R, BS = 110, 200
input = T.Placeholder((Ds[0], ), 'float32')
in_signs = T.Placeholder((np.sum(Ds[1:-1]), ), 'bool')
batch_in_signs = T.Placeholder((R, np.sum(Ds[1:-1])), 'bool')
x = T.Placeholder((BS, Ds[-1]), 'float32')
m0 = T.Placeholder((BS, R), 'float32')
m1 = T.Placeholder((BS, R, Ds[0]), 'float32')
m2 = T.Placeholder((BS, R, Ds[0], Ds[0]), 'float32')
f, g, h, all_g, train_f, sigma2 = utils.create_fns(input,
in_signs,
Ds,
x,
m0,
m1,
m2,
batch_in_signs,
sigma_x=np.log(1))
output, A, b, inequalities, signs = f(np.random.randn(Ds[0]) / 4)
regions = utils.search_region(all_g, g, signs)
assert len(regions) <= R
As = np.array([regions[s]['Ab'][0] for s in regions])
Bs = np.array([regions[s]['Ab'][1] for s in regions])
N = 140
L = 4
if Ds[0] == 1:
if Ds[0] == 1:
xx = np.linspace(-3, 3, 500)
else:
xx = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))
xx = np.vstack([xx[0].flatten(), xx[1].flatten()]).T
for ss in [0.05, 0.2, 0.5]:
np.random.seed(int(sys.argv[-1]) + 10)
sigma_x = np.eye(Ds[-1]) * ss
for i in range(5):
fig = plt.figure(figsize=(5, 5))
f, g, h, all_g, train_f = utils.create_fns(input,
in_signs,
Ds,
x,
m0,
m1,
m2,
batch_in_signs,
sigma=2)
z = np.random.randn(Ds[0])
output, A, b, inequalities, signs = f(z)
outpute = output + np.random.randn(Ds[-1]) * np.sqrt(0.05)
regions = utils.search_region(all_g, g, signs)
print(len(regions))
As = np.array([regions[s]['Ab'][0] for s in regions])
Bs = np.array([regions[s]['Ab'][1] for s in regions])
import matplotlib.pyplot as plt
import utils
from tqdm import tqdm
from scipy.spatial import ConvexHull
from multiprocessing import Pool
np.random.seed(14)
Ds = [1, 32, 2]
mu_z = np.zeros(Ds[0])
sigma_z = np.eye(Ds[0])
sigma_x = np.eye(Ds[-1]) * 0.2
input = T.Placeholder((Ds[0], ), 'float32')
in_signs = T.Placeholder((np.sum(Ds[1:-1]), ), 'bool')
f, g, h, all_g = utils.create_fns(input, in_signs, Ds)
x = np.random.randn(Ds[0])
output, A, b, inequalities, signs = f(x)
regions = utils.search_region(all_g, g, signs)
print('number of regions:', len(regions))
N = 25
L = 2.
xx = np.meshgrid(np.linspace(-L, L, N), np.linspace(-L, L, N))
xx = np.hstack([xx[0].flatten()[:, None], xx[1].flatten()[:, None]])
p = list()
for x in tqdm(xx):
p.append(utils.algo2(x, regions, sigma_x, mu_z, sigma_z)[0])
p = np.array(p).reshape((N, N))