def plot_gmm(params, ax, num_points=100):
angles = np.expand_dims(np.linspace(0, 2*np.pi, num_points), 1)
xs, ys = np.cos(angles), np.sin(angles)
circle_pts = np.concatenate([xs, ys], axis=1) * 2.0
for log_proportion, mean, chol in zip(*unpack_params(params)):
cur_pts = mean + np.dot(circle_pts, chol)
ax.plot(cur_pts[:, 0], cur_pts[:, 1], '-')
def build_toy_dataset(D=1, n_data=20, noise_std=0.1):
rs = npr.RandomState(0)
inputs = np.concatenate([np.linspace(0, 3, num=n_data/2),
np.linspace(6, 8, num=n_data/2)])
targets = (np.cos(inputs) + rs.randn(n_data) * noise_std) / 2.0
inputs = (inputs - 4.0) / 2.0
inputs = inputs.reshape((len(inputs), D))
return inputs, targets
def make_pinwheel_data(num_classes, num_per_class, rate=2.0, noise_std=0.001):
spoke_angles = np.linspace(0, 2*np.pi, num_classes+1)[:-1]
rs = npr.RandomState(0)
x = np.linspace(0.1, 1, num_per_class)
xs = np.concatenate([rate *x * np.cos(angle + x * rate) + noise_std * rs.randn(num_per_class)
for angle in spoke_angles])
ys = np.concatenate([rate *x * np.sin(angle + x * rate) + noise_std * rs.randn(num_per_class)
for angle in spoke_angles])
return np.concatenate([np.expand_dims(xs, 1), np.expand_dims(ys,1)], axis=1)
def make_pinwheel_data(num_spokes=5, points_per_spoke=40, rate=1.0, noise_std=0.005):
"""Make synthetic data in the shape of a pinwheel."""
spoke_angles = np.linspace(0, 2 * np.pi, num_spokes + 1)[:-1]
rs = npr.RandomState(0)
x = np.linspace(0.1, 1, points_per_spoke)
xs = np.concatenate([x * np.cos(angle + x * rate) + noise_std * rs.randn(len(x))
for angle in spoke_angles])
ys = np.concatenate([x * np.sin(angle + x * rate) + noise_std * rs.randn(len(x))
for angle in spoke_angles])
return np.concatenate([np.expand_dims(xs, 1), np.expand_dims(ys,1)], axis=1)
def build_toy_dataset(n_data=40, noise_std=0.1):
D = 1
rs = npr.RandomState(0)
inputs = np.concatenate([np.linspace(0, 2, num=n_data/2),
np.linspace(6, 8, num=n_data/2)])
inputs1 = np.concatenate([np.linspace(0, 2, num=n_data/2),
np.linspace(6, 8, num=n_data/2)])
targets = np.cos(inputs) + rs.randn(n_data) * noise_std
inputs = (inputs - 4.0) / 4.0
inputs1 = (inputs1 - 4.0) / 4.0
inputs = inputs.reshape((len(inputs), D))
inputs1 = inputs.reshape((len(inputs), D))
imputfull = np.concatenate((inputs , inputs1),axis=1)
targets = targets.reshape((len(targets), D))
return inputs, targets
