def test_speed_comparison1(n_range=None):
if n_range is None:
n_range = [2**k for k in range(2)]
numpy.random.seed(0)
perfplot.plot(
setup=lambda n: (numpy.random.rand(n, 100), numpy.random.rand(100, n)),
kernels=[
lambda xy: numpy.dot(*xy),
lambda xy: accupy.kdot(*xy, K=2),
lambda xy: accupy.kdot(*xy, K=3),
lambda xy: accupy.fdot(*xy),
],
labels=[
"numpy.dot", "accupy.kdot[2]", "accupy.kdot[3]", "accupy.fdot"
],
colors=plt.rcParams["axes.prop_cycle"].by_key()["color"][:4],
n_range=n_range,
title="dot(random(n, 100), random(100, n))",
xlabel="n",
logx=True,
logy=True,
automatic_order=False,
)
plt.gca().set_aspect(0.2)
lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
# plt.show()
plt.savefig(
"speed-comparison-dot1.png",
transparent=True,
bbox_extra_artists=(lgd, ),
bbox_inches="tight",
)
return
def test_speed_comparison2(filename=None, n_range=None):
if n_range is None:
n_range = [2**k for k in range(2)]
numpy.random.seed(0)
perfplot.plot(
setup=lambda n: (numpy.random.rand(100, n), numpy.random.rand(n, 100)),
kernels=[
lambda xy: numpy.dot(*xy),
lambda xy: accupy.kdot(*xy, K=2),
lambda xy: accupy.kdot(*xy, K=3),
lambda xy: accupy.fdot(*xy),
],
labels=[
"numpy.dot", "accupy.kdot[2]", "accupy.kdot[3]", "accupy.fdot"
],
n_range=n_range,
xlabel="n",
logx=True,
logy=True,
)
plt.title("dot(random(100, n), random(n, 100))")
# plt.show()
if filename:
plt.savefig(filename, transparent=True, bbox_inches="tight")
def test_discontiguous():
x = numpy.random.rand(3, 10)
y = numpy.random.rand(3, 10)
accupy.kdot(x.T, y)
accupy.fdot(x.T, y)
return
def test_speed_comparison1(n_range=None):
if n_range is None:
n_range = [2**k for k in range(2)]
perfplot.plot(setup=lambda n:
(numpy.random.rand(n, 100), numpy.random.rand(100, n)),
kernels=[
lambda xy: numpy.dot(*xy),
lambda xy: accupy.kdot(*xy, K=2),
lambda xy: accupy.kdot(*xy, K=3),
lambda xy: accupy.fdot(*xy),
],
labels=[
'numpy.dot',
'accupy.kdot[2]',
'accupy.kdot[3]',
'accupy.fdot',
],
colors=plt.rcParams['axes.prop_cycle'].by_key()['color'][:4],
n_range=n_range,
title='dot(random(n, 100), random(100, n))',
xlabel='n',
logx=True,
logy=True,
automatic_order=False)
plt.gca().set_aspect(0.2)
lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
# plt.show()
plt.savefig(
'speed-comparison-dot1.png',
transparent=True,
bbox_extra_artists=(lgd, ),
bbox_inches='tight',
)
return
def test_accuracy_comparison_illcond(target_cond=None):
if target_cond is None:
target_cond = [10**k for k in range(2)]
kernels = [
numpy.dot,
lambda x, y: accupy.kdot(x, y, K=2),
lambda x, y: accupy.kdot(x, y, K=3),
accupy.fdot,
]
labels = ["numpy.dot", "accupy.kdot[2]", "accupy.kdot[3]", "accupy.fdot"]
data = numpy.empty((len(target_cond), len(kernels)))
condition_numbers = numpy.empty(len(target_cond))
numpy.random.seed(0)
for k, tc in enumerate(target_cond):
x, y, ref, C = accupy.generate_ill_conditioned_dot_product(1000, tc)
condition_numbers[k] = C
data[k] = [abs(kernel(x, y) - ref) / abs(ref) for kernel in kernels]
# sort
s = numpy.argsort(condition_numbers)
condition_numbers = condition_numbers[s]
data = data[s]
for label, d in zip(labels, data.T):
plt.loglog(condition_numbers, d, label=label)
lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
plt.grid()
plt.ylim(5.0e-18, 1.0)
plt.xlabel("condition number")
plt.ylabel("relative error")
plt.gca().set_aspect(1.3)
# plt.show()
# <https://stackoverflow.com/a/10154763/353337>
plt.savefig(
"accuracy-dot.png",
transparent=True,
bbox_extra_artists=(lgd, ),
bbox_inches="tight",
)
return
def test_accuracy_comparison_illcond(filename=None, target_cond=None):
plt.style.use(dufte.style)
if target_cond is None:
target_cond = [10**k for k in range(2)]
kernels = [
numpy.dot,
lambda x, y: accupy.kdot(x, y, K=2),
lambda x, y: accupy.kdot(x, y, K=3),
accupy.fdot,
]
labels = ["numpy.dot", "accupy.kdot[2]", "accupy.kdot[3]", "accupy.fdot"]
data = numpy.empty((len(target_cond), len(kernels)))
condition_numbers = numpy.empty(len(target_cond))
numpy.random.seed(0)
for k, tc in enumerate(target_cond):
x, y, ref, C = accupy.generate_ill_conditioned_dot_product(1000, tc)
condition_numbers[k] = C
data[k] = [abs(kernel(x, y) - ref) / abs(ref) for kernel in kernels]
# sort
s = numpy.argsort(condition_numbers)
condition_numbers = condition_numbers[s]
data = data[s]
for label, d in zip(labels, data.T):
plt.loglog(condition_numbers, d, label=label)
dufte.legend()
plt.xlabel("condition number")
plt.ylabel("relative error")
# plt.show()
# <https://stackoverflow.com/a/10154763/353337>
if filename:
plt.savefig(filename, transparent=True, bbox_inches="tight")
def test_kdot3(cond):
x, y, ref, _ = accupy.generate_ill_conditioned_dot_product(100, cond)
assert abs(accupy.kdot(x, y, K=3) - ref) < 1.0e-13 * abs(ref)
