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toy_data.py
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toy_data.py
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import numpy as np
import sklearn
import sklearn.datasets
from sklearn.utils import shuffle as util_shuffle
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
# Dataset iterator for generation of dataset samples
def generate_2d_data(data, rng=None, batch_size=1000):
if rng is None:
rng = np.random.RandomState()
if data == "swissroll":
data = sklearn.datasets.make_swiss_roll(n_samples=batch_size, noise=1.0)[0]
data = data.astype("float32")[:, [0, 2]]
data /= 5
return data, np.max(data)
elif data == "circles":
data = sklearn.datasets.make_circles(n_samples=batch_size, factor=.5, noise=0.08)[0]
data = data.astype("float32")
data *= 3
return data, np.max(data)
elif data == "rings":
n_samples4 = n_samples3 = n_samples2 = batch_size // 4
n_samples1 = batch_size - n_samples4 - n_samples3 - n_samples2
# so as not to have the first point = last point, we set endpoint=False
linspace4 = np.linspace(0, 2 * np.pi, n_samples4, endpoint=False)
linspace3 = np.linspace(0, 2 * np.pi, n_samples3, endpoint=False)
linspace2 = np.linspace(0, 2 * np.pi, n_samples2, endpoint=False)
linspace1 = np.linspace(0, 2 * np.pi, n_samples1, endpoint=False)
circ4_x = np.cos(linspace4)
circ4_y = np.sin(linspace4)
circ3_x = np.cos(linspace4) * 0.75
circ3_y = np.sin(linspace3) * 0.75
circ2_x = np.cos(linspace2) * 0.5
circ2_y = np.sin(linspace2) * 0.5
circ1_x = np.cos(linspace1) * 0.25
circ1_y = np.sin(linspace1) * 0.25
X = np.vstack([
np.hstack([circ4_x, circ3_x, circ2_x, circ1_x]),
np.hstack([circ4_y, circ3_y, circ2_y, circ1_y])
]).T * 3.0
X = util_shuffle(X, random_state=rng)
# Add noise
X = X + rng.normal(scale=0.08, size=X.shape)
return X.astype("float32"), np.max(X)
elif data == "moons":
data = sklearn.datasets.make_moons(n_samples=batch_size, noise=0.1)[0]
data = data.astype("float32")
data = data * 2 + np.array([-1, -0.2], dtype="float32")
return data, np.max(data)
elif data == "4gaussians":
scale = 4.
centers = [(1, 0), (-1, 0), (0, 1), (0, -1)]
centers = [(scale * x, scale * y) for x, y in centers]
dataset = []
for i in range(batch_size):
point = rng.randn(2) * 0.5
idx = rng.randint(4)
center = centers[idx]
point[0] += center[0]
point[1] += center[1]
dataset.append(point)
dataset = np.array(dataset, dtype="float32")
dataset /= 1.414
return dataset, np.max(dataset)
elif data == "8gaussians":
scale = 4.
centers = [(1, 0), (-1, 0), (0, 1), (0, -1), (1. / np.sqrt(2), 1. / np.sqrt(2)),
(1. / np.sqrt(2), -1. / np.sqrt(2)), (-1. / np.sqrt(2),
1. / np.sqrt(2)), (-1. / np.sqrt(2), -1. / np.sqrt(2))]
centers = [(scale * x, scale * y) for x, y in centers]
dataset = []
for i in range(batch_size):
point = rng.randn(2) * 0.5
idx = rng.randint(8)
center = centers[idx]
point[0] += center[0]
point[1] += center[1]
dataset.append(point)
dataset = np.array(dataset, dtype="float32")
dataset /= 1.414
return dataset, np.max(dataset)
elif data == "pinwheel":
radial_std = 0.3
tangential_std = 0.1
num_classes = 5
num_per_class = batch_size // 5
rate = 0.25
rads = np.linspace(0, 2 * np.pi, num_classes, endpoint=False)
features = rng.randn(num_classes*num_per_class, 2) \
* np.array([radial_std, tangential_std])
features[:, 0] += 1.
labels = np.repeat(np.arange(num_classes), num_per_class)
angles = rads[labels] + rate * np.exp(features[:, 0])
rotations = np.stack([np.cos(angles), -np.sin(angles), np.sin(angles), np.cos(angles)])
rotations = np.reshape(rotations.T, (-1, 2, 2))
data = 2 * rng.permutation(np.einsum("ti,tij->tj", features, rotations))
return data, np.max(data)
elif data == "2spirals":
n = np.sqrt(np.random.rand(batch_size // 2, 1)) * 540 * (2 * np.pi) / 360
d1x = -np.cos(n) * n + np.random.rand(batch_size // 2, 1) * 0.5
d1y = np.sin(n) * n + np.random.rand(batch_size // 2, 1) * 0.5
x = np.vstack((np.hstack((d1x, d1y)), np.hstack((-d1x, -d1y)))) / 3
x += np.random.randn(*x.shape) * 0.1
return np.array(x, dtype='float32'), np.max(x)
elif data == "checkerboard":
x1 = np.random.rand(batch_size) * 4 - 2
x2_ = np.random.rand(batch_size) - np.random.randint(0, 2, batch_size) * 2
x2 = x2_ + (np.floor(x1) % 2)
data = np.concatenate([x1[:, None], x2[:, None]], 1) * 2
return np.array(data, dtype="float32"), np.max(data)
elif data == "line":
x = rng.rand(batch_size) * 5 - 2.5
y = x
data = np.stack((x, y), 1)
return data, np.max(data)
elif data == "cos":
x = rng.rand(batch_size) * 5 - 2.5
y = np.sin(x) * 2.5
data = np.stack((x, y), 1)
return data, np.max(data)
elif data == "tum":
mesh = np.zeros((400,400), dtype="float32")
mesh[20:140, 100:130] = 1
mesh[70:100, 120:300] = 1
mesh[140:170, 100:300] = 1
mesh[170:220, 250:300] = 1
mesh[220:250, 100:300] = 1
mesh[250:370, 100:130] = 1
mesh[280:310, 120:300] = 1
mesh[340:370, 120:300] = 1
index = np.argwhere(mesh == 1)
coordinates = index - 200
coordinates[:,1] *= -1
coordinates = coordinates / 50
index_2 = np.random.randint(len(coordinates), size=batch_size)
dataset = np.array(coordinates[index_2,:], dtype="float32")
return dataset, np.max(dataset)
else:
data = generate_2d_data("8gaussians", rng, batch_size)
return data, np.max(data)
# distribution generator for initial distribution and sampling
def generate_2d_dist(distribution, rng=None):
if rng is None:
rng = np.random.RandomState()
if distribution == "normal":
dist = tfd.MultivariateNormalDiag(loc=tf.zeros([2], tf.float32))
return dist
elif distribution == "4gaussians":
mix = [0.25, 0.25, 0.25, 0.25]
scale = 4.0/1.414
scale_diag = [0.5/1.414, 0.5/1.414]
centers = [(1, 0), (-1, 0), (0, 1), (0, -1)]
centers = tf.cast([(scale * x, scale * y) for x, y in centers], dtype=tf.float32)
dist = tfd.Mixture(
cat=tfd.Categorical(probs=mix),
components=[
tfd.MultivariateNormalDiag(
loc=centers[0],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[1],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[2],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[3],
scale_diag=scale_diag)
])
return dist
elif distribution == "8gaussians":
mix = [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125]
scale = 4.0/1.414
scale_diag = [0.5/1.414, 0.5/1.414]
centers = [(1, 0), (-1, 0), (0, 1), (0, -1), (1. / np.sqrt(2), 1. / np.sqrt(2)), (1. / np.sqrt(2), -1. / np.sqrt(2)),
(-1. / np.sqrt(2), 1. / np.sqrt(2)), (-1. / np.sqrt(2), -1. / np.sqrt(2))]
centers = tf.cast([(scale * x, scale * y) for x, y in centers], dtype=tf.float32)
dist = tfd.Mixture(
cat=tfd.Categorical(probs=mix),
components=[
tfd.MultivariateNormalDiag(
loc=centers[0],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[1],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[2],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[3],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[4],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[5],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[6],
scale_diag=scale_diag),
tfd.MultivariateNormalDiag(
loc=centers[7],
scale_diag=scale_diag)
])
return dist
else:
return generate_2d_dist("normal", rng)