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_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_comparison1(filename=None, n_range=None):
plt.style.use(dufte.style)
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),
kernels=[
sum,
lambda p: numpy.sum(p, axis=0),
accupy.kahan_sum,
lambda p: accupy.ksum(p, K=2),
lambda p: accupy.ksum(p, K=3),
accupy.fsum,
],
labels=[
"sum",
"numpy.sum",
"accupy.kahan_sum",
"accupy.ksum[2]",
"accupy.ksum[3]",
"accupy.fsum",
],
n_range=n_range,
xlabel="n",
)
plt.title("Sum(random(n, 100))")
# plt.show()
if filename:
plt.savefig(filename, transparent=True, bbox_inches="tight")
def performance_comparison_to():
import perfplot
Y_b = 20
L_A = 64 / numpy.pi / 5
c = 0.69 # average
cam16 = colorio.CAM16(c, Y_b, L_A)
def cio(x):
return cam16.to_xyz100(x, "JCh")
cam16_legacy = CAM16Legacy(c, Y_b, L_A)
def cio_legacy(x):
return cam16_legacy.to_xyz100(x, "JCh")
perfplot.plot(
setup=lambda n: numpy.random.rand(3, n),
kernels=[cio, cio_legacy],
n_range=100000 * numpy.arange(11),
xlabel="Number of input samples",
)
# import matplotlib2tikz
# matplotlib2tikz.save('fig.tikz')
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_no_labels():
def mytest(a):
return numpy.c_[a, a]
kernels = [mytest]
r = [2**k for k in range(4)]
perfplot.plot(setup=numpy.random.rand,
kernels=kernels,
n_range=r,
xlabel="len(a)")
return
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),
kernels=[
sum,
lambda p: numpy.sum(p, axis=0),
accupy.kahan_sum,
lambda p: accupy.ksum(p, K=2),
lambda p: accupy.ksum(p, K=3),
accupy.fsum,
],
labels=[
"sum",
"numpy.sum",
"accupy.kahan_sum",
"accupy.ksum[2]",
"accupy.ksum[3]",
"accupy.fsum",
],
colors=plt.rcParams["axes.prop_cycle"].by_key()["color"][:6],
n_range=n_range,
title="Sum(random(n, 100))",
xlabel="n",
logx=True,
logy=True,
automatic_order=False,
)
plt.gca().set_aspect(0.5)
lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
# plt.show()
plt.savefig(
"speed-comparison1.png",
transparent=True,
bbox_extra_artists=(lgd,),
bbox_inches="tight",
)
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),
kernels=[
sum,
lambda p: numpy.sum(p, axis=0),
accupy.kahan_sum,
lambda p: accupy.ksum(p, K=2),
lambda p: accupy.ksum(p, K=3),
accupy.fsum,
],
labels=[
'sum',
'numpy.sum',
'accupy.kahan_sum',
'accupy.ksum[2]',
'accupy.ksum[3]',
'accupy.fsum',
],
colors=plt.rcParams['axes.prop_cycle'].by_key()['color'][:6],
n_range=n_range,
title='Sum(random(n, 100))',
xlabel='n',
logx=True,
logy=True,
automatic_order=False)
plt.gca().set_aspect(0.5)
lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
# plt.show()
plt.savefig(
'speed-comparison1.png',
transparent=True,
bbox_extra_artists=(lgd, ),
bbox_inches='tight',
)
return
def test_speed(N=2):
numpy.random.seed(1)
osa = colorio.OsaUcs()
cielab = colorio.CIELAB()
# cam16 = colorio.CAM16(0.69, 20, L_A=64 / numpy.pi / 5)
ciecam02 = colorio.CIECAM02(0.69, 20, L_A=64 / numpy.pi / 5)
perfplot.plot(
# Don't use numpy.random.rand(3, n) to avoid the CIECAM breakdown
setup=lambda n: numpy.outer(numpy.random.rand(3), numpy.ones(n)) * 10,
equality_check=None,
kernels=[
osa.to_xyz100,
cielab.to_xyz100,
# cam16.to_xyz100,
lambda Jsh: ciecam02.to_xyz100(Jsh, "Jsh"),
numpy.cbrt,
],
labels=["OSA-UCS", "CIELAB", "CIECAM02", "cbrt"],
n_range=[2**n for n in range(N)],
logx=True,
logy=True,
# relative_to=3
)
""" Force sample from ChainerRL PrioritizedReplayBuffer
"""
def sample(n):
_rb.memory.wait_priority_after_sampling = False
return _rb.sample(n)
return sample
# ReplayBuffer.add
perfplot.plot(
setup=env,
time_unit="ms",
kernels=[add_b(brb),
add_r(rrb),
add_c(crb), lambda e: rb.add(**e)],
labels=["OpenAI/Baselines", "Ray/RLlib", "Chainer/ChainerRL", "cpprb"],
n_range=[n for n in range(1, 102, 10)],
xlabel="Step size added at once",
logx=False,
logy=False,
equality_check=None)
plt.title("Replay Buffer Add Speed")
plt.savefig("ReplayBuffer_add.png", transparent=True, bbox_inches="tight")
plt.close()
# Fill Buffers
o = np.random.rand(buffer_size, obs_shape)
e = {
"obs": o, # [0,1)
"act": np.random.rand(buffer_size, act_shape),
"rew": np.random.rand(buffer_size),
def test_no_labels():
perfplot.plot(setup=numpy.random.rand, kernels=kernels, n_range=r, xlabel="len(a)")
return
[4, 1, 1, 4, 1])
return itertools.islice(dataset, n)
return sample
# ReplayBuffer.add
perfplot.plot(setup=env,
time_unit="ms",
kernels=[
add_client_insert(client, "ReplayBuffer"),
add_client(client, "ReplayBuffer"),
add_tf_client(tf_client, "ReplayBuffer"),
lambda e: rb.add(**e)
],
labels=[
"DeepMind/Reverb: Client.insert",
"DeepMind/Reverb: Client.writer",
"DeepMind/Reverb: TFClient.insert", "cpprb"
],
n_range=[n for n in range(1, 102, 10)],
xlabel="Step size added at once",
logx=False,
logy=False,
equality_check=None)
plt.title("Replay Buffer Add Speed")
plt.savefig("ReplayBuffer_add2.png", transparent=True, bbox_inches="tight")
plt.close()
# Fill Buffers
for _ in range(buffer_size):
o = np.random.rand(obs_shape) # [0,1)
