def benchmark(self):
numMatrices = 20
matrixList = []
print("Generating matrices")
for i in range(numMatrices):
print("Iteration: " + str(i))
m = numpy.random.randint(5000, 20000)
n = numpy.random.randint(5000, 20000)
density = numpy.random.rand() * 0.1
X = scipy.sparse.rand(m, n, density)
r = numpy.random.randint(10, 50)
U, s, V = SparseUtils.generateLowRank((m, n), r)
print(m, n, density, r)
matrixList.append((X, U, s, V))
k = 10
times = []
print("Starting timings for ARPACK")
start = time.time()
for i, matrices in enumerate(matrixList):
print("Iteration: " + str(i))
X, U, s, V = matrices
SVDUpdate.addSparseArpack(U, s, V, X, k)
times.append(time.time() - start)
#Compare versus PROPACK
print("Starting timings for PROPACK")
start = time.time()
for i, matrices in enumerate(matrixList):
print("Iteration: " + str(i))
X, U, s, V = matrices
SparseUtils.svdSparseLowRank(X, U, s, V, k)
times.append(time.time() - start)
print(times)
""" import sys import logging import scipy.sparse import numpy from sparsesvd import sparsesvd from exp.util.SparseUtils import SparseUtils numpy.random.seed(21) logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) numpy.set_printoptions(precision=3, suppress=True, linewidth=100) m = 10 n = 10 r = 1 U0, s0, V0 = SparseUtils.generateLowRank((m, n), r) numInds = 10 inds = numpy.unique(numpy.random.randint(0, m * n, numInds)) A = SparseUtils.reconstructLowRank(U0, s0, V0, inds) #print(A.todense()) t0 = s0 + numpy.random.rand(s0.shape[0]) * 0.1 B = SparseUtils.reconstructLowRank(U0, t0, V0, inds) #print(B.todense()) k = 9 U, s, V = sparsesvd(A, k) U2, s2, V2 = sparsesvd(B, k) print(A.todense())
""" import sys import logging import scipy.sparse import numpy from sparsesvd import sparsesvd from exp.util.SparseUtils import SparseUtils numpy.random.seed(21) logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) numpy.set_printoptions(precision=3, suppress=True, linewidth=100) m = 10 n = 10 r = 1 U0, s0, V0 = SparseUtils.generateLowRank((m, n), r) numInds = 10 inds = numpy.unique(numpy.random.randint(0, m*n, numInds)) A = SparseUtils.reconstructLowRank(U0, s0, V0, inds) #print(A.todense()) t0 = s0 + numpy.random.rand(s0.shape[0])*0.1 B = SparseUtils.reconstructLowRank(U0, t0, V0, inds) #print(B.todense()) k = 9 U, s, V = sparsesvd(A, k) U2, s2, V2 = sparsesvd(B, k) print(A.todense())