def main():
import time
# bounder
bounder = Bounder()
# splitter
splitter = Splitter()
# proposal
proposal = Uniform()
# target
n = 10
w = np.triu(np.random.rand(n, n) * 0.2, 1)
f = np.random.rand(n) * 2 - 1
target = CliqueIsingModel(w, f)
# ======== RUN ===============
M = 1
N = 10
print("\nM samples per run:{0}, N runs:{1}".format(M, N))
start = time.time()
for i in range(N):
if i % 100 == 0:
print i
stream = osstar.osstar_sampling_iterator(target, proposal, bounder,
splitter)
for j in range(M):
X, G = stream.next()
end = time.time()
print("\nOS*")
print("splits/run: {0}".format(splitter.counter / float(N)))
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
samples = []
start = time.time()
for i in range(N):
if i % 100 == 0:
print i
stream = astar.astar_sampling_iterator(target, proposal, bounder,
splitter)
for j in range(M):
X, G = stream.next()
samples.append(X)
end = time.time()
print("\nA*")
print("splits/run: {0}".format(splitter.counter / float(N)))
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
def main():
import time
# bounder
bounder = Bounder()
# splitter
splitter = Splitter()
# proposal
proposal = Uniform()
# target
n = 10
w = np.triu(np.random.rand(n,n)*0.2, 1)
f = np.random.rand(n)*2 - 1
target = CliqueIsingModel(w, f)
# ======== RUN ===============
M = 1
N = 10
print("\nM samples per run:{0}, N runs:{1}".format(M, N))
start = time.time()
for i in range(N):
if i % 100 == 0:
print i
stream = osstar.osstar_sampling_iterator(target, proposal, bounder, splitter)
for j in range(M):
X, G = stream.next()
end = time.time()
print("\nOS*")
print("splits/run: {0}".format(splitter.counter/float(N)))
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
samples = []
start = time.time()
for i in range(N):
if i % 100 == 0:
print i
stream = astar.astar_sampling_iterator(target, proposal, bounder, splitter)
for j in range(M):
X, G = stream.next()
samples.append(X)
end = time.time()
print("\nA*")
print("splits/run: {0}".format(splitter.counter/float(N)))
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
def main():
import time
sigma = 2 * np.ones(1)
# bounder
bounder = Bounder()
# splitter
splitter = Splitter()
# proposal
proposal = IsotropicGaussian(1, sigma)
# target
x, y = generate_data(1000)
target = CauchyRegression(x, y, sigma)
# ======== RUN ===============
M = 1
N = 1000
print("\nM samples per run:{0}, N runs:{1}".format(M, N))
start = time.time()
for i in range(N):
if i % 100 == 0:
print i
stream = osstar.osstar_sampling_iterator(target, proposal, bounder,
splitter)
for j in range(M):
X, G = stream.next()
end = time.time()
print("\nOS*")
print("splits/run: {0}".format(splitter.counter / float(N)))
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
samples = np.empty((N * M)).squeeze()
start = time.time()
for i in range(N):
if i % 100 == 0:
print i
stream = astar.astar_sampling_iterator(target, proposal, bounder,
splitter)
for j in range(M):
X, G = stream.next()
samples[i * M + j] = X
end = time.time()
print("\nA*")
print("splits/run: {0}".format(splitter.counter / float(N)))
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
samples = samples.ravel()
plt.hist(samples, bins=25, normed=True)
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(-5, 5, 1000)
y = p(x)
plt.plot(x, y, "r", linewidth=4)
plt.draw()
plt.show()
def main():
import time
target = Sin(1)
proposal = Uniform()
bounder = Bounder()
splitter = Splitter()
M = 1
N = 5000
rej_samples = []
print("\nM samples per run:{0}, N runs:{1}".format(M, N))
start = time.time()
for i in range(N):
stream = osstar.rejection_sampling_iterator(target, proposal, bounder)
for j in range(M):
X, G = stream.next()
rej_samples.append(X)
end = time.time()
rej_samples = np.array(rej_samples).ravel()
print("\nRejection Sampling")
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
osstar_samples = []
start = time.time()
for i in range(N):
stream = osstar.osstar_sampling_iterator(target, proposal, bounder,
splitter)
for j in range(M):
X, G = stream.next()
osstar_samples.append(X)
end = time.time()
osstar_samples = np.array(osstar_samples).ravel()
print("\nOS*")
print("splits/run: {0}".format(splitter.counter / float(N)))
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
astar_iter_samples = []
start = time.time()
for i in range(N):
stream = astar.astar_sampling_iterator(target, proposal, bounder,
splitter)
for j in range(M):
X, G = stream.next()
astar_iter_samples.append(X)
end = time.time()
astar_iter_samples = np.array(astar_iter_samples).ravel()
print("\nA*")
print("splits/run: {0}".format(splitter.counter / float(N)))
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
plt.subplot(221)
plt.hist(rej_samples, bins=25, normed=True)
plt.title("Rejection")
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(0, 2 * pi, 1000)
y = p(x)
plt.plot(x, y, "r", linewidth=4)
plt.subplot(222)
plt.hist(osstar_samples, bins=25, normed=True)
plt.title("OS*")
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(0, 2 * pi, 1000)
y = p(x)
plt.plot(x, y, "r", linewidth=4)
plt.subplot(223)
plt.hist(astar_iter_samples, bins=25, normed=True)
plt.title("A*")
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(0, 2 * pi, 1000)
y = p(x)
plt.plot(x, y, "r", linewidth=4)
plt.draw()
plt.show()
def main():
import time
# bounder
bounder = Bounder()
# splitter
splitter = Splitter()
dim = 1
sigma = 2 * np.ones(dim)
# proposal
proposal = IsotropicGaussian(dim, sigma)
# target
pi = 0.5
num_points = 3
points = np.concatenate(
(np.linspace(-5, -3, num_points), np.linspace(2, 4, num_points)))
data = np.zeros((len(points), dim))
for d in range(dim):
data[:, d] = points
target = ClutterPosterior(sigma, pi, data)
# go time!
M = 1
N = 500
print("\nM samples per run:{0}, N runs:{1}".format(M, N))
start = time.time()
for i in range(N):
stream = osstar.osstar_sampling_iterator(target, proposal, bounder,
splitter)
for j in range(M):
X, G = stream.next()
end = time.time()
print("\nOS*")
print("splits/run: {0}".format(splitter.counter / float(N)))
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
samples = np.empty((N * M, dim)).squeeze()
start = time.time()
for i in range(N):
stream = astar.astar_sampling_iterator(target, proposal, bounder,
splitter)
for j in range(M):
X, G = stream.next()
samples[i * M + j] = X
end = time.time()
print("\nA*")
print("splits/run: {0}".format(splitter.counter / float(N)))
print("likelihoods/sample: {0}".format(proposal.counter / float(M * N)))
print("bounds/sample: {0}".format(bounder.counter / float(M * N)))
print("time taken: {0}s".format(end - start))
if dim == 1:
samples = samples.ravel()
plt.hist(samples, bins=25, normed=True)
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(-5, 5, 1000)
y = p(x)
plt.plot(x, y, "r", linewidth=4)
plt.draw()
plt.show()
if dim == 2:
plt.plot(samples[:, 0], samples[:, 1], '.')
plt.draw()
plt.show()
def main():
import time
sigma = 2 * np.ones(1)
# bounder
bounder = Bounder()
# splitter
splitter = Splitter()
# proposal
proposal = IsotropicGaussian(1, sigma)
# target
x, y = generate_data(1000)
target = CauchyRegression(x, y, sigma)
# ======== RUN ===============
M = 1
N = 1000
print("\nM samples per run:{0}, N runs:{1}".format(M, N))
start = time.time()
for i in range(N):
if i % 100 == 0:
print i
stream = osstar.osstar_sampling_iterator(target, proposal, bounder, splitter)
for j in range(M):
X, G = stream.next()
end = time.time()
print("\nOS*")
print("splits/run: {0}".format(splitter.counter/float(N)))
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
samples = np.empty((N*M)).squeeze()
start = time.time()
for i in range(N):
if i % 100 == 0:
print i
stream = astar.astar_sampling_iterator(target, proposal, bounder, splitter)
for j in range(M):
X, G = stream.next()
samples[i*M + j] = X
end = time.time()
print("\nA*")
print("splits/run: {0}".format(splitter.counter/float(N)))
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
samples = samples.ravel()
plt.hist(samples, bins=25, normed=True)
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(-5, 5, 1000)
y = p(x)
plt.plot(x,y, "r", linewidth=4)
plt.draw()
plt.show()
def main():
import time
# bounder
bounder = Bounder()
# splitter
splitter = Splitter()
dim = 1
sigma = 2 * np.ones(dim)
# proposal
proposal = IsotropicGaussian(dim, sigma)
# target
pi = 0.5
num_points = 3
points = np.concatenate((np.linspace(-5, -3, num_points), np.linspace(2, 4, num_points)))
data = np.zeros((len(points), dim))
for d in range(dim):
data[:,d] = points
target = ClutterPosterior(sigma, pi, data)
# go time!
M = 1
N = 500
print("\nM samples per run:{0}, N runs:{1}".format(M, N))
start = time.time()
for i in range(N):
stream = osstar.osstar_sampling_iterator(target, proposal, bounder, splitter)
for j in range(M):
X, G = stream.next()
end = time.time()
print("\nOS*")
print("splits/run: {0}".format(splitter.counter/float(N)))
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
samples = np.empty((N*M, dim)).squeeze()
start = time.time()
for i in range(N):
stream = astar.astar_sampling_iterator(target, proposal, bounder, splitter)
for j in range(M):
X, G = stream.next()
samples[i*M + j] = X
end = time.time()
print("\nA*")
print("splits/run: {0}".format(splitter.counter/float(N)))
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
if dim == 1:
samples = samples.ravel()
plt.hist(samples, bins=25, normed=True)
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(-5, 5, 1000)
y = p(x)
plt.plot(x,y, "r", linewidth=4)
plt.draw()
plt.show()
if dim == 2:
plt.plot(samples[:,0], samples[:,1], '.')
plt.draw()
plt.show()
def main():
import time
target = Sin(1)
proposal = Uniform()
bounder = Bounder()
splitter = Splitter()
M = 1
N = 5000
rej_samples = []
print("\nM samples per run:{0}, N runs:{1}".format(M, N))
start = time.time()
for i in range(N):
stream = osstar.rejection_sampling_iterator(target, proposal, bounder)
for j in range(M):
X, G = stream.next()
rej_samples.append(X)
end = time.time()
rej_samples = np.array(rej_samples).ravel()
print("\nRejection Sampling")
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
osstar_samples = []
start = time.time()
for i in range(N):
stream = osstar.osstar_sampling_iterator(target, proposal, bounder, splitter)
for j in range(M):
X, G = stream.next()
osstar_samples.append(X)
end = time.time()
osstar_samples = np.array(osstar_samples).ravel()
print("\nOS*")
print("splits/run: {0}".format(splitter.counter/float(N)))
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
splitter.counter = 0
proposal.counter = 0
bounder.counter = 0
astar_iter_samples = []
start = time.time()
for i in range(N):
stream = astar.astar_sampling_iterator(target, proposal, bounder, splitter)
for j in range(M):
X, G = stream.next()
astar_iter_samples.append(X)
end = time.time()
astar_iter_samples = np.array(astar_iter_samples).ravel()
print("\nA*")
print("splits/run: {0}".format(splitter.counter/float(N)))
print("likelihoods/sample: {0}".format(proposal.counter/float(M*N)))
print("bounds/sample: {0}".format(bounder.counter/float(M*N)))
print("time taken: {0}s".format(end-start))
plt.subplot(221)
plt.hist(rej_samples, bins=25, normed=True)
plt.title("Rejection")
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(0, 2*pi, 1000)
y = p(x)
plt.plot(x,y, "r", linewidth=4)
plt.subplot(222)
plt.hist(osstar_samples, bins=25, normed=True)
plt.title("OS*")
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(0, 2*pi, 1000)
y = p(x)
plt.plot(x,y, "r", linewidth=4)
plt.subplot(223)
plt.hist(astar_iter_samples, bins=25, normed=True)
plt.title("A*")
p = np.vectorize(lambda x: np.exp(target.log_density(x)))
x = np.linspace(0, 2*pi, 1000)
y = p(x)
plt.plot(x,y, "r", linewidth=4)
plt.draw()
plt.show()