def compression_vs_qr(m, n, q):
x = np.random.standard_normal(size=(m, n))
t = timeit.default_timer()
randcomp.compress(x, q, n_power_iter=0)
tim_comp = timeit.default_timer() - t
t = timeit.default_timer()
np.linalg.qr(x)
tim_qr = timeit.default_timer() - t
return tim_comp, tim_qr
def compression_vs_qr(m, n, q):
x = np.random.standard_normal(size=(m, n))
t = timeit.default_timer()
randcomp.compress(x, q, n_power_iter=0)
tim_comp = timeit.default_timer() - t
t = timeit.default_timer()
np.linalg.qr(x)
tim_qr = timeit.default_timer() - t
return tim_comp, tim_qr
def size_timing(m, n, q):
x = np.random.standard_normal(size=(m, n))
blockshape = (select_blocksize(m), select_blocksize(n))
data = from_array(x, blockshape=blockshape)
t = timeit.default_timer()
data_comp, _ = randcomp.compress(data, q)
np.array(data_comp)
tid = timeit.default_timer() - t
t = timeit.default_timer()
randcomp.compress(x, q, n_power_iter=0)
tim = timeit.default_timer() - t
return tid, tim
def size_timing(m, n, q):
x = np.random.standard_normal(size=(m, n))
blockshape = (select_blocksize(m), select_blocksize(n))
data = from_array(x, blockshape=blockshape)
t = timeit.default_timer()
data_comp, _ = randcomp.compress(data, q)
np.array(data_comp)
tid = timeit.default_timer() - t
t = timeit.default_timer()
randcomp.compress(x, q, n_power_iter=0)
tim = timeit.default_timer() - t
return tid, tim
