def matmul_maybe_select(A, B):
"""Perform Matrix multiplication C = A * B but A could be an integer id vector.
If A is an integer vector, we treat it as multiplying a one-hot encoded tensor.
In this case, the expensive dense matrix multiply can be replaced by a much
cheaper index lookup.
For example,
::
A = [2, 0, 1],
B = [[0.1, 0.2],
[0.3, 0.4],
[0.5, 0.6]]
then matmul_maybe_select(A, B) is equivalent to
::
[[0, 0, 1], [[0.1, 0.2],
[1, 0, 0], * [0.3, 0.4],
[0, 1, 0]] [0.5, 0.6]]
In all other cases, perform a normal matmul.
Parameters
----------
A : torch.Tensor
lhs tensor
B : torch.Tensor
rhs tensor
Returns
-------
C : torch.Tensor
result tensor
"""
if A.dtype == jnp.int64 and len(A.shape) == 1:
return jnp.take(B, A, 0)
else:
return jnp.matmul(A, B)
def extend(prev_three_coords, point, multi_m):
"""
Aligns an atom or an entire fragment depending on value of `multi_m`
with the preceding three atoms.
:param prev_three_coords: Named tuple storing the last three atom
coordinates ("a", "b", "c") where "c" is the current end of the
structure (i.e. closest to the atom/ fragment that will be added now).
Shape NUM_DIHEDRALS x [NUM_FRAGS/0, BATCH_SIZE, NUM_DIMENSIONS].
First rank depends on value of `multi_m`.
:param point: Point describing the atom that is added to the structure.
Shape [NUM_FRAGS/FRAG_SIZE, BATCH_SIZE, NUM_DIMENSIONS]
First rank depends on value of `multi_m`.
:param multi_m: If True, a single atom is added to the chain for
multiple fragments in parallel. If False, an single fragment is added.
Note the different parameter dimensions.
:return: Coordinates of the atom/ fragment.
"""
# Normalize rows: https://necromuralist.github.io/neural_networks/posts/normalizing-with-numpy/
Xbc = (prev_three_coords.c - prev_three_coords.b)
bc = Xbc / onp.linalg.norm(Xbc, axis=-1, keepdims=True)
Xn = onp.cross(prev_three_coords.b - prev_three_coords.a,
bc,
axisa=-1,
axisb=-1,
axisc=-1)
n = Xn / onp.linalg.norm(Xn, axis=-1, keepdims=True)
if multi_m: # multiple fragments, one atom at a time
m = onp.transpose(onp.stack([bc, onp.cross(n, bc), n]),
(1, 2, 3, 0))
else: # single fragment, reconstructed entirely at once.
s = point.shape + (3, ) # +
m = onp.transpose(onp.stack([bc, onp.cross(n, bc), n]), (1, 2, 0))
m = onp.tile(m, (s[0], 1, 1)).reshape(s)
coord = onp.squeeze(onp.matmul(m, onp.expand_dims(point, axis=3)),
axis=3) + prev_three_coords.c
return coord
def group_utility(particle_weights,
particles,
groups,
group_sensitivities,
group_specificities,
utility_fun):
"""Compute the utility of a set of groups.
This function computes the utility of a set of groups, given a distribution
over the population status encoded as a weighted sum of Dirac measures on
particles, the specificities and sensitivities of tests, and a utility
function.
Args:
particle_weights: weights of particles
particles: particles summarizing belief about infection status
groups: set of groups to be tested
group_sensitivities: sensitivies of test for each group
group_specificities: specificities of test for each group
utility_fun: a utility function that takes as input (particle_weights,
particles) and output the utility of the distribution
Returns:
The expected utility (over the test results) of the posterior
"""
num_groups = groups.shape[0]
proba_y_is_one_given_x = (np.matmul(particles, np.transpose(groups))
* (group_sensitivities + group_specificities - 1)
+ 1.0 - group_specificities)
proba_y_is_one_given_x = np.expand_dims(proba_y_is_one_given_x, axis=2)
test_res = np.array(list(itertools.product([0, 1], repeat=num_groups)))
test_res = np.expand_dims(np.transpose(test_res), axis=0)
proba_y_given_x = np.product(test_res * proba_y_is_one_given_x + (1-test_res)
* (1-proba_y_is_one_given_x), axis=1)
proba_y_and_x = proba_y_given_x * np.expand_dims(particle_weights, 1)
proba_y = np.sum(proba_y_and_x, axis=0)
proba_x_given_y = proba_y_and_x / np.expand_dims(proba_y, 0)
vutility_fun = jax.vmap(utility_fun, [1, None])
utility_x_given_y = vutility_fun(proba_x_given_y, particles)
return np.dot(proba_y, utility_x_given_y)
def ghq_2d_separable(f, locs=None, Ls=None, pts_and_weights=None, degree=5):
"""Computes an estimate of E[f(x)] using Gauss-Hermite quadrature.
Args:
f: The function to estimate E[f(x)] for. Must accept a [data_dim] input
point and return a scalar or vector.
loc: A vector of shape [data_dim], the means of the Normal distributions to
integrate against.
cov: A PSD matrix of shape [data_dim, data_dim], the covariance matrix of
the normal distributions to integrate against.
Returns:
The estimate of E[f(x)], a scalar.
"""
if pts_and_weights is None:
n = locs.shape[0]
# [degree]
xs_1d, ws_1d = np.polynomial.hermite.hermgauss(degree)
# [degree^2, 2]
xs_2d = np.array(list(itertools.product(xs_1d, xs_1d)))
# [degree^2, n, 2]
xs_nd = np.stack([xs_2d]*n, axis=1)
# [degree^2]
ws_2d = np.prod(np.array(list(itertools.product(ws_1d, ws_1d))), axis=1)
# [degree^2]
ws_nd = ws_2d * n
pts = np.matmul(xs_nd[:,:,np.newaxis,:], np.transpose(Ls[np.newaxis,:,:,:], axes=(0,1,3,2)))
pts = np.squeeze(pts, axis=2)
# [degree^2, n, 2]
pts = pts*np.sqrt(2.) + locs[np.newaxis,:,:]
else:
pts, ws_nd = pts_and_weights
n = pts.shape[1]
# [degree^2, n, ...]
gs = np.array([f(pts[i]) for i in range(pts.shape[0])])
# [degree^2, ...]
gs = np.sum(gs, axis=1)
ws_shape = [gs.shape[0]] + [1]*(gs.ndim-1)
return np.sum(gs * ws_nd.reshape(ws_shape), axis=0)/(n*np.pi), (pts, ws_nd)
def model(self, batch):
XL, XH = batch['XL'], batch['XH']
y = batch['y']
NL, NH = XL.shape[0], XH.shape[0]
D = XH.shape[1]
# set uninformative log-normal priors for low-fidelity kernel
var_L = sample('kernel_var_L',
dist.LogNormal(0.0, 1.0),
sample_shape=(1, ))
length_L = sample('kernel_length_L',
dist.LogNormal(0.0, 1.0),
sample_shape=(D, ))
theta_L = np.concatenate([var_L, length_L])
# set uninformative log-normal priors for high-fidelity kernel
var_H = sample('kernel_var_H',
dist.LogNormal(0.0, 1.0),
sample_shape=(1, ))
length_H = sample('kernel_length_H',
dist.LogNormal(0.0, 1.0),
sample_shape=(D, ))
theta_H = np.concatenate([var_H, length_H])
# prior for rho
rho = sample('rho', dist.Normal(0.0, 10.0), sample_shape=(1, ))
# Compute kernels
K_LL = self.kernel(XL, XL, theta_L) + np.eye(NL) * 1e-8
K_LH = rho * self.kernel(XL, XH, theta_L)
K_HH = rho**2 * self.kernel(XH, XH, theta_L) + \
self.kernel(XH, XH, theta_H) + np.eye(NH)*1e-8
K = np.vstack((np.hstack((K_LL, K_LH)), np.hstack((K_LH.T, K_HH))))
L = cholesky(K, lower=True)
# Generate latent function
beta_L = sample('beta_L', dist.Normal(0.0, 1.0))
beta_H = sample('beta_H', dist.Normal(0.0, 1.0))
eta_L = sample('eta_L', dist.Normal(0.0, 1.0), sample_shape=(NL, ))
eta_H = sample('eta_H', dist.Normal(0.0, 1.0), sample_shape=(NH, ))
beta = np.concatenate([beta_L * np.ones(NL), beta_H * np.ones(NH)])
eta = np.concatenate([eta_L, eta_H])
f = np.matmul(L, eta) + beta
# Bernoulli likelihood
sample('y', dist.Bernoulli(logits=f), obs=y)
def __getitem__(self, slice_spec):
"""Basic indexing, returns a TTMatrix containing the specified element / slice."""
d = self.ndim
if len(slice_spec) != 2 * d:
raise ValueError('Expected %d indices, got %d' %
(2 * d, len(slice_spec)))
for i in range(d):
if isinstance(slice_spec[i], slice) != isinstance(
slice_spec[d + i], slice):
raise ValueError(
'Elements i_%d and j_%d should be the same type, '
'instead: %s and %s.' %
(i, i, slice_spec[i], slice_spec[d + i]))
new_tt_cores = []
remainder = None
for i in range(self.ndim):
curr_core = self.tt_cores[i]
sliced_core = curr_core[..., :, slice_spec[i],
slice_spec[d + i], :]
if len(curr_core.shape) != len(sliced_core.shape):
# These indices are specified exactly and we want to collapse this axis.
if remainder is None:
remainder = sliced_core
else:
remainder = jnp.matmul(remainder, sliced_core)
else:
if remainder is not None:
# Add reminder from the previous collapsed cores to the current core.
sliced_core = jnp.einsum('...ab,...bijd->...aijd',
remainder, sliced_core)
remainder = None
new_tt_cores.append(sliced_core)
if remainder is not None:
# The reminder obtained from collapsing the last cores.
new_tt_cores[-1] = jnp.einsum('...aijb,...bd->...aijd',
new_tt_cores[-1], remainder)
remainder = None
return TTMatrix(new_tt_cores)
def driven_mu_cov(b, f, theta, dt):
"""
compute the twisted mean and covariance matrix of twisting parameters
arguments
b : jnp.array(N)
twisting vector
f : jnp.array(N)
push vector
theta : jnp.array(N,N)
twist matrix
dt : float
time increment
returns
mu : jnp.array(N)
twisted mean
cov : jnp.array(N,N)
twisted covariance matrix
"""
mu = jnp.matmul(theta, f - dt*b)
cov = dt*theta
return mu, cov
def pool_forward(X, W, size=2, stride=2):
n, d, h, w = X.shape
h_out = (h - size) // stride + 1
w_out = (w - size) // stride + 1
h_out, w_out = int(h_out), int(w_out)
X_reshaped = X.reshape(n * d, 1, h, w)
X_col = im2col_indices(X_reshaped, size, size, padding=0, stride=stride)
n_filter, v, h_filter, w_filter = W.shape
W_col = W.reshape(n_filter, -1)
out = jnp.matmul(W_col, X_col)
out = jnp.mean(out,axis=0)
out = out.reshape(h_out, w_out, n, d)
out = jnp.transpose(out, (2, 3, 0, 1))
out = jnp.array(out, dtype='float32')
return out
def test_shape_error(self):
"""Some of the examples from the README."""
with self.assertRaisesRegex(
TypeError,
re.escape("add got incompatible shapes for broadcasting: (v,), (4,)")):
self.CheckShapePolymorphism(
lambda x, y: x + y,
input_signature=[tf.TensorSpec([None]),
tf.TensorSpec([4])],
polymorphic_shapes=["(v,)", "(4,)"],
expected_output_signature=tf.TensorSpec([None]))
four_ones = np.ones((4,))
# We get the error even if we use correct actual arguments
with self.assertRaisesRegex(
TypeError,
re.escape("add got incompatible shapes for broadcasting: (v,), (4,)")):
jax2tf.convert(
lambda x, y: x + y, polymorphic_shapes=["(v,)", "(4,)"])(four_ones,
four_ones)
with self.assertRaisesRegex(core.InconclusiveDimensionOperation,
re.escape("Shape variable comparison v == 4 is inconclusive")):
jax2tf.convert(lambda x: jnp.matmul(x, x),
polymorphic_shapes=["(v, 4)"])(np.ones((4, 4)))
with self.assertRaisesRegex(TypeError,
re.escape("unsupported operand type(s) for *: 'DimVar' and 'int'")):
jax2tf.convert(lambda x: jnp.reshape(x, np.prod(x.shape)),
polymorphic_shapes=["(b, ...)"])(np.ones((3, 4, 5)))
jax2tf.convert(lambda x: jnp.reshape(x, (x.shape[0], np.prod(x.shape[1:]))),
polymorphic_shapes=["(b, _, _)"])(np.ones((3, 4, 5)))
with self.assertRaisesRegex(
TypeError,
re.escape("unsupported operand type(s) for /: 'TensorFlowTracer' and 'DimVar'")):
jax2tf.convert(lambda x: jnp.sum(x, axis=0) / x.shape[0],
polymorphic_shapes=["(v, _)"])(np.ones((4, 4)))
def test_full_range_integer_weights_with_float_scale_should_give_close_output(
self, weight_prec):
# If weights are ints (already quantized) with
# max(abs(weights[:, ch])) == 2**(prec-1)-1 in each channel
# and if these integer weights are multiplied by a float scale,
# the resulting error should still be very small (just float rounding).
num_features = 256
input_dim = 1024
inputs = random.uniform(self.rng_key, shape=(2, input_dim))
model, state = self.init_model_with_1_layer(
inputs, num_features, weight_prec=weight_prec)
minval = -2**(weight_prec - 1) + 1
maxval = 2**(weight_prec - 1) - 1
full_range_integer_weights = random.randint(self.rng_key,
(input_dim, num_features),
minval, maxval + 1)
# manually set one value in each output dim of weights to be exactly maxval
full_range_integer_weights = jax.ops.index_update(
full_range_integer_weights, jax.ops.index[0, :], maxval)
float_scale = jax.random.uniform(self.rng_key, (1, num_features))
state = state.unfreeze()
state['params']['kernel'] = full_range_integer_weights * float_scale
state = flax.core.freeze(state)
outputs = model.apply(state, inputs, padding_mask=None)
exp_outputs = jnp.matmul(inputs, state['params']['kernel'])
# TODO(wanglisa): Determine how much noise is expected for following test.
# We know that the noise should be proportional to the square root of
# input_dim and inversely proportional to 2**weight_prec.
# The following tol_const was obtained experimentally and should be derived
# more systematically.
tol_const = 8e-04
onp.testing.assert_allclose(
outputs,
exp_outputs,
rtol=jnp.sqrt(input_dim) * 2**(-weight_prec) * tol_const)
def build(self, sc: OrderedDict):
gp_matrices = OrderedDict()
gp_matrices["l_u_beta"] = aux_math.matrix_diag_transform(np.tril(sc["S_u_beta"]), nn.softplus)
gp_matrices["l_u_gamma"] = aux_math.matrix_diag_transform(np.tril(sc["S_u_gamma"]), nn.softplus)
# Kernel
gp_matrices["k_beta_beta"] = self.kernel.matrix(sc["X_u_beta"], sc["X_u_beta"])
gp_matrices["l_beta_beta"] = scipy.linalg.cholesky(gp_matrices["k_beta_beta"] +
self.jitter * np.eye(self.n_inducing_beta),
lower=True)
k_gamma_gamma = \
self.kernel.matrix(sc["X_u_gamma"], sc["X_u_gamma"]) + self.jitter * np.eye(self.n_inducing_gamma)
k_beta_gamma = self.kernel.matrix(sc["X_u_beta"], sc["X_u_gamma"])
l_beta_inv_k_beta_gamma = scipy.linalg.solve_triangular(gp_matrices["l_beta_beta"], k_beta_gamma,
lower=True)
gp_matrices["l_beta_inv_k_beta_gamma"] = l_beta_inv_k_beta_gamma
c_gamma_gamma = k_gamma_gamma - np.matmul(
np.transpose(gp_matrices["l_beta_inv_k_beta_gamma"], (0, 2, 1)), gp_matrices["l_beta_inv_k_beta_gamma"])
gp_matrices["l_gamma_gamma"] = scipy.linalg.cholesky(c_gamma_gamma +
self.jitter * np.eye(self.n_inducing_gamma), lower=True)
# U_beta_dists
gp_matrices["q_u_beta_mean"] = sc["mu_u_beta"]
gp_matrices["q_u_beta_tril"] = gp_matrices["l_u_beta"]
gp_matrices["p_u_beta_mean"] = np.zeros([self.ndims_out, self.n_inducing_beta])
gp_matrices["p_u_beta_tril"] = gp_matrices["l_beta_beta"]
# U_gamma_dists
gp_matrices["q_u_gamma_mean"] = sc["mu_u_gamma"]
gp_matrices["q_u_gamma_tril"] = gp_matrices["l_u_gamma"]
gp_matrices["p_u_gamma_mean"] = np.zeros([self.ndims_out, self.n_inducing_gamma])
gp_matrices["p_u_gamma_tril"] = gp_matrices["l_gamma_gamma"]
return gp_matrices
def __call__(self, x, features, bias=True, kernel_init=None):
def mu_init(key, shape):
# Initialization of mean noise parameters (Section 3.2)
low = -1 / jnp.power(x.shape[0], 0.5)
high = 1 / jnp.power(x.shape[0], 0.5)
return jax.random.uniform(key,
minval=low,
maxval=high,
shape=shape)
def sigma_init(key, shape, dtype=jnp.float32): # pylint: disable=unused-argument
# Initialization of sigma noise parameters (Section 3.2)
return jnp.ones(shape, dtype) * (0.1 / onp.sqrt(x.shape[0]))
if self.eval_mode:
# Turn off noise during evaluation
w_epsilon = onp.zeros(shape=(x.shape[0], features),
dtype=onp.float32)
b_epsilon = onp.zeros(shape=(features, ), dtype=onp.float32)
else:
# Factored gaussian noise in (10) and (11) in Fortunato et al. (2018).
p = NoisyNetwork.sample_noise(self.rng_key, [x.shape[0], 1])
q = NoisyNetwork.sample_noise(self.rng_key, [1, features])
f_p = NoisyNetwork.f(p)
f_q = NoisyNetwork.f(q)
w_epsilon = f_p * f_q
b_epsilon = jnp.squeeze(f_q)
# See (8) and (9) in Fortunato et al. (2018) for output computation.
w_mu = self.param('kernel_mu', mu_init, (x.shape[0], features))
w_sigma = self.param('kernel_sigma', sigma_init,
(x.shape[0], features))
w = w_mu + jnp.multiply(w_sigma, w_epsilon)
ret = jnp.matmul(x, w)
b_mu = self.param('bias_mu', mu_init, (features, ))
b_sigma = self.param('bias_sigma', sigma_init, (features, ))
b = b_mu + jnp.multiply(b_sigma, b_epsilon)
return jnp.where(bias, ret + b, ret)
def mean(
gp: NonConjugatePosterior,
param: dict,
test_inputs: Array,
train_inputs: Array,
train_outputs: Array,
):
ell, alpha, nu = param["lengthscale"], param["variance"], param["latent"]
Kff = gram(gp.prior.kernel, train_inputs, param)
Kfx = cross_covariance(gp.prior.kernel, train_inputs, test_inputs, param)
Kxx = gram(gp.prior.kernel, test_inputs, param)
L = jnp.linalg.cholesky(Kff + jnp.eye(train_inputs.shape[0]) * 1e-6)
A = solve_triangular(L, Kfx.T, lower=True)
latent_var = Kxx - jnp.sum(jnp.square(A), -2)
latent_mean = jnp.matmul(A.T, nu)
lvar = jnp.diag(latent_var)
moment_fn = predictive_moments(gp.likelihood)
pred_rv = moment_fn(latent_mean.ravel(), lvar)
return pred_rv.mean()
def _linear_correlate_color(t):
"""Multiply input by sqrt of empirical (ImageNet) color correlation matrix.
If you interpret t's innermost dimension as describing colors in a
decorrelated version of the color space (which is a very natural way to
describe colors -- see discussion in Feature Visualization article) the way
to map back to normal colors is multiply the square root of your color
correlations.
Args:
t: input whitened color array, with trailing dimension 3.
Returns:
t_correlated: RGB color array.
"""
assert t.shape[-1] == 3
t_flat = np.reshape(t, [-1, 3])
color_correlation_normalized = (color_correlation_svd_sqrt /
max_norm_svd_sqrt)
t_flat = np.matmul(t_flat, color_correlation_normalized.T)
t_correlated = np.reshape(t_flat, t.shape)
return t_correlated
def test_shape_error(self):
"""Some of the examples from the README."""
raise unittest.SkipTest("Failing after fixing Poly unsoundness #4878")
with self.assertRaisesRegex(
TypeError,
re.escape(
"add got incompatible shapes for broadcasting: (v,), (4,)")
):
self.CheckShapePolymorphism(
lambda x, y: x + y,
input_signature=[tf.TensorSpec([None]),
tf.TensorSpec([4])],
in_shapes=["(v,)", "(4,)"],
expected_output_signature=tf.TensorSpec([None]))
four_ones = np.ones((4, ))
# We get the error even if we use correct actual arguments
with self.assertRaisesRegex(
TypeError,
re.escape(
"add got incompatible shapes for broadcasting: (v,), (4,)")
):
jax2tf.convert(lambda x, y: x + y,
in_shapes=["(v,)", "(4,)"])(four_ones, four_ones)
with self.assertRaisesRegex(
TypeError,
re.escape(
"dot_general requires contracting dimensions to have the same shape, got [4] and [v]."
)):
jax2tf.convert(lambda x: jnp.matmul(x, x),
in_shapes=["(v, 4)"])(np.ones((4, 4)))
# TODO: this is an opportunity to improve the translation, should not error
with self.assertRaisesRegex(
TypeError,
"Only integers, .* tensors are valid indices, got 0"):
jax2tf.convert(lambda x: jnp.split(x, 2),
in_shapes=["(2*v,)"])(four_ones)
def parametric(subposteriors, diagonal=False):
"""
Merges subposteriors following (embarrassingly parallel) parametric Monte Carlo algorithm.
**References:**
1. *Asymptotically Exact, Embarrassingly Parallel MCMC*,
Willie Neiswanger, Chong Wang, Eric Xing
:param list subposteriors: a list in which each element is a collection of samples.
:param bool diagonal: whether to compute weights using variance or covariance, defaults to
`False` (using covariance).
:return: the estimated mean and variance/covariance parameters of the joined posterior
"""
joined_subposteriors = tree_multimap(lambda *args: np.stack(args),
*subposteriors)
joined_subposteriors = vmap(
vmap(lambda sample: ravel_pytree(sample)[0]))(joined_subposteriors)
submeans = np.mean(joined_subposteriors, axis=1)
if diagonal:
weights = vmap(lambda x: 1 / np.var(x, ddof=1, axis=0))(
joined_subposteriors)
var = 1 / np.sum(weights, axis=0)
normalized_weights = var * weights
# comparing to consensus implementation, we compute weighted mean here
mean = np.einsum('ij,ij->j', normalized_weights, submeans)
return mean, var
else:
weights = vmap(lambda x: np.linalg.inv(np.cov(x.T)))(
joined_subposteriors)
cov = np.linalg.inv(np.sum(weights, axis=0))
normalized_weights = np.matmul(cov, weights)
# comparing to consensus implementation, we compute weighted mean here
mean = np.einsum('ijk,ik->j', normalized_weights, submeans)
return mean, cov
def model_wo_c(T, T_forecast, x, obs=None):
# Define priors over beta, tau, sigma, z_1 (keep the shapes in mind)
W = numpyro.sample(name="W",
fn=dist.Normal(loc=jnp.zeros((2, 4)),
scale=jnp.ones((2, 4))))
beta = numpyro.sample(name="beta",
fn=dist.Normal(loc=jnp.zeros(2), scale=jnp.ones(2)))
tau = numpyro.sample(name="tau", fn=dist.HalfCauchy(scale=jnp.ones(2)))
sigma = numpyro.sample(name="sigma", fn=dist.HalfCauchy(scale=0.1))
z_prev = numpyro.sample(name="z_1",
fn=dist.Normal(loc=jnp.zeros(2),
scale=jnp.ones(2)))
# Define LKJ prior
L_Omega = numpyro.sample("L_Omega", dist.LKJCholesky(2, 10.0))
Sigma_lower = jnp.matmul(
jnp.diag(jnp.sqrt(tau)),
L_Omega) # lower cholesky factor of the covariance matrix
noises = numpyro.sample(
"noises",
fn=dist.MultivariateNormal(loc=jnp.zeros(2), scale_tril=Sigma_lower),
sample_shape=(T + T_forecast, ),
)
# Propagate the dynamics forward using jax.lax.scan
carry = (W, beta, z_prev, tau)
z_collection = [z_prev]
carry, zs_exp = lax.scan(f, carry, (x, noises), T + T_forecast)
z_collection = jnp.concatenate((jnp.array(z_collection), zs_exp), axis=0)
obs_mean = z_collection[:T, 1]
pred_mean = z_collection[T:, 1]
# Sample the observed y (y_obs)
numpyro.sample(name="y_obs",
fn=dist.Normal(loc=obs_mean, scale=sigma),
obs=obs)
numpyro.sample(name="y_pred",
fn=dist.Normal(loc=pred_mean, scale=sigma),
obs=None)
def _combine_gradients(self, px_grads_list, px_loss):
""" Combines the per-example gradients into the batch gradient and
applies the batch gradient transformation given as
`batch_grad_manipulation_fn`.
This is the third step of a full update iteration.
:param px_grads_list: List of transformed per-example gradients as returned
by `_apply_per_example_gradient_transformations`
:param px_loss: Array of per-example loss values as output by
`_compute_per_example_gradients`.
:returns: tuple consisting of the updated svi state, the loss value for
the batch and a jax tree of batch gradients per parameter site.
"""
# get total loss and loss combiner vjp func
loss_val, loss_combine_vjp = jax.vjp(self.loss.combiner_fn, px_loss)
# loss_combine_vjp gives us the backward differentiation function
# from combined loss to per-example losses. we use it to get the
# (1xbatch_size) Jacobian and construct a function that takes
# per-example gradients and left-multiplies them with that jacobian
# to get the final combined gradient
loss_jacobian = jnp.reshape(loss_combine_vjp(jnp.array(1.))[0], (1, -1))
# loss_vjp = lambda px_grads: jnp.sum(jnp.multiply(loss_jacobian, px_grads))
loss_vjp = lambda px_grads: jnp.matmul(loss_jacobian, px_grads)
# we map the loss combination vjp func over all secondary dimensions
# of gradient sites. This is necessary since some gradient
# sites might be matrices in itself (e.g., for NN layers), so a stack
# of those would be 3-dimensional and not admittable to jnp.matmul
loss_vjp = map_over_secondary_dims(loss_vjp)
# combine gradients for all parameters in the gradient jax tree
# according to the loss combination vjp func
grads_list = tuple(map(loss_vjp, px_grads_list))
return loss_val, grads_list
def __getitem__(self, slice_spec):
"""Basic indexing, returns a TT containing the specified element / slice.
Examples:
>>> a = ttax.random.tensor(rng, [2, 3, 4])
>>> a[1, :, :]
is a 2D TensorTrain 3 x 4.
>>> a[1:2, :, :]
is a 3D TensorTrain 1 x 3 x 4
"""
if len(slice_spec) != self.ndim:
raise ValueError('Expected %d indices, got %d' %
(self.ndim, len(slice_spec)))
new_tt_cores = []
remainder = None
for i in range(self.ndim):
curr_core = self.tt_cores[i]
sliced_core = curr_core[..., :, slice_spec[i], :]
if len(curr_core.shape) != len(sliced_core.shape):
# This index is specified exactly and we want to collapse this axis.
if remainder is None:
remainder = sliced_core
else:
remainder = jnp.matmul(remainder, sliced_core)
else:
if remainder is not None:
# Add reminder from the previous collapsed cores to the current core.
sliced_core = jnp.einsum('...ab,...bid->...aid', remainder,
sliced_core)
remainder = None
new_tt_cores.append(sliced_core)
if remainder is not None:
# The reminder obtained from collapsing the last cores.
new_tt_cores[-1] = jnp.einsum('...aib,...bd->...aid',
new_tt_cores[-1], remainder)
remainder = None
return TT(new_tt_cores)
def logmarglike_lineargaussianmodel_onetransfer_jit(M_T, y, yinvvar,
logyinvvar):
"""
Fit linear model to one Gaussian data set, with no (=uniform) prior on the linear components.
Parameters
----------
y, yinvvar, logyinvvar : ndarray (n_pix_y)
data and data inverse variances.
Zeros will be ignored.
M_T : ndarray (n_components, n_pix_y)
design matrix of linear model
Returns
-------
logfml : ndarray scalar
log likelihood values with parameters marginalised and at best fit
theta_map : ndarray (n_components)
Best fit MAP parameters
theta_cov : ndarray (n_components, n_components)
Parameter covariance
"""
log2pi = np.log(2.0 * np.pi)
nt = np.shape(M_T)[-2]
ny = np.count_nonzero(yinvvar)
M = np.transpose(M_T) # (n_pix_y, n_components)
Myinv = M * yinvvar[:, None] # (n_pix_y, n_components)
Hbar = np.matmul(M_T, Myinv) # (n_components, n_components)
etabar = np.sum(Myinv * y[:, None], axis=0) # (n_components)
theta_map = np.linalg.solve(Hbar, etabar) # (n_components)
theta_cov = np.linalg.inv(Hbar) # (n_components, n_components)
logdetH = np.sum(logyinvvar) # scalar
xi1 = -0.5 * (ny * log2pi - logdetH + np.sum(y * y * yinvvar)) # scalar
sign, logdetHbar = np.linalg.slogdet(Hbar)
xi2 = -0.5 * (nt * log2pi - logdetHbar + np.sum(etabar * theta_map))
logfml = xi1 - xi2
return logfml, theta_map, theta_cov
def _test_lanczos_dynamic_vs_static_once(self, seed=0):
def _safe_div(x1, x2):
return jnp.where(jnp.logical_and(x1 == 0, x2 == 0), x1, x1 / x2)
dim = 5
key = jax.random.PRNGKey(seed)
h_tmp = jax.random.normal(key, shape=(dim, dim))
h = h_tmp + jnp.transpose(h_tmp)
hv = lambda v: jnp.matmul(h, v)
tr1, vecs1 = eigenvector_utils.lanczos_alg(hv,
dim,
dim,
key,
dynamic_unroll=True)
tr2, vecs2 = eigenvector_utils.lanczos_alg(hv,
dim,
dim,
key,
dynamic_unroll=False)
assert jnp.max(jnp.abs(_safe_div(tr1 - tr2, tr2))) < 1e-4, (
f'Seed {seed}: large relative error in Lanczos tridiag')
assert jnp.max(jnp.abs(_safe_div(vecs1 - vecs2, vecs2))) < 1e-4, (
f'Seed {seed}: large relative error in Lanczos vecs')
def mll(
params: dict,
training: Dataset,
priors: dict = {"latent": tfd.Normal(loc=0.0, scale=1.0)},
static_params: dict = None,
):
x, y = training.X, training.y
n = training.n
params = transform(params)
if static_params:
params = concat_dictionaries(params, transform(static_params))
link = link_function(gp.likelihood)
gram_matrix = gram(gp.prior.kernel, x, params)
gram_matrix += I(n) * jitter
L = jnp.linalg.cholesky(gram_matrix)
F = jnp.matmul(L, params["latent"])
rv = link(F)
ll = jnp.sum(rv.log_prob(y))
priors = prior_checks(gp, priors)
log_prior_density = evaluate_prior(params, priors)
constant = jnp.array(-1.0) if negative else jnp.array(1.0)
return constant * (ll + log_prior_density)
def evaluate(self, params):
"""
Evaluates the circuit and returns its unitary operator as a matrix. You must
assemble the circuit before evaluating it. Uses a JIT compiler; for optimal
performance, do not modify the circuit nor its constituent gates between calls
to this method.
Parameters
----------
params: 1D ndarray
A vector of parameters for the gates.
Returns
-------
2D ndarray. A unitary matrix representing the circuit.
"""
assert len(params) == self.n_params
mat = jnp.eye(self.regInfo.dim)
for layer in self.gates:
g = layer.gate(params)
# reverse since operators act right-to-left
mat = jnp.matmul(g, mat)
return mat
def model(n, y, mu, tlist, AV, t0left, t0right):
t0 = numpyro.sample('t0',
numpyro.distributions.Uniform(t0left, t0right))
if Tau == 0:
light_curve = numpyro.distributions.Normal(t0, scale=Sigma)
pl = numpyro.primitives.deterministic(
'pl',
jnp.exp(light_curve.log_prob(tlist)) / n * mu)
else:
pl = numpyro.primitives.deterministic(
'pl',
Co * (1. - jax.scipy.special.erf(
(Alpha * Sigma**2 -
(tlist - t0)) / (math.sqrt(2.) * Sigma))) *
jnp.exp(-Alpha * (tlist - t0)) / n * mu)
A = numpyro.sample('A', mNormal(pl, mix0sigma, 1., gsigma / gmu))
with numpyro.plate('observations', len(y)):
obs = numpyro.sample('obs',
numpyro.distributions.Normal(jnp.matmul(
AV, A),
scale=std),
obs=y)
return obs
def get_dYdt(
P: Union[float, np.ndarray],
R: float,
gas_info: GasInfo,
nasa_poly: NASAPolynomials,
kinetics_coeffs: KineticsCoeffs,
kinetics_data: KineticsData,
t: Union[float, np.ndarray],
state_vec: np.ndarray,
) -> np.ndarray:
"""
get dYdt where dYdt[0] is dTdt and rest is dYdt
"""
T = state_vec[0]
Y = state_vec[1:]
T = np.clip(T, a_min=200.0, a_max=1e5)
Y = np.clip(Y, a_min=0.0, a_max=1.0)
mean_molecular_weight = get_mean_molecular_weight(
Y, gas_info.molecular_weights)
density_mass = P / R / T * mean_molecular_weight
X = Y2X(Y, gas_info.molecular_weights, mean_molecular_weight)
C = Y2C(Y, gas_info.molecular_weights, density_mass)
cp_data = calculate_cp(T, X, R, nasa_poly)
enthalpy_data = calculate_enthalpy(T, X, R, nasa_poly)
entropy_data = calculate_entropy(T, P, X, R, nasa_poly)
cp_mass = cp_data.cp_mole / mean_molecular_weight
Kc = get_equilibirum_constants(T, P, R, entropy_data.sdivR,
enthalpy_data.hdivRT, gas_info)
kf = get_forward_rate_constants(T, R, C, kinetics_coeffs, kinetics_data)
kr = get_reverse_rate_constants(kf, Kc, kinetics_data.is_reversible)
production_rates = get_production_rates(kf, kr, C, gas_info)
Ydot = (production_rates.wdot * gas_info.molecular_weights) / density_mass
Tdot = -(np.matmul(enthalpy_data.partial_molar_enthalpies,
production_rates.wdot)) / (density_mass * cp_mass)
dYdt = np.hstack((Tdot, Ydot))
# dYdt = delete_small_numbers(dYdt)
return dYdt
def update_curvature_matrix_estimate(self, info: _BlockInfo,
batch_size: int,
ema_old: Union[float, jnp.ndarray],
ema_new: Union[float, jnp.ndarray],
pmap_axis_name: str) -> None:
del pmap_axis_name
(x, ), (dy, ) = info["inputs"], info["outputs_tangent"]
utils.check_first_dim_is_batch_size(batch_size, x, dy)
grads = list()
if self._has_scale:
# Scale gradients
scale_shape = info["params"][0].shape
full_scale_shape = (1, ) * (len(x.shape) -
len(scale_shape)) + scale_shape
axis = [
i for i, s in enumerate(full_scale_shape) if s == 1 and i != 0
]
d_scale = jnp.sum(x * dy, axis=axis)
d_scale = d_scale.reshape([batch_size, -1])
grads.append(d_scale)
if self._has_shift:
# Shift gradients
shift_shape = info["params"][1].shape
full_shift_shape = (1, ) * (len(x.shape) -
len(shift_shape)) + shift_shape
axis = [
i for i, s in enumerate(full_shift_shape) if s == 1 and i != 0
]
d_shift = jnp.sum(dy, axis=axis)
d_shift = d_shift.reshape([batch_size, -1])
grads.append(d_shift)
grads = jnp.concatenate(grads, axis=1)
factor_update = jnp.matmul(grads.T, grads) / batch_size
self.factor.update(factor_update, ema_old, ema_new)
def test_dense_mass(kernel_cls, rho):
num_warmup, num_samples = 20000, 10000
true_cov = jnp.array([[10.0, rho], [rho, 0.1]])
def model():
numpyro.sample(
"x",
dist.MultivariateNormal(jnp.zeros(2), covariance_matrix=true_cov))
if kernel_cls is HMC or kernel_cls is NUTS:
kernel = kernel_cls(model, trajectory_length=2.0, dense_mass=True)
elif kernel_cls is BarkerMH:
kernel = BarkerMH(model, dense_mass=True)
mcmc = MCMC(kernel,
num_warmup=num_warmup,
num_samples=num_samples,
progress_bar=False)
mcmc.run(random.PRNGKey(0))
mass_matrix_sqrt = mcmc.last_state.adapt_state.mass_matrix_sqrt
if kernel_cls is HMC or kernel_cls is NUTS:
mass_matrix_sqrt = mass_matrix_sqrt[("x", )]
mass_matrix = jnp.matmul(mass_matrix_sqrt, jnp.transpose(mass_matrix_sqrt))
estimated_cov = jnp.linalg.inv(mass_matrix)
assert_allclose(estimated_cov, true_cov, rtol=0.10)
samples = mcmc.get_samples()["x"]
assert_allclose(jnp.mean(samples[:, 0]), jnp.array(0.0), atol=0.50)
assert_allclose(jnp.mean(samples[:, 1]), jnp.array(0.0), atol=0.05)
assert_allclose(jnp.mean(samples[:, 0] * samples[:, 1]),
jnp.array(rho),
atol=0.20)
assert_allclose(jnp.var(samples, axis=0),
jnp.array([10.0, 0.1]),
rtol=0.20)
def projective_inverse_warp(img,
transform,
mask_value,
intrinsics,
depth,
bilinear=True):
"""Inverse warp a source image to the target image plane based on projection.
Args:
img: the source image [batch, height_s, width_s, 3]
transform: 4x4 transformation matrix
mask_value: uint8 maks value of rgb/a mask value
depth: depth map of the target image [batch, height_t, width_t]
intrinsics: camera intrinsics [batch, 3, 3]
bilinear: bool use bilinear or nearest sampling.
Returns:
Source image inverse warped to the target image plane [batch, height_t,
width_t, 3]
"""
height, width, _ = img.shape
# Construct pixel grid coordinates
pixel_coords = meshgrid(height, width)
# Convert pixel coordinates to the camera frame
cam_coords = pixel2cam(depth, pixel_coords, intrinsics)
# Construct a 4x4 intrinsic matrix
filler = jnp.array([[0.0, 0.0, 0.0, 1.0]])
# filler = jnp.tile(filler, [batch, 1, 1])
intrinsics = jnp.concatenate([intrinsics, jnp.zeros([3, 1])], axis=1)
intrinsics = jnp.concatenate([intrinsics, filler], axis=0)
# Get a 4x4 transformation matrix from 'target' camera frame to 'source'
# pixel frame.
proj_tgt_cam_to_src_pixel = jnp.matmul(intrinsics, transform)
src_pixel_coords = cam2pixel(cam_coords, proj_tgt_cam_to_src_pixel)
output_img = jnp.where(bilinear,
bilinear_sampler(img, src_pixel_coords, mask_value),
nearest_sampler(img, src_pixel_coords, mask_value))
return output_img.astype('uint8')
def gen_values_outside_bounds(constraint, size, key=random.PRNGKey(11)):
if isinstance(constraint, constraints._Boolean):
return random.bernoulli(key, shape=size) - 2
elif isinstance(constraint, constraints._GreaterThan):
return constraint.lower_bound - np.exp(random.normal(key, size))
elif isinstance(constraint, constraints._IntegerInterval):
lower_bound = np.broadcast_to(constraint.lower_bound, size)
return random.randint(key, size, lower_bound - 1, lower_bound)
elif isinstance(constraint, constraints._IntegerGreaterThan):
return constraint.lower_bound - poisson(key, 5, shape=size)
elif isinstance(constraint, constraints._Interval):
upper_bound = np.broadcast_to(constraint.upper_bound, size)
return random.uniform(key, size, minval=upper_bound, maxval=upper_bound + 1.)
elif isinstance(constraint, (constraints._Real, constraints._RealVector)):
return lax.full(size, np.nan)
elif isinstance(constraint, constraints._Simplex):
return osp.dirichlet.rvs(alpha=np.ones((size[-1],)), size=size[:-1]) + 1e-2
elif isinstance(constraint, constraints._Multinomial):
n = size[-1]
return multinomial(key, p=np.ones((n,)) / n, n=constraint.upper_bound, shape=size[:-1]) + 1
elif isinstance(constraint, constraints._CorrCholesky):
return signed_stick_breaking_tril(
random.uniform(key, size[:-2] + (size[-1] * (size[-1] - 1) // 2,),
minval=-1, maxval=1)) + 1e-2
elif isinstance(constraint, constraints._CorrMatrix):
cholesky = 1e-2 + signed_stick_breaking_tril(
random.uniform(key, size[:-2] + (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))
return np.matmul(cholesky, np.swapaxes(cholesky, -2, -1))
elif isinstance(constraint, constraints._LowerCholesky):
return random.uniform(key, size)
elif isinstance(constraint, constraints._PositiveDefinite):
return random.normal(key, size)
elif isinstance(constraint, constraints._OrderedVector):
x = np.cumsum(random.exponential(key, size), -1)
return x[..., ::-1]
else:
raise NotImplementedError('{} not implemented.'.format(constraint))
def apply(self, x, features, bias=True, kernel_init=None):
#print("NoisyNetwork")
def sample_noise(shape):
#tf.random_normal
noise = jax.random.normal(random.PRNGKey(0), shape)
##noise = jax.random.normal(shape)
return noise
def f(x):
return jnp.multiply(jnp.sign(x), jnp.power(jnp.abs(x), 0.5))
# Initializer of \mu and \sigma
def mu_init(key, shape):
low = -1 * 1 / jnp.power(x.shape[1], 0.5)
high = 1 * 1 / jnp.power(x.shape[1], 0.5)
return onp.random.uniform(low, high, shape)
def sigma_init(key, shape, dtype=jnp.float32):
return jnp.ones(shape, dtype) * (0.1 / onp.sqrt(x.shape[1]))
# Sample noise from gaussian
p = sample_noise([x.shape[1], 1])
q = sample_noise([1, features])
f_p = f(p)
f_q = f(q)
w_epsilon = f_p * f_q
b_epsilon = jnp.squeeze(f_q)
w_mu = self.param('kernel', (x.shape[1], features), mu_init)
w_sigma = self.param('kernell', (x.shape[1], features), sigma_init)
w = w_mu + jnp.multiply(w_sigma, w_epsilon)
ret = jnp.matmul(x, w)
b_mu = self.param('bias', (features, ), mu_init)
b_sigma = self.param('biass', (features, ), sigma_init)
b = b_mu + jnp.multiply(b_sigma, b_epsilon)
return jnp.where(bias, ret + b, ret)
