def main():
args = parse_args()
if not args.scipy_only:
if args.sparsity > 0.:
if args.sparsity >= 1.:
raise (RuntimeError("Sparsity must be less than 1."))
nnz = (1. - args.sparsity) * args.num_row * args.num_col
v = np.random.rand(nnz)
i = np.random.randint(0, high=args.num_row, size=nnz)
j = np.random.randint(0, high=args.num_col, size=nnz)
A = sp.csc_matrix((v, (i, j)), shape=(args.num_row, args.num_col))
else:
A = np.random.rand(args.num_row * args.num_col).reshape(
args.num_row, args.num_col)
start_irlb = time.time()
X = irlb(A, args.nu, tol=args.tol, maxit=args.max_it)
end_irlb = time.time()
# Compare estimated values with np.linalg.svd:
if not args.irlb_only:
if args.sparsity > 0.:
start_svd = time.time()
S = splinalg.svds(A=A,
k=args.nu,
tol=args.tol,
maxiter=args.max_it,
return_singular_vectors=True)
end_svd = time.time()
else:
start_svd = time.time()
S = np.linalg.svd(A, 0)
end_svd = time.time()
abserr = np.max(np.abs(X[1] - S[1][0:args.nu]))
if (args.csv):
if args.irlb_only:
print("{}".format(end_irlb - start_irlb))
elif args.scipy_only:
print("{}".format(end_svd - start_svd))
else:
print("{},{},{}".format(end_irlb - start_irlb, end_svd - start_svd,
abserr))
else:
if not args.scipy_only:
print("IRLB timing: {}".format(end_irlb - start_irlb))
if not args.irlb_only:
print("SVD timing: {}".format(end_svd - start_svd))
if not args.irlb_only and not args.scipy_only:
print("Absolute error: {}".format(abserr))
def main():
args = parse_args()
if not args.scipy_only:
if args.sparsity > 0.0:
if args.sparsity >= 1.0:
raise (RuntimeError("Sparsity must be less than 1."))
nnz = (1.0 - args.sparsity) * args.num_row * args.num_col
v = np.random.rand(nnz)
i = np.random.randint(0, high=args.num_row, size=nnz)
j = np.random.randint(0, high=args.num_col, size=nnz)
A = sp.csc_matrix((v, (i, j)), shape=(args.num_row, args.num_col))
else:
A = np.random.rand(args.num_row * args.num_col).reshape(args.num_row, args.num_col)
start_irlb = time.time()
X = irlb(A, args.nu, tol=args.tol, maxit=args.max_it)
end_irlb = time.time()
# Compare estimated values with np.linalg.svd:
if not args.irlb_only:
if args.sparsity > 0.0:
start_svd = time.time()
S = splinalg.svds(A=A, k=args.nu, tol=args.tol, maxiter=args.max_it, return_singular_vectors=True)
end_svd = time.time()
else:
start_svd = time.time()
S = np.linalg.svd(A, 0)
end_svd = time.time()
abserr = np.max(np.abs(X[1] - S[1][0 : args.nu]))
if args.csv:
if args.irlb_only:
print("{}".format(end_irlb - start_irlb))
elif args.scipy_only:
print("{}".format(end_svd - start_svd))
else:
print("{},{},{}".format(end_irlb - start_irlb, end_svd - start_svd, abserr))
else:
if not args.scipy_only:
print("IRLB timing: {}".format(end_irlb - start_irlb))
if not args.irlb_only:
print("SVD timing: {}".format(end_svd - start_svd))
if not args.irlb_only and not args.scipy_only:
print("Absolute error: {}".format(abserr))
def main():
m = 2000 # Number of rows
n = 2000 # Number of columns
nnz = 10000 # Maximum number of nonzero elements
nu = 5 # Number of singular values to compute
v = np.random.rand(nnz)
i = np.random.randint(0, high=m, size=nnz)
j = np.random.randint(0, high=n, size=nnz)
A = sp.csc_matrix((v, (i, j)), shape=(m, n))
X = irlb(A, nu, tol=0.000001)
# A gut check measurement of absolute decomposition error:
print("TSVD: ||AV - US||_F = %f" % np.linalg.norm(A.dot(sp.csr_matrix(X[2])).todense().A - X[0] * X[1], "fro"))
# Compare with reference svd
S = np.linalg.svd(A.todense(), 0)
abserr = np.max(np.abs(X[1] - S[1][0:nu]))
print("Estmated/accurate singular values:")
print(X[1])
print(S[1][0:nu])
print("||S_tsvd - S_svd||_inf = %f" % abserr)
def main():
m = 2000 # Number of rows
n = 2000 # Number of columns
nnz = 10000 # Maximum number of nonzero elements
nu = 5 # Number of singular values to compute
v = np.random.rand(nnz)
i = np.random.randint(0, high=m, size=nnz)
j = np.random.randint(0, high=n, size=nnz)
A = sp.csc_matrix((v, (i, j)), shape=(m, n))
X = irlb(A, nu, tol=0.000001)
# A gut check measurement of absolute decomposition error:
print("TSVD: ||AV - US||_F = %f" % np.linalg.norm(
A.dot(sp.csr_matrix(X[2])).todense().A - X[0] * X[1], "fro"))
# Compare with reference svd
S = np.linalg.svd(A.todense(), 0)
abserr = np.max(np.abs(X[1] - S[1][0:nu]))
print("Estmated/accurate singular values:")
print(X[1])
print(S[1][0:nu])
print("||S_tsvd - S_svd||_inf = %f" % abserr)