def __init__(self):
numpy.random.seed(21)
#Create a low rank matrix
# n = 100000
# m = 100000
# self.r = 50
# nKnown = 10**4
# netflix-like
# n = 480000
# m = 18000
# self.r = 200
# nKnown = 10**8
# close from netflix
n = 480000
m = 18000
self.r = 200
nKnown = 10**6
# focusing on scalar-product
n = 480000
m = 18000
self.r = 50
nKnown = 10**5
self.X = SparseUtils.generateSparseLowRank((n, m), self.r, nKnown)
print(self.X.nnz)
def __init__(self):
numpy.random.seed(21)
#Create a low rank matrix
n = 100000
m = 100000
self.r = 200
k = 10**6
self.X = SparseUtils.generateSparseLowRank((n, m), self.r, k)
def __init__(self):
numpy.random.seed(21)
#Create a low rank matrix
n = 500000
m = 500000
self.r = 200
k = 10**6
print("Generating low rank")
self.X = SparseUtils.generateSparseLowRank((n, m), self.r, k)
print("Generating csarray")
self.X = csarray.fromScipySparse(self.X, storageType="rowMajor")
print("Done")
#Test if we can easily get the SVD of a set of matrices with low rank but under
#a fixed structure
import numpy
import scipy.sparse
from exp.util.SparseUtils import SparseUtils
numpy.set_printoptions(suppress=True, precision=3, linewidth=150)
shape = (15, 20)
r = 10
k = 50
X, U, s, V = SparseUtils.generateSparseLowRank(shape, r, k, verbose=True)
X = numpy.array(X.todense())
Y = numpy.zeros(X.shape)
Y[X.nonzero()] = 1
print(Y)
U2, s2, V2 = numpy.linalg.svd(Y)
print(s2)
X2 = numpy.zeros(X.shape)
for i in range(r):
X2 += s[i]*numpy.diag(U[:,i]).dot(Y).dot(numpy.diag(V[:, i]))