def demo(M=6, N=200, D=3, maxiter=100, debug=False, seed=42, rotate=True,
precompute=False, plot=True, monitor=True):
"""
Run the demo for linear state-space model.
"""
# Use deterministic random numbers
if seed is not None:
np.random.seed(seed)
# Get data
(y, f) = simulate_data(M, N)
# Add missing values randomly
mask = random.mask(M, N, p=0.3)
# Add missing values to a period of time
mask[:,30:80] = False
y[~mask] = np.nan # BayesPy doesn't require this. Just for plotting.
# Run inference
Q = infer(y, D,
mask=mask,
rotate=rotate,
debug=debug,
monitor=monitor,
maxiter=maxiter)
if plot:
# Show results
plt.figure()
bpplt.timeseries_normal(Q['F'], scale=2)
bpplt.timeseries(f, linestyle='-', color='b')
bpplt.timeseries(y, linestyle='None', color='r', marker='.')
plt.show()
def demo(M=6, N=200, D=3, maxiter=100, debug=False, seed=42, rotate=True,
precompute=False, plot=True, monitor=True):
"""
Run the demo for linear state-space model.
"""
# Use deterministic random numbers
if seed is not None:
np.random.seed(seed)
# Get data
(y, f) = simulate_data(M, N)
# Add missing values randomly
mask = random.mask(M, N, p=0.3)
# Add missing values to a period of time
mask[:,30:80] = False
y[~mask] = np.nan # BayesPy doesn't require this. Just for plotting.
# Run inference
Q = infer(y, D,
mask=mask,
rotate=rotate,
debug=debug,
monitor=monitor,
maxiter=maxiter)
if plot:
# Show results
plt.figure()
bpplt.timeseries_normal(Q['F'], scale=2)
bpplt.timeseries(f, linestyle='-', color='b')
bpplt.timeseries(y, linestyle='None', color='r', marker='.')
def run(M=10,
N=100,
D_y=3,
D=5,
seed=42,
rotate=False,
maxiter=100,
debug=False,
plot=True):
if seed is not None:
np.random.seed(seed)
# Generate data
w = np.random.normal(0, 1, size=(M, 1, D_y))
x = np.random.normal(0, 1, size=(1, N, D_y))
f = misc.sum_product(w, x, axes_to_sum=[-1])
y = f + np.random.normal(0, 0.2, size=(M, N))
# Construct model
(Y, F, W, X, tau, alpha) = model(M, N, D)
# Data with missing values
mask = random.mask(M, N, p=0.5) # randomly missing
y[~mask] = np.nan
Y.observe(y, mask=mask)
# Construct inference machine
Q = VB(Y, W, X, tau, alpha)
# Initialize some nodes randomly
X.initialize_from_random()
W.initialize_from_random()
# Run inference algorithm
if rotate:
# Use rotations to speed up learning
rotW = transformations.RotateGaussianARD(W, alpha)
rotX = transformations.RotateGaussianARD(X)
R = transformations.RotationOptimizer(rotW, rotX, D)
for ind in range(maxiter):
Q.update()
if debug:
R.rotate(check_bound=True, check_gradient=True)
else:
R.rotate()
else:
# Use standard VB-EM alone
Q.update(repeat=maxiter)
# Plot results
if plot:
plt.figure()
bpplt.timeseries_normal(F, scale=2)
bpplt.timeseries(f, color='g', linestyle='-')
bpplt.timeseries(y, color='r', linestyle='None', marker='+')
def run(M=10, N=100, D_y=3, D=5, seed=42, rotate=False, maxiter=100, debug=False):
if seed is not None:
np.random.seed(seed)
# Generate data
w = np.random.normal(0, 1, size=(M,1,D_y))
x = np.random.normal(0, 1, size=(1,N,D_y))
f = misc.sum_product(w, x, axes_to_sum=[-1])
y = f + np.random.normal(0, 0.2, size=(M,N))
# Construct model
(Y, F, W, X, tau, alpha) = model(M, N, D)
# Data with missing values
mask = random.mask(M, N, p=0.5) # randomly missing
y[~mask] = np.nan
Y.observe(y, mask=mask)
# Construct inference machine
Q = VB(Y, W, X, tau, alpha)
# Initialize some nodes randomly
X.initialize_from_random()
W.initialize_from_random()
# Run inference algorithm
if rotate:
# Use rotations to speed up learning
rotW = transformations.RotateGaussianARD(W, alpha)
rotX = transformations.RotateGaussianARD(X)
R = transformations.RotationOptimizer(rotW, rotX, D)
for ind in range(maxiter):
Q.update()
if debug:
R.rotate(check_bound=True,
check_gradient=True)
else:
R.rotate()
else:
# Use standard VB-EM alone
Q.update(repeat=maxiter)
# Plot results
plt.figure()
bpplt.timeseries_normal(F, scale=2)
bpplt.timeseries(f, color='g', linestyle='-')
bpplt.timeseries(y, color='r', linestyle='None', marker='+')
plt.show()
def run(M=10,
N=100,
D_y=3,
D=5,
seed=42,
rotate=False,
maxiter=1000,
debug=False,
plot=True):
if seed is not None:
np.random.seed(seed)
# Generate data
w = np.random.normal(0, 1, size=(M, 1, D_y))
x = np.random.normal(0, 1, size=(1, N, D_y))
f = misc.sum_product(w, x, axes_to_sum=[-1])
y = f + np.random.normal(0, 0.1, size=(M, N))
# Construct model
Q = model(M, N, D)
# Data with missing values
mask = random.mask(M, N, p=0.5) # randomly missing
y[~mask] = np.nan
Q['Y'].observe(y, mask=mask)
# Run inference algorithm
if rotate:
# Use rotations to speed up learning
rotW = transformations.RotateGaussianARD(Q['W'], Q['alpha'])
rotX = transformations.RotateGaussianARD(Q['X'])
R = transformations.RotationOptimizer(rotW, rotX, D)
if debug:
Q.callback = lambda: R.rotate(check_bound=True,
check_gradient=True)
else:
Q.callback = R.rotate
# Use standard VB-EM alone
Q.update(repeat=maxiter)
# Plot results
if plot:
plt.figure()
bpplt.timeseries_normal(Q['F'], scale=2)
bpplt.timeseries(f, color='g', linestyle='-')
bpplt.timeseries(y, color='r', linestyle='None', marker='+')
def run(M=10, N=100, D_y=3, D=5, seed=42, rotate=False, maxiter=1000, debug=False, plot=True):
if seed is not None:
np.random.seed(seed)
# Generate data
w = np.random.normal(0, 1, size=(M,1,D_y))
x = np.random.normal(0, 1, size=(1,N,D_y))
f = misc.sum_product(w, x, axes_to_sum=[-1])
y = f + np.random.normal(0, 0.1, size=(M,N))
# Construct model
Q = model(M, N, D)
# Data with missing values
mask = random.mask(M, N, p=0.5) # randomly missing
y[~mask] = np.nan
Q['Y'].observe(y, mask=mask)
# Run inference algorithm
if rotate:
# Use rotations to speed up learning
rotW = transformations.RotateGaussianARD(Q['W'], Q['alpha'])
rotX = transformations.RotateGaussianARD(Q['X'])
R = transformations.RotationOptimizer(rotW, rotX, D)
if debug:
Q.callback = lambda : R.rotate(check_bound=True,
check_gradient=True)
else:
Q.callback = R.rotate
# Use standard VB-EM alone
Q.update(repeat=maxiter)
# Plot results
if plot:
plt.figure()
bpplt.timeseries_normal(Q['F'], scale=2)
bpplt.timeseries(f, color='g', linestyle='-')
bpplt.timeseries(y, color='r', linestyle='None', marker='+')
def demo(N=1000, D=5, K=4, seed=42, maxiter=200, rotate=True, debug=False,
precompute=False, plot=True):
# Seed for random number generator
if seed is not None:
np.random.seed(seed)
# Create data
(y, f) = simulate_data(N)
# Create some gaps
mask_gaps = misc.trues(N)
for m in range(100, N, 140):
start = m
end = min(m+15, N-1)
mask_gaps[start:end] = False
# Randomly missing values
mask_random = np.logical_or(random.mask(N, p=0.8),
np.logical_not(mask_gaps))
# Remove the observations
mask = np.logical_and(mask_gaps, mask_random)
y[~mask] = np.nan # BayesPy doesn't require NaNs, they're just for plotting.
# Add row axes
y = y[None,...]
f = f[None,...]
mask = mask[None,...]
mask_gaps = mask_gaps[None,...]
mask_random = mask_random[None,...]
# Run the method
Q = infer(y, D, K,
mask=mask,
maxiter=maxiter,
rotate=rotate,
debug=debug,
precompute=precompute,
update_hyper=10,
start_rotating_weights=20,
monitor=True)
if plot:
# Plot observations
plt.figure()
bpplt.timeseries_normal(Q['F'], scale=2)
bpplt.timeseries(f, linestyle='-', color='b')
bpplt.timeseries(y, linestyle='None', color='r', marker='.')
plt.ylim([-2, 2])
# Plot latent space
Q.plot('X')
# Plot mixing weight space
Q.plot('S')
# Compute RMSE
rmse_random = misc.rmse(Q['Y'].get_moments()[0][~mask_random],
f[~mask_random])
rmse_gaps = misc.rmse(Q['Y'].get_moments()[0][~mask_gaps],
f[~mask_gaps])
print("RMSE for randomly missing values: %f" % rmse_random)
print("RMSE for gap values: %f" % rmse_gaps)
plt.show()
def demo(N=1000,
D=5,
K=4,
seed=42,
maxiter=200,
rotate=True,
debug=False,
precompute=False,
plot=True):
# Seed for random number generator
if seed is not None:
np.random.seed(seed)
# Create data
(y, f) = simulate_data(N)
# Create some gaps
mask_gaps = misc.trues(N)
for m in range(100, N, 140):
start = m
end = min(m + 15, N - 1)
mask_gaps[start:end] = False
# Randomly missing values
mask_random = np.logical_or(random.mask(N, p=0.8),
np.logical_not(mask_gaps))
# Remove the observations
mask = np.logical_and(mask_gaps, mask_random)
y[~mask] = np.nan # BayesPy doesn't require NaNs, they're just for plotting.
# Add row axes
y = y[None, ...]
f = f[None, ...]
mask = mask[None, ...]
mask_gaps = mask_gaps[None, ...]
mask_random = mask_random[None, ...]
# Run the method
Q = infer(y,
D,
K,
mask=mask,
maxiter=maxiter,
rotate=rotate,
debug=debug,
precompute=precompute,
update_hyper=10,
start_rotating_weights=20,
monitor=True)
if plot:
# Plot observations
plt.figure()
bpplt.timeseries_normal(Q['F'], scale=2)
bpplt.timeseries(f, linestyle='-', color='b')
bpplt.timeseries(y, linestyle='None', color='r', marker='.')
plt.ylim([-2, 2])
# Plot latent space
Q.plot('X')
# Plot mixing weight space
Q.plot('S')
# Compute RMSE
rmse_random = misc.rmse(Q['Y'].get_moments()[0][~mask_random],
f[~mask_random])
rmse_gaps = misc.rmse(Q['Y'].get_moments()[0][~mask_gaps], f[~mask_gaps])
print("RMSE for randomly missing values: %f" % rmse_random)
print("RMSE for gap values: %f" % rmse_gaps)
