("Analytic low-rank solution:")
print()
print(low_rank_solution)
print()
print("Rank-{} approximation:".format(rank))
print()
print(low_rank_approximation)
print()
print("Frobenius norm error:",
la.norm(low_rank_approximation - low_rank_solution))
print()
def fixedrank(A, YT, r):
""" Solves the AX=YT problem on the manifold of r-rank matrices with
"""
# Instantiate a manifold
manifold = FixedRankEmbedded(N, r, r)
# Define the cost function (here using autograd.numpy)
def cost(X):
U = X[0]
cst = 0
for n in range(N):
cst = cst + huber(U[n, :])
Mat = np.matmul(np.matmul(X[0], np.diag(X[1])), X[2])
fidelity = LA.norm(np.subtract(np.matmul(A, Mat), YT))
return cst + lambd * fidelity**2
problem = Problem(manifold=manifold, cost=cost)
solver = ConjugateGradient(maxiter=maxiter)
# Let Pymanopt do the rest
Xopt = solver.solve(problem)
#Solve
Sol = np.dot(np.dot(Xopt[0], np.diag(Xopt[1])), Xopt[2])
return Sol
def setUp(self):
self.m = m = 10
self.n = n = 5
self.k = k = 3
self.manifold = FixedRankEmbedded(m, n, k)
u, s, vt = self.manifold.random_point()
matrix = (u * s) @ vt
@pymanopt.function.autograd(self.manifold)
def cost(u, s, vt):
return np.linalg.norm((u * s) @ vt - matrix) ** 2
self.cost = cost
def setUp(self):
self.m = m = 20
self.n = n = 10
self.rank = rank = 3
A = np.random.normal(size=(m, n))
self.manifold = Product([FixedRankEmbedded(m, n, rank), Euclidean(n)])
@pymanopt.function.autograd(self.manifold)
def cost(u, s, vt, x):
return np.linalg.norm(((u * s) @ vt - A) @ x) ** 2
self.cost = cost
self.gradient = self.cost.get_gradient_operator()
self.hessian = self.cost.get_hessian_operator()
self.problem = pymanopt.Problem(self.manifold, self.cost)
def setUp(self):
self.m = m = 20
self.n = n = 10
self.rank = rank = 3
A = np.random.randn(m, n)
@pymanopt.function.Autograd
def cost(u, s, vt, x):
return np.linalg.norm(((u * s) @ vt - A) @ x)**2
self.cost = cost
self.gradient = self.cost.compute_gradient()
self.hvp = self.cost.compute_hessian_vector_product()
self.manifold = Product([FixedRankEmbedded(m, n, rank), Euclidean(n)])
self.problem = pymanopt.Problem(self.manifold, self.cost)
def run(backend=SUPPORTED_BACKENDS[0], quiet=True):
m, n, rank = 5, 4, 2
matrix = np.random.normal(size=(m, n))
manifold = FixedRankEmbedded(m, n, rank)
cost, euclidean_gradient = create_cost_and_derivates(
manifold, matrix, backend
)
problem = pymanopt.Problem(
manifold, cost, euclidean_gradient=euclidean_gradient
)
optimizer = ConjugateGradient(
verbosity=2 * int(not quiet), beta_rule="PolakRibiere"
)
(
left_singular_vectors,
singular_values,
right_singular_vectors,
) = optimizer.run(problem).point
low_rank_approximation = (
left_singular_vectors
@ np.diag(singular_values)
@ right_singular_vectors
)
if not quiet:
u, s, vt = np.linalg.svd(matrix, full_matrices=False)
indices = np.argsort(s)[-rank:]
low_rank_solution = (
u[:, indices] @ np.diag(s[indices]) @ vt[indices, :]
)
print("Analytic low-rank solution:")
print()
print(low_rank_solution)
print()
print(f"Rank-{rank} approximation:")
print()
print(low_rank_approximation)
print()
print(
"Frobenius norm error:",
np.linalg.norm(low_rank_approximation - low_rank_solution),
)
print()
import autograd.numpy as np
from pymanopt import Problem
from pymanopt.solvers import ConjugateGradient
# Let A be a (5 x 4) matrix to be approximated
A = np.random.randn(5, 4)
k = 2
# (a) Instantiation of a manifold
# points on the manifold are parameterized via their singular value
# decomposition (u, s, vt) where
# u is a 5 x 2 matrix with orthonormal columns,
# s is a vector of length 2,
# vt is a 2 x 4 matrix with orthonormal rows,
# so that u*diag(s)*vt is a 5 x 4 matrix of rank 2.
manifold = FixedRankEmbedded(A.shape[0], A.shape[1], k)
# (b) Definition of a cost function (here using autograd.numpy)
# Note that the cost must be defined in terms of u, s and vt, where
# X = u * diag(s) * vt.
def cost(usv):
delta = .5
u = usv[0]
s = usv[1]
vt = usv[2]
X = np.dot(np.dot(u, np.diag(s)), vt)
return np.sum(np.sqrt((X - A)**2 + delta**2) - delta)
# define the Pymanopt problem
def setUp(self):
self.m = m = 10
self.n = n = 5
self.k = k = 3
self.man = FixedRankEmbedded(m, n, k)
m = 4 n = 2 k = 2 X = np.random.rand(m, n) U, S, Vt = np.linalg.svd(X, full_matrices=False) U = U[:, :k] S = S[:k] Vt = Vt[:k, :] grad_f = grad(f) grad_g = grad(g) man = FixedRankEmbedded(m, n, k) dU, dS, dVt = grad_g((U, S, Vt)) Up, M, Vp = man.egrad2rgrad((U, S, Vt), (dU, dS, dVt)) U_, S_, V_ = man.tangent2ambient((U, S, Vt), (Up, M, Vp)) tangent_grad = U_.dot(S_).dot(V_.T) print print print 'X:' print X print print 'U,S,Vt:' print U, S, Vt
#e_correct_prediction = tf.equal(tf.argmax(e_y, 1), tf.argmax(y_, 1))
#e_accuracy = tf.reduce_mean(tf.cast(e_correct_prediction, "float"))
#e_accuracy_summary =tf.summary.scalar("e_accuracy", e_accuracy)
summaries = tf.summary.merge_all()
manifold_b = Euclidean(1,10)
if use_parameterization:
manifold_A = Euclidean(784, k)
manifold_B = Euclidean(k, 10)
manifold_W = Product([manifold_A,manifold_B])
arg = [A, B, b]
else:
manifold_W = FixedRankEmbedded(784, 10, k)
#manifold_W = Product([Stiefel(784,k), Euclidean(k), Stiefel(k,10)])
arg = [A, M, B, b]
manifold = Product([manifold_W, manifold_b])
e_manifold_W = Euclidean(784, 10)
e_arg = [eW, b]
e_manifold = Product([e_manifold_W, manifold_b])
problem = Problem(manifold=manifold, cost=loss, accuracy=accuracy, summary=summaries, arg=arg, data=[x,y_], verbosity=1)
e_problem = Problem(manifold=e_manifold, cost=e_loss, accuracy=accuracy, summary=summaries, arg=e_arg, data=[x, y_],
verbosity=1)
solver = SGD(maxiter=10000000,logverbosity=10)
if not os.path.isdir('result'):
os.makedirs('result')
path = os.path.join('result', experiment_name + '.csv')
m, n, rank = 10, 8, 4
p = 1 / 2
for i in range(n_exp):
matrix = rnd.randn(m, n)
P_omega = np.zeros_like(matrix)
for j in range(m):
for k in range(n):
P_omega[j][k] = random.choice([0, 1])
cost = create_cost(matrix, P_omega)
manifold = FixedRankEmbedded(m, n, rank)
problem = pymanopt.Problem(manifold, cost=cost, egrad=None)
res_list = []
for beta_type in BetaTypes:
solver = ConjugateGradient(beta_type=beta_type, maxiter=10000)
res = solver.solve(problem)
res_list.append(res[1])
res_list.append(res[2])
with open(path, 'a') as f:
writer = csv.writer(f)
writer.writerow(res_list)
