def projectParam_vec(param, N, D, G, M, K, lb=1e-6):
# unpack the input parameter vector
tp_1 = [0, M, 2*M, 3*M, 4*M, 4*M+M*G, 4*M+M*G+G, 4*M+M*G+2*G,
4*M+M*G+2*G+G*N*K, 4*M+M*G+2*G+G*(N+D)*K, 4*M+M*G+2*G+G*(N+2*D)*K,
4*M+M*G+2*G+G*(N+2*D+1)*K, 4*M+M*G+2*G+G*(N+2*D+2)*K]
tp_2 = []
for i in np.arange(len(tp_1)-1):
tp_2.append(param[tp_1[i] : tp_1[i+1]])
[tau_a1, tau_a2, tau_b1, tau_b2, phi, tau_v1, tau_v2, eta, mu_w, sigma_w,\
mu_b, sigma_b] = tp_2
phi = np.reshape(phi, (M,G))
eta = np.reshape(eta, (G,N,K))
# apply projections
w_tau_ab = projectLB(np.concatenate((tau_a1,tau_a2,tau_b1,tau_b2)), lb)
w_phi = np.zeros((M,G))
for m in np.arange(M):
w_phi[m] = projectSimplex_vec(phi[m])
w_tau_v = projectLB(np.concatenate((tau_v1,tau_v2)), lb)
w_eta = np.zeros((G,N,K))
for g in np.arange(G):
for n in np.arange(N):
w_eta[g,n] = projectSimplex_vec(eta[g,n])
w = np.concatenate((w_tau_ab, w_phi.reshape(M*G), w_tau_v, \
w_eta.reshape(G*N*K), mu_w, projectLB(sigma_w,lb), mu_b, \
projectLB(sigma_b,lb)))
return w
def callback(params):
print("Log likelihood {}".format(-objective(params)))
plt.cla()
print(params)
# Show posterior marginals.
plot_xs = np.reshape(np.linspace(-7, 7, 300), (300,1))
pred_mean, pred_cov = predict(params, X, y, plot_xs)
marg_std = np.sqrt(np.diag(pred_cov))
ax.plot(plot_xs, pred_mean, 'b')
ax.fill(np.concatenate([plot_xs, plot_xs[::-1]]),
np.concatenate([pred_mean - 1.96 * marg_std,
(pred_mean + 1.96 * marg_std)[::-1]]),
alpha=.15, fc='Blue', ec='None')
# Show samples from posterior.
rs = npr.RandomState(0)
sampled_funcs = rs.multivariate_normal(pred_mean, pred_cov, size=10)
ax.plot(plot_xs, sampled_funcs.T)
ax.plot(X, y, 'kx')
ax.set_ylim([-1.5, 1.5])
ax.set_xticks([])
ax.set_yticks([])
plt.draw()
plt.pause(1.0/60.0)
def plot_single_gp(ax, params, layer, unit, plot_xs):
ax.cla()
rs = npr.RandomState(0)
deep_map = create_deep_map(params)
gp_details = deep_map[layer][unit]
gp_params = pack_gp_params(gp_details)
pred_mean, pred_cov = predict_layer_funcs[layer][unit](gp_params, plot_xs, with_noise = False, FITC = False)
x0 = deep_map[layer][unit]['x0']
y0 = deep_map[layer][unit]['y0']
noise_scale = deep_map[layer][unit]['noise_scale']
marg_std = np.sqrt(np.diag(pred_cov))
if n_samples_to_plot > 19:
ax.plot(plot_xs, pred_mean, 'b')
ax.fill(np.concatenate([plot_xs, plot_xs[::-1]]),
np.concatenate([pred_mean - 1.96 * marg_std,
(pred_mean + 1.96 * marg_std)[::-1]]),
alpha=.15, fc='Blue', ec='None')
# Show samples from posterior.
sampled_funcs = rs.multivariate_normal(pred_mean, pred_cov*(random), size=n_samples_to_plot)
ax.plot(plot_xs, sampled_funcs.T)
ax.plot(x0, y0, 'ro')
#ax.errorbar(x0, y0, yerr = noise_scale, fmt='o')
ax.set_xticks([])
ax.set_yticks([])
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 Lambda(self):
lam = super(LinearDiscreteHawkes, self).Lambda
w = self.W[self.K:, :]
assert w.shape == (self.K, self.K), "unmatched w shape: should be KxK"
Rs = []
for t in range(self.conv_dyad_data.shape[2]):
Rs.append(self.conv_dyad_data[:, :, t] * w)
Rs = np.concatenate(Rs, axis=1).reshape(self.conv_dyad_data.shape, order='F')
return np.concatenate([lam[i, :, :] + Rs[:, :, i] for i in range(self.data.shape[0])]).reshape(lam.shape)
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 flatten(value):
"""value can be any nesting of tuples, arrays, dicts.
returns 1D numpy array and an unflatten function."""
if isinstance(getval(value), np.ndarray):
def unflatten(vector):
return np.reshape(vector, value.shape)
return np.ravel(value), unflatten
elif isinstance(getval(value), float):
return np.array([value]), lambda x : x[0]
elif isinstance(getval(value), tuple):
if not value:
return np.array([]), lambda x : ()
flattened_first, unflatten_first = flatten(value[0])
flattened_rest, unflatten_rest = flatten(value[1:])
def unflatten(vector):
N = len(flattened_first)
return (unflatten_first(vector[:N]),) + unflatten_rest(vector[N:])
return np.concatenate((flattened_first, flattened_rest)), unflatten
elif isinstance(getval(value), list):
if not value:
return np.array([]), lambda x : []
flattened_first, unflatten_first = flatten(value[0])
flattened_rest, unflatten_rest = flatten(value[1:])
def unflatten(vector):
N = len(flattened_first)
return [unflatten_first(vector[:N])] + unflatten_rest(vector[N:])
return np.concatenate((flattened_first, flattened_rest)), unflatten
elif isinstance(getval(value), dict):
flattened = []
unflatteners = []
lengths = []
keys = []
for k, v in sorted(iteritems(value), key=itemgetter(0)):
cur_flattened, cur_unflatten = flatten(v)
flattened.append(cur_flattened)
unflatteners.append(cur_unflatten)
lengths.append(len(cur_flattened))
keys.append(k)
def unflatten(vector):
split_ixs = np.cumsum(lengths)
pieces = np.split(vector, split_ixs)
return {key: unflattener(piece)
for piece, unflattener, key in zip(pieces, unflatteners, keys)}
return np.concatenate(flattened), unflatten
else:
raise Exception("Don't know how to flatten type {}".format(type(value)))
def ridgeData(X_train, Y_train, regularization_factor):
# This wasn't tested for dim(X_train) != 2 or dim(Y_train) != 1
N, D = X_train.shape
assert(Y_train.shape == (N,))
ridge_matrix = np.sqrt(ridge_precision) * np.identity(D)
X_trainp = np.concatenate((X_train, ridge_matrix), 0)
zeros = np.zeros([D for i in range(dim(Y_train))])
assert(dim(zeros) == dim(Y_train))
Y_trainp = np.concatenate((Y_train, zeros), 0)
return X_trainp, Y_trainp
def calc_side_matrices(operators, operators_bar, obs, test_points, op_cache, fun_args=None):
obs_points = np.r_[[p for p, _ in obs]]
L = []
Lbar = []
for op, op_bar, point in zip(operators, operators_bar, obs_points):
f = op_cache[(op,)]
fbar = op_cache[(op_bar,)]
L.append(f(point, test_points, fun_args))
Lbar.append(fbar(test_points, point, fun_args))
L = np.concatenate(L)
Lbar = np.concatenate(Lbar, axis=1)
return L, Lbar
def get_Aopt(inX, iny):
X_train, y_train, X_test, y_test = ascdata.split_train_test(inX, iny)
X_train = np.concatenate((X_train, np.ones((X_train.shape[ 0 ], 1))), 1)
X_test = np.concatenate((X_test, np.ones((X_test.shape[ 0 ], 1))), 1)
X_train_less, s_train = ascdata.split_X_s(X_train)
X_test_less, s_test = ascdata.split_X_s(X_test)
s_train_phi = ascdata.generate_phi(s_train, d, A_phi, b_phi)
s_test_phi = ascdata.generate_phi(s_test, d, A_phi, b_phi)
nfeatures = X_train.shape[1] - 1
# Dimensions of phi(s)
nfeatures_phi = d
invT2 = 10
def logprob(inA, inX, iny, ins_phi):
RMS = 0
for i in range(len(iny)):
wi = np.dot(inA, inX[i])
RMS_current = (iny[i] - np.dot(wi, ins_phi[i]))**2
RMS += RMS_current
return -RMS
objective = lambda inA, t: -logprob(inA, X_train_less, y_train, s_train_phi)
LLHs = []
LLH_xs = []
def callback(params, t, g):
LLH = -objective(params, t)
LLHs.append(LLH)
LLH_xs.append(t)
print("Iteration {} log likelihood {}".format(t, LLH))
init_A = 0.00000000001*(np.ones((nfeatures_phi, nfeatures)))
# init_A = [[ -3.05236728e-04, -9.50015728e-04, -3.80139503e-04, 1.44010470e-04, -3.05236728e-04,
# -4.96117987e-04, -1.02736409e-04, -1.86416292e-04, -9.52628589e-04, -1.55023279e-03,
# 1.44717581e-04, 1.00000000e-11, -9.50028200e-04, -4.96117987e-04, 1.00000000e-11,
# -3.05236728e-04, 1.77416412e-06, -8.16665436e-06, 3.12622951e-05, -8.25700143e-04,
# 1.44627987e-04, 1.90211243e-05, -8.28273186e-04, -9.41349990e-04, -4.56671031e-04,
# 9.79097070e-03, -6.41866046e-04, -7.79274856e-05, 1.44539330e-04, -3.05236728e-04,
# -5.99188450e-04, -7.29470175e-04, -6.69558174e-04, -9.50028200e-04]]
init_A = np.array(init_A)
print("Optimizing network parameters...")
optimized_params = adam(grad(objective), init_A,
step_size=0.01, num_iters=1000, callback=callback)
Aopt = optimized_params
print "Aopt = ", Aopt
return Aopt, X_train_less, y_train, s_train, X_test_less, y_test, s_test, LLHs, LLH_xs
def projectParam(param, N, D, G, M, K, lb=1e-6):
""" project variational parameter vector onto the constraint set, including
positive constraints for parameters of Beta distributions, simplex
constraints for parameters of Categorical distributions
Parameters
----------
param: length (2M + 2M + MG + 2G + GDK + GDK + GK + GK)
variational parameters, including:
1) tau_a1: len(M), first parameter of q(alpha_m)
2) tau_a2: len(M), second parameter of q(alpha_m)
3) tau_b1: len(M), first parameter of q(beta_m)
4) tau_b2: len(M), second parameter of q(beta_m)
5) phi: shape(M, G), phi[m,:] is the paramter vector of q(c_m)
6) tau_v1: len(G), first parameter of q(nu_g)
7) tau_v2: len(G), second parameter of q(nu_g)
8) mu_w: shape(G, D, K), mu_w[g,d,k] is the mean parameter of
q(W^g_{dk})
9) sigma_w: shape(G, D, K), sigma_w[g,d,k] is the std parameter of
q(W^g_{dk})
10) mu_b: shape(G, K), mu_b[g,k] is the mean parameter of q(b^g_k)
11) sigma_b: shape(G, K), sigma_b[g,k] is the std parameter of q(b^g_k)
N,D,G,M,K: number of samples (N), features(D), groups(G), experts(M),
clusters(K)
lb: float, lower bound of positive constraints
Returns
-------
w: length (2M + 2M + MG + 2G + GNK + GDK + GDK + GK + GK)
"""
# unpack the input parameter vector
tp_1 = [0, M, 2*M, 3*M, 4*M, 4*M+M*G, 4*M+M*G+G,
4*M+M*G+2*G, 4*M+M*G+2*G+G*D*K, 4*M+M*G+2*G+G*(2*D)*K,
4*M+M*G+2*G+G*(2*D+1)*K, 4*M+M*G+2*G+G*(2*D+2)*K]
tp_2 = []
for i in np.arange(len(tp_1)-1):
tp_2.append(param[tp_1[i] : tp_1[i+1]])
[tau_a1, tau_a2, tau_b1, tau_b2, phi, tau_v1, tau_v2, mu_w, sigma_w,\
mu_b, sigma_b] = tp_2
phi = np.reshape(phi, (M,G))
# apply projections
w_tau_ab = projectLB(np.concatenate((tau_a1,tau_a2,tau_b1,tau_b2)), lb)
w_phi_vec = np.reshape(projectSimplex(phi), M*G)
w_tau_v = projectLB(np.concatenate((tau_v1,tau_v2)), lb)
w = np.concatenate((w_tau_ab, w_phi_vec, w_tau_v, \
mu_w, projectLB(sigma_w,lb), mu_b, projectLB(sigma_b,lb)))
return w
def mean(self, test_points, g=None):
if g is None:
g = np.concatenate([val for _, val in self.__obs])
L, Lbar = calc_side_matrices(self.__operators, self.__operators_bar, self.__obs, test_points, self.__op_cache, self.__fun_args)
mu_multiplier = np.dot(Lbar, self.__LLbar_inv)
return np.dot(mu_multiplier, g)
def preprocess_data(df, nodes_nbrs, graph_idxs, graph_nodes, graph_array):
intersect = set(df['seqid'].values).intersection(graph_idxs.keys())
# Get a reduced list of graph_idxs.
graph_idxs_reduced = dict()
graph_nodes_reduced = dict()
for g in intersect:
graph_idxs_reduced[g] = graph_idxs[g]
graph_nodes_reduced[g] = graph_nodes[g]
# return intersect, graph_idxs_reduced, graph_nodes_reduced
# Initialize a zero-matrix.
idxs = np.concatenate([i for i in graph_idxs_reduced.values()])
graph_arr_fin = np.zeros(shape=graph_array[idxs].shape)
# Initialize empty maps of graph indices from the old to the new.
nodes_oldnew = dict() # {old_idx: new_idx}.
nodes_newold = dict() # {new_idx: old_idx}
# Re-assign reduced graphs to the zero-matrix.
curr_idx = 0
for seqid, idxs in sorted(graph_idxs_reduced.items()):
for idx in idxs:
nodes_oldnew[idx] = curr_idx
nodes_newold[curr_idx] = idx
graph_arr_fin[curr_idx] = graph_array[idx]
curr_idx += 1
nodes_nbrs_fin = filter_and_reindex_nodes_nbrs(nodes_nbrs, nodes_oldnew)
graph_idxs_fin = filter_and_reindex_graph_idxs(graph_idxs, nodes_oldnew)
graph_nodes_fin = filter_and_reindex_graph_nodes(graph_nodes, nodes_oldnew)
return graph_arr_fin, nodes_nbrs_fin, graph_idxs_fin, graph_nodes_fin
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 initParam(prior, X, N, D, G, M, K, dir_param, prng):
""" initialize variational parameters with prior parameters
"""
[tpM, tpG, lb, ub] = [np.ones(M), np.ones(G), 10., 10.]
tpR = prng.rand(2*M)
[tau_a1, tau_a2, tau_b1, tau_b2, tau_v1, tau_v2] = \
[lb+(ub-lb)*tpR[0 : M], tpM,\
lb+(ub-lb)*tpR[M : 2*M], tpM, \
tpG, tpG]
mu_w = prng.randn(G,D,K)/np.sqrt(D)
sigma_w = np.ones(G*D*K) * 1e-3
mu_b = prng.randn(G, K)/np.sqrt(D)
sigma_b = np.ones(G*K) * 1e-3
phi = np.reshape(prng.dirichlet(np.ones(G)*dir_param, M), M*G)
mu_w = np.reshape(mu_w, G*D*K)
mu_b = np.reshape(mu_b, G*K)
param_init = np.concatenate((tau_a1, tau_a2, tau_b1, tau_b2, phi, tau_v1,\
tau_v2, mu_w, sigma_w, mu_b, sigma_b))
return param_init
def handle_time_inds(times, h=None):
"""
Takes a list of time vectors and returns the unique, potentially augmented,
vector
"""
# get size of each vector
t_sizes = [len(t) for t in times]
# concatenate to single time vector
tt = np.concatenate(times)
# get the distinct times, and the indices
tt_uni, inv_ind = np.unique(tt, return_inverse=True)
# split inv_ind up
ind_ti = util._unpack_vector(inv_ind, t_sizes)
if h is None:
return tt_uni, ind_ti
elif isinstance(h, float) and h > 0:
# augment the time vector so that diff is at most h
ttc, inds_c = augment_times(tt_uni, h)
data_inds = [inds_c[ind_i] for ind_i in ind_ti]
return ttc, data_inds
else:
raise ValueError("h should be a float > 0")
def jacobian(fun, argnum=0):
"""
Returns a function which computes the Jacobian of `fun` with respect to
positional argument number `argnum`, which must be a scalar or array. Unlike
`grad` it is not restricted to scalar-output functions, but also it cannot
take derivatives with respect to some argument types (like lists or dicts).
If the input to `fun` has shape (in1, in2, ...) and the output has shape
(out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).
"""
def getshape(val):
val = getval(val)
assert np.isscalar(val) or isinstance(val, np.ndarray), \
'Jacobian requires input and output to be scalar- or array-valued'
return np.shape(val)
def unit_vectors(shape):
for idxs in it.product(*map(range, shape)):
vect = np.zeros(shape)
vect[idxs] = 1
yield vect
concatenate = lambda lst: np.concatenate(map(np.atleast_1d, lst))
@attach_name_and_doc(fun, argnum, 'Jacobian')
def jacfun(*args, **kwargs):
vjp, ans = make_vjp(fun, argnum)(*args, **kwargs)
outshape = getshape(ans)
grads = map(vjp, unit_vectors(outshape))
jacobian_shape = outshape + getshape(args[argnum])
return np.reshape(concatenate(grads), jacobian_shape)
return jacfun
def Lambda(self):
w = self.W
Rs = []
for k in range(self.K):
wk = tile(w[:self.K, k], (self.conv_data.shape[0], 1))
Rs.append((self.conv_data * wk)[:, None])
return np.concatenate(Rs, axis=2).reshape((self.conv_data.shape[0], self.K, self.K), order='F')
def GenerateDataset(Size, Nfeat, Npoints, Nneurons):
X, Y = [], []
for i in range(Size):
# generate random ffnn
#P = FFNN_Parameters(Nfeat, Nneurons, 1, 20)
x = rnd(Npoints, Nfeat)*20
#P[2] = np.abs(P[2])
#y = np.dot( np.tanh( np.dot(x, P[0]) + P[1]), P[2] )
pr = np.random.randn(x.shape[1], 1)
pr = pr > 0
y = np.dot(x, pr)*0.5
xy = np.concatenate((x,y), axis=1)
"""
W = np.vstack((P[0], [P[1]] , np.transpose( P[2] ))) #
W = np.transpose(W)
W = W[np.lexsort(np.fliplr(W).T)]
W = np.transpose(W)"""
xval = xy.flatten()
yval = pr.flatten()
if X == []:
X = np.zeros((Size, xval.shape[0]))
Y = np.zeros((Size, yval.shape[0]))
X[i,:] = xval
Y[i,:] = yval
return X,Y
def TrackNormal(x):
xx = np.concatenate([x[-1:], x, x[:1]])
p0 = xx[:-2]
p2 = xx[2:]
T = p2 - p0 # track derivative
uT = np.abs(T)
return T / uT
def jacobian(fun, argnum=0):
"""
Returns a function which computes the Jacobian of `fun` with respect to
positional argument number `argnum`, which must be a scalar or array. Unlike
`grad` it is not restricted to scalar-output functions, but also it cannot
take derivatives with respect to some argument types (like lists or dicts).
If the input to `fun` has shape (in1, in2, ...) and the output has shape
(out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).
"""
dummy = lambda: None
def getshape(val):
val = getval(val)
assert np.isscalar(val) or isinstance(val, np.ndarray), \
'Jacobian requires input and output to be scalar- or array-valued'
return np.shape(val)
def list_fun(*args, **kwargs):
val = fun(*args, **kwargs)
dummy.outshape = getshape(val)
return list(np.ravel(val))
concatenate = lambda lst: np.concatenate(list(map(np.atleast_1d, lst)))
@attach_name_and_doc(fun, argnum, 'Jacobian')
def jacfun(*args, **kwargs):
start_node, end_nodes, tape = forward_pass(list_fun, args, kwargs, argnum)
grads = list(map(partial(backward_pass, start_node, tape=tape), end_nodes))
shape = dummy.outshape + getshape(args[argnum])
return np.reshape(concatenate(grads), shape) if shape else grads[0]
return jacfun
def flatten(self, value, covector=False):
if self.shape:
return np.concatenate(
[s.flatten(value[k], covector)
for k, s in sorted(iteritems(self.shape))])
else:
return np.zeros((0,))
def TrackVelocity(x, k, vmax, acmax, Ta):
''' compute the velocity at each point along the track (given
already-computed curvatures) assuming a certain accelration profile '''
v = np.minimum(np.abs(acmax / k)**0.5, vmax)
# also compute arc distance between successive points in x given curvature
# k; for now we'll just use the linear distance though as it's close enough
s = np.abs(np.concatenate([x[1:] - x[:-1], x[:1] - x[-1:]]))
va = 0
T = 0
vout = []
# first pass is just to get the initial velocity
# let's assume it's zero
# for i in range(1, len(k)):
# va = va + (v[i] - va) / Ta
for i in range(0, len(k)):
a = (v[i] - va) / Ta # acceleration
dt = s[i] / (va + a/2) # time to reach next waypoint
va = np.minimum(va + dt * (v[i] - va) / Ta, v[i])
T += dt
vout.append(va)
return np.array(vout), T
def predict(self, x): if self.prob_func_ == "sigmoid": prob = (1.0 / (1.0 + np.exp(-np.dot(x, self.coef_) - self.intercept_)))[:,np.newaxis] prob = np.concatenate((1.0-prob, prob), axis=1) else: # self.prob_func_ == "softmax" prob = np.exp(np.dot(x, self.coef_.T) + self.intercept_) prob /= np.sum(prob, axis=1)[:,np.newaxis] return np.array([self.classes_[i] for i in np.argmax(prob, axis=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 log_marginal_likelihood(params, data):
cluster_lls = []
for log_proportion, mean, chol in zip(*unpack_params(params)):
cov = np.dot(chol.T, chol) + 0.000001 * np.eye(D)
cluster_log_likelihood = log_proportion + mvn.logpdf(data, mean, cov)
cluster_lls.append(np.expand_dims(cluster_log_likelihood, axis=0))
cluster_lls = np.concatenate(cluster_lls, axis=0)
return np.sum(logsumexp(cluster_lls, axis=0))
def sample(var_mixture_params, num_samples, rs):
"""Sample locations aren't a continuous function of parameters
due to multinomial sampling."""
log_weights, var_params = unpack_mixture_params(var_mixture_params)
samples = np.concatenate([component_sample(params_k, num_samples, rs)[:, np.newaxis, :]
for params_k in var_params], axis=1)
ixs = np.random.choice(k, size=num_samples, p=np.exp(log_weights))
return np.array([samples[i, ix, :] for i, ix in enumerate(ixs)])
def plot_single_gp(ax, x0, y0, pred_mean, pred_cov, plot_xs):
ax.cla()
marg_std = np.sqrt(np.diag(pred_cov))
if n_samples > 1:
ax.plot(plot_xs, pred_mean, 'b')
ax.fill(np.concatenate([plot_xs, plot_xs[::-1]]),
np.concatenate([pred_mean - 1.96 * marg_std,
(pred_mean + 1.96 * marg_std)[::-1]]),
alpha=.15, fc='Blue', ec='None')
# Show samples from posterior.
rs = npr.RandomState(0)
sampled_funcs = rs.multivariate_normal(pred_mean, pred_cov*(random), size=n_samples)
ax.plot(plot_xs, sampled_funcs.T)
ax.plot(x0, y0, 'ro')
ax.set_xticks([])
ax.set_yticks([])
def plot_gp(ax, X, y, pred_mean, pred_cov, plot_xs):
ax.cla()
marg_std = np.sqrt(np.diag(pred_cov))
ax.plot(plot_xs, pred_mean, 'b')
ax.fill(np.concatenate([plot_xs, plot_xs[::-1]]),
np.concatenate([pred_mean - 1.96 * marg_std,
(pred_mean + 1.96 * marg_std)[::-1]]),
alpha=.15, fc='Blue', ec='None')
# Show samples from posterior.
rs = npr.RandomState(0)
sampled_funcs = rs.multivariate_normal(pred_mean, pred_cov, size=10)
ax.plot(plot_xs, sampled_funcs.T)
ax.plot(X, y, 'kx')
ax.set_ylim([-1.5, 1.5])
ax.set_xticks([])
ax.set_yticks([])
def x_with_bias(x):
"""Add a row of vector which value=1 for the bias to the input X
x.shape=(batch_size, input_vector_length)
=> x_with_bias(x).shape=(batch_size, input_vector_length + 1)
"""
batch_size = x.shape[0]
return np.concatenate((x, np.ones([batch_size, 1])), axis=1)
def fit_weights_and_save(
weights_file,
ca_data_file='rs_vm_denoise_200605.npy',
opto_silencing_data_file='vip_halo_data_for_sim.npy',
opto_activation_data_file='vip_chrimson_data_for_sim.npy',
constrain_wts=None,
allow_var=True,
fit_s02=True,
constrain_isn=True,
tv=False,
l2_penalty=0.01,
init_noise=0.1,
init_W_from_lsq=False,
scale_init_by=1,
init_W_from_file=False,
init_file=None,
correct_Eta=False,
init_Eta_with_s02=False,
init_Eta12_with_dYY=False,
use_opto_transforms=False,
share_residuals=False,
stimwise=False,
simulate1=True,
simulate2=False,
help_constrain_isn=True,
ignore_halo_vip=False,
verbose=True,
free_amplitude=False,
norm_opto_transforms=False,
zero_extra_weights=None,
no_halo_res=False,
l23_as_l4=False):
nsize, ncontrast = 6, 6
npfile = np.load(ca_data_file, allow_pickle=True)[(
)] #,{'rs':rs,'rs_denoise':rs_denoise},allow_pickle=True)
rs = npfile['rs']
#rs_denoise = npfile['rs_denoise']
nsize, ncontrast, ndir = 6, 6, 8
#ori_dirs = [[0,4],[2,6]] #[[0,4],[1,3,5,7],[2,6]]
ori_dirs = [[0, 1, 2, 3, 4, 5, 6, 7]]
nT = len(ori_dirs)
nS = len(rs[0])
def sum_to_1(r):
R = r.reshape((r.shape[0], -1))
#R = R/np.nansum(R[:,~np.isnan(R.sum(0))],axis=1)[:,np.newaxis]
R = R / np.nansum(R, axis=1)[:, np.newaxis] # changed 8/28
return R
def norm_to_mean(r):
R = r.reshape((r.shape[0], -1))
R = R / np.nanmean(R[:, ~np.isnan(R.sum(0))], axis=1)[:, np.newaxis]
return R
Rs = [[None, None] for i in range(len(rs))]
Rso = [[[None for iT in range(nT)] for iS in range(nS)]
for icelltype in range(len(rs))]
rso = [[[None for iT in range(nT)] for iS in range(nS)]
for icelltype in range(len(rs))]
for iR, r in enumerate(rs): #rs_denoise):
#print(iR)
for ialign in range(nS):
#Rs[iR][ialign] = r[ialign][:,:nsize,:]
#sm = np.nanmean(np.nansum(np.nansum(Rs[iR][ialign],1),1))
#Rs[iR][ialign] = Rs[iR][ialign]/sm
#print('frac isnan Rs %d,%d: %f'%(iR,ialign,np.isnan(r[ialign]).mean()))
Rs[iR][ialign] = sum_to_1(r[ialign][:, :nsize, :])
# Rs[iR][ialign] = von_mises_denoise(Rs[iR][ialign].reshape((-1,nsize,ncontrast,ndir)))
kernel = np.ones((1, 2, 2))
kernel = kernel / kernel.sum()
for iR, r in enumerate(rs):
for ialign in range(nS):
for iori in range(nT):
#print('this Rs shape: '+str(Rs[iR][ialign].shape))
#print('this Rs reshaped shape: '+str(Rs[iR][ialign].reshape((-1,nsize,ncontrast,ndir))[:,:,:,ori_dirs[iori]].shape))
#print('this Rs max percent nan: '+str(np.isnan(Rs[iR][ialign].reshape((-1,nsize,ncontrast,ndir))[:,:,:,ori_dirs[iori]]).mean(-1).max()))
Rso[iR][ialign][iori] = np.nanmean(
Rs[iR][ialign].reshape(
(-1, nsize, ncontrast, ndir))[:, :, :, ori_dirs[iori]],
-1)
Rso[iR][ialign][iori][:, :, 0] = np.nanmean(
Rso[iR][ialign][iori][:, :, 0],
1)[:, np.newaxis] # average 0 contrast values
#print('frac isnan pre-conv Rso %d,%d,%d: %f'%(iR,ialign,iori,np.isnan(Rso[iR][ialign][iori]).mean()))
Rso[iR][ialign][iori][:, 1:, 1:] = ssi.convolve(
Rso[iR][ialign][iori], kernel, 'valid')
Rso[iR][ialign][iori] = Rso[iR][ialign][iori].reshape(
Rso[iR][ialign][iori].shape[0], -1)
#print('frac isnan Rso %d,%d,%d: %f'%(iR,ialign,iori,np.isnan(Rso[iR][ialign][iori]).mean()))
#print('sum of Rso isnan: '+str(np.isnan(Rso[iR][ialign][iori]).sum(1)))
#Rso[iR][ialign][iori] = Rso[iR][ialign][iori]/np.nanmean(Rso[iR][ialign][iori],-1)[:,np.newaxis]
def set_bound(bd, code, val=0):
# set bounds to 0 where 0s occur in 'code'
for iitem in range(len(bd)):
bd[iitem][code[iitem]] = val
nN = 36
nS = 2
nP = 2
nT = 1
nQ = 4
# code for bounds: 0 , constrained to 0
# +/-1 , constrained to +/-1
# 1.5, constrained to [0,1]
# 2 , constrained to [0,inf)
# -2 , constrained to (-inf,0]
# 3 , unconstrained
Wmx_bounds = 3 * np.ones((nP, nQ), dtype=int)
Wmx_bounds[0, :] = 2 # L4 PCs are excitatory
Wmx_bounds[0, 1] = 0 # SSTs don't receive L4 input
if allow_var:
Wsx_bounds = 3 * np.ones(
Wmx_bounds.shape) #Wmx_bounds.copy()*0 #np.zeros_like(Wmx_bounds)
Wsx_bounds[0, 1] = 0
else:
Wsx_bounds = np.zeros(
Wmx_bounds.shape) #Wmx_bounds.copy()*0 #np.zeros_like(Wmx_bounds)
Wmy_bounds = 3 * np.ones((nQ, nQ), dtype=int)
Wmy_bounds[0, :] = 2 # PCs are excitatory
Wmy_bounds[1:, :] = -2 # all the cell types except PCs are inhibitory
Wmy_bounds[1, 1] = 0 # SSTs don't inhibit themselves
# Wmy_bounds[3,1] = 0 # PVs are allowed to inhibit SSTs, consistent with Hillel's unpublished results, but not consistent with Pfeffer et al.
Wmy_bounds[
2,
0] = 0 # VIPs don't inhibit L2/3 PCs. According to Pfeffer et al., only L5 PCs were found to get VIP inhibition
if not zero_extra_weights is None:
Wmx_bounds[zero_extra_weights[0]] = 0
Wmy_bounds[zero_extra_weights[1]] = 0
if allow_var:
Wsy_bounds = 3 * np.ones(
Wmy_bounds.shape) #Wmy_bounds.copy()*0 #np.zeros_like(Wmy_bounds)
Wsy_bounds[1, 1] = 0
Wsy_bounds[3, 1] = 0
Wsy_bounds[2, 0] = 0
else:
Wsy_bounds = np.zeros(
Wmy_bounds.shape) #Wmy_bounds.copy()*0 #np.zeros_like(Wmy_bounds)
if not constrain_wts is None:
for wt in constrain_wts:
Wmy_bounds[wt[0], wt[1]] = 0
Wsy_bounds[wt[0], wt[1]] = 0
def tile_nS_nT_nN(kernel):
row = np.concatenate([kernel for idim in range(nS * nT)],
axis=0)[np.newaxis, :]
tiled = np.concatenate([row for irow in range(nN)], axis=0)
return tiled
def set_bounds_by_code(lb, ub, bdlist):
set_bound(lb, [bd == 0 for bd in bdlist], val=0)
set_bound(ub, [bd == 0 for bd in bdlist], val=0)
set_bound(lb, [bd == 2 for bd in bdlist], val=0)
set_bound(ub, [bd == -2 for bd in bdlist], val=0)
set_bound(lb, [bd == 1 for bd in bdlist], val=1)
set_bound(ub, [bd == 1 for bd in bdlist], val=1)
set_bound(lb, [bd == 1.5 for bd in bdlist], val=0)
set_bound(ub, [bd == 1.5 for bd in bdlist], val=1)
set_bound(lb, [bd == -1 for bd in bdlist], val=-1)
set_bound(ub, [bd == -1 for bd in bdlist], val=-1)
if fit_s02:
s02_bounds = 2 * np.ones(
(nQ, )) # permitting noise as a free parameter
else:
s02_bounds = np.ones((nQ, ))
k_bounds = 1.5 * np.ones((nQ * (nS - 1), ))
#k_bounds[1] = 0 # temporary: spatial kernel constrained to 0 for SST
#k_bounds[2] = 0 # temporary: spatial kernel constrained to 0 for VIP
kappa_bounds = np.ones((1, ))
# kappa_bounds = 2*np.ones((1,))
T_bounds = 1.5 * np.ones((nQ * (nT - 1), ))
X_bounds = tile_nS_nT_nN(np.array([2, 1]))
# X_bounds = np.array([np.array([2,1,2,1])]*nN)
Xp_bounds = tile_nS_nT_nN(np.array([3, 1]))
# Xp_bounds = np.array([np.array([3,1,3,1])]*nN)
# Y_bounds = tile_nS_nT_nN(2*np.ones((nQ,)))
# # Y_bounds = 2*np.ones((nN,nT*nS*nQ))
Eta_bounds = tile_nS_nT_nN(3 * np.ones((nQ, )))
# Eta_bounds = 3*np.ones((nN,nT*nS*nQ))
if allow_var:
Xi_bounds = tile_nS_nT_nN(3 * np.ones((nQ, )))
else:
Xi_bounds = tile_nS_nT_nN(np.zeros((nQ, )))
# Xi_bounds = 3*np.ones((nN,nT*nS*nQ))
h1_bounds = -2 * np.ones((1, ))
h2_bounds = 2 * np.ones((1, ))
bl_bounds = 3 * np.ones((nQ, ))
if free_amplitude:
amp_bounds = 2 * np.ones((nT * nS * nQ, ))
else:
amp_bounds = 1 * np.ones((nT * nS * nQ, ))
# shapes = [(nP,nQ),(nQ,nQ),(nP,nQ),(nQ,nQ),(nQ,),(nQ,),(1,),(nN,nS*nP),(nN,nS*nQ),(nN,nS*nQ),(nN,nS*nQ)]
shapes1 = [(nP, nQ), (nQ, nQ), (nP, nQ),
(nQ, nQ), (nQ, ), (nQ * (nS - 1), ), (1, ), (nQ * (nT - 1), ),
(1, ), (1, ), (nQ, ), (nQ * nS * nT, )]
shapes2 = [(nN, nT * nS * nP), (nN, nT * nS * nP), (nN, nT * nS * nQ),
(nN, nT * nS * nQ), (nN, nT * nS * nP), (nN, nT * nS * nP)]
#print('size of shapes1: '+str(np.sum([np.prod(shp) for shp in shapes1])))
#print('size of shapes2: '+str(np.sum([np.prod(shp) for shp in shapes2])))
# Wmx, Wmy, Wsx, Wsy, s02, k, kappa,T, h1, h2
#XX, XXp, Eta, Xi
#bdlist = [Wmx_bounds,Wmy_bounds,Wsx_bounds,Wsy_bounds,s02_bounds,k_bounds,kappa_bounds,T_bounds,X_bounds,Xp_bounds,Eta_bounds,Xi_bounds,h1_bounds,h2_bounds]
bd1list = [
Wmx_bounds, Wmy_bounds, Wsx_bounds, Wsy_bounds, s02_bounds, k_bounds,
kappa_bounds, T_bounds, h1_bounds, h2_bounds, bl_bounds, amp_bounds
]
bd2list = [X_bounds, Xp_bounds, Eta_bounds, Xi_bounds, X_bounds, X_bounds]
lb1, ub1 = [[sgn * np.inf * np.ones(shp) for shp in shapes1]
for sgn in [-1, 1]]
set_bounds_by_code(lb1, ub1, bd1list)
lb2, ub2 = [[sgn * np.inf * np.ones(shp) for shp in shapes2]
for sgn in [-1, 1]]
set_bounds_by_code(lb2, ub2, bd2list)
#set_bound(lb,[bd==0 for bd in bdlist],val=0)
#set_bound(ub,[bd==0 for bd in bdlist],val=0)
#
#set_bound(lb,[bd==2 for bd in bdlist],val=0)
#
#set_bound(ub,[bd==-2 for bd in bdlist],val=0)
#
#set_bound(lb,[bd==1 for bd in bdlist],val=1)
#set_bound(ub,[bd==1 for bd in bdlist],val=1)
#
#set_bound(lb,[bd==1.5 for bd in bdlist],val=0)
#set_bound(ub,[bd==1.5 for bd in bdlist],val=1)
#
#set_bound(lb,[bd==-1 for bd in bdlist],val=-1)
#set_bound(ub,[bd==-1 for bd in bdlist],val=-1)
# for bd in [lb,ub]:
# for ind in [2,3]:
# bd[ind][:,1] = 0
# temporary for no variation expt.
# lb[2] = np.zeros_like(lb[2])
# lb[3] = np.zeros_like(lb[3])
# lb[4] = np.ones_like(lb[4])
# lb[5] = np.zeros_like(lb[5])
# ub[2] = np.zeros_like(ub[2])
# ub[3] = np.zeros_like(ub[3])
# ub[4] = np.ones_like(ub[4])
# ub[5] = np.ones_like(ub[5])
# temporary for no variation expt.
lb1 = np.concatenate([a.flatten() for a in lb1])
ub1 = np.concatenate([b.flatten() for b in ub1])
lb2 = np.concatenate([a.flatten() for a in lb2])
ub2 = np.concatenate([b.flatten() for b in ub2])
bounds1 = [(a, b) for a, b in zip(lb1, ub1)]
bounds2 = [(a, b) for a, b in zip(lb2, ub2)]
nS = 2
#print('nT: '+str(nT))
ndims = 5
ncelltypes = 5
Yhat = [[None for iT in range(nT)] for iS in range(nS)]
Xhat = [[None for iT in range(nT)] for iS in range(nS)]
Ypc_list = [[None for iT in range(nT)] for iS in range(nS)]
Xpc_list = [[None for iT in range(nT)] for iS in range(nS)]
mx = [None for iS in range(nS)]
for iS in range(nS):
mx[iS] = np.zeros((ncelltypes, ))
yy = [None for icelltype in range(ncelltypes)]
for icelltype in range(ncelltypes):
yy[icelltype] = np.nanmean(Rso[icelltype][iS][0], 0)
mx[iS][icelltype] = np.nanmax(yy[icelltype])
for iT in range(nT):
y = [
np.nanmean(Rso[icelltype][iS][iT], axis=0)[:, np.newaxis] /
mx[iS][icelltype] for icelltype in range(1, ncelltypes)
]
Ypc_list[iS][iT] = [None for icelltype in range(1, ncelltypes)]
for icelltype in range(1, ncelltypes):
rss = Rso[icelltype][iS][iT].copy(
) #/mx[iS][icelltype] #.reshape(Rs[icelltype][ialign].shape[0],-1)
#print('sum of isnan: '+str(np.isnan(rss).sum(1)))
#rss = Rso[icelltype][iS][iT].copy() #.reshape(Rs[icelltype][ialign].shape[0],-1)
rss = rss[np.isnan(rss).sum(1) == 0]
# print(rss.max())
# rss[rss<0] = 0
# rss = rss[np.random.randn(rss.shape[0])>0]
try:
u, s, v = np.linalg.svd(rss - np.mean(rss, 0)[np.newaxis])
Ypc_list[iS][iT][icelltype - 1] = [
(s[idim], v[idim]) for idim in range(ndims)
]
# print('yep on Y')
# print(np.min(np.sum(rs[icelltype][iS][iT],axis=1)))
except:
print('nope on Y')
#print('shape of rss: '+str(rss.shape))
#print('mean of rss: '+str(np.mean(np.isnan(rss))))
#print('min of this rs: '+str(np.min(np.sum(rs[icelltype][iS][iT],axis=1))))
Yhat[iS][iT] = np.concatenate(y, axis=1)
# x = sim_utils.columnize(Rso[0][iS][iT])[:,np.newaxis]
icelltype = 0
#x = np.nanmean(Rso[icelltype][iS][iT],0)[:,np.newaxis]#/mx[iS][icelltype]
x = np.nanmean(Rso[icelltype][iS][iT],
0)[:, np.newaxis] / mx[iS][icelltype]
# opto_column = np.concatenate((np.zeros((nN,)),np.zeros((nNO/2,)),np.ones((nNO/2,))),axis=0)[:,np.newaxis]
Xhat[iS][iT] = np.concatenate((x, np.ones_like(x)), axis=1)
# Xhat[iS][iT] = np.concatenate((x,np.ones_like(x),opto_column),axis=1)
icelltype = 0
#rss = Rso[icelltype][iS][iT].copy()/mx[iS][icelltype]
rss = Rso[icelltype][iS][iT].copy()
rss = rss[np.isnan(rss).sum(1) == 0]
# try:
u, s, v = np.linalg.svd(rss - rss.mean(0)[np.newaxis])
Xpc_list[iS][iT] = [None for iinput in range(2)]
Xpc_list[iS][iT][0] = [(s[idim], v[idim]) for idim in range(ndims)]
Xpc_list[iS][iT][1] = [(0, np.zeros((Xhat[0][0].shape[0], )))
for idim in range(ndims)]
# except:
# print('nope on X')
# print(np.mean(np.isnan(rss)))
# print(np.min(np.sum(Rso[icelltype][iS][iT],axis=1)))
nN, nP = Xhat[0][0].shape
#print('nP: '+str(nP))
nQ = Yhat[0][0].shape[1]
def compute_f_(Eta, Xi, s02):
return sim_utils.f_miller_troyer(
Eta, Xi**2 + np.concatenate([s02 for ipixel in range(nS * nT)]))
def compute_fprime_m_(Eta, Xi, s02):
return sim_utils.fprime_miller_troyer(
Eta, Xi**2 + np.concatenate([s02
for ipixel in range(nS * nT)])) * Xi
def compute_fprime_s_(Eta, Xi, s02):
s2 = Xi**2 + np.concatenate((s02, s02), axis=0)
return sim_utils.fprime_s_miller_troyer(Eta, s2) * (Xi / s2)
def sorted_r_eigs(w):
drW, prW = np.linalg.eig(w)
srtinds = np.argsort(drW)
return drW[srtinds], prW[:, srtinds]
# 0.Wmx, 1.Wmy, 2.Wsx, 3.Wsy, 4.s02,5.K, 6.kappa,7.T,8.XX, 9.XXp, 10.Eta, 11.Xi, 12.h1, 13.h2
shapes1 = [(nP, nQ), (nQ, nQ), (nP, nQ),
(nQ, nQ), (nQ, ), (nQ * (nS - 1), ), (1, ), (nQ * (nT - 1), ),
(1, ), (1, ), (nQ, ), (nT * nS * nQ, )]
shapes2 = [(nN, nT * nS * nP), (nN, nT * nS * nP), (nN, nT * nS * nQ),
(nN, nT * nS * nQ), (nN, nT * nS * nP), (nN, nT * nS * nP)]
#print('size of shapes1: '+str(np.sum([np.prod(shp) for shp in shapes1])))
#print('size of shapes2: '+str(np.sum([np.prod(shp) for shp in shapes2])))
import calnet.fitting_spatial_feature
import sim_utils
YYhat = calnet.utils.flatten_nested_list_of_2d_arrays(Yhat)
XXhat = calnet.utils.flatten_nested_list_of_2d_arrays(Xhat)
opto_dict = np.load(opto_silencing_data_file, allow_pickle=True)[()]
Yhat_opto = opto_dict['Yhat_opto']
Yhat_opto = np.nanmean(np.reshape(Yhat_opto, (nN, 2, nS, 2, nQ)),
3).reshape((nN * 2, -1))
Yhat_opto[0::12] = np.nanmean(Yhat_opto[0::12], axis=0)[np.newaxis]
Yhat_opto[1::12] = np.nanmean(Yhat_opto[1::12], axis=0)[np.newaxis]
Yhat_opto = Yhat_opto / np.nanmax(Yhat_opto[0::2], 0)[np.newaxis, :]
#print(Yhat_opto.shape)
h_opto = opto_dict['h_opto']
#dYY1 = Yhat_opto[1::2]-Yhat_opto[0::2]
Xhat_opto = opto_dict['Xhat_opto']
Xhat_opto = np.nanmean(np.reshape(Xhat_opto, (nN, 2, nS, 2, nP)),
3).reshape((nN * 2, -1))
Xhat_opto[0::12] = np.nanmean(Xhat_opto[0::12], axis=0)[np.newaxis]
Xhat_opto[1::12] = np.nanmean(Xhat_opto[1::12], axis=0)[np.newaxis]
Xhat_opto = Xhat_opto / np.nanmax(Xhat_opto[0::2], 0)[np.newaxis, :]
YYhat_halo = Yhat_opto.reshape((nN, 2, -1))
opto_transform1 = calnet.utils.fit_opto_transform(
YYhat_halo, norm01=norm_opto_transforms)
if l23_as_l4:
Xhat_opto[:, 0::2] = Yhat_opto[:, 0::4]
Xhat_halo = Xhat_opto.reshape((nN, 2, -1))
opto_transform1x = calnet.utils.fit_opto_transform(
Xhat_halo, norm01=norm_opto_transforms)
if no_halo_res:
opto_transform1.res[:, [0, 2, 3, 4, 6, 7]] = 0
opto_transform1x.res[:, :] = 0
dYY1 = opto_transform1.transform(YYhat) - opto_transform1.preprocess(YYhat)
dXX1 = opto_transform1x.transform(XXhat) - opto_transform1x.preprocess(
XXhat)
print('delta bias: %f' % dXX1[:, 1].mean())
#YYhat_halo_sim = calnet.utils.simulate_opto_effect(YYhat,YYhat_halo)
#dYY1 = YYhat_halo_sim[:,1,:] - YYhat_halo_sim[:,0,:]
def overwrite_plus_n(arr, to_overwrite, n):
arr[:, to_overwrite] = arr[:, int(to_overwrite + n)]
return arr
for to_overwrite in [1, 2]:
n = 4
dYY1,opto_transform1.slope,opto_transform1.intercept,opto_transform1.res \
= [overwrite_plus_n(x,to_overwrite,n) for x in \
[dYY1,opto_transform1.slope,opto_transform1.intercept,opto_transform1.res]]
for to_overwrite in [7]:
n = -4
dYY1,opto_transform1.slope,opto_transform1.intercept,opto_transform1.res \
= [overwrite_plus_n(x,to_overwrite,n) for x in \
[dYY1,opto_transform1.slope,opto_transform1.intercept,opto_transform1.res]]
for to_overwrite in [2]:
n = -2
dXX1,opto_transform1x.slope,opto_transform1x.intercept,opto_transform1x.res \
= [overwrite_plus_n(x,to_overwrite,n) for x in \
[dXX1,opto_transform1x.slope,opto_transform1x.intercept,opto_transform1x.res]]
if ignore_halo_vip:
dYY1[:, 2::nQ] = np.nan
#for to_overwrite in [1,2]:
# dYY1[:,to_overwrite] = dYY1[:,to_overwrite+4]
#for to_overwrite in [7]:
# dYY1[:,to_overwrite] = dYY1[:,to_overwrite-4]
#Yhat_opto = opto_dict['Yhat_opto']
#for iS in range(nS):
# mx = np.zeros((nQ,))
# for iQ in range(nQ):
# slicer = slice(nQ*nT*iS+iQ,nQ*nT*(1+iS),nQ)
# mx[iQ] = np.nanmax(Yhat_opto[0::2][:,slicer])
# Yhat_opto[:,slicer] = Yhat_opto[:,slicer]/mx[iQ]
##Yhat_opto = Yhat_opto/Yhat_opto[0::2].max(0)[np.newaxis,:]
#print(Yhat_opto.shape)
#h_opto = opto_dict['h_opto']
#dYY1 = Yhat_opto[1::2]-Yhat_opto[0::2]
#for to_overwrite in [1,2,5,6]: # overwrite sst and vip with off-centered values
# dYY1[:,to_overwrite] = dYY1[:,to_overwrite+8]
#for to_overwrite in [11,15]:
# dYY1[:,to_overwrite] = np.nan #dYY1[:,to_overwrite-8]
opto_dict = np.load(opto_activation_data_file, allow_pickle=True)[()]
Yhat_opto = opto_dict['Yhat_opto']
Yhat_opto = np.nanmean(np.reshape(Yhat_opto, (nN, 2, nS, 2, nQ)),
3).reshape((nN * 2, -1))
Yhat_opto[0::12] = np.nanmean(Yhat_opto[0::12], axis=0)[np.newaxis]
Yhat_opto[1::12] = np.nanmean(Yhat_opto[1::12], axis=0)[np.newaxis]
Yhat_opto = Yhat_opto / Yhat_opto[0::2].max(0)[np.newaxis, :]
#print(Yhat_opto.shape)
h_opto = opto_dict['h_opto']
#dYY2 = Yhat_opto[1::2]-Yhat_opto[0::2]
YYhat_chrimson = Yhat_opto.reshape((nN, 2, -1))
opto_transform2 = calnet.utils.fit_opto_transform(
YYhat_chrimson, norm01=norm_opto_transforms)
Xhat_opto = np.nan * np.ones((Yhat_opto.shape[0], nP * nS * nT))
Xhat_opto[:, 1::2] = 1
if l23_as_l4:
Xhat_opto[:, 0::2] = Yhat_opto[:, 0::4]
Xhat_chrimson = Xhat_opto.reshape((nN, 2, -1))
opto_transform2x = calnet.utils.fit_opto_transform(
Xhat_chrimson, norm01=norm_opto_transforms)
dYY2 = opto_transform2.transform(YYhat) - opto_transform2.preprocess(YYhat)
dXX2 = opto_transform2x.transform(XXhat) - opto_transform2x.preprocess(
XXhat)
#YYhat_chrimson_sim = calnet.utils.simulate_opto_effect(YYhat,YYhat_chrimson)
#dYY2 = YYhat_chrimson_sim[:,1,:] - YYhat_chrimson_sim[:,0,:]
#Yhat_opto = opto_dict['Yhat_opto']
#for iS in range(nS):
# mx = np.zeros((nQ,))
# for iQ in range(nQ):
# slicer = slice(nQ*nT*iS+iQ,nQ*nT*(1+iS),nQ)
# mx[iQ] = np.nanmax(Yhat_opto[0::2][:,slicer])
# Yhat_opto[:,slicer] = Yhat_opto[:,slicer]/mx[iQ]
##Yhat_opto = Yhat_opto/Yhat_opto[0::2].max(0)[np.newaxis,:]
#print(Yhat_opto.shape)
#h_opto = opto_dict['h_opto']
#dYY2 = Yhat_opto[1::2]-Yhat_opto[0::2]
#print('dYY1 mean: %03f'%np.nanmean(np.abs(dYY1)))
#print('dYY2 mean: %03f'%np.nanmean(np.abs(dYY2)))
dYY = np.concatenate((dYY1, dYY2), axis=0)
dXX = np.concatenate((dXX1, dXX2), axis=0)
#titles = ['VIP silencing','VIP activation']
#for itype in [0,1,2,3]:
# plt.figure(figsize=(5,2.5))
# for iyy,dyy in enumerate([dYY1,dYY2]):
# plt.subplot(1,2,iyy+1)
# if np.sum(np.isnan(dyy[:,itype]))==0:
# sca.scatter_size_contrast(YYhat[:,itype],YYhat[:,itype]+dyy[:,itype],nsize=6,ncontrast=6)#,mn=0)
# plt.title(titles[iyy])
# plt.xlabel('cell type %d event rate, \n light off'%itype)
# plt.ylabel('cell type %d event rate, \n light on'%itype)
# ut.erase_top_right()
# plt.tight_layout()
# ut.mkdir('figures')
# plt.savefig('figures/scatter_light_on_light_off_target_celltype_%d.eps'%itype)
opto_mask = ~np.isnan(dYY)
opto_maskX = ~np.isnan(dXX)
#dYY[nN:][~opto_mask[nN:]] = -dYY[:nN][~opto_mask[nN:]]
#print('mean of opto_mask: '+str(opto_mask.mean()))
#dYY[~opto_mask] = 0
def zero_nans(arr):
arr[np.isnan(arr)] = 0
return arr
#dYY,opto_transform1.slope,opto_transform1.intercept,opto_transform1.res,\
# opto_transform2.slope,opto_transform2.intercept,opto_transform2.res\
# = [zero_nans(x) for x in \
# [dYY,opto_transform1.slope,opto_transform1.intercept,opto_transform1.res,\
# opto_transform2.slope,opto_transform2.intercept,opto_transform2.res]]
dYY = zero_nans(dYY)
dXX = zero_nans(dXX)
# for cell types that were not measured with chrimson, fill with values inferred from halo data (this shouldn't matter, as these entries are masked by opto_mask)
to_adjust = np.logical_or(np.isnan(opto_transform2.slope[0]),
np.isnan(opto_transform2.intercept[0]))
opto_transform2.slope[:,
to_adjust] = 1 / opto_transform1.slope[:, to_adjust]
opto_transform2.intercept[:,
to_adjust] = -opto_transform1.intercept[:,
to_adjust] / opto_transform1.slope[:,
to_adjust]
opto_transform2.res[:,
to_adjust] = -opto_transform1.res[:,
to_adjust] / opto_transform1.slope[:,
to_adjust]
#np.save('/Users/dan/Documents/notebooks/mossing-PC/shared_data/calnet_data/dYY.npy',dYY)
from importlib import reload
reload(calnet)
#reload(calnet.fitting_2step_spatial_feature_opto_tight_nonlinear)
reload(sim_utils)
# reload(calnet.fitting_spatial_feature)
# W0list = [np.ones(shp) for shp in shapes]
wt_dict = {}
wt_dict['X'] = 1
wt_dict['Y'] = 3
wt_dict['Eta'] = 3 # 1 #
wt_dict['Xi'] = 0.1
wt_dict['stims'] = np.ones((nN, 1)) #(np.arange(30)/30)[:,np.newaxis]**1 #
wt_dict['barrier'] = 0. #30.0 #0.1
wt_dict['opto'] = 1 #1e1
wt_dict['isn'] = 0.3
wt_dict['tv'] = 1
spont_frac = 0.5
pc_frac = 0.5
wt_dict['stimsOpto'] = (1 - spont_frac) * 6 / 5 * np.ones((nN, 1))
wt_dict['stimsOpto'][0::6] = spont_frac * 6
wt_dict['celltypesOpto'] = (1 - pc_frac) * 4 / 3 * np.ones(
(1, nQ * nS * nT))
wt_dict['celltypesOpto'][0, 0::nQ] = pc_frac * 4
wt_dict['dirOpto'] = np.array((1, 0.3))
wt_dict['dYY'] = 10 #10
wt_dict['dXX'] = 10 #10
wt_dict['coupling'] = 1e-3
wt_dict['smi'] = 0.1
wt_dict['smi_halo'] = 30
wt_dict['smi_chrimson'] = 0.1
##temporary no_opto
wt_dict['opto'] = 0.01 #0
wt_dict['dirOpto'] = np.array((1, 1))
wt_dict['stimsOpto'] = np.ones((nN, 1))
wt_dict['celltypesOpto'] = np.ones((1, nQ * nS * nT))
wt_dict['smi'] = 0 #0.01 # 0
wt_dict['smi_halo'] = 0 #1 # 0
wt_dict['smi_chrimson'] = 0 #0.01 # 0
wt_dict['isn'] = 0.1
wt_dict['tv'] = 0.1
wt_dict['X'] = 3
#wt_dict['Eta'] = 300 # 1 #
## temporary opto from no_opto
#wt_dict['opto'] = 0.01
#wt_dict['tv'] = 0.3#0.1
np.save(
'XXYYhat.npy', {
'YYhat': YYhat,
'XXhat': XXhat,
'rs': rs,
'Rs': Rs,
'Rso': Rso,
'Ypc_list': Ypc_list,
'Xpc_list': Xpc_list
})
Eta0 = invert_f_mt(YYhat)
# Wmx, Wmy, Wsx, Wsy, s02, k, kappa,T, h1, h2
#XX, XXp, Eta, Xi, XX1, XX2
ntries = 1
nhyper = 1
dt = 1e-1
niter = int(np.round(10 / dt)) #int(1e4)
perturbation_size = 5e-2
# learning_rate = 1e-4 # 1e-5 #np.linspace(3e-4,1e-3,niter+1) # 1e-5
#l2_penalty = 0.1
W1t = [[None for itry in range(ntries)] for ihyper in range(nhyper)]
W2t = [[None for itry in range(ntries)] for ihyper in range(nhyper)]
loss = np.zeros((nhyper, ntries))
is_neg = np.array([b[1] for b in bounds1]) == 0
counter = 0
negatize = [np.zeros(shp, dtype='bool') for shp in shapes1]
#print(shapes1)
for ishp, shp in enumerate(shapes1):
nel = np.prod(shp)
negatize[ishp][:][is_neg[counter:counter + nel].reshape(shp)] = True
counter = counter + nel
for ihyper in range(nhyper):
for itry in range(ntries):
#print((ihyper,itry))
W10list = [
init_noise * (ihyper + 1) * np.random.rand(*shp)
for shp in shapes1
]
W20list = [
init_noise * (ihyper + 1) * np.random.rand(*shp)
for shp in shapes2
]
#print('size of shapes1: '+str(np.sum([np.prod(shp) for shp in shapes1])))
#print('size of w10: '+str(np.sum([np.size(x) for x in W10list])))
#print('len(W10list) : '+str(len(W10list)))
counter = 0
for ishp, shp in enumerate(shapes1):
W10list[ishp][negatize[ishp]] = -W10list[ishp][negatize[ishp]]
W10list[4] = np.ones(shapes1[4]) # s02
W10list[5] = np.ones(shapes1[5]) # K
W10list[6] = np.ones(shapes1[6]) # kappa
W10list[7] = np.ones(shapes1[7]) # T
W20list[0] = np.concatenate(Xhat, axis=1) #XX
W20list[1] = np.zeros_like(W20list[1]) #XXp
W20list[2] = Eta0.copy() #np.zeros(shapes[10]) #Eta
W20list[3] = np.zeros(shapes2[3]) #Xi
W20list[4] = np.concatenate(Xhat, axis=1) #XX
W20list[5] = np.concatenate(Xhat, axis=1) #XX
#[Wmx,Wmy,Wsx,Wsy,s02,k,kappa,T,XX,XXp,Eta,Xi]
if init_W_from_lsq:
W10list[0], W10list[1] = initialize_W(Xhat,
Yhat,
scale_by=scale_init_by)
for ivar in range(0, 2):
W10list[
ivar] = W10list[ivar] + init_noise * np.random.randn(
*W10list[ivar].shape)
if constrain_isn:
W10list[1][0, 0] = 3
if help_constrain_isn:
W10list[1][0, 3] = 5
W10list[1][3, 0] = -5
W10list[1][3, 3] = -5
else:
W10list[1][0, 1:4] = 5
W10list[1][1:4, 0] = -5
if init_W_from_file:
npyfile = np.load(init_file, allow_pickle=True)[()]
#Wmx,Wmy,Wsx,Wsy,s02,K,kappa,T,h1,h2,bl,amp = parse_W1(W1)
#XX,XXp,Eta,Xi,XX1,XX2 = parse_W2(W2)
#Wmx,Wmy,Wsx,Wsy,s02,K,kappa,T,XX,XXp,Eta,Xi,XX1,XX2,h1,h2,bl,amp = parse_W1(W1)
if len(npyfile['as_list']) == 18:
W10list = [
npyfile['as_list'][ivar]
for ivar in [0, 1, 2, 3, 4, 5, 6, 7, 14, 15, 16, 17]
]
W20list = [
npyfile['as_list'][ivar]
for ivar in [8, 9, 10, 11, 12, 13]
]
elif len(npyfile['as_list']) == 16:
W10list = [
npyfile['as_list'][ivar]
for ivar in [0, 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15]
]
W20list = [
npyfile['as_list'][ivar]
for ivar in [8, 9, 10, 11, 8, 8]
]
if W20list[0].size == nN * nS * 2 * nP:
#assert(True==False)
W10list[7] = np.array(())
W10list[1][1, 0] = W10list[1][1, 0]
W20list[0] = np.nanmean(
W20list[0].reshape((nN, nS, 2, nP)), 2).flatten() #XX
W20list[1] = np.nanmean(
W20list[1].reshape((nN, nS, 2, nP)), 2).flatten() #XXp
W20list[2] = np.nanmean(
W20list[2].reshape((nN, nS, 2, nQ)), 2).flatten() #Eta
W20list[3] = np.nanmean(
W20list[3].reshape((nN, nS, 2, nQ)), 2).flatten() #Xi
W20list[4] = np.nanmean(
W20list[4].reshape((nN, nS, 2, nP)), 2).flatten() #XX1
W20list[5] = np.nanmean(
W20list[5].reshape((nN, nS, 2, nP)), 2).flatten() #XX2
if correct_Eta:
#assert(True==False)
W20list[2] = Eta0.copy()
if len(W10list) < len(shapes1):
#assert(True==False)
W10list = W10list + [
np.array(1),
np.zeros((nQ, )),
np.zeros((nT * nS * nQ, ))
] # add h2, bl, amp
if init_Eta_with_s02:
#assert(True==False)
s02 = W10list[4].copy()
Eta0 = invert_f_mt_with_s02(YYhat, s02, nS=nS, nT=nT)
W20list[2] = Eta0.copy()
#if init_Eta12_with_dYY:
# Eta0 = W20list[2].copy().reshape((nN,nQ*nS*nT))
# Xi0 = W20list[3].copy().reshape((nN,nQ*nS*nT))
# s020 = W10list[4].copy()
# YY0s = compute_f_(Eta0,Xi0,s020)
#titles = ['VIP silencing','VIP activation']
#for itype in [0,1,2,3]:
# plt.figure(figsize=(5,2.5))
# for iyy,yy in enumerate([YY10s,YY20s]):
# plt.subplot(1,2,iyy+1)
# if np.sum(np.isnan(yy[:,itype]))==0:
# sca.scatter_size_contrast(YY0s[:,itype],yy[:,itype],nsize=6,ncontrast=6)#,mn=0)
# plt.title(titles[iyy])
# plt.xlabel('cell type %d event rate, \n light off'%itype)
# plt.ylabel('cell type %d event rate, \n light on'%itype)
# ut.erase_top_right()
# plt.tight_layout()
# ut.mkdir('figures')
# plt.savefig('figures/scatter_light_on_light_off_init_celltype_%d.eps'%itype)
for ivar in [0, 1, 4, 5]: # Wmx, Wmy, s02, k
print(init_noise)
W10list[
ivar] = W10list[ivar] + init_noise * np.random.randn(
*W10list[ivar].shape)
#print('size of bounds1: '+str(np.sum([np.size(x) for x in bd1list])))
#print('size of w10: '+str(np.sum([np.size(x) for x in W10list])))
#print('size of shapes1: '+str(np.sum([np.prod(shp) for shp in shapes1])))
W1t[ihyper][itry], W2t[ihyper][itry], loss[ihyper][
itry], gr, hess, result = calnet.fitting_2step_opto_layers.fit_W_sim(
Xhat,
Xpc_list,
Yhat,
Ypc_list,
pop_rate_fn=sim_utils.f_miller_troyer,
pop_deriv_fn=sim_utils.fprime_miller_troyer,
neuron_rate_fn=sim_utils.evaluate_f_mt,
W10list=W10list.copy(),
W20list=W20list.copy(),
bounds1=bounds1,
bounds2=bounds2,
niter=niter,
wt_dict=wt_dict,
l2_penalty=l2_penalty,
compute_hessian=False,
dt=dt,
perturbation_size=perturbation_size,
dYY=dYY,
dXX=dXX,
constrain_isn=constrain_isn,
tv=tv,
opto_mask=opto_mask,
opto_maskX=opto_maskX,
use_opto_transforms=use_opto_transforms,
opto_transform1=opto_transform1,
opto_transform1x=opto_transform1x,
opto_transform2=opto_transform2,
opto_transform2x=opto_transform2x,
share_residuals=share_residuals,
stimwise=stimwise,
simulate1=simulate1,
simulate2=simulate2,
verbose=verbose)
#def parse_W(W):
# Wmx,Wmy,Wsx,Wsy,s02,K,kappa,T,XX,XXp,Eta,Xi,h1,h2 = W
# return Wmx,Wmy,Wsx,Wsy,s02,K,kappa,T,XX,XXp,Eta,Xi,h1,h2
def parse_W1(W):
Wmx, Wmy, Wsx, Wsy, s02, K, kappa, T, h1, h2, bl, amp = W
return Wmx, Wmy, Wsx, Wsy, s02, K, kappa, T, h1, h2, bl, amp
def parse_W2(W):
XX, XXp, Eta, Xi, XX1, XX2 = W
return XX, XXp, Eta, Xi, XX1, XX2
itry = 0
Wmx, Wmy, Wsx, Wsy, s02, K, kappa, T, h1, h2, bl, amp = parse_W1(W1t[0][0])
XX, XXp, Eta, Xi, XX1, XX2 = parse_W2(W2t[0][0])
labels1 = [
'Wmx', 'Wmy', 'Wsx', 'Wsy', 's02', 'K', 'kappa', 'T', 'h1', 'h2', 'bl',
'amp'
]
labels2 = ['XX', 'XXp', 'Eta', 'Xi', 'XX1', 'XX2']
Wstar_dict = {}
for i, label in enumerate(labels1):
Wstar_dict[label] = W1t[0][0][i]
for i, label in enumerate(labels2):
Wstar_dict[label] = W2t[0][0][i]
Wstar_dict['as_list'] = [
Wmx, Wmy, Wsx, Wsy, s02, K, kappa, T, XX, XXp, Eta, Xi, XX1, XX2, h1,
h2, bl, amp
]
Wstar_dict['loss'] = loss[0][0]
Wstar_dict['wt_dict'] = wt_dict
np.save(weights_file, Wstar_dict, allow_pickle=True)
def make_L_col(i):
nelems = d - i - 1
col = np.concatenate(
(np.zeros(i + 1), icf[constructL.Lparamidx:(constructL.Lparamidx + nelems)]))
constructL.Lparamidx += nelems
return col
def wake(self, wake_data, it):
ddc = self.reg
self.wake_data = wake_data.copy()
gs = []
nl_obs = self.nlayer - 1
mean_name_higher = "mx%d_x%d" % (self.nlayer - 2, nl_obs)
fun_name = "x%d->x%d" % (nl_obs, self.nlayer - 2)
self.wake_data[mean_name_higher] = ddc.predict(self.wake_data,
fun_name)
if self.layer_plastic[-1]:
A = self.reg.Ws["A"]
B = self.reg.Ws["B"]
z_mean = self.wake_data["mx%d_x%d" % (nl_obs - 1, nl_obs)]
x_suff = self.model.dists[-1].suff(self.wake_data["x%d" % nl_obs])
x = np.einsum("ij,ik->ijk", z_mean,
x_suff).reshape(z_mean.shape[0], -1)
y = z_mean
dnatsuff = x.dot(A)
dnorm = y.dot(B)
g = dnatsuff - dnorm
self.wake_data["dnatsuff%d" % nl_obs] = dnatsuff
self.wake_data["dnorm%d" % nl_obs] = dnorm
self.wake_data["dlogp%d" % (nl_obs)] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[-1].ps))
if self.nlayer > 1:
for i in range(self.nlayer - 2, 0, -1):
mean_name_lower = mean_name_higher
mean_name_higher = "mx%d_x%d" % (i - 1, nl_obs)
fun_name = "mx%d_x%d->x%d" % (i, nl_obs, i - 1)
self.wake_data[mean_name_higher] = ddc.predict(
self.wake_data, fun_name)
if self.layer_plastic[i]:
grad_name = "x%d->dnatsuff%d" % (i, i)
dnatsuff = self.approx_E(mean_name_lower, grad_name)
self.wake_data["dnatsuff%d" % i] = dnatsuff
#dnatsuff = self.model.dists[i].dnatsuff(self.wake_data["x%d"%(i-1)], self.wake_data["x%d"%(i)])
grad_name = "x%d->dnorm%d" % (i - 1, i)
dnorm = self.approx_E(mean_name_higher, grad_name)
self.wake_data["dnorm%d" % i] = dnorm
#dnorm = self.model.dists[i].dnorm(self.wake_data["x%d"%(i-1)])
g = (dnatsuff - dnorm)
#g2 = (self.model.dists[i].dlogp(self.wake_data["x%d"%(i-1)], self.wake_data["x%d"%(i)])).mean(0)
#assert np.allclose(g, g2)
self.wake_data["dlogp%d" % i] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[i].ps))
if self.layer_plastic[0]:
grad_name = "x0->fsuff0"
suff = self.approx_E(mean_name_higher, grad_name)
#suff = self.model.dists[0].suff(self.wake_data["x0"])
dnat = self.model.dists[0].dnat()
dnatsuff = self.model.dists[0].dnatsuff_from_dnatsuff(
dnat, suff)
self.wake_data["dnatsuff0"] = dnatsuff
dnorm = self.model.dists[0].dnorm(n=dnatsuff.shape[0])
self.wake_data["dnorm0"] = dnorm
g = (dnatsuff - dnorm)
#g = self.model.dists[0].dlogp(self.wake_data["x0"]).mean(0)
#assert np.allclose(g_exp,g)
self.wake_data["dlogp0"] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[0].ps))
gs = np.concatenate(gs)
self.gradient_step(gs, it)
def test_slogdet_3d():
fun = lambda x: np.sum(np.linalg.slogdet(x)[1])
mat = np.concatenate([(rand_psd(5) + 5 * np.eye(5))[None, ...]
for _ in range(3)])
check_grads(fun)(mat)
def test_cholesky_broadcast():
fun = lambda A: np.linalg.cholesky(A)
A = np.concatenate([rand_psd(6)[None, :, :] for i in range(3)], axis=0)
check_symmetric_matrix_grads(fun)(A)
def compare_2d3d(func1, func2, **kwargs):
view = [20, -50]
if 'view' in kwargs:
view = kwargs['view']
# construct figure
fig = plt.figure(figsize=(12, 4))
# remove whitespace from figure
fig.subplots_adjust(left=0, right=1, bottom=0, top=1) # remove whitespace
fig.subplots_adjust(wspace=0.01, hspace=0.01)
# create subplot with 3 panels, plot input function in center plot
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 2, 4])
### draw 2d version ###
ax1 = plt.subplot(gs[1])
grad = compute_grad(func1)
# generate a range of values over which to plot input function, and derivatives
w_plot = np.linspace(-3, 3, 200) # input range for original function
g_plot = func1(w_plot)
g_range = max(g_plot) - min(g_plot) # used for cleaning up final plot
ggap = g_range * 0.2
w_vals = np.linspace(-2.5, 2.5, 200)
# grab the next input/output tangency pair, the center of the next approximation(s)
w_val = float(0)
g_val = func1(w_val)
# plot original function
ax1.plot(w_plot, g_plot, color='k', zorder=1, linewidth=2)
# plot axis
ax1.plot(w_plot, g_plot * 0, color='k', zorder=1, linewidth=1)
# plot the input/output tangency point
ax1.scatter(w_val,
g_val,
s=80,
c='lime',
edgecolor='k',
linewidth=2,
zorder=3) # plot point of tangency
#### plot first order approximation ####
# plug input into the first derivative
g_grad_val = grad(w_val)
# determine width to plot the approximation -- so its length == width
width = 4
div = float(1 + g_grad_val**2)
w1 = w_val - math.sqrt(width / div)
w2 = w_val + math.sqrt(width / div)
# compute first order approximation
wrange = np.linspace(w1, w2, 100)
h = g_val + g_grad_val * (wrange - w_val)
# plot the first order approximation
ax1.plot(wrange, h, color='lime', alpha=0.5, linewidth=3,
zorder=2) # plot approx
#### clean up panel ####
# fix viewing limits on panel
v = 5
ax1.set_xlim([-v, v])
ax1.set_ylim([-1 - 0.3, v - 0.3])
# label axes
ax1.set_xlabel('$w$', fontsize=12, labelpad=-60)
ax1.set_ylabel('$g(w)$', fontsize=25, rotation=0, labelpad=50)
ax1.grid(False)
ax1.yaxis.set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['left'].set_visible(False)
### draw 3d version ###
ax2 = plt.subplot(gs[2], projection='3d')
grad = compute_grad(func2)
w_val = [float(0), float(0)]
# define input space
w_in = np.linspace(-2, 2, 200)
w1_vals, w2_vals = np.meshgrid(w_in, w_in)
w1_vals.shape = (len(w_in)**2, 1)
w2_vals.shape = (len(w_in)**2, 1)
w_vals = np.concatenate((w1_vals, w2_vals), axis=1).T
g_vals = func2(w_vals)
# evaluation points
w_val = np.array([float(w_val[0]), float(w_val[1])])
w_val.shape = (2, 1)
g_val = func2(w_val)
grad_val = grad(w_val)
grad_val.shape = (2, 1)
# create and evaluate tangent hyperplane
w_tan = np.linspace(-1, 1, 200)
w1tan_vals, w2tan_vals = np.meshgrid(w_tan, w_tan)
w1tan_vals.shape = (len(w_tan)**2, 1)
w2tan_vals.shape = (len(w_tan)**2, 1)
wtan_vals = np.concatenate((w1tan_vals, w2tan_vals), axis=1).T
#h = lambda weh: g_val + np.dot( (weh - w_val).T,grad_val)
h = lambda weh: g_val + (weh[0] - w_val[0]) * grad_val[0] + (weh[
1] - w_val[1]) * grad_val[1]
h_vals = h(wtan_vals + w_val)
zmin = min(np.min(h_vals), -0.5)
zmax = max(np.max(h_vals), +0.5)
# vals for cost surface, reshape for plot_surface function
w1_vals.shape = (len(w_in), len(w_in))
w2_vals.shape = (len(w_in), len(w_in))
g_vals.shape = (len(w_in), len(w_in))
w1tan_vals += w_val[0]
w2tan_vals += w_val[1]
w1tan_vals.shape = (len(w_tan), len(w_tan))
w2tan_vals.shape = (len(w_tan), len(w_tan))
h_vals.shape = (len(w_tan), len(w_tan))
### plot function ###
ax2.plot_surface(w1_vals,
w2_vals,
g_vals,
alpha=0.5,
color='w',
rstride=25,
cstride=25,
linewidth=1,
edgecolor='k',
zorder=2)
### plot z=0 plane ###
ax2.plot_surface(w1_vals,
w2_vals,
g_vals * 0,
alpha=0.1,
color='w',
zorder=1,
rstride=25,
cstride=25,
linewidth=0.3,
edgecolor='k')
### plot tangent plane ###
ax2.plot_surface(w1tan_vals,
w2tan_vals,
h_vals,
alpha=0.4,
color='lime',
zorder=1,
rstride=50,
cstride=50,
linewidth=1,
edgecolor='k')
# scatter tangency
ax2.scatter(w_val[0],
w_val[1],
g_val,
s=70,
c='lime',
edgecolor='k',
linewidth=2)
### clean up plot ###
# plot x and y axes, and clean up
ax2.xaxis.pane.fill = False
ax2.yaxis.pane.fill = False
ax2.zaxis.pane.fill = False
ax2.xaxis.pane.set_edgecolor('white')
ax2.yaxis.pane.set_edgecolor('white')
ax2.zaxis.pane.set_edgecolor('white')
# remove axes lines and tickmarks
ax2.w_zaxis.line.set_lw(0.)
ax2.set_zticks([])
ax2.w_xaxis.line.set_lw(0.)
ax2.set_xticks([])
ax2.w_yaxis.line.set_lw(0.)
ax2.set_yticks([])
# set viewing angle
ax2.view_init(20, -65)
# set vewing limits
y = 4
ax2.set_xlim([-y, y])
ax2.set_ylim([-y, y])
ax2.set_zlim([zmin, zmax])
# label plot
fontsize = 12
ax2.set_xlabel(r'$w_1$', fontsize=fontsize, labelpad=-35)
ax2.set_ylabel(r'$w_2$', fontsize=fontsize, rotation=0, labelpad=-40)
plt.show()
def dynamics_fn(t, coords):
dcoords = autograd.grad(hamiltonian_fn)(coords)
dqdt, dpdt = np.split(dcoords, 2)
S = np.concatenate([dpdt, -dqdt], axis=-1)
return S
def animate_it_3d(self, w_hist, **kwargs):
self.w_hist = w_hist
##### setup figure to plot #####
# initialize figure
fig = plt.figure(figsize=(8, 3))
artist = fig
# create subplot with 3 panels, plot input function in center plot
gs = gridspec.GridSpec(1, 2, width_ratios=[2, 1])
ax1 = plt.subplot(gs[0], projection='3d')
ax2 = plt.subplot(gs[1])
# produce color scheme
s = np.linspace(0, 1, len(self.w_hist[:round(len(self.w_hist) / 2)]))
s.shape = (len(s), 1)
t = np.ones(len(self.w_hist[round(len(self.w_hist) / 2):]))
t.shape = (len(t), 1)
s = np.vstack((s, t))
self.colorspec = []
self.colorspec = np.concatenate((s, np.flipud(s)), 1)
self.colorspec = np.concatenate((self.colorspec, np.zeros(
(len(s), 1))), 1)
# seed left panel plotting range
viewmax = 3
if 'viewmax' in kwargs:
viewmax = kwargs['viewmax']
r = np.linspace(-viewmax, viewmax, 200)
# create grid from plotting range
x1_vals, x2_vals = np.meshgrid(r, r)
x1_vals.shape = (len(r)**2, 1)
x2_vals.shape = (len(r)**2, 1)
x1_vals.shape = (np.size(r), np.size(r))
x2_vals.shape = (np.size(r), np.size(r))
# seed left panel view
view = [20, 50]
if 'view' in kwargs:
view = kwargs['view']
# set zaxis to the left
self.move_axis_left(ax1)
# start animation
num_frames = len(self.w_hist)
print('starting animation rendering...')
def animate(k):
# clear panels
ax1.cla()
# set axis in left panel
self.move_axis_left(ax1)
# current color
color = self.colorspec[k]
# print rendering update
if np.mod(k + 1, 25) == 0:
print('rendering animation frame ' + str(k + 1) + ' of ' +
str(num_frames))
if k == num_frames - 1:
print('animation rendering complete!')
time.sleep(1.5)
clear_output()
###### make left panel - plot data and fit ######
# initialize fit
w = self.w_hist[k]
# reshape and plot the surface, as well as where the zero-plane is
y_fit = w[0] + w[1] * x1_vals + w[2] * x2_vals
# plot cost surface
ax1.plot_surface(x1_vals,
x2_vals,
y_fit,
alpha=0.1,
color=color,
rstride=25,
cstride=25,
linewidth=0.25,
edgecolor='k',
zorder=2)
# scatter data
self.scatter_pts(ax1)
#ax1.view_init(view[0],view[1])
# plot connector between points for visualization purposes
if k == 0:
w_new = self.w_hist[k]
g_new = self.least_squares(w_new)[0]
ax2.scatter(k,
g_new,
s=0.1,
color='w',
linewidth=2.5,
alpha=0,
zorder=1) # plot approx
if k > 0:
w_old = self.w_hist[k - 1]
w_new = self.w_hist[k]
g_old = self.least_squares(w_old)[0]
g_new = self.least_squares(w_new)[0]
ax2.plot([k - 1, k], [g_old, g_new],
color=color,
linewidth=2.5,
alpha=1,
zorder=2) # plot approx
ax2.plot([k - 1, k], [g_old, g_new],
color='k',
linewidth=3.5,
alpha=1,
zorder=1) # plot approx
# set viewing limits for second panel
ax2.axhline(y=0, color='k', zorder=0, linewidth=0.5)
ax2.set_xlabel('iteration', fontsize=12)
ax2.set_ylabel(r'$g(\mathbf{w})$',
fontsize=12,
rotation=0,
labelpad=25)
ax2.set_xlim([-0.5, len(self.w_hist)])
# set axis in left panel
self.move_axis_left(ax1)
return artist,
anim = animation.FuncAnimation(fig,
animate,
frames=num_frames,
interval=num_frames,
blit=True)
return (anim)
def animate_it_2d(self, w_hist, **kwargs):
self.w_hist = w_hist
##### setup figure to plot #####
# initialize figure
fig = plt.figure(figsize=(8, 3))
artist = fig
# create subplot with 3 panels, plot input function in center plot
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1])
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
# produce color scheme
s = np.linspace(0, 1, len(self.w_hist[:round(len(self.w_hist) / 2)]))
s.shape = (len(s), 1)
t = np.ones(len(self.w_hist[round(len(self.w_hist) / 2):]))
t.shape = (len(t), 1)
s = np.vstack((s, t))
self.colorspec = []
self.colorspec = np.concatenate((s, np.flipud(s)), 1)
self.colorspec = np.concatenate((self.colorspec, np.zeros(
(len(s), 1))), 1)
# seed left panel plotting range
xmin = copy.deepcopy(min(self.x))
xmax = copy.deepcopy(max(self.x))
xgap = (xmax - xmin) * 0.1
xmin -= xgap
xmax += xgap
x_fit = np.linspace(xmin, xmax, 300)
# seed right panel contour plot
viewmax = 3
if 'viewmax' in kwargs:
viewmax = kwargs['viewmax']
view = [20, 100]
if 'view' in kwargs:
view = kwargs['view']
num_contours = 15
if 'num_contours' in kwargs:
num_contours = kwargs['num_contours']
self.contour_plot(ax2, viewmax, num_contours)
# start animation
num_frames = len(self.w_hist)
print('starting animation rendering...')
def animate(k):
# clear panels
ax1.cla()
# current color
color = self.colorspec[k]
# print rendering update
if np.mod(k + 1, 25) == 0:
print('rendering animation frame ' + str(k + 1) + ' of ' +
str(num_frames))
if k == num_frames - 1:
print('animation rendering complete!')
time.sleep(1.5)
clear_output()
###### make left panel - plot data and fit ######
# initialize fit
w = self.w_hist[k]
y_fit = w[0] + x_fit * w[1]
# scatter data
self.scatter_pts(ax1)
# plot fit to data
ax1.plot(x_fit, y_fit, color=color, linewidth=3)
###### make right panel - plot contour and steps ######
if k == 0:
ax2.scatter(w[0],
w[1],
s=90,
facecolor=color,
edgecolor='k',
linewidth=0.5,
zorder=3)
if k > 0 and k < num_frames:
self.plot_pts_on_contour(ax2, k, color)
if k == num_frames - 1:
ax2.scatter(w[0],
w[1],
s=90,
facecolor=color,
edgecolor='k',
linewidth=0.5,
zorder=3)
return artist,
anim = animation.FuncAnimation(fig,
animate,
frames=num_frames,
interval=num_frames,
blit=True)
return (anim)
def tile_nS_nT_nN(kernel):
row = np.concatenate([kernel for idim in range(nS * nT)],
axis=0)[np.newaxis, :]
tiled = np.concatenate([row for irow in range(nN)], axis=0)
return tiled
def compute_fprime_m_(Eta, Xi, s02):
return sim_utils.fprime_miller_troyer(
Eta, Xi**2 + np.concatenate([s02
for ipixel in range(nS * nT)])) * Xi
def wake(self, wake_data, it):
ddc = self.reg
self.wake_data = wake_data.copy()
gs = []
nl_obs = self.nlayer - 1
mean_name_higher = "mx%d_x%d" % (self.nlayer - 2, nl_obs)
fun_name = "x%d->x%d" % (nl_obs, self.nlayer - 2)
self.wake_data[mean_name_higher] = ddc.predict(self.wake_data,
fun_name)
if self.layer_plastic[-1]:
grad_name = "x%d->dnat%d" % (self.nlayer - 2, nl_obs)
dnat = self.approx_E(mean_name_higher, grad_name)
#dnat = self.model.dists[-1].dnat(self.wake_data["x%d"%(self.nlayer-2)])
grad_name = "x%d->dnorm%d" % (self.nlayer - 2, nl_obs)
dnorm = self.approx_E(mean_name_higher, grad_name)
#dnorm = self.model.dists[-1].dnorm(self.wake_data["x%d"%(self.nlayer-2)])
suff = self.model.dists[-1].suff(self.wake_data["x%d" % (nl_obs)])
g = (self.model.dists[-1].dnatsuff_from_dnatsuff(dnat, suff) -
dnorm)
self.wake_data["dlogp%d" % (nl_obs)] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[-1].ps))
if self.nlayer > 1:
for i in range(self.nlayer - 2, 0, -1):
mean_name_lower = mean_name_higher
mean_name_higher = "mx%d_x%d" % (i - 1, nl_obs)
fun_name = "mx%d_x%d->x%d" % (i, nl_obs, i - 1)
self.wake_data[mean_name_higher] = ddc.predict(
self.wake_data, fun_name)
if self.layer_plastic[i]:
grad_name = "x%d->dnatsuff%d" % (i, i)
dnatsuff = self.approx_E(mean_name_lower, grad_name)
#dnatsuff = self.model.dists[i].dnatsuff(self.wake_data["x%d"%(i-1)], self.wake_data["x%d"%(i)])
grad_name = "x%d->dnorm%d" % (i - 1, i)
dnorm = self.approx_E(mean_name_higher, grad_name)
#dnorm = self.model.dists[i].dnorm(self.wake_data["x%d"%(i-1)])
g = (dnatsuff - dnorm)
#g2 = (self.model.dists[i].dlogp(self.wake_data["x%d"%(i-1)], self.wake_data["x%d"%(i)])).mean(0)
#assert np.allclose(g, g2)
self.wake_data["dlogp%d" % i] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[i].ps))
if self.layer_plastic[0]:
grad_name = "x0->fsuff0"
suff = self.approx_E(mean_name_higher, grad_name)
#suff = self.model.dists[0].suff(self.wake_data["x0"])
dnat = self.model.dists[0].dnat()
dnatsuff = self.model.dists[0].dnatsuff_from_dnatsuff(
dnat, suff)
dnorm = self.model.dists[0].dnorm()
g = (dnatsuff - dnorm)
#g = self.model.dists[0].dlogp(self.wake_data["x0"]).mean(0)
#assert np.allclose(g_exp,g)
self.wake_data["dlogp0"] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[0].ps))
gs = np.concatenate(gs)
self.gradient_step(gs, it)
xm = np.array(x)[:, 0] / 50 # 50 pixels / meter
track_k = TrackCurvature(xm)
Nx = TrackNormal(xm)
u = 1j * Nx
np.savetxt("track_x.txt",
np.vstack([np.real(xm), np.imag(xm)]).T.reshape(-1),
newline=",\n")
np.savetxt("track_u.txt",
np.vstack([np.real(u), np.imag(u)]).T.reshape(-1),
newline=",\n")
np.savetxt("track_k.txt", track_k, newline=",\n")
ye, val, stuff = OptimizeTrack(xm, 1.0, 0.05, 0.4)
psie = RelativePsie(ye, xm)
rx = u * ye + xm
raceline_k = TrackCurvature(rx)
np.savetxt("raceline_k.txt", raceline_k, newline=",\n")
np.savetxt("raceline_ye.txt", ye, newline=",\n")
np.savetxt("raceline_psie.txt", psie, newline=",\n")
np.savetxt(
"raceline_ds.txt",
np.abs( # distance between successive points
np.concatenate([rx[1:] - rx[:-1], rx[:1] - rx[-1:]])),
newline=",\n")
print "saved track_k.txt"
print "saved raceline_{k, ye, psie}.txt"
def ReverseBinaryFlip(z, j):
return np.concatenate(reversed(ReverseBinarySplit(z, j)), -1)
def reconstruct_fullfield(fname,
theta_st=0,
theta_end=PI,
n_epochs='auto',
crit_conv_rate=0.03,
max_nepochs=200,
alpha=1e-7,
alpha_d=None,
alpha_b=None,
gamma=1e-6,
learning_rate=1.0,
output_folder=None,
minibatch_size=None,
save_intermediate=False,
full_intermediate=False,
energy_ev=5000,
psize_cm=1e-7,
n_epochs_mask_release=None,
cpu_only=False,
save_path='.',
phantom_path='phantom',
shrink_cycle=20,
core_parallelization=True,
free_prop_cm=None,
multiscale_level=1,
n_epoch_final_pass=None,
initial_guess=None,
n_batch_per_update=5,
dynamic_rate=True,
probe_type='plane',
probe_initial=None,
probe_learning_rate=1e-3,
pupil_function=None,
theta_downsample=None,
forward_algorithm='fresnel',
random_theta=True,
object_type='normal',
kernel_size=17,
debug=False,
**kwargs):
"""
Reconstruct a beyond depth-of-focus object.
:param fname: Filename and path of raw data file. Must be in HDF5 format.
:param theta_st: Starting rotation angle.
:param theta_end: Ending rotation angle.
:param n_epochs: Number of epochs to be executed. If given 'auto', optimizer will stop
when reduction rate of loss function goes below crit_conv_rate.
:param crit_conv_rate: Reduction rate of loss function below which the optimizer should
stop.
:param max_nepochs: The maximum number of epochs to be executed if n_epochs is 'auto'.
:param alpha: Weighting coefficient for both delta and beta regularizer. Should be None
if alpha_d and alpha_b are specified.
:param alpha_d: Weighting coefficient for delta regularizer.
:param alpha_b: Weighting coefficient for beta regularizer.
:param gamma: Weighting coefficient for TV regularizer.
:param learning_rate: Learning rate of ADAM.
:param output_folder: Name of output folder. Put None for auto-generated pattern.
:param downsample: Downsampling (not implemented yet).
:param minibatch_size: Size of minibatch.
:param save_intermediate: Whether to save the object after each epoch.
:param energy_ev: Beam energy in eV.
:param psize_cm: Pixel size in cm.
:param n_epochs_mask_release: The number of epochs after which the finite support mask
is released. Put None to disable this feature.
:param cpu_only: Whether to disable GPU.
:param save_path: The location of finite support mask, the prefix of output_folder and
other metadata.
:param phantom_path: The location of phantom objects (for test version only).
:param shrink_cycle: Shrink-wrap is executed per every this number of epochs.
:param core_parallelization: Whether to use Horovod for parallelized computation within
this function.
:param free_prop_cm: The distance to propagate the wavefront in free space after exiting
the sample, in cm.
:param multiscale_level: The level of multiscale processing. When this number is m and
m > 1, m - 1 low-resolution reconstructions will be performed
before reconstructing with the original resolution. The downsampling
factor for these coarse reconstructions will be [2^(m - 1),
2^(m - 2), ..., 2^1].
:param n_epoch_final_pass: specify a number of iterations for the final pass if multiscale
is activated. If None, it will be the same as n_epoch.
:param initial_guess: supply an initial guess. If None, object will be initialized with noises.
:param n_batch_per_update: number of minibatches during which gradients are accumulated, after
which obj is updated.
:param dynamic_rate: when n_batch_per_update > 1, adjust learning rate dynamically to allow it
to decrease with epoch number
:param probe_type: type of wavefront. Can be 'plane', ' fixed', or 'optimizable'. If 'optimizable',
the probe function will be optimized along with the object.
:param probe_initial: can be provided for 'optimizable' probe_type, and must be provided for
'fixed'.
"""
def forward_pass(obj_delta, obj_beta, this_ind_batch):
obj_stack = np.stack([obj_delta, obj_beta], axis=3)
obj_rot_batch = []
for i in range(minibatch_size):
obj_rot_batch.append(
apply_rotation(
obj_stack, coord_ls[this_ind_batch[i]],
'arrsize_{}_{}_{}_ntheta_{}'.format(
dim_y, dim_x, dim_x, n_theta)))
obj_rot_batch = np.stack(obj_rot_batch)
exiting_batch = multislice_propagate_cnn(obj_rot_batch[:, :, :, :, 0],
obj_rot_batch[:, :, :, :, 1],
probe_real,
probe_imag,
energy_ev,
[psize_cm * ds_level] * 3,
free_prop_cm=free_prop_cm,
kernel_size=kernel_size)
return exiting_batch
def calculate_loss(obj_delta, obj_beta, this_ind_batch, this_prj_batch):
obj_stack = np.stack([obj_delta, obj_beta], axis=3)
obj_rot_batch = []
for i in range(minibatch_size):
obj_rot_batch.append(
apply_rotation(
obj_stack, coord_ls[this_ind_batch[i]],
'arrsize_{}_{}_{}_ntheta_{}'.format(
dim_y, dim_x, dim_x, n_theta)))
obj_rot_batch = np.stack(obj_rot_batch)
exiting_batch = multislice_propagate_cnn(obj_rot_batch[:, :, :, :, 0],
obj_rot_batch[:, :, :, :, 1],
probe_real,
probe_imag,
energy_ev,
[psize_cm * ds_level] * 3,
free_prop_cm=free_prop_cm,
kernel_size=kernel_size)
loss = np.mean((np.abs(exiting_batch) - np.abs(this_prj_batch))**2)
if alpha_d is None:
reg_term = alpha * (np.sum(np.abs(obj_delta)) + np.sum(
np.abs(obj_delta))) + gamma * total_variation_3d(obj_delta)
else:
if gamma == 0:
reg_term = alpha_d * np.sum(
np.abs(obj_delta)) + alpha_b * np.sum(np.abs(obj_beta))
else:
reg_term = alpha_d * np.sum(
np.abs(obj_delta)) + alpha_b * np.sum(np.abs(
obj_beta)) + gamma * total_variation_3d(obj_delta)
loss = loss + reg_term
print('Current loss: {}'.format(loss))
return loss
t_zero = time.time()
try:
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
mpi_ok = True
except:
from pseudo import Mpi
comm = Mpi()
size = 1
rank = 0
mpi_ok = False
# read data
t0 = time.time()
print_flush('Reading data...', 0, rank)
f = h5py.File(os.path.join(save_path, fname), 'r')
prj_0 = f['exchange/data'][...].astype('complex64')
theta = -np.linspace(theta_st, theta_end, prj_0.shape[0], dtype='float32')
n_theta = len(theta)
prj_theta_ind = np.arange(n_theta, dtype=int)
if theta_downsample is not None:
prj_0 = prj_0[::theta_downsample]
theta = theta[::theta_downsample]
prj_theta_ind = prj_theta_ind[::theta_downsample]
n_theta = len(theta)
original_shape = prj_0.shape
comm.Barrier()
print_flush('Data reading: {} s'.format(time.time() - t0), 0, rank)
print_flush('Data shape: {}'.format(original_shape), 0, rank)
comm.Barrier()
initializer_flag = False
if output_folder is None:
output_folder = 'recon_360_minibatch_{}_' \
'mskrls_{}_' \
'shrink_{}_' \
'iter_{}_' \
'alphad_{}_' \
'alphab_{}_' \
'gamma_{}_' \
'rate_{}_' \
'energy_{}_' \
'size_{}_' \
'ntheta_{}_' \
'prop_{}_' \
'ms_{}_' \
'cpu_{}' \
.format(minibatch_size, n_epochs_mask_release, shrink_cycle,
n_epochs, alpha_d, alpha_b,
gamma, learning_rate, energy_ev,
prj_0.shape[-1], prj_0.shape[0], free_prop_cm,
multiscale_level, cpu_only)
if abs(PI - theta_end) < 1e-3:
output_folder += '_180'
if save_path != '.':
output_folder = os.path.join(save_path, output_folder)
for ds_level in range(multiscale_level - 1, -1, -1):
ds_level = 2**ds_level
print_flush('Multiscale downsampling level: {}'.format(ds_level), 0,
rank)
comm.Barrier()
# downsample data
prj = np.copy(prj_0)
if ds_level > 1:
prj = prj[:, ::ds_level, ::ds_level]
prj = prj.astype('complex64')
comm.Barrier()
ind_ls = np.arange(n_theta)
np.random.shuffle(ind_ls)
n_tot_per_batch = size * minibatch_size
if n_theta % n_tot_per_batch > 0:
ind_ls = np.concatenate(
ind_ls, ind_ls[:n_tot_per_batch - n_theta % n_tot_per_batch])
ind_ls = split_tasks(ind_ls, n_tot_per_batch)
ind_ls = [np.sort(x) for x in ind_ls]
print(len(ind_ls), n_theta % n_tot_per_batch)
dim_y, dim_x = prj.shape[-2:]
comm.Barrier()
# read rotation data
try:
coord_ls = read_all_origin_coords(
'arrsize_{}_{}_{}_ntheta_{}'.format(dim_y, dim_x, dim_x,
n_theta), n_theta)
except:
save_rotation_lookup([dim_y, dim_x, dim_x], n_theta)
coord_ls = read_all_origin_coords(
'arrsize_{}_{}_{}_ntheta_{}'.format(dim_y, dim_x, dim_x,
n_theta), n_theta)
if minibatch_size is None:
minibatch_size = n_theta
if n_epochs_mask_release is None:
n_epochs_mask_release = np.inf
try:
mask = dxchange.read_tiff_stack(
os.path.join(save_path, 'fin_sup_mask', 'mask_00000.tiff'),
range(prj_0.shape[1]), 5)
except:
try:
mask = dxchange.read_tiff(
os.path.join(save_path, 'fin_sup_mask', 'mask.tiff'))
except:
obj_pr = dxchange.read_tiff_stack(
os.path.join(save_path, 'paganin_obj/recon_00000.tiff'),
range(prj_0.shape[1]), 5)
obj_pr = gaussian_filter(np.abs(obj_pr),
sigma=3,
mode='constant')
mask = np.zeros_like(obj_pr)
mask[obj_pr > 1e-5] = 1
dxchange.write_tiff_stack(mask,
os.path.join(save_path,
'fin_sup_mask/mask'),
dtype='float32',
overwrite=True)
if ds_level > 1:
mask = mask[::ds_level, ::ds_level, ::ds_level]
dim_z = mask.shape[-1]
# unify random seed for all threads
comm.Barrier()
seed = int(time.time() / 60)
np.random.seed(seed)
comm.Barrier()
if initializer_flag == False:
if initial_guess is None:
print_flush('Initializing with Gaussian random.', 0, rank)
obj_delta = np.random.normal(
size=[dim_y, dim_x, dim_z], loc=8.7e-7, scale=1e-7) * mask
obj_beta = np.random.normal(
size=[dim_y, dim_x, dim_z], loc=5.1e-8, scale=1e-8) * mask
obj_delta[obj_delta < 0] = 0
obj_beta[obj_beta < 0] = 0
else:
print_flush('Using supplied initial guess.', 0, rank)
sys.stdout.flush()
obj_delta = initial_guess[0]
obj_beta = initial_guess[1]
else:
print_flush('Initializing with Gaussian random.', 0, rank)
obj_delta = dxchange.read_tiff(
os.path.join(output_folder,
'delta_ds_{}.tiff'.format(ds_level * 2)))
obj_beta = dxchange.read_tiff(
os.path.join(output_folder,
'beta_ds_{}.tiff'.format(ds_level * 2)))
obj_delta = upsample_2x(obj_delta)
obj_beta = upsample_2x(obj_beta)
obj_delta += np.random.normal(
size=[dim_y, dim_x, dim_z], loc=8.7e-7, scale=1e-7) * mask
obj_beta += np.random.normal(
size=[dim_y, dim_x, dim_z], loc=5.1e-8, scale=1e-8) * mask
obj_delta[obj_delta < 0] = 0
obj_beta[obj_beta < 0] = 0
obj_size = obj_delta.shape
if object_type == 'phase_only':
obj_beta[...] = 0
elif object_type == 'absorption_only':
obj_delta[...] = 0
# ====================================================
if probe_type == 'plane':
probe_real = np.ones([dim_y, dim_x])
probe_imag = np.zeros([dim_y, dim_x])
elif probe_type == 'optimizable':
if probe_initial is not None:
probe_mag, probe_phase = probe_initial
probe_real, probe_imag = mag_phase_to_real_imag(
probe_mag, probe_phase)
else:
# probe_mag = np.ones([dim_y, dim_x])
# probe_phase = np.zeros([dim_y, dim_x])
back_prop_cm = (free_prop_cm + (psize_cm * obj_size[2])
) if free_prop_cm is not None else (
psize_cm * obj_size[2])
probe_init = create_probe_initial_guess(
os.path.join(save_path, fname), back_prop_cm * 1.e7,
energy_ev, psize_cm * 1.e7)
probe_real = probe_init.real
probe_imag = probe_init.imag
if pupil_function is not None:
probe_real = probe_real * pupil_function
probe_imag = probe_imag * pupil_function
elif probe_type == 'fixed':
probe_mag, probe_phase = probe_initial
probe_real, probe_imag = mag_phase_to_real_imag(
probe_mag, probe_phase)
elif probe_type == 'point':
# this should be in spherical coordinates
probe_real = np.ones([dim_y, dim_x])
probe_imag = np.zeros([dim_y, dim_x])
elif probe_type == 'gaussian':
probe_mag_sigma = kwargs['probe_mag_sigma']
probe_phase_sigma = kwargs['probe_phase_sigma']
probe_phase_max = kwargs['probe_phase_max']
py = np.arange(obj_size[0]) - (obj_size[0] - 1.) / 2
px = np.arange(obj_size[1]) - (obj_size[1] - 1.) / 2
pxx, pyy = np.meshgrid(px, py)
probe_mag = np.exp(-(pxx**2 + pyy**2) / (2 * probe_mag_sigma**2))
probe_phase = probe_phase_max * np.exp(-(pxx**2 + pyy**2) /
(2 * probe_phase_sigma**2))
probe_real, probe_imag = mag_phase_to_real_imag(
probe_mag, probe_phase)
else:
raise ValueError(
'Invalid wavefront type. Choose from \'plane\', \'fixed\', \'optimizable\'.'
)
# =============finite support===================
obj_delta = obj_delta * mask
obj_beta = obj_beta * mask
obj_delta = np.clip(obj_delta, 0, None)
obj_beta = np.clip(obj_beta, 0, None)
# ==============================================
# generate Fresnel kernel
voxel_nm = np.array([psize_cm] * 3) * 1.e7 * ds_level
lmbda_nm = 1240. / energy_ev
delta_nm = voxel_nm[-1]
h = get_kernel(delta_nm, lmbda_nm, voxel_nm, [dim_y, dim_y, dim_x])
loss_grad = grad(calculate_loss, [0, 1])
print_flush('Optimizer started.', 0, rank)
if rank == 0:
create_summary(output_folder, locals(), preset='fullfield')
cont = True
i_epoch = 0
while cont:
m, v = (None, None)
t0 = time.time()
for i_batch in range(len(ind_ls)):
t00 = time.time()
this_ind_batch = ind_ls[i_batch][rank *
minibatch_size:(rank + 1) *
minibatch_size]
this_prj_batch = prj[this_ind_batch]
grads = loss_grad(obj_delta, obj_beta, this_ind_batch,
this_prj_batch)
grads = np.array(grads)
this_grads = np.copy(grads)
if mpi_ok:
grads = np.zeros_like(this_grads)
comm.Allreduce(this_grads, grads)
grads = grads / size
(obj_delta, obj_beta), m, v = apply_gradient_adam(
np.array([obj_delta, obj_beta]),
grads,
i_batch,
m,
v,
step_size=learning_rate)
dxchange.write_tiff(obj_delta,
fname=os.path.join(
output_folder, 'intermediate',
'current'.format(ds_level)),
dtype='float32',
overwrite=True)
# finite support
obj_delta = obj_delta * mask
obj_beta = obj_beta * mask
obj_delta = np.clip(obj_delta, 0, None)
obj_beta = np.clip(obj_beta, 0, None)
# shrink wrap
if shrink_cycle is not None:
if i_epoch >= shrink_cycle:
boolean = obj_delta > 1e-15
mask = mask * boolean
print_flush('Minibatch done in {} s (rank {})'.format(
time.time() - t00, rank))
if i_batch % 10 == 0 and debug:
temp_exit = forward_pass(obj_delta, obj_beta,
this_ind_batch)
dxchange.write_tiff(abs(temp_exit),
os.path.join(
output_folder, 'exits',
'{}-{}'.format(i_epoch, i_batch)),
dtype='float32',
overwrite=True)
if n_epochs == 'auto':
pass
else:
if i_epoch == n_epochs - 1: cont = False
i_epoch = i_epoch + 1
print_flush(
'Epoch {} (rank {}); loss = {}; Delta-t = {} s; current time = {}.'
.format(
i_epoch, rank,
calculate_loss(obj_delta, obj_beta, this_ind_batch,
this_prj_batch),
time.time() - t0,
time.time() - t_zero))
dxchange.write_tiff(obj_delta,
fname=os.path.join(output_folder,
'delta_ds_{}'.format(ds_level)),
dtype='float32',
overwrite=True)
dxchange.write_tiff(obj_beta,
fname=os.path.join(output_folder,
'beta_ds_{}'.format(ds_level)),
dtype='float32',
overwrite=True)
print_flush('Current iteration finished.', 0, rank)
def test_lds_log_probability_perf(T=1000, D=10, N_iter=10):
"""
Compare performance of banded method vs message passing in pylds.
"""
print("Comparing methods for T={} D={}".format(T, D))
from pylds.lds_messages_interface import kalman_info_filter, kalman_filter
# Convert LDS parameters into info form for pylds
As, bs, Qi_sqrts, ms, Ri_sqrts = make_lds_parameters(T, D)
Qis = np.matmul(Qi_sqrts, np.swapaxes(Qi_sqrts, -1, -2))
Ris = np.matmul(Ri_sqrts, np.swapaxes(Ri_sqrts, -1, -2))
x = npr.randn(T, D)
print("Timing banded method")
start = time.time()
for itr in range(N_iter):
lds_log_probability(x, As, bs, Qi_sqrts, ms, Ri_sqrts)
stop = time.time()
print("Time per iter: {:.4f}".format((stop - start) / N_iter))
# Compare to Kalman Filter
mu_init = np.zeros(D)
sigma_init = np.eye(D)
Bs = np.ones((D, 1))
sigma_states = np.linalg.inv(Qis)
Cs = np.eye(D)
Ds = np.zeros((D, 1))
sigma_obs = np.linalg.inv(Ris)
inputs = bs
data = ms
print("Timing PyLDS message passing (kalman_filter)")
start = time.time()
for itr in range(N_iter):
kalman_filter(mu_init, sigma_init,
np.concatenate([As, np.eye(D)[None, :, :]]), Bs,
np.concatenate([sigma_states,
np.eye(D)[None, :, :]]), Cs, Ds,
sigma_obs, inputs, data)
stop = time.time()
print("Time per iter: {:.4f}".format((stop - start) / N_iter))
# Info form comparison
J_init = np.zeros((D, D))
h_init = np.zeros(D)
log_Z_init = 0
J_diag, J_lower_diag, h = convert_lds_to_block_tridiag(
As, bs, Qi_sqrts, ms, Ri_sqrts)
J_pair_21 = J_lower_diag
J_pair_22 = J_diag[1:]
J_pair_11 = J_diag[:-1]
J_pair_11[1:] = 0
h_pair_2 = h[1:]
h_pair_1 = h[:-1]
h_pair_1[1:] = 0
log_Z_pair = 0
J_node = np.zeros((T, D, D))
h_node = np.zeros((T, D))
log_Z_node = 0
print("Timing PyLDS message passing (kalman_info_filter)")
start = time.time()
for itr in range(N_iter):
kalman_info_filter(J_init, h_init, log_Z_init, J_pair_11, J_pair_21,
J_pair_22, h_pair_1, h_pair_2, log_Z_pair, J_node,
h_node, log_Z_node)
stop = time.time()
print("Time per iter: {:.4f}".format((stop - start) / N_iter))
# Author: Pierre Ablin <*****@*****.**>
# License: MIT
# Example with several variables
import autograd.numpy as np
from autoptim import minimize
n = 1000
n_components = 3
x = np.concatenate(
(np.random.randn(n) - 1, 3 * np.random.randn(n), np.random.randn(n) + 2))
# Here, the model should fit both the means and the variances. Using
# scipy.optimize.minimize, one would have to vectorize by hand these variables.
def loss(means, variances, x):
tmp = np.zeros(n_components * n)
for m, v in zip(means, variances):
tmp += np.exp(-(x - m)**2 / (2 * v**2)) / v
return -np.sum(np.log(tmp))
# autoptim can handle lists of unknown variables
means0 = np.random.randn(n_components)
variances0 = np.random.rand(n_components)
optim_vars = [means0, variances0]
x = x[None]
if fulldim:
x_resc = x * (2. * np.pi)
else:
x_resc = np.copy(x[:, relevant_dims]) * (2. * np.pi) # [0, 1] -> [0, 2pi]
f_x = np.sin(x_resc[:, 0])[:, None] * np.prod(np.sin(x_resc), axis=1)[:, None] * 10 # rescale by factor
if noisy:
noise = np.random.normal(loc=0., scale=scale, size=f_x.shape[0])[:, None]
y = f_x + noise
return y
return f_x
func = lambda x: ProductSines10D(x, noisy=False, fulldim=True)
func_grad = grad(func)
minimizer = np.ones(shape=[1, int(100)], dtype=np.float64) * (np.pi * (3. / 2.)) / (2*np.pi)
g0 = func_grad(minimizer)
np.random.seed(123)
num = int(5e06)
shape_x = [num, int(10)]
x = np.random.uniform(low=0., high=1., size=np.prod(shape_x)).reshape(shape_x)
grads = []
for x_i, i in zip(list(x), list(range(num))):
print(i)
grad_i = func_grad(x_i[None])
grads.append(grad_i)
grads_all = np.concatenate(grads, axis=0)
# f_sample = objective.f(x, noisy=False, fulldim=True)
# f_min_sample = f_sample.min()
def __init__(self, frame, sky_coord, observations, entropic=False):
"""Source intialized with a single pixel
Parameters
----------
frame: `~scarlet.Frame`
The frame of the model
sky_coord: tuple
Center of the source
observations: instance or list of `~scarlet.Observation`
Observation(s) to initialize this source
entropic: `bool`
Whether or not to enforce more information on SED
"""
C, Ny, Nx = frame.shape
self.entropic = entropic
self.center = np.array(frame.get_pixel(sky_coord), dtype="float")
# initialize SED from sky_coord
try:
iter(observations)
except TypeError:
observations = [observations]
# determine initial SED from peak position
# SED in the frame for source detection
seds = []
for obs in observations:
_sed = get_psf_sed(sky_coord, obs, frame)
seds.append(_sed)
sed = np.concatenate(seds).reshape(-1)
if np.any(sed <= 0):
# If the flux in all channels is <=0,
# the new sed will be filled with NaN values,
# which will cause the code to crash later
msg = "Zero or negative SED {} at y={}, x={}".format(
sed, *sky_coord)
if np.all(sed <= 0):
logger.warning(msg)
else:
logger.info(msg)
# set up parameters
sed_constraints = PositivityConstraint()
if entropic:
sed_constraints = ConstraintChain(sed_constraints,
EntropyConstraint())
sed = Parameter(
sed,
name="sed",
step=partial(relative_step, factor=1e-2),
constraint=sed_constraints,
)
center = Parameter(self.center, name="center", step=1e-1)
# define bbox
pixel_center = tuple(np.round(center).astype("int"))
front, back = 0, C
bottom = pixel_center[0] - frame.psf.shape[1] // 2
top = pixel_center[0] + frame.psf.shape[1] // 2
left = pixel_center[1] - frame.psf.shape[2] // 2
right = pixel_center[1] + frame.psf.shape[2] // 2
bbox = Box.from_bounds((front, back), (bottom, top), (left, right))
super().__init__(frame, sed, center, self._psf_wrapper, bbox=bbox)
def animate(k):
ax1.cla()
ax2.cla()
# print rendering update
if np.mod(k+1,25) == 0:
print ('rendering animation frame ' + str(k+1) + ' of ' + str(num_frames))
if k == num_frames - 1:
print ('animation rendering complete!')
time.sleep(1.5)
clear_output()
# plot initial point and evaluation
if k == 0:
w_val = self.w_init
g_val = self.g(w_val)
ax1.scatter(w_val,g_val,s = 100,c = 'm',edgecolor = 'k',linewidth = 0.7,zorder = 2) # plot point of tangency
# ax1.scatter(w_val,0,s = 100,c = 'm',edgecolor = 'k',linewidth = 0.7, zorder = 2, marker = 'X')
# plot function
ax1.plot(w_plot,g_plot,color = 'k',zorder = 0) # plot function
# plot function alone first along with initial point
if k > 0:
alpha = self.steplength_range[k-1]
# run gradient descent method
self.w_hist = []
self.run_gradient_descent(alpha = alpha)
# plot function
self.plot_function(ax1)
# colors for points
s = np.linspace(0,1,len(self.w_hist[:round(len(self.w_hist)/2)]))
s.shape = (len(s),1)
t = np.ones(len(self.w_hist[round(len(self.w_hist)/2):]))
t.shape = (len(t),1)
s = np.vstack((s,t))
self.colorspec = []
self.colorspec = np.concatenate((s,np.flipud(s)),1)
self.colorspec = np.concatenate((self.colorspec,np.zeros((len(s),1))),1)
# plot everything for each iteration
for j in range(len(self.w_hist)):
w_val = self.w_hist[j]
g_val = self.g(w_val)
grad_val = self.grad(w_val)
ax1.scatter(w_val,g_val,s = 90,c = self.colorspec[j],edgecolor = 'k',linewidth = 0.7,zorder = 3) # plot point of tangency
# ax1.scatter(w_val,0,s = 90,facecolor = self.colorspec[j],marker = 'X',edgecolor = 'k',linewidth = 0.7, zorder = 2)
# determine width to plot the approximation -- so its length == width defined above
div = float(1 + grad_val**2)
w1 = w_val - math.sqrt(width/div)
w2 = w_val + math.sqrt(width/div)
# use point-slope form of line to plot
wrange = np.linspace(w1,w2, 100)
h = g_val + grad_val*(wrange - w_val)
# plot tracers connecting consecutive points on the cost (for visualization purposes)
if tracers == 'on':
if j > 0:
w_old = self.w_hist[j-1]
w_new = self.w_hist[j]
g_old = self.g(w_old)
g_new = self.g(w_new)
ax1.quiver(w_old, g_old, w_new - w_old, g_new - g_old, scale_units='xy', angles='xy', scale=1, color = self.colorspec[j],linewidth = 1.5,alpha = 0.2,linestyle = '-',headwidth = 4.5,edgecolor = 'k',headlength = 10,headaxislength = 7)
### plot all on cost function decrease plot
ax2.scatter(j,g_val,s = 90,c = self.colorspec[j],edgecolor = 'k',linewidth = 0.7,zorder = 3) # plot point of tangency
# clean up second axis, set title on first
ax2.set_xticks(np.arange(len(self.w_hist)))
ax1.set_title(r'$\alpha = $' + r'{:.2f}'.format(alpha),fontsize = 14)
# plot connector between points for visualization purposes
if j > 0:
w_old = self.w_hist[j-1]
w_new = self.w_hist[j]
g_old = self.g(w_old)
g_new = self.g(w_new)
ax2.plot([j-1,j],[g_old,g_new],color = self.colorspec[j],linewidth = 2,alpha = 0.4,zorder = 1) # plot approx
### clean up function plot ###
# fix viewing limits on function plot
#ax1.set_xlim([-3,3])
#ax1.set_ylim([min(g_plot) - ggap,max(g_plot) + ggap])
# draw axes and labels
ax1.set_xlabel(r'$w$',fontsize = 13)
ax1.set_ylabel(r'$g(w)$',fontsize = 13,rotation = 0,labelpad = 25)
ax2.set_xlabel('iteration',fontsize = 13)
ax2.set_ylabel(r'$g(w)$',fontsize = 13,rotation = 0,labelpad = 25)
ax1.axhline(y=0, color='k',zorder = 0,linewidth = 0.5)
ax2.axhline(y=0, color='k',zorder = 0,linewidth = 0.5)
return artist,
# pickle.dump(lam_list, f)
#############################################
# Nonparametric variational inference code #
# --- save posterior parameters #
#############################################
if args.npvi:
init_with_mfvi = True
if init_with_mfvi:
mfvi_lam = mfvi_init()
# initialize theta
theta_mfvi = np.atleast_2d(
np.concatenate([mfvi_lam[:D], [2 * mfvi_lam[D:].mean()]]))
mu0 = vi.bbvi_npvi.mogsamples(args.ncomp, theta_mfvi)
# create npvi object
theta0 = np.column_stack(
[mu0, np.ones(args.ncomp) * theta_mfvi[0, -1]])
else:
theta0 = np.column_stack(
[10 * np.random.randn(args.ncomp, D), -2 * np.ones(args.ncomp)])
# create initial theta and sample
npvi = vi.NPVI(lnpdf, D=D)
mu, s2, elbo_vals, theta = npvi.run(theta0.copy(), verbose=False)
print elbo_vals
def _merge_array(self, *args):
self.sleep_data["".join(args)] = np.concatenate(
[self.sleep_data[k] for k in args], -1)
def init_gaussian_var_params(D, mean_mean=-1, log_std_mean=-5, scale=0.1, rs=npr.RandomState(0)):
init_mean = mean_mean * np.ones(D) + rs.randn(D) * scale
init_log_std = log_std_mean * np.ones(D) + rs.randn(D) * scale
return np.concatenate([init_mean, init_log_std])
def wake(self, wake_data, it):
ddc = self.reg
self.wake_data = wake_data.copy()
gs = []
nl_obs = self.nlayer - 1
mean_name_higher = "mx%d_x%d" % (self.nlayer - 2, nl_obs)
fun_name = "x%d->x%d" % (nl_obs, self.nlayer - 2)
self.wake_data[mean_name_higher] = ddc.predict(self.wake_data,
fun_name)
if self.layer_plastic[-1]:
grad_name = "x%d->dnat%d" % (self.nlayer - 2, nl_obs)
dnat = self.approx_E(mean_name_higher, grad_name)
#dnat = self.model.dists[-1].dnat(self.wake_data["x%d"%(self.nlayer-2)])
grad_name = "x%d->dnorm%d" % (self.nlayer - 2, nl_obs)
dnorm = self.approx_E(mean_name_higher, grad_name)
#dnorm = self.model.dists[-1].dnorm(self.wake_data["x%d"%(self.nlayer-2)])
suff = self.model.dists[-1].suff(self.wake_data["x%d" % (nl_obs)])
g = (self.model.dists[-1].dnatsuff_from_dnatsuff(dnat, suff) -
dnorm)
self.wake_data["dlogp%d" % (nl_obs)] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[-1].ps))
if self.layer_plastic[-2]:
fun_name = "x%d->x%d" % (nl_obs, self.nlayer - 2)
grad_name = "x%d->dlogp%d" % (self.nlayer - 2, self.nlayer - 2)
g = self.approx_E(mean_name_higher, grad_name)
self.wake_data["dlogp%d" % (self.nlayer - 2)] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[-2].ps))
for i in range(self.nlayer - 3, -1, -1):
mean_name = "mx%d_x%d" % (i, nl_obs)
fun_name = "mx%d_x%d->x%d" % (i + 1, nl_obs, i)
grad_name = "x%d->dlogp%d" % (i, i)
self.wake_data[mean_name] = ddc.predict(self.wake_data, fun_name)
if self.layer_plastic[i]:
g = self.approx_E(mean_name, grad_name)
self.wake_data["dlogp%d" % i] = g
gs.insert(0, g.mean(0))
else:
gs.insert(0, np.zeros_like(self.model.dists[i].ps))
gs = np.concatenate(gs)
self.gradient_step(gs, it)
def draw_surface(self, g, ax, **kwargs):
xmin = -3.1
xmax = 3.1
ymin = -3.1
ymax = 3.1
if 'xmin' in kwargs:
xmin = kwargs['xmin']
if 'xmax' in kwargs:
xmax = kwargs['xmax']
if 'ymin' in kwargs:
ymin = kwargs['ymin']
if 'ymax' in kwargs:
ymax = kwargs['ymax']
#### define input space for function and evaluate ####
w1 = np.linspace(xmin, xmax, 200)
w2 = np.linspace(ymin, ymax, 200)
w1_vals, w2_vals = np.meshgrid(w1, w2)
w1_vals.shape = (len(w1)**2, 1)
w2_vals.shape = (len(w2)**2, 1)
h = np.concatenate((w1_vals, w2_vals), axis=1)
func_vals = np.asarray([g(np.reshape(s, (2, 1))) for s in h])
### plot function as surface ###
w1_vals.shape = (len(w1), len(w2))
w2_vals.shape = (len(w1), len(w2))
func_vals.shape = (len(w1), len(w2))
ax.plot_surface(w1_vals,
w2_vals,
func_vals,
alpha=0.1,
color='w',
rstride=25,
cstride=25,
linewidth=1,
edgecolor='k',
zorder=2)
# plot z=0 plane
ax.plot_surface(w1_vals,
w2_vals,
func_vals * 0,
alpha=0.1,
color='w',
zorder=1,
rstride=25,
cstride=25,
linewidth=0.3,
edgecolor='k')
# clean up axis
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('white')
ax.yaxis.pane.set_edgecolor('white')
ax.zaxis.pane.set_edgecolor('white')
ax.xaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.yaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.zaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.set_xlabel('$w_0$', fontsize=14)
ax.set_ylabel('$w_1$', fontsize=14, rotation=0)
ax.set_title('$g(w_0,w_1)$', fontsize=14)
import matplotlib.pyplot as plt
if not os.path.exists('plots'):
os.mkdir('plots')
# Set priors, dataset, problem
D = 2 # dimensions
K = 3 # number of components, unnecessary ones should go to zero
N = 100 # number of points in synthetic dataset
N_its = 0 # number of updates
# Dataset
centres = [np.array([0., 8.]), np.array([5., 0.])]
covs = [np.eye(2), np.array([[0.6, 0.4], [0.4, 0.6]])]
X1 = multivariate_normal(mean=centres[0], cov=covs[0], size=int(N / 2))
X2 = multivariate_normal(mean=centres[1], cov=covs[1], size=int(N / 2))
X = np.concatenate((X1, X2))
# Variational priors
alpha0 = 1e-3 # as alpha0 -> 0, pi_k -> 0. As alpha0 -> Inf, pi_k -> 1/K
beta0 = 1e-10 # ???
m0 = np.zeros(2) # zero by convention (symmetry)
W0 = np.eye(2) #
nu0 = 2 #
# r needs to be randomised (not uniform) because if its all the same nothing
# changes - not sure of the mathematical reasoning for this
r = np.array([np.random.dirichlet(np.ones(K)) for _ in range(N)])
r = [r[:, k] for k in range(K)]
# neaten up the output
from tqdm import tqdm
def single_input_plot(self, g, weight_histories, cost_histories, **kwargs):
# adjust viewing range
wmin = -3.1
wmax = 3.1
if 'wmin' in kwargs:
wmin = kwargs['wmin']
if 'wmax' in kwargs:
wmax = kwargs['wmax']
onerun_perplot = False
if 'onerun_perplot' in kwargs:
onerun_perplot = kwargs['onerun_perplot']
### initialize figure
fig = plt.figure(figsize=(9, 4))
artist = fig
# remove whitespace from figure
#fig.subplots_adjust(left=0, right=1, bottom=0, top=1) # remove whitespace
#fig.subplots_adjust(wspace=0.01,hspace=0.01)
# create subplot with 2 panels, plot input function in center plot
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1])
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
### plot function in both panels
w_plot = np.linspace(wmin, wmax, 500)
g_plot = g(w_plot)
gmin = np.min(g_plot)
gmax = np.max(g_plot)
g_range = gmax - gmin
ggap = g_range * 0.1
gmin -= ggap
gmax += ggap
# plot function, axes lines
ax1.plot(w_plot, g_plot, color='k', zorder=2) # plot function
ax1.axhline(y=0, color='k', zorder=1, linewidth=0.25)
ax1.axvline(x=0, color='k', zorder=1, linewidth=0.25)
ax1.set_xlabel(r'$w$', fontsize=13)
ax1.set_ylabel(r'$g(w)$', fontsize=13, rotation=0, labelpad=25)
ax1.set_xlim(wmin, wmax)
ax1.set_ylim(gmin, gmax)
ax2.plot(w_plot, g_plot, color='k', zorder=2) # plot function
ax2.axhline(y=0, color='k', zorder=1, linewidth=0.25)
ax2.axvline(x=0, color='k', zorder=1, linewidth=0.25)
ax2.set_xlabel(r'$w$', fontsize=13)
ax2.set_ylabel(r'$g(w)$', fontsize=13, rotation=0, labelpad=25)
ax2.set_xlim(wmin, wmax)
ax2.set_ylim(gmin, gmax)
#### loop over histories and plot each
for j in range(len(weight_histories)):
w_hist = weight_histories[j]
c_hist = cost_histories[j]
# colors for points --> green as the algorithm begins, yellow as it converges, red at final point
s = np.linspace(0, 1, len(w_hist[:round(len(w_hist) / 2)]))
s.shape = (len(s), 1)
t = np.ones(len(w_hist[round(len(w_hist) / 2):]))
t.shape = (len(t), 1)
s = np.vstack((s, t))
self.colorspec = []
self.colorspec = np.concatenate((s, np.flipud(s)), 1)
self.colorspec = np.concatenate(
(self.colorspec, np.zeros((len(s), 1))), 1)
### plot all history points
ax = ax2
if onerun_perplot == True:
if j == 0:
ax = ax1
if j == 1:
ax = ax2
for k in range(len(w_hist)):
# pick out current weight and function value from history, then plot
w_val = w_hist[k]
g_val = c_hist[k]
ax.scatter(w_val,
g_val,
s=90,
color=self.colorspec[k],
edgecolor='k',
linewidth=0.5 * ((1 / (float(k) + 1)))**(0.4),
zorder=3,
marker='X') # evaluation on function
ax.scatter(w_val,
0,
s=90,
facecolor=self.colorspec[k],
edgecolor='k',
linewidth=0.5 * ((1 / (float(k) + 1)))**(0.4),
zorder=3)
def TrackSmoothness(x):
k = TrackCurvature(x)
kdiff = np.concatenate([k[1:] - k[:-1], k[-1:] - k[:1]])
return np.sum(kdiff**2)
def pack(x, dc, dv):
return np.concatenate([x.ravel(), dc.ravel(), dv.ravel()])
def compute_fprime_s_(Eta, Xi, s02):
s2 = Xi**2 + np.concatenate((s02, s02), axis=0)
return sim_utils.fprime_s_miller_troyer(Eta, s2) * (Xi / s2)