def benchmark_ssvd(ctx, timer):
DIM = (1280, 1280)
#A = expr.randn(*DIM, dtype=np.float64)
A = np.random.randn(*DIM)
A = expr.from_numpy(A)
t1 = datetime.now()
U, S, VT = svd(A)
t2 = datetime.now()
cost_time = millis(t1, t2)
print "total cost time:%s ms" % (cost_time)
def benchmark_ssvd(ctx, timer):
DIM = (1280, 1280)
#A = expr.randn(*DIM, dtype=np.float64)
A = np.random.randn(*DIM)
A = expr.from_numpy(A)
t1 = datetime.now()
U,S,VT = svd(A)
t2 = datetime.now()
cost_time = millis(t1, t2)
print "total cost time:%s ms" % (cost_time)
def test_ssvd(self):
ctx = blob_ctx.get()
# Create a sparse matrix.
A = expr.randn(*DIM, tile_hint = (int(DIM[0]/ctx.num_workers), DIM[1]),
dtype=np.float64)
U,S,VT = svd(A)
U2,S2,VT2 = linalg.svd(A.glom(), full_matrices=0)
assert np.allclose(absolute(U.glom()), absolute(U2))
assert np.allclose(absolute(S), absolute(S2))
assert np.allclose(absolute(VT), absolute(VT2))
def test_ssvd(self):
ctx = blob_ctx.get()
# Create a sparse matrix.
A = expr.randn(*DIM,
tile_hint=(int(DIM[0] / ctx.num_workers), DIM[1]),
dtype=np.float64)
U, S, VT = svd(A)
U2, S2, VT2 = linalg.svd(A.glom(), full_matrices=0)
assert np.allclose(absolute(U.glom()), absolute(U2))
assert np.allclose(absolute(S), absolute(S2))
assert np.allclose(absolute(VT), absolute(VT2))
