def partial_trace(A, A_label):
""" Partial trace on tensor A over repeated labels in A_label """
num_cont = len(A_label) - len(np.unique(A_label))
if num_cont > 0:
dup_list = []
for ele in np.unique(A_label):
if sum(A_label == ele) > 1:
dup_list.append([np.where(A_label == ele)[0]])
cont_ind = np.array(dup_list).reshape(2*num_cont,order='F')
free_ind = onp.delete(np.arange(len(A_label)),cont_ind)
cont_dim = np.prod(np.array(A.shape)[cont_ind[:num_cont]])
free_dim = np.array(A.shape)[free_ind]
B_label = onp.delete(A_label, cont_ind)
cont_label = np.unique(A_label[cont_ind])
B = np.zeros(np.prod(free_dim))
A = A.transpose(np.append(free_ind, cont_ind)).reshape(np.prod(free_dim),cont_dim,cont_dim)
for ip in range(cont_dim):
B = B + A[:,ip,ip]
return B.reshape(free_dim), B_label, cont_label
else:
return A, A_label, []
def rde(lr: Union[float, Schedule] = 2**-15,
train: Union[bool, Schedule] = False,
Rs: Array = jnp.unique(jnp.abs(comm.const("16QAM", norm=True))),
const: Optional[Array] = None) -> AdaptiveFilter:
"""Radius Directed adaptive Equalizer
Args:
lr: learning rate. scalar or Schedule
train: schedule training mode, which can be a bool for global control within one call
or an array of bool to swich training on iteration basis
Rs: the radii of the target constellation
const: Optional; constellation used to infer R2 when R2 is None
Returns:
an ``AdaptiveFilter`` object
References:
- [1] Fatadin, I., Ives, D. and Savory, S.J., 2009. Blind equalization and
carrier phase recovery in a 16-QAM optical coherent system. Journal
of lightwave technology, 27(15), pp.3042-3049.
"""
lr = cxopt.make_schedule(lr)
train = cxopt.make_schedule(train)
if const is not None:
Rs = jnp.array(jnp.unique(jnp.abs(const)))
def init(dims=2, w0=None, taps=32, dtype=np.complex64):
if w0 is None:
w0 = np.zeros((dims, dims, taps), dtype=dtype)
ctap = (taps + 1) // 2 - 1
w0[np.arange(dims), np.arange(dims), ctap] = 1.
return w0
def loss_fn(w, u, x, i):
v = r2c(mimo(w, u)[None, :])
R2 = jnp.where(
train(i),
jnp.abs(x)**2,
Rs[jnp.argmin(jnp.abs(Rs[:, None] * v / jnp.abs(v) - v),
axis=0)]**2)
l = jnp.sum(jnp.abs(R2 - jnp.abs(v[0, :])**2))
return l
def update(i, w, inp):
u, x = inp
l, g = jax.value_and_grad(loss_fn)(w, u, x, i)
out = (w, l)
w = w - lr(i) * g.conj()
return w, out
def apply(ws, yf):
return jax.vmap(mimo)(ws, yf)
return AdaptiveFilter(init, update, apply)
def bdd_message_func(self, edges):
"""Message function for block-diagonal-decomposition regularizer"""
if edges.src['h'].dtype == jnp.int64 and len(edges.src['h'].shape) == 1:
raise TypeError('Block decomposition does not allow integer ID feature.')
# calculate msg @ W_r before put msg into edge
if self.low_mem:
etypes = jnp.unique(edges.data['type'])
msg = jnp.zeros((edges.src['h'].shape[0], self.out_feat))
for etype in etypes:
loc = edges.data['type'] == etype
w = self.weight[etype].reshape((self.num_bases, self.submat_in, self.submat_out))
src = edges.src['h'][loc].reshape((-1, self.num_bases, self.submat_in))
sub_msg = jnp.einsum('abc,bcd->abd', src, w)
sub_msg = sub_msg.reshape((-1, self.out_feat))
msg = jax.ops.index_update(
msg,
loc,
sub_msg
)
else:
weight = jnp.take(
self.weight,
edges.data['type'],
0,
).reshape(
(-1, self.submat_in, self.submat_out),
)
node = edges.src['h'].reshape((-1, 1, self.submat_in))
msg = jax.lax.batch_matmul(node, weight).reshape((-1, self.out_feat))
if 'norm' in edges.data:
msg = msg * edges.data['norm']
return {'msg': msg}
def basis_message_func(self, edges):
"""Message function for basis regularizer"""
if self.num_bases < self.num_rels:
# generate all weights from bases
weight = self.weight.reshape((self.num_bases,
self.in_feat * self.out_feat))
weight = jnp.matmul(self.w_comp, weight).reshape((
self.num_rels, self.in_feat, self.out_feat))
else:
weight = self.weight
# calculate msg @ W_r before put msg into edge
# if src is jnp.int64 we expect it is an index select
if edges.src['h'].dtype != jnp.int64 and self.low_mem:
etypes = jnp.unique(edges.data['type'])
msg = jnp.zeros((edges.src['h'].shape[0], self.out_feat))
for etype in etypes:
loc = edges.data['type'] == etype
w = weight[etype]
src = edges.src['h'][loc]
sub_msg = jnp.matmul(src, w)
msg = jax.ops.index_update(
msg,
loc,
sub_msg
)
else:
# put W_r into edges then do msg @ W_r
msg = utils.bmm_maybe_select(edges.src['h'], weight, edges.data['type'])
if 'norm' in edges.data:
msg = msg * edges.data['norm']
return {'msg': msg}
def pre2post_mean(pre_values, post_num, post_ids, pre_ids=None):
"""The pre-to-post synaptic mean computation.
Parameters
----------
pre_values: float, jax.numpy.ndarray, JaxArray, Variable
The pre-synaptic values.
pre_ids: jax.numpy.ndarray, JaxArray
The connected pre-synaptic neuron ids.
post_ids: jax.numpy.ndarray, JaxArray
The connected post-synaptic neuron ids.
post_num: int
Output dimension. The number of post-synaptic neurons.
Returns
-------
post_val: jax.numpy.ndarray, JaxArray
The value with the size of post-synaptic neurons.
"""
out = jnp.zeros(post_num, dtype=profile.float_)
pre_values = as_device_array(pre_values)
post_ids = as_device_array(post_ids)
if jnp.ndim(pre_values) == 0:
# return out.at[post_ids].set(pre_values)
return out.at[jnp.unique(post_ids)].set(pre_values)
else:
_raise_pre_ids_is_none(pre_ids)
pre_ids = as_device_array(pre_ids)
pre_values = pre2syn(pre_values, pre_ids)
return syn2post_mean(pre_values, post_ids, post_num)
def sum_from_unique(
cls,
input: np.array,
mean: bool = True) -> Tuple[np.array, np.array, "SparseReduce"]:
un, cts = np.unique(input, return_counts=True)
un_idx = [
np.argwhere(input == un[i]).flatten() for i in range(un.size)
]
l_arr = np.array([i.size for i in un_idx])
argsort = np.argsort(l_arr)
un_sorted = un[argsort]
cts_sorted = cts[argsort]
un_idx_sorted = [un_idx[i] for i in argsort]
change = list(
np.argwhere(
l_arr[argsort][:-1] - l_arr[argsort][1:] != 0).flatten() + 1)
change.insert(0, 0)
change.append(len(l_arr))
change = np.array(change)
el = []
for i in range(len(change) - 1):
el.append(
np.array([
un_idx_sorted[j] for j in range(change[i], change[i + 1])
]))
#assert False
return un_sorted, cts_sorted, SparseReduce(el, mean)
def _log_compare(mat, cats, significance_test=scipy.stats.ttest_ind):
"""Calculates pairwise log ratios between all features and performs a
significiance test (i.e. t-test) to determine if there is a significant
difference in feature ratios with respect to the variable of interest.
Parameters
----------
mat: np.array
rows correspond to samples and columns correspond to
features (i.e. OTUs)
cats: np.array, float
Vector of categories
significance_test: function
statistical test to run
Returns:
--------
log_ratio : np.array
log ratio pvalue matrix
"""
r, c = mat.shape
log_ratio = np.zeros((c, c))
log_mat = np.log(mat)
cs = np.unique(cats)
def func(x):
return significance_test(*[x[cats == k] for k in cs])
for i in range(c - 1):
ratio = (log_mat[:, i].T - log_mat[:, i + 1:].T).T
m, p = np.apply_along_axis(func, axis=0, arr=ratio)
log_ratio[i, i + 1:] = np.squeeze(np.array(p.T))
return log_ratio
def compute_fun(R, **kwargs):
D_fn = partial(displacement, **kwargs)
D_fn = space.map_product(D_fn)
D_different_types = [
D_fn(R[species == atom_type, :], R) for atom_type in np.unique(species)
]
out = []
atom_types = np.unique(species)
for i in range(len(atom_types)):
for j in range(i, len(atom_types)):
out += [
np.sum(
_all_pairs_angular(D_different_types[i], D_different_types[j]),
axis=[1, 2])
]
return np.hstack(out)
def prob_inf_house_size_iter(state, hh_sizes_, house_dist):
""" Function that computes the probability of an individual getting infected given their household size.
@param state : A Device Array that encodes the state of each individual in the population at the end of each iteration of the simulation
@type : Device Array of shape (# of iterations, population size)
@param hh_sizes_ : An array which keeps track of the size of each individual's household
@type : Array of length = population size
@param house_dist : Distribution of household sizes
@type : List or 1D array
@return : Returns the probability of infection given household size and the mean probability of infection
@type : Tuple
"""
hh_sizes = np.asarray(hh_sizes_)
iterations = len(state)
prob_hh_size = np.zeros((iterations, len(house_dist)))
pop = len(state[0])
mean_inf_prob = np.zeros(iterations)
# First compute the probability of the household size given that the person was infected and then use Bayes rule
for i in range(iterations):
if_inf = np.where(state[i] > 0)[0]
inf_size = len(if_inf)
hh_inf = hh_sizes[if_inf]
prob = ((np.array(np.unique(hh_inf, return_counts= True))[-1])/inf_size) * (inf_size/pop) * (1/house_dist) # Bayes rule
prob_hh_size = index_add(prob_hh_size, i, prob)
mean_inf_prob = index_add(mean_inf_prob, i, inf_size/pop)
# Returns the probability of infection given household size
return np.average(prob_hh_size, axis = 0) , np.average(mean_inf_prob)
def arithmetic_encoding_num_bits(v: jnp.ndarray) -> int: """Computes number of bits needed to store v via arithmetic coding.""" v = jnp.nan_to_num(v) v = v.flatten() uniq = jnp.unique(v) entropy = _entropy(v, uniq) hist_bits = _hist_bits(v, uniq) return hist_bits + (v.size * entropy) + (2 * 32) + 2
def unique(x, return_index=False, return_inverse=False,
return_counts=False, axis=None):
if isinstance(x, JaxArray): x = x.value
return JaxArray(jnp.unique(x,
return_index=return_index,
return_inverse=return_inverse,
return_counts=return_counts,
axis=axis))
def pair_correlation(displacement_or_metric: Union[DisplacementFn, MetricFn],
radii: Array,
sigma: float,
species: Array = None):
"""Computes the pair correlation function at a mesh of distances.
The pair correlation function measures the number of particles at a given
distance from a central particle. The pair correlation function is defined
by $g(r) = <\sum_{i\neq j}\delta(r - |r_i - r_j|)>.$ We make the
approximation
$\delta(r) \approx {1 \over \sqrt{2\pi\sigma^2}e^{-r / (2\sigma^2)}}$.
Args:
displacement_or_metric: A function that computes the displacement or
distance between two points.
radii: An array of radii at which we would like to compute g(r).
sigima: A float specifying the width of the approximating Gaussian.
species: An optional array specifying the species of each particle. If
species is None then we compute a single g(r) for all particles,
otherwise we compute one g(r) for each species.
Returns:
A function `g_fn` that computes the pair correlation function for a
collection of particles.
"""
d = space.canonicalize_displacement_or_metric(displacement_or_metric)
d = space.map_product(d)
def pairwise(dr, dim):
return jnp.exp(-f32(0.5) *
(dr - radii)**2 / sigma**2) / radii**(dim - 1)
pairwise = vmap(vmap(pairwise, (0, None)), (0, None))
if species is None:
def g_fn(R):
dim = R.shape[-1]
mask = 1 - jnp.eye(R.shape[0], dtype=R.dtype)
return jnp.sum(mask[:, :, jnp.newaxis] * pairwise(d(R, R), dim),
axis=(1, ))
else:
if not (isinstance(species, jnp.ndarray) and is_integer(species)):
raise TypeError('Malformed species; expecting array of integers.')
species_types = jnp.unique(species)
def g_fn(R):
dim = R.shape[-1]
g_R = []
mask = 1 - jnp.eye(R.shape[0], dtype=R.dtype)
for s in species_types:
Rs = R[species == s]
mask_s = mask[:, species == s, jnp.newaxis]
g_R += [jnp.sum(mask_s * pairwise(d(Rs, R), dim), axis=(1, ))]
return g_R
return g_fn
def heavy_atoms(self):
unique, counts = jnp.unique(self.Z, return_counts=True)
dictionary = dict(zip(unique, counts))
try:
heavy_atoms = self.Z.size - dictionary[1]
except KeyError:
print("In file %s no hydrogens were reported" % self.filename)
heavy_atoms = self.Z.size
return (heavy_atoms)
def check_inputs(connect_list, flat_connect, dims_list, cont_order):
""" Check consistancy of NCON inputs"""
pos_ind = flat_connect[flat_connect > 0]
neg_ind = flat_connect[flat_connect < 0]
# check that lengths of lists match
if len(dims_list) != len(connect_list):
raise ValueError(('NCON error: %i tensors given but %i index sublists given')
%(len(dims_list), len(connect_list)))
# check that tensors have the right number of indices
for ele in range(len(dims_list)):
if len(dims_list[ele]) != len(connect_list[ele]):
raise ValueError(('NCON error: number of indices does not match number of labels on tensor %i: '
'%i-indices versus %i-labels')%(ele,len(dims_list[ele]),len(connect_list[ele])))
# check that contraction order is valid
if not np.array_equal(np.sort(cont_order),np.unique(pos_ind)):
raise ValueError(('NCON error: invalid contraction order'))
# check that negative indices are valid
for ind in np.arange(-1,-len(neg_ind)-1,-1):
if sum(neg_ind == ind) == 0:
raise ValueError(('NCON error: no index labelled %i') %(ind))
elif sum(neg_ind == ind) > 1:
raise ValueError(('NCON error: more than one index labelled %i')%(ind))
# check that positive indices are valid and contracted tensor dimensions match
flat_dims = np.array([item for sublist in dims_list for item in sublist])
for ind in np.unique(pos_ind):
if sum(pos_ind == ind) == 1:
raise ValueError(('NCON error: only one index labelled %i')%(ind))
elif sum(pos_ind == ind) > 2:
raise ValueError(('NCON error: more than two indices labelled %i')%(ind))
cont_dims = flat_dims[flat_connect == ind]
if cont_dims[0] != cont_dims[1]:
raise ValueError(('NCON error: tensor dimension mismatch on index labelled %i: '
'dim-%i versus dim-%i')%(ind,cont_dims[0],cont_dims[1]))
return True
def sum_from_unique(
cls,
input: Array,
mean: bool = True) -> Tuple[np.array, np.array, "LinearReduce"]:
un, cts = np.unique(input, return_counts=True)
un_idx = [
np.argwhere(input == un[i]).flatten() for i in range(un.size)
]
m = np.zeros((len(un_idx), input.shape[0]))
for i, idx in enumerate(un_idx):
b = np.ones(int(cts[i].squeeze())).squeeze()
m = m.at[i, idx.squeeze()].set(b / cts[i].squeeze() if mean else b)
return un, cts, LinearReduce(m)
def get_group_zellner(groups, X, isgmom=False):
"""Note that V=(XtX)^-1 and Vinv=XtX."""
n, p = X.shape
Vinv = jnp.zeros((p, p))
V = jnp.zeros((p, p))
for group, p_j in zip(*jnp.unique(groups, return_counts=True)):
mask = jnp.arange(p)[groups == group]
X_j = X[:, mask]
p_term = cond(isgmom, p_j, lambda x: x, p_j, lambda x: x + 2)
aux = jnp.dot(X_j.T, X_j) * n / p_term
Vinv = Vinv.at[jnp.ix_(mask, mask)].set(aux)
V = V.at[jnp.ix_(mask, mask)].set(jnp.linalg.inv(aux))
return V, Vinv
def random_adjacency(key: jnp.ndarray,
num_nodes: int,
num_edges: int,
dtype=jnp.float32) -> COO:
"""
Get the adjacency matrix of a random fully connected undirected graph.
Note that `num_edges` is only approximate. The process of creating edges it:
- sample `num_edges` random edges
- remove self-edges
- add ring edges
- add reverse edges
- filter duplicates
Args:
key: `jax.random.PRNGKey`.
num_nodes: number of nodes in returned graph.
num_edges: number of random internal edges initially added.
dtype: dtype of returned JAXSparse.
Returns:
COO, shape (num_nodes, num_nodes), weights all ones.
"""
shape = num_nodes, num_nodes
internal_indices = jax.random.uniform(
key,
shape=(num_edges, ),
dtype=jnp.float32,
maxval=num_nodes**2,
).astype(jnp.int32)
# remove randomly sampled self-edges.
self_edges = (internal_indices // num_nodes) == (internal_indices %
num_nodes)
internal_indices = internal_indices[jnp.logical_not(self_edges)]
# add a ring so we know the graph is connected
r = jnp.arange(num_nodes, dtype=jnp.int32)
ring_indices = r * num_nodes + (r + 1) % num_nodes
indices = jnp.concatenate((internal_indices, ring_indices))
# add reverse indices
coords = jnp.unravel_index(indices, shape)
coords_rev = coords[-1::-1]
indices_rev = jnp.ravel_multi_index(coords_rev, shape)
indices = jnp.concatenate((indices, indices_rev))
# filter out duplicates
indices = jnp.unique(indices)
row, col = jnp.unravel_index(indices, shape)
return COO((jnp.ones((row.size, ), dtype=dtype), row, col), shape=shape)
def create_spatiotemporal_grid(X, Y):
"""
create a grid of data sized [T, R1, R2]
note that this function removes full duplicates (i.e. where all dimensions match)
TODO: generalise to >5D
"""
if Y.ndim < 2:
Y = Y[:, None]
num_spatial_dims = X.shape[1] - 1
if num_spatial_dims == 4:
sort_ind = nnp.lexsort(
(X[:, 4], X[:, 3], X[:, 2], X[:, 1], X[:,
0])) # sort by 0, 1, 2, 4
elif num_spatial_dims == 3:
sort_ind = nnp.lexsort(
(X[:, 3], X[:, 2], X[:, 1], X[:, 0])) # sort by 0, 1, 2, 3
elif num_spatial_dims == 2:
sort_ind = nnp.lexsort((X[:, 2], X[:, 1], X[:, 0])) # sort by 0, 1, 2
elif num_spatial_dims == 1:
sort_ind = nnp.lexsort((X[:, 1], X[:, 0])) # sort by 0, 1
else:
raise NotImplementedError
X = X[sort_ind]
Y = Y[sort_ind]
unique_time = np.unique(X[:, 0])
unique_space = nnp.unique(X[:, 1:], axis=0)
N_t = unique_time.shape[0]
N_r = unique_space.shape[0]
if num_spatial_dims == 4:
R = np.tile(unique_space, [N_t, 1, 1, 1, 1])
elif num_spatial_dims == 3:
R = np.tile(unique_space, [N_t, 1, 1, 1])
elif num_spatial_dims == 2:
R = np.tile(unique_space, [N_t, 1, 1])
elif num_spatial_dims == 1:
R = np.tile(unique_space, [N_t, 1])
else:
raise NotImplementedError
R_flat = R.reshape(-1, num_spatial_dims)
Y_dummy = np.nan * np.zeros([N_t * N_r, 1])
time_duplicate = np.tile(unique_time, [N_r, 1]).T.flatten()
X_dummy = np.block([time_duplicate[:, None], R_flat])
X_all = np.vstack([X, X_dummy])
Y_all = np.vstack([Y, Y_dummy])
X_unique, ind = nnp.unique(X_all, axis=0, return_index=True)
Y_unique = Y_all[ind]
grid_shape = (unique_time.shape[0], ) + unique_space.shape
R_grid = X_unique[:, 1:].reshape(grid_shape)
Y_grid = Y_unique.reshape(grid_shape[:-1] + (1, ))
return unique_time[:, None], R_grid, Y_grid
def compute_fun(R, **kwargs):
_metric = partial(metric, **kwargs)
_metric = space.map_product(_metric)
radial_fn = lambda eta, dr: (np.exp(-eta * dr**2) *
_behler_parrinello_cutoff_fn(dr, cutoff_distance))
def return_radial(atom_type):
"""Returns the radial symmetry functions for neighbor type atom_type."""
R_neigh = R[species == atom_type, :]
dr = _metric(R, R_neigh)
radial = vmap(radial_fn, (0, None))(etas, dr)
return np.sum(radial, axis=1).T
return np.hstack([return_radial(atom_type) for
atom_type in np.unique(species)])
def union_bad_ants(JDs):
"""Return all the bad antennas for the specified JDs
:param: Julian Days
:type: ndarray, list
:return: Union of bad antennas for JDs
:rtype: ndarray
"""
bad_ants_fn = os.path.join(os.path.dirname(__file__), 'bad_ants_idr2.pkl')
with open(bad_ants_fn, 'rb') as f:
bad_ants_dict = pickle.load(f)
bad_ants = np.array([], dtype=int)
for JD in JDs:
bad_ants = np.append(bad_ants, bad_ants_dict[JD])
return np.sort(np.unique(bad_ants))
def updateMeans(self, X, clusters, means):
clusters = clusters.reshape(clusters.shape[0], 1)
n = X.shape[1]
X = jnp.hstack((X, clusters))
X = X[X[:, n].argsort()]
spilited = jnp.split(X[:, :n],
jnp.unique(X[:, n], return_index=True)[1][1:])
temp = [0 for j in range(len(spilited))] #jnp.zeros((len(spilited),n))
for i in range(len(spilited)):
temp[i] = jnp.mean(spilited[i], axis=0)
temp = jnp.array(temp)
newmean = (means + temp) / 2
return newmean
def _load_dataset():
_, fetch = load_dataset(COVTYPE, shuffle=False)
features, labels = fetch()
# normalize features and add intercept
features = (features - features.mean(0)) / features.std(0)
features = jnp.hstack([features, jnp.ones((features.shape[0], 1))])
# make binary feature
_, counts = jnp.unique(labels, return_counts=True)
specific_category = jnp.argmax(counts)
labels = labels == specific_category
N, dim = features.shape
print("Data shape:", features.shape)
print("Label distribution: {} has label 1, {} has label 0".format(
labels.sum(), N - labels.sum()))
return features, labels
def plot_gmm_changepoints(ax, gmm_output, timesteps=None):
X = gmm_output["observed"]
states = gmm_output["latent"]
n_states = len(jnp.unique(states))
T = len(X)
timesteps = jnp.arange(T) if timesteps is None else timesteps
ax[0].plot(timesteps, X, marker="o", markersize=3, linewidth=1, c="tab:gray")
ax[1].scatter(timesteps, states, c="tab:gray")
ax[1].set_yticks(jnp.arange(n_states))
for y in range(n_states):
ax[1].axhline(y=y, c="tab:gray", alpha=0.3)
for changepoint, axi in product(changepoints, ax):
axi.axvline(x=changepoint, c="tab:red", linestyle="dotted")
for axi in ax:
axi.set_xlim(timesteps[0], timesteps[-1])
def glmm(dept, male, applications, admit=None):
v_mu = numpyro.sample('v_mu', dist.Normal(0, jnp.array([4., 1.])))
sigma = numpyro.sample('sigma', dist.HalfNormal(jnp.ones(2)))
L_Rho = numpyro.sample('L_Rho', dist.LKJCholesky(2, concentration=2))
scale_tril = sigma[..., jnp.newaxis] * L_Rho
# non-centered parameterization
num_dept = len(jnp.unique(dept))
z = numpyro.sample('z', dist.Normal(jnp.zeros((num_dept, 2)), 1))
v = jnp.dot(scale_tril, z.T).T
logits = v_mu[0] + v[dept, 0] + (v_mu[1] + v[dept, 1]) * male
if admit is None:
# we use a Delta site to record probs for predictive distribution
probs = expit(logits)
numpyro.sample('probs', dist.Delta(probs), obs=probs)
numpyro.sample('admit',
dist.Binomial(applications, logits=logits),
obs=admit)
def compute_fun(R: Array, neighbor: NeighborList, **kwargs) -> Array:
_metric = partial(metric, **kwargs)
_metric = space.map_neighbor(_metric)
radial_fn = lambda eta, dr: (np.exp(
-eta * dr**2) * _behler_parrinello_cutoff_fn(dr, cutoff_distance))
def return_radial(atom_type):
"""Returns the radial symmetry functions for neighbor type atom_type."""
R_neigh = R[neighbor.idx]
species_neigh = species[neighbor.idx]
mask = np.logical_and(neighbor.idx < R.shape[0],
species_neigh == atom_type)
dr = _metric(R, R_neigh)
radial = vmap(radial_fn, (0, None))(etas, dr)
return util.high_precision_sum(radial * mask[np.newaxis, :, :],
axis=2).T
return np.hstack(
[return_radial(atom_type) for atom_type in np.unique(species)])
def lib_as_grid(self):
"""Convert the library parameters to pixel indices in each dimension,
and build and store a KDTree for the pixel coordinates.
"""
# Get the unique gridpoints in each param
self.gridpoints = {}
self.binwidths = {}
for p in self.labels:
self.gridpoints[p] = np.unique(self.libparams[p])
self.binwidths[p] = np.diff(self.gridpoints[p])
# Digitize the library parameters
X = np.array([
np.digitize(self.libparams[p], bins=self.gridpoints[p], right=True)
for p in self.labels
])
self.X = X.T
# Build the KDTree
startime = datetime.now()
self._kdt = KDTree(self.X, leafsize=1000) # , metric='euclidean')
print('built KDTree: {}'.format(datetime.now() - startime))
def rde(lr=1e-4, Rs=jnp.unique(jnp.abs(comm.const("16QAM", norm=True)))):
'''
References:
[1] Fatadin, I., Ives, D. and Savory, S.J., 2009. Blind equalization and
carrier phase recovery in a 16-QAM optical coherent system. Journal
of lightwave technology, 27(15), pp.3042-3049.
'''
def init(w0=None, taps=19, dims=2, unitarize=False):
if w0 is None:
w0 = np.zeros((2, 2, taps), dtype=np.complex64)
ctap = (taps + 1) // 2 - 1
w0[np.arange(dims), np.arange(dims), ctap] = 1.
elif unitarize:
try:
w0 = unitarize_mimo_weights(w0)
except:
pass
return w0
def update(w, inp):
u, Rx, train = inp
def loss_fn(w, u):
v = mimo(w, u)[None,:]
R2 = jnp.where(train,
Rx**2,
Rs[jnp.argmin(
jnp.abs(Rs[:,None] * v / jnp.abs(v) - v),
axis=0)]**2)
l = jnp.sum(jnp.abs(R2 - jnp.abs(v[0,:])**2))
return l
l, g = jax.value_and_grad(loss_fn)(w, u)
out = (l, w)
w = w - lr * g.conj()
return w, out
def static_map(ws, yf):
return jax.vmap(mimo)(ws, yf)
return AdaptiveFilter(init, update, static_map)
def get_theta_grid(self):
self.theta_grid = dict()
tg = self.theta_grid
ntheta = 11
ntheta_fine = 121 # preliminary
theta_min = 0.01
theta_max = 0.99
tg['theta_grid_coarse'] = np.linspace(theta_min, theta_max, ntheta)
tg['ntheta_coarse'] = ntheta
tfine = np.unique(
np.concatenate(
(np.linspace(theta_min, theta_max,
ntheta_fine), tg['theta_grid_coarse'])))
tg['theta_gird_fine'] = tfine
tg['ntheta_fine'] = tfine.size
tg['v_theta'] = VecOnGrid(tg['theta_grid_coarse'],
tg['theta_gird_fine'])
def compute_fun(R, neighbor, **kwargs):
D_fn = partial(displacement, **kwargs)
D_fn = space.map_neighbor(D_fn)
R_neigh = R[neighbor.idx]
species_neigh = species[neighbor.idx]
atom_types = np.unique(species)
base_mask = neighbor.idx < len(R)
mask = [
np.logical_and(base_mask, species_neigh == t) for t in atom_types
]
out = []
dR = D_fn(R, R_neigh)
all_angular = _all_pairs_angular(dR, dR)
for i in range(len(atom_types)):
mask_i = mask[i][:, :, np.newaxis, np.newaxis]
for j in range(i, len(atom_types)):
mask_j = mask[j][:, np.newaxis, :, np.newaxis]
out += [np.sum(all_angular * mask_i * mask_j, axis=[1, 2])]
return np.hstack(out)
def restricted_hartree_fock(geom, basis_name, xyz_path, nuclear_charges, charge, options, deriv_order=0, return_aux_data=False):
# Load keyword options
maxit = options['maxit']
damping = options['damping']
damp_factor = options['damp_factor']
spectral_shift = options['spectral_shift']
convergence = 1e-10
nelectrons = int(jnp.sum(nuclear_charges)) - charge
ndocc = nelectrons // 2
# If we are doing MP2 or CCSD after, might as well use jit-compiled JK-build, since HF will not be memory bottleneck
if return_aux_data:
jk_build = jax.jit(jax.vmap(jax.vmap(lambda x,y: jnp.tensordot(x, y, axes=[(0,1),(0,1)]), in_axes=(0,None)), in_axes=(0,None)))
else:
jk_build = jax.vmap(jax.vmap(lambda x,y: jnp.tensordot(x, y, axes=[(0,1),(0,1)]), in_axes=(0,None)), in_axes=(0,None))
# Canonical orthogonalization via cholesky decomposition
S, T, V, G = compute_integrals(geom, basis_name, xyz_path, nuclear_charges, charge, deriv_order, options)
A = cholesky_orthogonalization(S)
nbf = S.shape[0]
# For slightly shifting eigenspectrum of transformed Fock for degenerate eigenvalues
# (JAX cannot differentiate degenerate eigenvalue eigh)
if spectral_shift:
# Shifting eigenspectrum requires lower convergence.
convergence = 1e-8
fudge = jnp.asarray(np.linspace(0, 1, nbf)) * convergence
shift = jnp.diag(fudge)
else:
shift = jnp.zeros_like(S)
H = T + V
Enuc = nuclear_repulsion(geom.reshape(-1,3),nuclear_charges)
D = jnp.zeros_like(H)
def rhf_iter(F,D):
E_scf = jnp.einsum('pq,pq->', F + H, D) + Enuc
Fp = jnp.dot(A.T, jnp.dot(F, A))
Fp = Fp + shift
eps, C2 = jnp.linalg.eigh(Fp)
C = jnp.dot(A,C2)
Cocc = C[:, :ndocc]
D = jnp.dot(Cocc, Cocc.T)
return E_scf, D, C, eps
iteration = 0
E_scf = 1.0
E_old = 0.0
Dold = jnp.zeros_like(D)
dRMS = 1.0
# Converge according to energy and DIIS residual to ensure eigenvalues and eigenvectors are maximally converged.
# This is crucial for numerical stability for higher order derivatives of correlated methods.
while ((abs(E_scf - E_old) > convergence) or (dRMS > convergence)):
E_old = E_scf * 1
if damping:
if iteration < 10:
D = Dold * damp_factor + D * damp_factor
Dold = D * 1
# Build JK matrix: 2 * J - K
JK = 2 * jk_build(G, D)
JK -= jk_build(G.transpose((0,2,1,3)), D)
# Build Fock
F = H + JK
# Update convergence error
if iteration > 1:
diis_e = jnp.einsum('ij,jk,kl->il', F, D, S) - jnp.einsum('ij,jk,kl->il', S, D, F)
diis_e = A.dot(diis_e).dot(A)
dRMS = jnp.mean(diis_e**2)**0.5
# Compute energy, transform Fock and diagonalize, get new density
E_scf, D, C, eps = rhf_iter(F,D)
iteration += 1
if iteration == maxit:
break
print(iteration, " RHF iterations performed")
# If many orbitals are degenerate, warn that higher order derivatives may be unstable
tmp = jnp.round(eps,6)
ndegen_orbs = tmp.shape[0] - jnp.unique(tmp).shape[0]
if (ndegen_orbs / nbf) > 0.20:
print("Hartree-Fock warning: More than 20% of orbitals have degeneracies. Higher order derivatives may be unstable due to eigendecomposition AD rule")
if not return_aux_data:
return E_scf
else:
return E_scf, C, eps, G
