def compare_smoother_grads(lds):
init_params, pair_params, node_params = lds
symmetrize = make_unop(lambda x: (x + x.T)/2. if np.ndim(x) == 2 else x, tuple)
messages, _ = natural_filter_forward_general(*lds)
dotter = randn_like(natural_smoother_general(messages, *lds))
def py_fun(messages):
result = natural_smoother_general(messages, *lds)
assert shape(result) == shape(dotter)
return contract(dotter, result)
dense_messages, _ = _natural_filter_forward_general(
init_params, pair_params, node_params)
def cy_fun(messages):
result = _natural_smoother_general(messages, pair_params)
result = result[0][:3], result[1], result[2]
assert shape(result) == shape(dotter)
return contract(dotter, result)
result_py = py_fun(messages)
result_cy = cy_fun(dense_messages)
assert np.isclose(result_py, result_cy)
g_py = grad(py_fun)(messages)
g_cy = unpack_dense_messages(grad(cy_fun)(dense_messages))
assert allclose(g_py, g_cy)
def compare_smoother_grads(lds):
init_params, pair_params, node_params = lds
symmetrize = make_unop(
lambda x: (x + x.T) / 2. if np.ndim(x) == 2 else x, tuple)
messages, _ = natural_filter_forward_general(*lds)
dotter = randn_like(natural_smoother_general(messages, *lds))
def py_fun(messages):
result = natural_smoother_general(messages, *lds)
assert shape(result) == shape(dotter)
return contract(dotter, result)
dense_messages, _ = _natural_filter_forward_general(
init_params, pair_params, node_params)
def cy_fun(messages):
result = _natural_smoother_general(messages, pair_params)
result = result[0][:3], result[1], result[2]
assert shape(result) == shape(dotter)
return contract(dotter, result)
result_py = py_fun(messages)
result_cy = cy_fun(dense_messages)
assert np.isclose(result_py, result_cy)
g_py = grad(py_fun)(messages)
g_cy = unpack_dense_messages(grad(cy_fun)(dense_messages))
assert allclose(g_py, g_cy)
def test_compute_stats_grad():
F = make_unop(lambda x: np.require(x, np.double, 'F'), tuple)
dotter = F(randn_like(compute_stats(Ex, ExxT, ExnxT, True)))
g1 = grad(lambda x: contract(dotter, compute_stats(*x)))((Ex, ExxT, ExnxT, 1.))
g2 = _compute_stats_grad(dotter)
assert allclose(g1[:3], g2)
dotter = F(randn_like(compute_stats(Ex, ExxT, ExnxT, False)))
g1 = grad(lambda x: contract(dotter, compute_stats(*x)))((Ex, ExxT, ExnxT, 0.))
g2 = _compute_stats_grad(dotter)
assert allclose(g1[:3], g2)
init_natparam, pair_natparam = lds_natparam
nu, S, M, K = mniw.natural_to_standard(pair_natparam)
# new_M = M
# new_M = npr.randn(*M.shape)
new_M = M + 0.2*npr.randn(*M.shape)
return init_natparam, mniw.standard_to_natural(nu, S, new_M, K)
natparam = make_slds_global_natparam_from_truth(As, Bs)
hmm_natparam, lds_natparams = natparam
return hmm_natparam, map(replace_lds_natparam, lds_natparams)
def _zero(arr):
arr = np.copy(arr)
arr[120:140] = 0
return arr
zero = make_unop(_zero, tuple)
def show_states(params, data):
from svae.models.slds_svae import optimize_local_meanfield
natparam, phi, psi = params
hmm_global_natparam, lds_global_natparam = natparam
node_potentials = zero(linear_recognize(data, psi))
(hmm_stats, _), _, _ = optimize_local_meanfield(natparam, node_potentials)
pcolor_states(data, hmm_stats[2].argmax(1))
def pcolor_states(data, stateseq, cmap=plt.cm.Set1):
fig, ax = plt.subplots()
def matshow(A):
print(A)
# plt.matshow(A, cmap='plasma')
# plt.axis('off')
# plt.savefig('lds_synth_fit.png')
# plt.close()
def _zero(x):
sl = slice(len(x) // 3, 2 * (len(x) // 3))
out = np.copy(x)
out[sl] = 0
return out
zero = make_unop(lambda x: _zero(x), tuple)
def sample_states(params, data, num_samples=1):
natparam, phi, psi = params
local_natparam = lds_prior_expectedstats(natparam)
node_potentials = zero(linear_recognize(data, psi))
return natural_lds_sample(local_natparam, node_potentials,
num_samples=1).mean(1)
def sample(params, data, num_samples=1):
_, _, psi = params
C, D = phi
return np.dot(sample_states(params, data, num_samples), C.T)
from scipy.signal import sawtooth
from time import time
from svae.svae import make_gradfun
from svae.optimizers import adam, adadelta
from svae.util import zeros_like, make_unop
from svae.recognition_models import mlp_recognize, init_mlp_recognize
from svae.forward_models import mlp_loglike, mlp_decode, init_mlp_loglike
from svae.models.lds_svae import cython_run_inference as run_inference, \
make_prior_natparam, lds_prior_expectedstats
from svae.lds.lds_inference import cython_natural_lds_sample as natural_lds_sample
import svae.lds.mniw as mniw
import svae.lds.niw as niw
zero_after_prefix = lambda prefix: make_unop(
lambda x: np.concatenate((x[:prefix], np.zeros_like(x[prefix:]))), tuple)
### data generation
triangle = lambda t: sawtooth(np.pi * t, width=0.5)
make_dot_trajectory = lambda x0, v: lambda t: triangle(v * (t + (1 + x0) / 2.))
make_renderer = lambda grid, sigma: lambda x: np.exp(-1. / 2 *
(x - grid)**2 / sigma**2)
def make_dot_data(image_width,
T,
num_steps,
x0=0.0,
v=0.5,
render_sigma=0.2,
from scipy.signal import sawtooth
from time import time
from svae.svae import make_gradfun
from svae.optimizers import adam, adadelta
from svae.util import zeros_like, make_unop
from svae.recognition_models import mlp_recognize, init_mlp_recognize
from svae.forward_models import mlp_loglike, mlp_decode, init_mlp_loglike
from svae.models.lds_svae import cython_run_inference as run_inference, \
make_prior_natparam, lds_prior_expectedstats
from svae.lds.lds_inference import cython_natural_lds_sample as natural_lds_sample
import svae.lds.mniw as mniw
import svae.lds.niw as niw
zero_after_prefix = lambda prefix: make_unop(lambda x: np.concatenate(
(x[:prefix], np.zeros_like(x[prefix:]))), tuple)
### data generation
triangle = lambda t: sawtooth(np.pi*t, width=0.5)
make_dot_trajectory = lambda x0, v: lambda t: triangle(v*(t + (1+x0)/2.))
make_renderer = lambda grid, sigma: lambda x: np.exp(-1./2 * (x - grid)**2/sigma**2)
def make_dot_data(image_width, T, num_steps, x0=0.0, v=0.5, render_sigma=0.2, noise_sigma=0.1):
grid = np.linspace(-1, 1, image_width, endpoint=True)
render = make_renderer(grid, render_sigma)
x = make_dot_trajectory(x0, v)
images = np.vstack([render(x(t)) for t in np.linspace(0, T, num_steps)])
return images + noise_sigma * npr.randn(*images.shape)
return y
def matshow(A):
print(A)
# plt.matshow(A, cmap='plasma')
# plt.axis('off')
# plt.savefig('lds_synth_fit.png')
# plt.close()
def _zero(x):
sl = slice(len(x)//3, 2*(len(x)//3))
out = np.copy(x)
out[sl] = 0
return out
zero = make_unop(lambda x: _zero(x), tuple)
def sample_states(params, data, num_samples=1):
natparam, phi, psi = params
local_natparam = lds_prior_expectedstats(natparam)
node_potentials = zero(linear_recognize(data, psi))
return natural_lds_sample(local_natparam, node_potentials, num_samples=1).mean(1)
def sample(params, data, num_samples=1):
_, _, psi = params
C, D = phi
return np.dot(sample_states(params, data, num_samples), C.T)
if __name__ == "__main__":
npr.seed(0)
# new_M = npr.randn(*M.shape)
new_M = M + 0.2 * npr.randn(*M.shape)
return init_natparam, mniw.standard_to_natural(nu, S, new_M, K)
natparam = make_slds_global_natparam_from_truth(As, Bs)
hmm_natparam, lds_natparams = natparam
return hmm_natparam, map(replace_lds_natparam, lds_natparams)
def _zero(arr):
arr = np.copy(arr)
arr[120:140] = 0
return arr
zero = make_unop(_zero, tuple)
def show_states(params, data):
from svae.models.slds_svae import optimize_local_meanfield
natparam, phi, psi = params
hmm_global_natparam, lds_global_natparam = natparam
node_potentials = zero(linear_recognize(data, psi))
(hmm_stats, _), _, _ = optimize_local_meanfield(natparam, node_potentials)
pcolor_states(data, hmm_stats[2].argmax(1))
def pcolor_states(data, stateseq, cmap=plt.cm.Set1):