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)