def testEigpsd(self):
tol = 10**-3
N=50
A = numpy.random.rand(N, N)
A = A.dot(A.T)
lmbda, V = Nystrom.eigpsd(A, N)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A)*tol)
#Approximation should be good when n ~ N
lmbda, V = Nystrom.eigpsd(A, N-1)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A)*tol)
#Now let's test on positive semi-definite
w, U = numpy.linalg.eig(A)
w[-1] = 0
A = U.dot(numpy.diag(w)).dot(U.T)
lmbda, V = Nystrom.eigpsd(A, N)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A)*tol)
#Approximation should be good when n ~ N
for n in range(N-2, N+1):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
lmbda, V = Nystrom.eigpsd(A, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A)*tol)
self.assertAlmostEqual(numpy.linalg.norm(A - AHat), numpy.linalg.norm(A - AHat2))
def testEigpsd3(self):
# These tests are on big matrices
tol = 10**-3
n = 1000 # size of the matrices
m = 100 # rank of the matrices
max_k = int(m * 1.1) # maximum rank of the approximation
# relevant matrix
Arel = numpy.random.rand(m, m)
Arel = Arel.dot(Arel.T)
w, U = numpy.linalg.eigh(Arel)
Arel = U.dot(numpy.diag(w + 1)).dot(U.T)
tolArel = tol * numpy.linalg.norm(Arel)
# big matrix
P = numpy.random.rand(n, n)
A = P.dot(
scipy.linalg.block_diag(
Arel,
numpy.identity(n - m) / numpy.sqrt(n - m) * tolArel / 10)).dot(
P.T)
#Resulting matrix is really badly conditioned so we reduce the largest eigenvalue
lmbda, V = numpy.linalg.eigh(A)
inds = numpy.argsort(lmbda)
lmbda[inds[-1]] = 5000
A = (V * lmbda).dot(V.T)
tolA = tol * numpy.linalg.norm(A)
min_error = float('infinity')
for k in map(int, 2 + numpy.array(range(11)) * (max_k - 2) / 10):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:k])
lmbda, V = Nystrom.eigpsd(A, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A))
min_error = min(min_error, numpy.linalg.norm(A - AHat))
a, b, places = numpy.linalg.norm(A - AHat), numpy.linalg.norm(
A - AHat2), -int(numpy.log10(tolA))
self.assertAlmostEqual(
a,
b,
places=places,
msg="both approximations differ: " + str(a) + " != " + str(b) +
" within " + str(places) + " places (with rank " + str(k) +
" approximation)")
def testMatrixApprox(self):
tol = 10**-6
A = numpy.random.rand(10, 10)
A = A.dot(A.T)
n = 5
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
AHat = Nystrom.matrixApprox(A, inds)
n = 10
AHat2 = Nystrom.matrixApprox(A, n)
self.assertTrue(numpy.linalg.norm(A - AHat2) < numpy.linalg.norm(A - AHat))
self.assertTrue(numpy.linalg.norm(A - AHat2) < tol)
#Test on a sparse matrix
As = scipy.sparse.csr_matrix(A)
n = 5
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
AHat = Nystrom.matrixApprox(As, inds)
n = 10
AHat2 = Nystrom.matrixApprox(As, n)
self.assertTrue(SparseUtils.norm(As - AHat2) < SparseUtils.norm(As - AHat))
self.assertTrue(SparseUtils.norm(As - AHat2) < tol)
#Compare dense and sparse solutions
for n in range(1, 9):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
AHats = Nystrom.matrixApprox(As, inds)
AHat = Nystrom.matrixApprox(A, inds)
self.assertTrue(numpy.linalg.norm(AHat - numpy.array(AHats.todense())) < tol)
def testEig(self):
tol = 10**-5
#Test with an indeterminate matrix
A = numpy.random.rand(10, 10)
A = A.dot(A.T)
w, U = numpy.linalg.eig(A)
A = U.dot(numpy.diag(w-1)).dot(U.T)
n = 10
lmbda, V = Nystrom.eig(A, n)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
self.assertTrue(numpy.linalg.norm(A - AHat) < tol)
#Approximation should be good when n < 10
for n in range(2, 11):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
lmbda, V = Nystrom.eig(A, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
#self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A))
#print(n)
#print(numpy.linalg.norm(A - AHat))
#print(numpy.linalg.norm(A - AHat2))
#Test with a positive definite matrix
A = numpy.random.rand(10, 10)
A = A.dot(A.T)
w, U = numpy.linalg.eig(A)
A = U.dot(numpy.diag(w+1)).dot(U.T)
#Approximation should be good when n < 10
for n in range(2, 11):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
lmbda, V = Nystrom.eig(A, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A))
def testMatrixApprox(self):
tol = 10**-6
A = numpy.random.rand(10, 10)
A = A.dot(A.T)
n = 5
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
AHat = Nystrom.matrixApprox(A, inds)
n = 10
AHat2 = Nystrom.matrixApprox(A, n)
self.assertTrue(
numpy.linalg.norm(A - AHat2) < numpy.linalg.norm(A - AHat))
self.assertTrue(numpy.linalg.norm(A - AHat2) < tol)
#Test on a sparse matrix
As = scipy.sparse.csr_matrix(A)
n = 5
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
AHat = Nystrom.matrixApprox(As, inds)
n = 10
AHat2 = Nystrom.matrixApprox(As, n)
self.assertTrue(
SparseUtils.norm(As - AHat2) < SparseUtils.norm(As - AHat))
self.assertTrue(SparseUtils.norm(As - AHat2) < tol)
#Compare dense and sparse solutions
for n in range(1, 9):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
AHats = Nystrom.matrixApprox(As, inds)
AHat = Nystrom.matrixApprox(A, inds)
self.assertTrue(
numpy.linalg.norm(AHat - numpy.array(AHats.todense())) < tol)
def testEig(self):
tol = 10**-5
#Test with an indeterminate matrix
A = numpy.random.rand(10, 10)
A = A.dot(A.T)
w, U = numpy.linalg.eig(A)
A = U.dot(numpy.diag(w - 1)).dot(U.T)
n = 10
lmbda, V = Nystrom.eig(A, n)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
self.assertTrue(numpy.linalg.norm(A - AHat) < tol)
#Approximation should be good when n < 10
for n in range(2, 11):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
lmbda, V = Nystrom.eig(A, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
#self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A))
#print(n)
#print(numpy.linalg.norm(A - AHat))
#print(numpy.linalg.norm(A - AHat2))
#Test with a positive definite matrix
A = numpy.random.rand(10, 10)
A = A.dot(A.T)
w, U = numpy.linalg.eig(A)
A = U.dot(numpy.diag(w + 1)).dot(U.T)
#Approximation should be good when n < 10
for n in range(2, 11):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
lmbda, V = Nystrom.eig(A, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A))
def testEigpsd2(self):
#These tests are on sparse matrices
tol = 10**-5
A = numpy.random.rand(10, 10)
A = A.dot(A.T)
w, U = numpy.linalg.eig(A)
A = U.dot(numpy.diag(w+1)).dot(U.T)
As = scipy.sparse.csr_matrix(A)
n = 10
lmbda, V = Nystrom.eigpsd(As, n)
AHat = scipy.sparse.csr_matrix(V.dot(numpy.diag(lmbda)).dot(V.T))
self.assertTrue(numpy.linalg.norm(A - AHat) < tol)
for n in range(2, 11):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
lmbda, V = Nystrom.eigpsd(As, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A))
self.assertAlmostEqual(numpy.linalg.norm(A - AHat), numpy.linalg.norm(A - AHat2))
def testEigpsd(self):
tol = 10**-3
N = 50
A = numpy.random.rand(N, N)
A = A.dot(A.T)
lmbda, V = Nystrom.eigpsd(A, N)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
self.assertTrue(
numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A) * tol)
#Approximation should be good when n ~ N
lmbda, V = Nystrom.eigpsd(A, N - 1)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
self.assertTrue(
numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A) * tol)
#Now let's test on positive semi-definite
w, U = numpy.linalg.eig(A)
w[-1] = 0
A = U.dot(numpy.diag(w)).dot(U.T)
lmbda, V = Nystrom.eigpsd(A, N)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
self.assertTrue(
numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A) * tol)
#Approximation should be good when n ~ N
for n in range(N - 2, N + 1):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
lmbda, V = Nystrom.eigpsd(A, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
self.assertTrue(
numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A) * tol)
self.assertAlmostEqual(numpy.linalg.norm(A - AHat),
numpy.linalg.norm(A - AHat2))
def testEigpsd2(self):
#These tests are on sparse matrices
tol = 10**-5
A = numpy.random.rand(10, 10)
A = A.dot(A.T)
w, U = numpy.linalg.eig(A)
A = U.dot(numpy.diag(w + 1)).dot(U.T)
As = scipy.sparse.csr_matrix(A)
n = 10
lmbda, V = Nystrom.eigpsd(As, n)
AHat = scipy.sparse.csr_matrix(V.dot(numpy.diag(lmbda)).dot(V.T))
self.assertTrue(numpy.linalg.norm(A - AHat) < tol)
for n in range(2, 11):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:n])
lmbda, V = Nystrom.eigpsd(As, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A))
self.assertAlmostEqual(numpy.linalg.norm(A - AHat),
numpy.linalg.norm(A - AHat2))
def testEigpsd3(self):
# These tests are on big matrices
tol = 10**-3
n = 1000 # size of the matrices
m = 100 # rank of the matrices
max_k = int(m*1.1) # maximum rank of the approximation
# relevant matrix
Arel = numpy.random.rand(m, m)
Arel = Arel.dot(Arel.T)
w, U = numpy.linalg.eigh(Arel)
Arel = U.dot(numpy.diag(w+1)).dot(U.T)
tolArel = tol*numpy.linalg.norm(Arel)
# big matrix
P = numpy.random.rand(n, n)
A = P.dot(scipy.linalg.block_diag(Arel, numpy.identity(n-m)/numpy.sqrt(n-m)*tolArel/10)).dot(P.T)
#Resulting matrix is really badly conditioned so we reduce the largest eigenvalue
lmbda, V = numpy.linalg.eigh(A)
inds = numpy.argsort(lmbda)
lmbda[inds[-1]] = 5000
A = (V*lmbda).dot(V.T)
tolA = tol*numpy.linalg.norm(A)
min_error = float('infinity')
for k in map(int,2+numpy.array(range(11))*(max_k-2)/10):
inds = numpy.sort(numpy.random.permutation(A.shape[0])[0:k])
lmbda, V = Nystrom.eigpsd(A, inds)
AHat = V.dot(numpy.diag(lmbda)).dot(V.T)
AHat2 = Nystrom.matrixApprox(A, inds)
self.assertTrue(numpy.linalg.norm(A - AHat) < numpy.linalg.norm(A))
min_error = min(min_error, numpy.linalg.norm(A - AHat))
a, b, places = numpy.linalg.norm(A - AHat), numpy.linalg.norm(A - AHat2), -int(numpy.log10(tolA))
self.assertAlmostEqual(a, b, places=places, msg= "both approximations differ: " + str(a) + " != " + str(b) + " within " + str(places) + " places (with rank " + str(k) + " approximation)")
def run():
W = createDataset()
numVertices = W.shape[0]
graph = SparseGraph(GeneralVertexList(numVertices))
graph.setWeightMatrix(W)
L = graph.normalisedLaplacianSym()
#L = GraphUtils.shiftLaplacian(scipy.sparse.csr_matrix(W)).todense()
n = 100
omega, Q = numpy.linalg.eigh(L)
omega2, Q2 = Nystrom.eigpsd(L, n)
print(omega)
print(omega2)
plt.figure(1)
plt.plot(numpy.arange(omega.shape[0]), omega)
plt.plot(numpy.arange(omega2.shape[0]), omega2)
plt.show()
#run()
def clusterFromIterator(self, graphListIterator, verbose=False):
"""
Find a set of clusters for the graphs given by the iterator. If verbose
is true the each iteration is timed and bounded the results are returned
as lists.
The difference between a weight matrix and the previous one should be
positive.
"""
clustersList = []
decompositionTimeList = []
kMeansTimeList = []
boundList = []
sinThetaList = []
i = 0
for subW in graphListIterator:
if __debug__:
Parameter.checkSymmetric(subW)
if self.logStep and i % self.logStep == 0:
logging.debug("Graph index: " + str(i))
logging.debug("Clustering graph of size " + str(subW.shape))
if self.alg != "efficientNystrom":
ABBA = GraphUtils.shiftLaplacian(subW)
# --- Eigen value decomposition ---
startTime = time.time()
if self.alg == "IASC":
if i % self.T != 0:
omega, Q = self.approxUpdateEig(subW, ABBA, omega, Q)
if self.computeBound:
inds = numpy.flipud(numpy.argsort(omega))
Q = Q[:, inds]
omega = omega[inds]
bounds = self.pertBound(omega, Q, omegaKbot, AKbot,
self.k2)
#boundList.append([i, bounds[0], bounds[1]])
#Now use accurate values of norm of R and delta
rank = Util.rank(ABBA.todense())
gamma, U = scipy.sparse.linalg.eigsh(ABBA,
rank - 1,
which="LM",
ncv=ABBA.shape[0])
#logging.debug("gamma=" + str(gamma))
bounds2 = self.realBound(omega, Q, gamma, AKbot,
self.k2)
boundList.append(
[bounds[0], bounds[1], bounds2[0], bounds2[1]])
else:
logging.debug("Computing exact eigenvectors")
self.storeInformation(subW, ABBA)
if self.computeBound:
#omega, Q = scipy.sparse.linalg.eigsh(ABBA, min(self.k2*2, ABBA.shape[0]-1), which="LM", ncv = min(10*self.k2, ABBA.shape[0]))
rank = Util.rank(ABBA.todense())
omega, Q = scipy.sparse.linalg.eigsh(ABBA,
rank - 1,
which="LM",
ncv=ABBA.shape[0])
inds = numpy.flipud(numpy.argsort(omega))
omegaKbot = omega[inds[self.k2:]]
QKbot = Q[:, inds[self.k2:]]
AKbot = (QKbot * omegaKbot).dot(QKbot.T)
omegaSort = numpy.flipud(numpy.sort(omega))
boundList.append([0] * 4)
else:
omega, Q = scipy.sparse.linalg.eigsh(
ABBA,
min(self.k2, ABBA.shape[0] - 1),
which="LM",
ncv=min(10 * self.k2, ABBA.shape[0]))
elif self.alg == "nystrom":
omega, Q = Nystrom.eigpsd(ABBA, self.k3)
elif self.alg == "exact":
omega, Q = scipy.sparse.linalg.eigsh(
ABBA,
min(self.k1, ABBA.shape[0] - 1),
which="LM",
ncv=min(15 * self.k1, ABBA.shape[0]))
elif self.alg == "efficientNystrom":
omega, Q = EfficientNystrom.eigWeight(subW, self.k2, self.k1)
elif self.alg == "randomisedSvd":
Q, omega, R = RandomisedSVD.svd(ABBA, self.k4)
else:
raise ValueError("Invalid Algorithm: " + str(self.alg))
if self.computeSinTheta:
omegaExact, QExact = scipy.linalg.eigh(ABBA.todense())
inds = numpy.flipud(numpy.argsort(omegaExact))
QExactKbot = QExact[:, inds[self.k1:]]
inds = numpy.flipud(numpy.argsort(omega))
QApproxK = Q[:, inds[:self.k1]]
sinThetaList.append(
scipy.linalg.norm(QExactKbot.T.dot(QApproxK)))
decompositionTimeList.append(time.time() - startTime)
if self.alg == "IASC":
self.storeInformation(subW, ABBA)
# --- Kmeans ---
startTime = time.time()
inds = numpy.flipud(numpy.argsort(omega))
standardiser = Standardiser()
#For some very strange reason we get an overflow when computing the
#norm of the rows of Q even though its elements are bounded by 1.
#We'll ignore it for now
try:
V = standardiser.normaliseArray(Q[:, inds[0:self.k1]].real.T).T
except FloatingPointError as e:
logging.warn("FloatingPointError: " + str(e))
V = VqUtils.whiten(V)
if i == 0:
centroids, distortion = vq.kmeans(V,
self.k1,
iter=self.nb_iter_kmeans)
else:
centroids = self.findCentroids(V, clusters[:subW.shape[0]])
if centroids.shape[0] < self.k1:
nb_missing_centroids = self.k1 - centroids.shape[0]
random_centroids = V[numpy.random.randint(
0, V.shape[0], nb_missing_centroids), :]
centroids = numpy.vstack((centroids, random_centroids))
centroids, distortion = vq.kmeans(
V, centroids) #iter can only be 1
clusters, distortion = vq.vq(V, centroids)
kMeansTimeList.append(time.time() - startTime)
clustersList.append(clusters)
#logging.debug("subW.shape: " + str(subW.shape))
#logging.debug("len(clusters): " + str(len(clusters)))
#from sandbox.util.ProfileUtils import ProfileUtils
#logging.debug("Total memory usage: " + str(ProfileUtils.memory()/10**6) + "MB")
if ProfileUtils.memory() > 10**9:
ProfileUtils.memDisplay(locals())
i += 1
if verbose:
eigenQuality = {
"boundList": boundList,
"sinThetaList": sinThetaList
}
return clustersList, numpy.array(
(decompositionTimeList, kMeansTimeList)).T, eigenQuality
else:
return clustersList
print(omega)
print(Q)
omegaHat, Qhat = numpy.linalg.eigh(L2.todense())
inds = numpy.argsort(omegaHat)
omegaHat, Qhat = omegaHat[inds], Qhat[:, inds]
omegaHatk, Qhatk = omegaHat[0:k], Qhat[:, 0:k]
print(computeInnerProd(Qk, Qhatk))
for i, nystromN in enumerate(nystromNs):
print(nystromN)
#omega2, Q2 = numpy.linalg.eigh(L.todense())
omega2, Q2 = Nystrom.eigpsd(L, int(nystromN))
inds = numpy.flipud(numpy.argsort(omega2))
omega2, Q2 = omega2[inds], Q2[:, inds]
omega2k, Q2k = omega2[0:k], Q2[:, 0:k]
errors[i, 0] = computeBound(L, omega, Q, omega2k, Q2k, k)
innerProds[i, 0] = computeInnerProd(Qk, Q2k)
#omega2, Q2 = numpy.linalg.eigh(L2.todense())
omega2, Q2 = Nystrom.eigpsd(L2, int(nystromN))
inds = numpy.argsort(omega2)
omega2, Q2 = omega2[inds], Q2[:, inds]
omega2k, Q2k = omega2[0:k], Q2[:, 0:k]
print(omega2)
errors[i, 1] = computeBound(L2, omegaHat, Qhat, omega2k, Q2k, k)
def clusterFromIterator(self, graphListIterator, verbose=False):
"""
Find a set of clusters for the graphs given by the iterator. If verbose
is true the each iteration is timed and bounded the results are returned
as lists.
The difference between a weight matrix and the previous one should be
positive.
"""
clustersList = []
decompositionTimeList = []
kMeansTimeList = []
boundList = []
sinThetaList = []
i = 0
for subW in graphListIterator:
if __debug__:
Parameter.checkSymmetric(subW)
if self.logStep and i % self.logStep == 0:
logging.debug("Graph index: " + str(i))
logging.debug("Clustering graph of size " + str(subW.shape))
if self.alg!="efficientNystrom":
ABBA = GraphUtils.shiftLaplacian(subW)
# --- Eigen value decomposition ---
startTime = time.time()
if self.alg=="IASC":
if i % self.T != 0:
omega, Q = self.approxUpdateEig(subW, ABBA, omega, Q)
if self.computeBound:
inds = numpy.flipud(numpy.argsort(omega))
Q = Q[:, inds]
omega = omega[inds]
bounds = self.pertBound(omega, Q, omegaKbot, AKbot, self.k2)
#boundList.append([i, bounds[0], bounds[1]])
#Now use accurate values of norm of R and delta
rank = Util.rank(ABBA.todense())
gamma, U = scipy.sparse.linalg.eigsh(ABBA, rank-1, which="LM", ncv = ABBA.shape[0])
#logging.debug("gamma=" + str(gamma))
bounds2 = self.realBound(omega, Q, gamma, AKbot, self.k2)
boundList.append([bounds[0], bounds[1], bounds2[0], bounds2[1]])
else:
logging.debug("Computing exact eigenvectors")
self.storeInformation(subW, ABBA)
if self.computeBound:
#omega, Q = scipy.sparse.linalg.eigsh(ABBA, min(self.k2*2, ABBA.shape[0]-1), which="LM", ncv = min(10*self.k2, ABBA.shape[0]))
rank = Util.rank(ABBA.todense())
omega, Q = scipy.sparse.linalg.eigsh(ABBA, rank-1, which="LM", ncv = ABBA.shape[0])
inds = numpy.flipud(numpy.argsort(omega))
omegaKbot = omega[inds[self.k2:]]
QKbot = Q[:, inds[self.k2:]]
AKbot = (QKbot*omegaKbot).dot(QKbot.T)
omegaSort = numpy.flipud(numpy.sort(omega))
boundList.append([0]*4)
else:
omega, Q = scipy.sparse.linalg.eigsh(ABBA, min(self.k2, ABBA.shape[0]-1), which="LM", ncv = min(10*self.k2, ABBA.shape[0]))
elif self.alg == "nystrom":
omega, Q = Nystrom.eigpsd(ABBA, self.k3)
elif self.alg == "exact":
omega, Q = scipy.sparse.linalg.eigsh(ABBA, min(self.k1, ABBA.shape[0]-1), which="LM", ncv = min(15*self.k1, ABBA.shape[0]))
elif self.alg == "efficientNystrom":
omega, Q = EfficientNystrom.eigWeight(subW, self.k2, self.k1)
elif self.alg == "randomisedSvd":
Q, omega, R = RandomisedSVD.svd(ABBA, self.k4)
else:
raise ValueError("Invalid Algorithm: " + str(self.alg))
if self.computeSinTheta:
omegaExact, QExact = scipy.linalg.eigh(ABBA.todense())
inds = numpy.flipud(numpy.argsort(omegaExact))
QExactKbot = QExact[:, inds[self.k1:]]
inds = numpy.flipud(numpy.argsort(omega))
QApproxK = Q[:,inds[:self.k1]]
sinThetaList.append(scipy.linalg.norm(QExactKbot.T.dot(QApproxK)))
decompositionTimeList.append(time.time()-startTime)
if self.alg=="IASC":
self.storeInformation(subW, ABBA)
# --- Kmeans ---
startTime = time.time()
inds = numpy.flipud(numpy.argsort(omega))
standardiser = Standardiser()
#For some very strange reason we get an overflow when computing the
#norm of the rows of Q even though its elements are bounded by 1.
#We'll ignore it for now
try:
V = standardiser.normaliseArray(Q[:, inds[0:self.k1]].real.T).T
except FloatingPointError as e:
logging.warn("FloatingPointError: " + str(e))
V = VqUtils.whiten(V)
if i == 0:
centroids, distortion = vq.kmeans(V, self.k1, iter=self.nb_iter_kmeans)
else:
centroids = self.findCentroids(V, clusters[:subW.shape[0]])
if centroids.shape[0] < self.k1:
nb_missing_centroids = self.k1 - centroids.shape[0]
random_centroids = V[numpy.random.randint(0, V.shape[0], nb_missing_centroids),:]
centroids = numpy.vstack((centroids, random_centroids))
centroids, distortion = vq.kmeans(V, centroids) #iter can only be 1
clusters, distortion = vq.vq(V, centroids)
kMeansTimeList.append(time.time()-startTime)
clustersList.append(clusters)
#logging.debug("subW.shape: " + str(subW.shape))
#logging.debug("len(clusters): " + str(len(clusters)))
#from sandbox.util.ProfileUtils import ProfileUtils
#logging.debug("Total memory usage: " + str(ProfileUtils.memory()/10**6) + "MB")
if ProfileUtils.memory() > 10**9:
ProfileUtils.memDisplay(locals())
i += 1
if verbose:
eigenQuality = {"boundList" : boundList, "sinThetaList" : sinThetaList}
return clustersList, numpy.array((decompositionTimeList, kMeansTimeList)).T, eigenQuality
else:
return clustersList
lastL = initialL
lastOmegas = [initialOmega]*len(IASCL)
lastQs = [initialQ]*len(IASCL)
#Compute exact eigenvalues
omega, Q = numpy.linalg.eigh(L.todense())
inds = numpy.flipud(numpy.argsort(omega))
omega, Q = omega[inds], Q[:, inds]
omegak, Qk = omega[0:k], Q[:, 0:k]
omegakbot, Qkbot = omega[k:], Q[:, k:]
#Nystrom method
print("Running Nystrom")
for j, nystromN in enumerate(nystromNs):
omega2, Q2 = Nystrom.eigpsd(L, nystromN)
inds = numpy.flipud(numpy.argsort(omega2))
omega2, Q2 = omega2[inds], Q2[:, inds]
omega2k, Q2k = omega2[0:k], Q2[:, 0:k]
# errors[i, j] += computeBound(L, omega, Q, omega2k, Q2k, k)
errors[i, j] += computeSinTheta(Qkbot, Q2k)
#Randomised SVD method
print("Running Random SVD")
for j, r in enumerate(randSVDVecs):
Q4, omega4, R4 = RandomisedSVD.svd(L, r)
inds = numpy.flipud(numpy.argsort(omega4))
omega4, Q4 = omega4[inds], Q4[:, inds]
omega4k, Q4k = omega4[0:k], Q4[:, 0:k]